system

Abstract

The system according to the embodiment aims to enable users to interact with AI in natural language and receive assistance in daily life. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a control unit. The reception unit receives natural language input from a user. The analysis unit analyzes the natural language received by the reception unit. The provision unit provides information based on the results of the analysis by the analysis unit. The control unit controls the smart device based on the information provided by the provision 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] Conventional technologies do not adequately enable users to interact with AI in natural language and receive assistance in their daily lives, and there is room for improvement.

[0005] The system according to the embodiment aims to enable users to interact with AI in natural language and receive assistance in daily life. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a control unit. The reception unit receives natural language input from a user. The analysis unit analyzes the natural language received by the reception unit. The provision unit provides information based on the analysis result by the analysis unit. The control unit controls the smart device based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to interact with AI in natural language and receive assistance in daily life. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI terminal according to an embodiment of the present invention is a system that accepts and analyzes natural language input from users, provides information, and controls smart devices. The generative AI terminal provides users with a natural and intuitive AI connection, opening up new possibilities for using AI as an extension of the body and supporting daily life. For example, when a user asks, "What's the weather like today?", the generative AI instantly provides weather information. The generative AI terminal also provides personalized information based on the user's lifestyle and preferences. For example, if a user has a habit of drinking coffee every morning, the generative AI can suggest, "Good morning. Would you like to try a new coffee bean today?" Furthermore, the generative AI terminal can also help users manage their health. For example, if a user feels "tired easily lately," the generative AI can advise, "It seems you've been getting less sleep recently. I recommend you get an early night." The generative AI terminal can also connect with smart devices in the home. For example, if a user commands, "Turn down the lights in the living room," the generative AI will operate the smart lighting and dim the lights. Finally, generative AI devices can also be used to support users in their studies and work. For example, if a user says, "Prepare the materials for the next meeting," the generative AI will search for the necessary materials and provide them all together. This allows generative AI devices to provide users with a natural and intuitive AI connection, opening up new possibilities for using generative AI to assist in everyday life. Users can easily access generative AI through the device and receive the information and support they need. This will promote the integration of generative AI technology into everyday life and support smooth adaptation to the new AI era.

[0029] A dedicated terminal for generation AI according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a control unit. The reception unit receives natural language input from a user. The natural language input includes, but is not limited to, speech input and text input. The reception unit converts the speech input into text data using, for example, speech recognition technology. The reception unit can also directly receive text input. The analysis unit analyzes the natural language received by the reception unit. Examples of analysis include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the input text into words using morphological analysis and analyzes the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the input text using semantic analysis. The provision unit provides information based on the results of the analysis by the analysis unit. The provided information includes, but is not limited to, text information, audio information, and image information. For example, the provision unit displays the text information on a screen. The provision unit can also play audio information through a speaker. The providing unit can also display image information on a screen. The control unit controls a smart device based on the information provided by the providing unit. Smart devices to be controlled include, but are not limited to, lighting, air conditioners, and televisions, for example. The control unit can, for example, adjust the brightness of lighting. The control unit can also set the temperature of an air conditioner. The control unit can also change the television channel. As a result, the generation AI-dedicated terminal according to the embodiment analyzes natural language input from a user, provides information, and controls smart devices to provide assistance in daily life.

[0030] The providing unit can provide personalized information based on the user's lifestyle habits and preferences. For example, if the user has the habit of drinking coffee every morning, the providing unit might suggest, "Good morning. Why don't you try some new coffee beans today?" The providing unit can also provide information that matches the user's preferences based on the user's past behavior history. For example, the providing unit might provide related information based on information the user has searched for in the past. This allows for more personalized support by providing information based on the user's lifestyle habits and preferences.

[0031] The providing unit can monitor the user's health condition and provide appropriate advice. For example, if the user feels that they have been getting tired easily recently, the providing unit may advise them by saying, "It seems that you have been sleeping less recently. I recommend that you go to bed early tonight." The providing unit can also provide health management advice based on the user's health data. For example, the providing unit may provide appropriate exercise and diet advice based on the user's heart rate and blood pressure data. In this way, the providing unit monitors the user's health condition and provides appropriate advice to support health management.

[0032] The control unit can operate smart devices in the home. The control unit, for example, operates smart devices in the home. For example, when a user commands, "Turn off the lights in the living room," the control unit operates the smart lighting to dim the lights in the living room. The control unit can also set the temperature of the air conditioner. For example, when a user commands, "Set the temperature of the air conditioner to 25 degrees," the control unit sets the temperature of the air conditioner to 25 degrees. The control unit can also change the TV channel. For example, when a user commands, "Change the TV channel to news," the control unit changes the TV channel to news. In this way, operating smart devices in the home makes the user's life more convenient.

[0033] The provision unit can support the user's learning or work. The provision unit, for example, supports the user's learning or work. For example, when the user instructs, "Prepare materials for the next meeting," the provision unit searches for the necessary materials and provides them in a compiled form. The provision unit can also provide related information to support the user's learning. For example, when the user asks, "I want to know more about this topic," the provision unit provides related articles and materials. This supports the user's learning or work, thereby enabling efficient work.

[0034] The analysis unit can analyze the user's natural language input and provide appropriate information. For example, when a user asks, "What's the weather like today?", the analysis unit analyzes the question and provides weather information. The analysis unit can also search for related information to generate an answer to the user's question. For example, when a user instructs, "Prepare materials for the next meeting," the analysis unit searches for the necessary materials and provides them to the providing unit. In this way, the analysis unit analyzes the user's natural language input and provides appropriate information, thereby providing information that meets the user's needs.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, it preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also provide predictive input based on content that the user has previously input, thereby reducing the effort required for input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history, improving user convenience.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving natural language input. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving natural language input. For example, if the user is at work, the reception unit can preferentially receive information related to work. Also, if the user is traveling, the reception unit can preferentially receive information related to travel. Also, if the user is interested in health, the reception unit can preferentially receive information related to health. In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0037] The reception unit can select the optimal reception means depending on the user's input method when receiving natural language input. For example, when receiving natural language input, the reception unit selects the optimal reception means depending on the user's input method (voice, text, gesture, etc.). For example, if the user uses voice input, the reception unit can receive the input using voice recognition technology. Also, if the user uses text input, the reception unit can also preferentially receive keyboard input. Also, if the user uses gesture input, the reception unit can also receive input using gesture recognition technology. In this way, convenience for the user is improved by selecting the optimal reception means depending on the user's input method.

[0038] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving natural language input. For example, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving natural language input. For example, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving information related to the user's home. In this way, highly relevant information is provided preferentially by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and receive related input when receiving natural language input. For example, the reception unit analyzes the user's social media activity and receives related input when receiving natural language input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also prioritize receiving related information by taking into account the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving natural language input. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving natural language input. For example, the reception unit suggests the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. In this way, the optimal reception method is provided by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis for important input. Also, the analysis unit can perform a concise analysis for general input. Also, the analysis unit can perform a quick analysis for urgent input. In this way, important information is analyzed in detail by adjusting the level of detail of the analysis based on the importance of the input.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the input during analysis. For example, for health-related input, the analysis unit applies a health analysis algorithm. For work-related input, the analysis unit can also apply a work analysis algorithm. For learning-related input, the analysis unit can also apply a learning analysis algorithm. In this way, by applying different analysis algorithms depending on the category of the input, more appropriate analysis results can be provided.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the current analysis results can be adjusted based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. In this way, the analysis accuracy can be improved by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time of input submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of input submission during analysis. For example, the analysis unit prioritizes analysis for urgent input. Furthermore, the analysis unit can also analyze general input with normal priority. Furthermore, the analysis unit can postpone analysis for past input. In this way, by determining the analysis priority based on the time of input submission, input with high urgency is analyzed with priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the input during analysis. For example, the analysis unit prioritizes analysis of highly relevant input. Also, the analysis unit can postpone analysis of low-relevance input. Also, the analysis unit can analyze input with medium relevance in the normal order. In this way, by adjusting the order of analysis based on the relevance of the input, highly relevant information is prioritized in analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user has general knowledge, the analysis unit can provide concise analysis results. Also, if the user is a beginner, the analysis unit can provide easy-to-understand analysis results. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0047] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit adjusts the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. Also, the providing unit can provide a concise explanation for general information. Also, the providing unit can provide urgent information quickly. In this way, by adjusting the level of detail of the information provided based on the importance of the information, important information is provided in detail.

[0048] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a health providing algorithm to health-related information. Furthermore, the providing unit can also apply a work providing algorithm to work-related information. Furthermore, the providing unit can also apply a learning providing algorithm to learning-related information. In this way, by applying different providing algorithms depending on the category of information, more appropriate information is provided.

[0049] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit improves the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit adjusts the current provision results based on the user's past provision results. It can also extract specific patterns from the user's past provision results to improve the accuracy of provision. It can also analyze the user's past provision results and optimize the provision algorithm. In this way, the accuracy of provision is improved by referring to the user's past provision results.

[0050] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides urgent information with priority. Furthermore, the providing unit can provide general information with normal priority. Furthermore, the providing unit can provide past information at a later date. In this way, by determining the priority of provision based on the time of submission of information, information with high urgency is provided with priority.

[0051] The providing unit can adjust the order of provision based on the relevance of information when providing the information. For example, the providing unit adjusts the order of provision based on the relevance of information when providing the information. For example, the providing unit provides highly relevant information with priority. Also, the providing unit can provide information with low relevance at a later date. Also, the providing unit can provide information with medium relevance in the normal order. In this way, by adjusting the order of provision based on the relevance of information, highly relevant information is provided with priority.

[0052] The providing unit can adjust the use of technical terminology provided in accordance with the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terminology provided in accordance with the user's level of expertise when providing the information. For example, if the user has technical knowledge, the providing unit can provide information that uses a lot of technical terminology. Also, if the user has general knowledge, the providing unit can provide concise information. Also, if the user is a beginner, the providing unit can provide easy-to-understand information. In this way, by adjusting the use of technical terminology provided in accordance with the user's level of expertise, information that is easy for the user to understand is provided.

[0053] The control unit can adjust the level of detail of control based on the importance of the device when controlling the device. For example, the control unit adjusts the level of detail of control based on the importance of the device when controlling the device. For example, the control unit performs detailed control on an important device. Also, the control unit can perform simple control on a general device. Also, the control unit can perform quick control on an urgent device. In this way, by adjusting the level of detail of control based on the importance of the device, important devices are controlled in detail.

[0054] The control unit can apply different control algorithms depending on the category of the device during control. For example, the control unit applies different control algorithms depending on the category of the device during control. For example, the control unit applies a lighting control algorithm to a lighting device. The control unit can also apply a music control algorithm to a music device. The control unit can also apply a temperature control algorithm to a temperature adjustment device. In this way, by applying different control algorithms depending on the category of the device, more appropriate control can be achieved.

[0055] The control unit can improve the accuracy of control by referring to the user's past control results during control. For example, the control unit can improve the accuracy of control by referring to the user's past control results during control. For example, the control unit can adjust the current control result based on the user's past control results. It can also extract specific patterns from the user's past control results to improve the control accuracy. It can also analyze the user's past control results and optimize the control algorithm. In this way, the control accuracy can be improved by referring to the user's past control results.

[0056] The control unit can, at the time of control, prioritize control of highly relevant devices by taking into account the geographical location information of the device. For example, at the time of control, the control unit prioritizes control of highly relevant devices by taking into account the geographical location information of the device. For example, when the user is in a specific location, the control unit can prioritize control of devices related to that location. Also, when the user is traveling, the control unit can also prioritize control of devices related to the travel destination. Also, when the user is at home, the control unit can prioritize control of devices related to the home. In this way, highly relevant devices are prioritized by taking into account the geographical location information of the device.

[0057] The control unit can improve the accuracy of control by referring to literature related to the device during control. For example, the control unit can improve the accuracy of control by referring to literature related to the device during control. For example, the control unit can refer to the instruction manual for the device and apply the optimal control method. The control unit can also optimize the control algorithm by referring to technical literature for the device. The control unit can also perform control that reflects user feedback by referring to user reviews for the device. In this way, the accuracy of control is improved by referring to literature related to the device.

[0058] The control unit can perform control taking into consideration the market value of the device when controlling. For example, the control unit performs control taking into consideration the market value of the device when controlling. For example, the control unit performs careful control for expensive devices. Furthermore, the control unit can perform normal control for general devices. Furthermore, the control unit can perform simple control for inexpensive devices. In this way, important devices are carefully controlled by taking into consideration the market value of the device.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past input history and select the optimal reception method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also provide predictive input based on the content the user has previously entered, reducing the effort required for input. The reception unit can also predict and suggest the input method to be used during a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history, improving user convenience.

[0061] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, the analysis unit performs a detailed analysis for important input. For general input, the analysis unit can also perform a concise analysis. For urgent input, the analysis unit can also perform a quick analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the input, important information can be analyzed in detail.

[0062] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide a detailed explanation for important information. For general information, the providing unit can provide a brief explanation. For urgent information, the providing unit can provide the information quickly. Thus, by adjusting the level of detail of the information provided based on the importance of the information, important information can be provided in detail.

[0063] The control unit can adjust the level of detail of control based on the importance of the device when controlling the device. For example, the control unit can perform detailed control on an important device. For a general device, the control unit can perform simple control. For an urgent device, the control unit can perform quick control. Thus, by adjusting the level of detail of control based on the importance of the device, important devices can be controlled in detail.

[0064] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. If the user has general knowledge, the analysis unit can also provide concise analysis results. If the user is a beginner, the analysis unit can also provide easy-to-understand analysis results. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives natural language input from the user. Natural language input includes voice input and text input. The reception unit can convert voice input into text data using voice recognition technology, and can also directly receive text input. Step 2: The analysis unit analyzes the natural language received by the reception unit. The analysis includes morphological analysis, grammatical analysis, semantic analysis, etc. The analysis unit uses morphological analysis to divide the input text into words, grammatical analysis to analyze the structure of sentences, and semantic analysis to understand the meaning of the input text. Step 3: The providing unit provides information based on the results of the analysis by the analyzing unit. The provided information includes text information, audio information, image information, etc. The providing unit can display the text information on a screen, play the audio information through a speaker, and display the image information on a screen. Step 4: The control unit controls the smart devices based on the information provided by the providing unit. The smart devices to be controlled include lighting, air conditioners, televisions, etc. The control unit can adjust the brightness of the lighting, set the temperature of the air conditioner, and change the channel on the television.

[0067] (Example 2) A generative AI terminal according to an embodiment of the present invention is a system that accepts and analyzes natural language input from users, provides information, and controls smart devices. The generative AI terminal provides users with a natural and intuitive AI connection, opening up new possibilities for using AI as an extension of the body and supporting daily life. For example, when a user asks, "What's the weather like today?", the generative AI instantly provides weather information. The generative AI terminal also provides personalized information based on the user's lifestyle and preferences. For example, if a user has a habit of drinking coffee every morning, the generative AI can suggest, "Good morning. Would you like to try a new coffee bean today?" Furthermore, the generative AI terminal can also help users manage their health. For example, if a user feels "tired easily lately," the generative AI can advise, "It seems you've been getting less sleep recently. I recommend you get an early night." The generative AI terminal can also connect with smart devices in the home. For example, if a user commands, "Turn down the lights in the living room," the generative AI will operate the smart lighting and dim the lights. Finally, generative AI devices can also be used to support users in their studies and work. For example, if a user says, "Prepare the materials for the next meeting," the generative AI will search for the necessary materials and provide them all together. This allows generative AI devices to provide users with a natural and intuitive AI connection, opening up new possibilities for using generative AI to assist in everyday life. Users can easily access generative AI through the device and receive the information and support they need. This will promote the integration of generative AI technology into everyday life and support smooth adaptation to the new AI era.

[0068] A dedicated terminal for generation AI according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a control unit. The reception unit receives natural language input from a user. The natural language input includes, but is not limited to, speech input and text input. The reception unit converts the speech input into text data using, for example, speech recognition technology. The reception unit can also directly receive text input. The analysis unit analyzes the natural language received by the reception unit. Examples of analysis include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the input text into words using morphological analysis and analyzes the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the input text using semantic analysis. The provision unit provides information based on the results of the analysis by the analysis unit. The provided information includes, but is not limited to, text information, audio information, and image information. For example, the provision unit displays the text information on a screen. The provision unit can also play audio information through a speaker. The providing unit can also display image information on a screen. The control unit controls a smart device based on the information provided by the providing unit. Smart devices to be controlled include, but are not limited to, lighting, air conditioners, and televisions, for example. The control unit can, for example, adjust the brightness of lighting. The control unit can also set the temperature of an air conditioner. The control unit can also change the television channel. As a result, the generation AI-dedicated terminal according to the embodiment analyzes natural language input from a user, provides information, and controls smart devices to provide assistance in daily life.

[0069] The providing unit can provide personalized information based on the user's lifestyle habits and preferences. For example, if the user has the habit of drinking coffee every morning, the providing unit might suggest, "Good morning. Why don't you try some new coffee beans today?" The providing unit can also provide information that matches the user's preferences based on the user's past behavior history. For example, the providing unit might provide related information based on information the user has searched for in the past. This allows for more personalized support by providing information based on the user's lifestyle habits and preferences.

[0070] The providing unit can monitor the user's health condition and provide appropriate advice. For example, if the user feels that they have been getting tired easily recently, the providing unit may advise them by saying, "It seems that you have been sleeping less recently. I recommend that you go to bed early tonight." The providing unit can also provide health management advice based on the user's health data. For example, the providing unit may provide appropriate exercise and diet advice based on the user's heart rate and blood pressure data. In this way, the providing unit monitors the user's health condition and provides appropriate advice to support health management.

[0071] The control unit can operate smart devices in the home. The control unit, for example, operates smart devices in the home. For example, when a user commands, "Turn off the lights in the living room," the control unit operates the smart lighting to dim the lights in the living room. The control unit can also set the temperature of the air conditioner. For example, when a user commands, "Set the temperature of the air conditioner to 25 degrees," the control unit sets the temperature of the air conditioner to 25 degrees. The control unit can also change the TV channel. For example, when a user commands, "Change the TV channel to news," the control unit changes the TV channel to news. In this way, operating smart devices in the home makes the user's life more convenient.

[0072] The provision unit can support the user's learning or work. The provision unit, for example, supports the user's learning or work. For example, when the user instructs, "Prepare materials for the next meeting," the provision unit searches for the necessary materials and provides them in a compiled form. The provision unit can also provide related information to support the user's learning. For example, when the user asks, "I want to know more about this topic," the provision unit provides related articles and materials. This supports the user's learning or work, thereby enabling efficient work.

[0073] The analysis unit can analyze the user's natural language input and provide appropriate information. For example, when a user asks, "What's the weather like today?", the analysis unit analyzes the question and provides weather information. The analysis unit can also search for related information to generate an answer to the user's question. For example, when a user instructs, "Prepare materials for the next meeting," the analysis unit searches for the necessary materials and provides them to the providing unit. In this way, the analysis unit analyzes the user's natural language input and provides appropriate information, thereby providing information that meets the user's needs.

[0074] The reception unit can estimate the user's emotion and adjust the timing of accepting natural language input based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of accepting natural language input based on the estimated user emotion. For example, if the user is stressed, the reception unit delays the timing of accepting input and waits until the user relaxes. Also, if the user is excited, the reception unit can immediately accept input and respond quickly. Also, if the user is tired, the reception unit can adjust the timing of accepting input and prompt the user to enter input again after taking a rest. In this way, adjusting the timing of accepting input according to the user's emotion enables input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, it preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also provide predictive input based on content that the user has previously input, thereby reducing the effort required for input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history, improving user convenience.

[0076] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving natural language input. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving natural language input. For example, if the user is at work, the reception unit can preferentially receive information related to work. Also, if the user is traveling, the reception unit can preferentially receive information related to travel. Also, if the user is interested in health, the reception unit can preferentially receive information related to health. In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0077] The reception unit can select the optimal reception means depending on the user's input method when receiving natural language input. For example, when receiving natural language input, the reception unit selects the optimal reception means depending on the user's input method (voice, text, gesture, etc.). For example, if the user uses voice input, the reception unit can receive the input using voice recognition technology. Also, if the user uses text input, the reception unit can also preferentially receive keyboard input. Also, if the user uses gesture input, the reception unit can also receive input using gesture recognition technology. In this way, convenience for the user is improved by selecting the optimal reception means depending on the user's input method.

[0078] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of inputs to be received based on the estimated user's emotion. For example, if the user is expressing an urgent emotion, the reception unit can preferentially receive urgent inputs. Also, if the user is relaxed, the reception unit can preferentially receive normal inputs. Also, if the user is feeling stressed, the reception unit can preferentially receive inputs for reducing stress. In this way, by determining the priority of inputs based on the user's emotion, inputs with high urgency are preferentially processed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving natural language input. For example, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving natural language input. For example, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving information related to the user's home. In this way, highly relevant information is provided preferentially by taking the user's geographical location information into consideration.

[0080] The reception unit can analyze the user's social media activity and receive related input when receiving natural language input. For example, the reception unit analyzes the user's social media activity and receives related input when receiving natural language input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also prioritize receiving related information by taking into account the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving natural language input. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving natural language input. For example, the reception unit suggests the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. In this way, the optimal reception method is provided by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis for important input. Also, the analysis unit can perform a concise analysis for general input. Also, the analysis unit can perform a quick analysis for urgent input. In this way, important information is analyzed in detail by adjusting the level of detail of the analysis based on the importance of the input.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the input during analysis. For example, for health-related input, the analysis unit applies a health analysis algorithm. For work-related input, the analysis unit can also apply a work analysis algorithm. For learning-related input, the analysis unit can also apply a learning analysis algorithm. In this way, by applying different analysis algorithms depending on the category of the input, more appropriate analysis results can be provided.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the current analysis results can be adjusted based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. In this way, the analysis accuracy can be improved by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Alternatively, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The analysis unit can determine the analysis priority based on the time of input submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of input submission during analysis. For example, the analysis unit prioritizes analysis for urgent input. Furthermore, the analysis unit can also analyze general input with normal priority. Furthermore, the analysis unit can postpone analysis for past input. In this way, by determining the analysis priority based on the time of input submission, input with high urgency is analyzed with priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the input during analysis. For example, the analysis unit prioritizes analysis of highly relevant input. Also, the analysis unit can postpone analysis of low-relevance input. Also, the analysis unit can analyze input with medium relevance in the normal order. In this way, by adjusting the order of analysis based on the relevance of the input, highly relevant information is prioritized in analysis.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user has general knowledge, the analysis unit can provide concise analysis results. Also, if the user is a beginner, the analysis unit can provide easy-to-understand analysis results. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0090] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the presentation method of the information to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can also provide concise information. If the user is excited, the providing unit can also provide visually stimulating information. In this way, by adjusting the presentation method of information based on the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit adjusts the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. Also, the providing unit can provide a concise explanation for general information. Also, the providing unit can provide urgent information quickly. In this way, by adjusting the level of detail of the information provided based on the importance of the information, important information is provided in detail.

[0092] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a health providing algorithm to health-related information. Furthermore, the providing unit can also apply a work providing algorithm to work-related information. Furthermore, the providing unit can also apply a learning providing algorithm to learning-related information. In this way, by applying different providing algorithms depending on the category of information, more appropriate information is provided.

[0093] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit improves the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit adjusts the current provision results based on the user's past provision results. It can also extract specific patterns from the user's past provision results to improve the accuracy of provision. It can also analyze the user's past provision results and optimize the provision algorithm. In this way, the accuracy of provision is improved by referring to the user's past provision results.

[0094] The providing unit can estimate the user's emotion and adjust the length of information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of information to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Alternatively, if the user is relaxed, the providing unit can provide detailed information. Alternatively, if the user is excited, the providing unit can provide visually stimulating information. In this way, by adjusting the length of information based on the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The providing unit, for example, determines the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides urgent information with priority. Furthermore, the providing unit can provide general information with normal priority. Furthermore, the providing unit can provide past information at a later date. In this way, by determining the priority of provision based on the time of submission of information, information with high urgency is provided with priority.

[0096] The providing unit can adjust the order of provision based on the relevance of information when providing the information. For example, the providing unit adjusts the order of provision based on the relevance of information when providing the information. For example, the providing unit provides highly relevant information with priority. Also, the providing unit can provide information with low relevance at a later date. Also, the providing unit can provide information with medium relevance in the normal order. In this way, by adjusting the order of provision based on the relevance of information, highly relevant information is provided with priority.

[0097] The providing unit can adjust the use of technical terminology provided in accordance with the user's level of expertise when providing the information. For example, the providing unit adjusts the use of technical terminology provided in accordance with the user's level of expertise when providing the information. For example, if the user has technical knowledge, the providing unit can provide information that uses a lot of technical terminology. Also, if the user has general knowledge, the providing unit can provide concise information. Also, if the user is a beginner, the providing unit can provide easy-to-understand information. In this way, by adjusting the use of technical terminology provided in accordance with the user's level of expertise, information that is easy for the user to understand is provided.

[0098] The control unit can estimate the user's emotion and adjust the control method of the smart device based on the estimated user's emotion. The control unit, for example, estimates the user's emotion and adjusts the control method of the smart device based on the estimated user's emotion. For example, if the user is relaxed, the control unit can adjust the lighting to softer light. If the user is in a hurry, the control unit can speed up the device's response speed. If the user is excited, the control unit can increase the volume of the music. This allows for more appropriate control by adjusting the control method of the smart device based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The control unit can adjust the level of detail of control based on the importance of the device when controlling the device. For example, the control unit adjusts the level of detail of control based on the importance of the device when controlling the device. For example, the control unit performs detailed control on an important device. Also, the control unit can perform simple control on a general device. Also, the control unit can perform quick control on an urgent device. In this way, by adjusting the level of detail of control based on the importance of the device, important devices are controlled in detail.

[0100] The control unit can apply different control algorithms depending on the category of the device during control. For example, the control unit applies different control algorithms depending on the category of the device during control. For example, the control unit applies a lighting control algorithm to a lighting device. The control unit can also apply a music control algorithm to a music device. The control unit can also apply a temperature control algorithm to a temperature adjustment device. In this way, by applying different control algorithms depending on the category of the device, more appropriate control can be achieved.

[0101] The control unit can improve the accuracy of control by referring to the user's past control results during control. For example, the control unit can improve the accuracy of control by referring to the user's past control results during control. For example, the control unit can adjust the current control result based on the user's past control results. It can also extract specific patterns from the user's past control results to improve the control accuracy. It can also analyze the user's past control results and optimize the control algorithm. In this way, the control accuracy can be improved by referring to the user's past control results.

[0102] The control unit can estimate the user's emotions and determine the priority of devices to be controlled based on the estimated user emotions. The control unit, for example, estimates the user's emotions and determines the priority of devices to be controlled based on the estimated user emotions. For example, if the user is expressing an urgent emotion, the control unit prioritizes controlling the urgent device. Also, if the user is relaxed, the control unit can prioritize controlling a normal device. Also, if the user is feeling stressed, the control unit can prioritize controlling a device for reducing stress. In this way, by determining the priority of devices based on the user's emotions, devices with high urgency are prioritized and controlled. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The control unit can, at the time of control, prioritize control of highly relevant devices by taking into account the geographical location information of the device. For example, at the time of control, the control unit prioritizes control of highly relevant devices by taking into account the geographical location information of the device. For example, when the user is in a specific location, the control unit can prioritize control of devices related to that location. Also, when the user is traveling, the control unit can also prioritize control of devices related to the travel destination. Also, when the user is at home, the control unit can prioritize control of devices related to the home. In this way, highly relevant devices are prioritized by taking into account the geographical location information of the device.

[0104] The control unit can improve the accuracy of control by referring to literature related to the device during control. For example, the control unit can improve the accuracy of control by referring to literature related to the device during control. For example, the control unit can refer to the instruction manual for the device and apply the optimal control method. The control unit can also optimize the control algorithm by referring to technical literature for the device. The control unit can also perform control that reflects user feedback by referring to user reviews for the device. In this way, the accuracy of control is improved by referring to literature related to the device.

[0105] The control unit can perform control taking into consideration the market value of the device when controlling. For example, the control unit performs control taking into consideration the market value of the device when controlling. For example, the control unit performs careful control for expensive devices. Furthermore, the control unit can perform normal control for general devices. Furthermore, the control unit can perform simple control for inexpensive devices. In this way, important devices are carefully controlled by taking into consideration the market value of the device. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and control 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 natural language input from a user using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs morphological analysis and grammatical analysis. The provision unit provides information using the display 40A and speaker 40B of the smart device 14. The control unit is realized by the control unit 46A of the smart device 14 and controls smart devices such as lighting and air conditioners. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, and control 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 natural language input from a user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs morphological analysis and grammatical analysis. The provision unit provides information using the speaker 240 of the smart glasses 214. The control unit is realized by the control unit 46A of the smart glasses 214 and controls smart devices such as lighting and air conditioners. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and control 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 natural language input from a user using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs morphological analysis and grammatical analysis. The provision unit provides information using the speaker 240 of the headset type terminal 314. The control unit is realized by the control unit 46A of the headset type terminal 314 and controls smart devices such as lighting and air conditioners. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and control 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 natural language input from a user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs morphological analysis and grammatical analysis. The provision unit provides information using the speaker 240 of the robot 414. The control unit is realized by the control unit 46A of the robot 414 and controls smart devices such as lighting and air conditioners.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can estimate the user's emotions and adjust the timing of accepting natural language input based on the estimated user emotions. For example, if the user is stressed, the reception unit can delay the timing of accepting input and wait until the user is relaxed. Also, if the user is excited, the reception unit can immediately accept input and respond quickly. Also, if the user is tired, the reception unit can adjust the timing of accepting input and prompt the user to enter input again after taking a rest. In this way, by adjusting the timing of accepting input according to the user's emotions, input can be performed at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise information. If the user is excited, the providing unit can provide visually stimulating information. This allows for more appropriate information to be provided by adjusting the way information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] The control unit can estimate the user's emotions and adjust the control method of the smart device based on the estimated user's emotions. For example, if the user is relaxed, the control unit can adjust the lighting to softer light. If the user is in a hurry, the control unit can speed up the device's response speed. If the user is excited, the control unit can increase the volume of the music. This allows for more appropriate control by adjusting the control method of the smart device based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can provide detailed information. If the user is excited, the providing unit can provide visually stimulating information. In this way, by adjusting the length of information based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The reception unit can analyze the user's past input history and select the optimal reception method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also provide predictive input based on the content the user has previously entered, reducing the effort required for input. The reception unit can also predict and suggest the input method to be used during a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history, improving user convenience.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, the analysis unit performs a detailed analysis for important input. For general input, the analysis unit can also perform a concise analysis. For urgent input, the analysis unit can also perform a quick analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the input, important information can be analyzed in detail.

[0114] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide a detailed explanation for important information. For general information, the providing unit can provide a brief explanation. For urgent information, the providing unit can provide the information quickly. Thus, by adjusting the level of detail of the information provided based on the importance of the information, important information can be provided in detail.

[0115] The control unit can adjust the level of detail of control based on the importance of the device when controlling the device. For example, the control unit can perform detailed control on an important device. For a general device, the control unit can perform simple control. For an urgent device, the control unit can perform quick control. Thus, by adjusting the level of detail of control based on the importance of the device, important devices can be controlled in detail.

[0116] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. If the user has general knowledge, the analysis unit can also provide concise analysis results. If the user is a beginner, the analysis unit can also provide easy-to-understand analysis results. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives natural language input from the user. Natural language input includes voice input and text input. The reception unit can convert voice input into text data using voice recognition technology, and can also directly receive text input. Step 2: The analysis unit analyzes the natural language received by the reception unit. The analysis includes morphological analysis, grammatical analysis, semantic analysis, etc. The analysis unit uses morphological analysis to divide the input text into words, grammatical analysis to analyze the structure of sentences, and semantic analysis to understand the meaning of the input text. Step 3: The providing unit provides information based on the results of the analysis by the analyzing unit. The provided information includes text information, audio information, image information, etc. The providing unit can display the text information on a screen, play the audio information through a speaker, and display the image information on a screen. Step 4: The control unit controls the smart devices based on the information provided by the providing unit. The smart devices to be controlled include lighting, air conditioners, televisions, etc. The control unit can adjust the brightness of the lighting, set the temperature of the air conditioner, and change the channel on the television.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 input in natural language from a user; an analysis unit that analyzes the natural language received by the reception unit; a providing unit that provides information based on the results of the analysis by the analyzing unit; a control unit that controls the smart device based on the information provided by the providing unit. A system characterized by:

2. The providing unit Providing personalized information based on users' lifestyles and preferences 2. The system of claim 1.

3. The providing unit Monitor the user's health and provide appropriate advice 2. The system of claim 1.

4. The control unit Control smart devices in your home 2. The system of claim 1.

5. The providing unit Support users in their studies and work 2. The system of claim 1.

6. The analysis unit Analyze the user's natural language input and provide appropriate information 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of accepting natural language input based on the estimated user emotions.

2. The system of claim 1.

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

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