System

Abstract

The system according to the embodiment aims to provide a natural interactive customer experience using voice input.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a voice input from a user. The analyzer analyzes the voice accepted by the acceptor and generates a response. The providing unit provides the response generated by the analyzing unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies fall short in providing a natural, interactive customer experience using voice input, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a natural interactive customer experience using voice input. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, and a providing unit. The receiving unit receives a voice input from a user. The analyzing unit analyzes the voice received by the receiving unit and generates a response. The providing unit provides the response generated by the analyzing unit by voice. [Effects of the Invention]

[0007] An embodiment of the system can provide a natural, interactive customer experience using voice input. [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 voice platform according to an embodiment of the present invention is a system that accepts voice input from a user, analyzes the voice, generates an appropriate response, and provides it via voice. In the voice platform, a user inputs a question or instruction via voice, and a generative AI analyzes the voice to generate a response, which is then provided to the user using voice technology. For example, if a user asks, "What's the weather like today?", the generative AI obtains weather information and responds, "It's sunny today." Furthermore, the voice platform allows for more natural and intuitive access to the generative AI through dedicated devices. This allows generative AI to be more deeply integrated into everyday life. This enables the voice platform to realize a new, conversational customer experience and facilitates human augmentation in the age of AGI. Generative AI is expected to be utilized in a variety of situations, including information acquisition, entertainment, and business efficiency at home.

[0029] A voice platform according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives voice input from a user. For example, when a user vocally asks, "What's the weather like today?", the reception unit receives the voice. The analysis unit uses a generation AI to analyze the voice received by the reception unit and generate an appropriate response. For example, the analysis unit converts the voice into text using speech recognition technology, analyzes the text, and generates a response such as, "It's sunny today." The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit converts the text response generated by the generation AI into speech using speech synthesis technology and provides it to the user. For example, the provision unit converts the text generated by the generation AI, "It's sunny today," into speech and provides it to the user. This allows the user to interact with the generation AI in a natural conversational format. This allows the voice platform according to an embodiment to analyze the voice input from the user and provide an appropriate response by speech.

[0030] The reception unit can receive voice input from the user. The reception unit can receive voice input from the user using, for example, a microphone. The reception unit can also receive voice input from a smartphone. For example, the user can receive voice input by speaking into the smartphone's microphone. This allows the user's voice input to be reliably received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the voice input to AI and have the AI ​​perform preprocessing of the voice data.

[0031] The analysis unit can convert speech to text using speech recognition technology and analyze the text to generate a response. The analysis unit can convert speech to text using, for example, speech recognition technology that uses deep learning. For example, the analysis unit receives speech data as input, performs phoneme recognition and grammar analysis, and generates text data. The analysis unit can also convert speech to text using an HMM (hidden Markov model). For example, the analysis unit inputs speech data into an HMM, extracts speech features, and generates text data. The analysis unit then analyzes the text data using a generation AI to generate an appropriate response. For example, the analysis unit inputs the text "What's the weather like today?" to the generation AI, which then generates the response "It's sunny today." This allows speech to be converted to text and an appropriate response to be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without an AI. For example, the analysis unit can input speech data to the generation AI and have the generation AI perform speech recognition and response generation.

[0032] The providing unit can convert the text response generated by the generation AI into speech using speech synthesis technology and provide it to the user. The providing unit can convert the text response generated by the generation AI into speech using, for example, Text-to-Speech (TTS) technology. For example, the providing unit can convert the text "It's sunny today" into speech using TTS technology and provide it to the user. The providing unit can also convert into speech using a speech sample. For example, the providing unit can convert the text generated by the generation AI into speech using a pre-recorded speech sample. This allows the text response generated by the generation AI to be converted into speech and provided to the user. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can perform speech synthesis using an AI model that receives text data generated by the generation AI as input and outputs speech data.

[0033] Furthermore, the audio platform includes a navigation unit that displays video in accordance with the user's moving speed. The navigation unit, for example, uses GPS to measure the user's moving speed and displays video in accordance with that speed. For example, if the user is walking, the navigation unit displays video in accordance with the walking speed. The navigation unit can also measure the moving speed using an acceleration sensor and display video in accordance with that speed. For example, if the user is traveling by bicycle, the navigation unit displays video in accordance with the bicycle speed. This makes it possible to display video in accordance with the user's moving speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input moving speed data to a generation AI and cause the generation AI to adjust the video display.

[0034] Furthermore, the voice platform includes a terminal unit that improves the accuracy of voice input using a dedicated terminal. The terminal unit improves the accuracy of voice input using, for example, specific hardware. For example, the terminal unit accepts voice input using a high-performance microphone. The terminal unit can also improve the accuracy of voice input using specific software. For example, the terminal unit improves the accuracy of voice input using noise canceling technology. This allows the accuracy of voice input to be improved using a dedicated terminal. Some or all of the above-described processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input voice input data to a generation AI and cause the generation AI to improve the accuracy of the voice input.

[0035] The reception unit can analyze the user's past voice input history and select an appropriate reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also predict and receive commands to be used in a specific time period based on the user's past voice input history. Furthermore, the reception unit can also suggest an optimal voice input reception method based on the user's past voice input history. This makes it possible to select an optimal reception method based on the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice input history data to a generation AI and have the generation AI select an optimal reception method.

[0036] The reception unit can filter the user's current environmental sound to remove noise when receiving voice input. For example, when the user is in a noisy environment, the reception unit can filter the environmental sound to remove noise and improve the accuracy of the voice input. Furthermore, when the user is in a quiet environment, the reception unit can also filter the environmental sound to a minimum and receive natural voice input. Furthermore, when the user is moving, the reception unit can also filter the environmental sound in real time to remove noise. This allows the user's current environmental sound to be filtered and noise to be removed. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to perform noise removal.

[0037] When receiving a voice input, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit receives the voice input using voice recognition technology. Furthermore, if the user selects gesture input, the reception unit can also receive the input using gesture recognition technology. Furthermore, if the user selects touch input, the reception unit can also receive the input using a touch screen. This makes it possible to select the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input input method data to a generation AI and cause the generation AI to select the optimal reception means.

[0038] When receiving a voice input, the reception unit can prioritize receiving relevant inputs by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving voice inputs related to that location. Furthermore, when the user is moving, the reception unit can prioritize receiving highly relevant voice inputs based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can prioritize receiving voice inputs related to that area. This makes it possible to prioritize receiving highly relevant inputs by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant inputs.

[0039] When receiving a voice input, the reception unit can analyze the user's online activity and receive related voice input. For example, the reception unit can preferentially receive voice input 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 preferentially receive related voice input. Furthermore, the reception unit can also refer to the activities of the user's friends on social media to preferentially receive related voice input. In this way, the user's social media activity can be analyzed and related voice input can be received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related voice input.

[0040] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving voice input. For example, the reception unit can suggest an optimal voice input reception method based on feedback provided by the user in the past. The reception unit can also preferentially receive a specific voice input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the voice input reception method. This makes it possible to customize the reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input feedback data to a generation AI and cause the generation AI to adjust the reception method.

[0041] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the voice input. For example, the analysis unit performs a detailed analysis on important voice input. The analysis unit can also perform a standard analysis on general voice input. Furthermore, the analysis unit can also perform a simplified analysis on voice input with low urgency. This makes it possible to adjust the level of detail of the analysis based on the importance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice input data to a generation AI and cause the generation AI to adjust the analysis based on the importance.

[0042] During analysis, the analysis unit can apply multiple analysis algorithms depending on the category of the voice input. For example, the analysis unit can apply a news analysis algorithm to a voice input related to news. The analysis unit can also apply a weather analysis algorithm to a voice input related to weather. Furthermore, the analysis unit can apply an entertainment analysis algorithm to a voice input related to entertainment. This makes it possible to apply different analysis algorithms depending on the category of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice input data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result 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 accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the order of analysis based on the submission time of the voice input. For example, the analysis unit prioritizes analysis of voice inputs with high urgency. The analysis unit can also analyze general voice inputs with standard priority. Furthermore, the analysis unit can postpone analysis of voice inputs with low urgency. This makes it possible to determine the priority of analysis based on the submission time of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to a generation AI and have the generation AI execute the analysis order.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the voice input. For example, the analysis unit prioritizes analysis of highly relevant voice input. The analysis unit can also analyze voice input of general relevance in a standard order. Furthermore, the analysis unit can also postpone analysis of voice input of low relevance. This makes it possible to adjust the order of analysis based on the relevance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI and have the generation AI execute the order of analysis.

[0046] During analysis, the analysis unit can determine the use of technical terminology 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 terminology. Furthermore, if the user has general knowledge, the analysis unit can provide analysis results that use standard terminology. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that use simple terminology. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input technical expertise level data into a generation AI and have the generation AI execute the use of technical terminology.

[0047] The providing unit can select an appropriate delivery method by referring to the user's past response history when providing the message. The providing unit can select the optimal delivery method based on, for example, the user's preferred voice tone in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and customize the delivery method. This makes it possible to select the optimal delivery method based on the user's past response history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input response history data to a generation AI and cause the generation AI to select the optimal delivery method.

[0048] The providing unit can adjust the content to be provided according to the user's current situation when providing the content. For example, when the user is on the move, the providing unit selects concise content to be provided. Furthermore, when the user is relaxed, the providing unit can select detailed content to be provided. Furthermore, when the user is in a hurry, the providing unit can select quick and to-the-point content to be provided. This makes it possible to customize the content to be provided according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data to the generating AI and cause the generating AI to adjust the content to be provided.

[0049] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, if a user provides feedback on the content provided, the providing unit improves the delivery method based on that feedback. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can analyze the user's feedback and customize the delivery method. This makes it possible to improve the delivery method by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data into a generating AI and cause the generating AI to improve the delivery method.

[0050] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, when the user is in a specific location, the providing unit can prioritize providing information related to that location. Furthermore, when the user is moving, the providing unit can also provide highly relevant information based on the user's current location. Furthermore, when the user is in a specific area, the providing unit can also provide information related to that area. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0051] The providing unit can adjust the content of the provision by analyzing the user's social media activity at the time of provision. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This makes it possible to customize the content of the provision by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and cause the generation AI to adjust the content of the provision.

[0052] The providing unit can adjust the delivery method by reflecting the user's past feedback when providing the information. The providing unit can, for example, suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and customize the delivery method. This makes it possible to customize the delivery method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input feedback data to a generation AI and cause the generation AI to adjust the delivery method.

[0053] During navigation, the navigation unit can provide an appropriate route by referring to the user's past travel history. The navigation unit, for example, can propose an optimal route based on routes the user has used in the past. The navigation unit can also propose a route that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and propose the most efficient route. This makes it possible to provide an optimal route based on the user's past travel history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input travel history data into a generation AI and have the generation AI propose an optimal route.

[0054] During navigation, the navigation unit can adjust the display content according to the user's current moving speed. For example, the navigation unit can provide detailed navigation information when the user is walking slowly. Furthermore, the navigation unit can provide concise navigation information when the user is walking fast. Furthermore, the navigation unit can provide appropriate navigation information when the user is traveling by bicycle. This makes it possible to customize the display content according to the user's current moving speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input moving speed data to the generation AI and have the generation AI adjust the display content.

[0055] The navigation unit can improve the navigation method by reflecting user feedback during navigation. For example, when a user provides feedback on navigation, the navigation unit improves the navigation method based on that feedback. The navigation unit can also preferentially select a specific navigation method based on the user's past feedback. Furthermore, the navigation unit can analyze the user's feedback and customize the navigation method. This allows the navigation method to be improved by reflecting the user's feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input feedback data to a generation AI and cause the generation AI to improve the navigation method.

[0056] During navigation, the navigation unit can provide an appropriate route by taking into account the user's geographical location information. For example, if the user is in a specific location, the navigation unit can preferentially provide a route related to that location. In addition, if the user is moving, the navigation unit can also provide an optimal route based on the user's current location. Furthermore, if the user is in a specific area, the navigation unit can also provide a route related to that area. This makes it possible to provide an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or may be performed without using AI. For example, the navigation unit can input geographical location information data to a generation AI and cause the generation AI to provide an optimal route.

[0057] During navigation, the navigation unit can analyze the user's social media activity and adjust the navigation content. For example, the navigation unit provides navigation information related to places where the user has checked in on social media. The navigation unit can also analyze the user's social media posts and provide related navigation information. Furthermore, the navigation unit can provide related navigation information by referring to the activities of the user's friends on social media. This makes it possible to customize the navigation content by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input social media activity data into a generation AI and have the generation AI adjust the navigation content.

[0058] During navigation, the navigation unit can adjust the navigation method by reflecting the user's past feedback. For example, the navigation unit can suggest an optimal navigation method based on feedback provided by the user in the past. The navigation unit can also preferentially select a specific navigation method based on the user's past feedback. Furthermore, the navigation unit can analyze the user's past feedback and customize the navigation method. This makes it possible to customize the navigation method by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input feedback data to a generation AI and have the generation AI adjust the navigation method.

[0059] When operating the terminal, the terminal unit can provide an appropriate operation method by referring to the user's past operation history. The terminal unit can provide an optimal operation method based on, for example, operation methods that the user has preferred in the past. The terminal unit can also preferentially provide a specific operation method based on the user's past operation history. Furthermore, the terminal unit can analyze the user's past operation history and customize the operation method. This makes it possible to provide an optimal operation method based on the user's past operation history. Some or all of the above-mentioned processing in the terminal unit can be performed using, for example, AI, or can be performed without using AI. For example, the terminal unit can input operation history data to a generation AI and cause the generation AI to provide an optimal operation method.

[0060] The terminal unit can adjust the operation content according to the user's current situation when operating the terminal. For example, when the user is on the move, the terminal unit can provide simple operation content. Furthermore, when the user is relaxed, the terminal unit can also provide detailed operation content. Furthermore, when the user is in a hurry, the terminal unit can also provide quick and to the point operation content. This makes it possible to customize the operation content according to the user's current situation. Some or all of the above-mentioned processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input current situation data to the generation AI and have the generation AI adjust the operation content.

[0061] The terminal unit can improve the operation method by reflecting user feedback when operating the terminal. For example, when the user provides feedback on the operation content, the terminal unit improves the operation method based on the feedback. The terminal unit can also preferentially provide a specific operation method based on the user's past feedback. Furthermore, the terminal unit can analyze the user's feedback and customize the operation method. This makes it possible to improve the operation method by reflecting the user's feedback. Some or all of the above-mentioned processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input feedback data into a generation AI and cause the generation AI to improve the operation method.

[0062] When operating the terminal, the terminal unit can provide an appropriate operation method by taking into account the user's geographical location information. For example, when the user is in a specific location, the terminal unit can provide an operation method related to that location. Furthermore, when the user is moving, the terminal unit can also provide an optimal operation method based on the user's current location. Furthermore, when the user is in a specific area, the terminal unit can also provide an operation method related to that area. This makes it possible to provide an optimal operation method by taking into account the user's geographical location information. Some or all of the above-described processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input geographical location information data to a generation AI and cause the generation AI to provide an optimal operation method.

[0063] When operating the terminal, the terminal unit can analyze the user's social media activity and adjust the operation content. For example, the terminal unit provides operation content related to places where the user has checked in on social media. The terminal unit can also analyze the user's social media posts and provide related operation content. Furthermore, the terminal unit can provide related operation content with reference to the activities of the user's friends on social media. This makes it possible to analyze the user's social media activity and customize the operation content. Some or all of the above-mentioned processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input social media activity data into a generation AI and have the generation AI adjust the operation content.

[0064] The terminal unit can adjust the operation method by reflecting the user's past feedback when operating the terminal. For example, the terminal unit can suggest an optimal operation method based on feedback provided by the user in the past. The terminal unit can also preferentially provide a specific operation method based on the user's past feedback. Furthermore, the terminal unit can analyze the user's past feedback and customize the operation method. This makes it possible to customize the operation method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input feedback data to a generation AI and cause the generation AI to adjust the operation method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The providing unit can learn the user's preferred response style based on the user's past voice input history and provide an appropriate response. For example, if the user has preferred short and concise responses in the past, the providing unit generates a response that matches that style. Also, if the user prefers detailed explanations, the providing unit can provide a detailed response. Furthermore, the providing unit can preferentially provide responses related to specific topics based on the user's past voice input history. This allows the providing unit to provide more personalized responses based on the user's past voice input history.

[0067] The analysis unit can analyze background sounds included in the user's voice input and estimate the user's current environment. For example, the analysis unit can detect the sound of a car included in the voice input and estimate that the user is moving. The analysis unit can also detect human voices included in the voice input and estimate that the user is in a public place. Furthermore, the analysis unit can detect natural sounds included in the voice input and estimate that the user is outdoors. This allows the analysis unit to estimate the user's current environment and generate an appropriate response according to that environment.

[0068] The navigation unit can display videos taking into consideration not only the user's moving speed but also the user's moving direction. For example, if the user is moving north, the navigation unit can display videos related to the north direction. Also, if the user is moving east, the navigation unit can display videos related to the east direction. Furthermore, if the user is moving toward a specific destination, the navigation unit can display videos related to that destination. This allows the navigation unit to display videos according to the user's moving direction.

[0069] The terminal unit may collect voice using multiple microphones to improve the accuracy of the user's voice input. For example, the terminal unit may use microphones arranged at the front and rear to collect the user's voice more accurately. Alternatively, the terminal unit may use microphones arranged on the left and right to collect the user's voice in stereo. Furthermore, the terminal unit may use noise canceling technology to remove ambient noise and improve the accuracy of the voice input. This allows the terminal unit to improve the accuracy of the voice input by using multiple microphones.

[0070] The providing unit can analyze the user's current environmental sound and select an appropriate audio output method. For example, if the user is in a noisy environment, the providing unit can provide audio at an increased volume. Also, if the user is in a quiet environment, the providing unit can provide audio at a decreased volume. Furthermore, if the user is moving, the providing unit can provide audio using noise canceling technology. This allows the providing unit to select an appropriate audio output method according to the user's current environmental sound.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives voice input from the user. For example, when the user asks a question by voice, such as "What's the weather like today?", the reception unit receives the voice. Step 2: The analysis unit uses a generation AI to analyze the speech received by the reception unit and generate an appropriate response. For example, the analysis unit converts the speech into text using speech recognition technology, analyzes the text, and generates a response such as "It's sunny today." The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The providing unit converts the text response generated by the generation AI into speech using speech synthesis technology and provides it to the user. For example, the providing unit converts the text generated by the generation AI, "It's sunny today," into speech and provides it to the user.

[0073] (Example 2) A voice platform according to an embodiment of the present invention is a system that accepts voice input from a user, analyzes the voice, generates an appropriate response, and provides it via voice. In the voice platform, a user inputs a question or instruction via voice, and a generative AI analyzes the voice to generate a response, which is then provided to the user using voice technology. For example, if a user asks, "What's the weather like today?", the generative AI obtains weather information and responds, "It's sunny today." Furthermore, the voice platform allows for more natural and intuitive access to the generative AI through dedicated devices. This allows generative AI to be more deeply integrated into everyday life. This enables the voice platform to realize a new, conversational customer experience and facilitates human augmentation in the age of AGI. Generative AI is expected to be utilized in a variety of situations, including information acquisition, entertainment, and business efficiency at home.

[0074] A voice platform according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives voice input from a user. For example, when a user vocally asks, "What's the weather like today?", the reception unit receives the voice. The analysis unit uses a generation AI to analyze the voice received by the reception unit and generate an appropriate response. For example, the analysis unit converts the voice into text using speech recognition technology, analyzes the text, and generates a response such as, "It's sunny today." The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit converts the text response generated by the generation AI into speech using speech synthesis technology and provides it to the user. For example, the provision unit converts the text generated by the generation AI, "It's sunny today," into speech and provides it to the user. This allows the user to interact with the generation AI in a natural conversational format. This allows the voice platform according to an embodiment to analyze the voice input from the user and provide an appropriate response by speech.

[0075] The reception unit can receive voice input from the user. The reception unit can receive voice input from the user using, for example, a microphone. The reception unit can also receive voice input from a smartphone. For example, the user can receive voice input by speaking into the smartphone's microphone. This allows the user's voice input to be reliably received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the voice input to AI and have the AI ​​perform preprocessing of the voice data.

[0076] The analysis unit can convert speech to text using speech recognition technology and analyze the text to generate a response. The analysis unit can convert speech to text using, for example, speech recognition technology that uses deep learning. For example, the analysis unit receives speech data as input, performs phoneme recognition and grammar analysis, and generates text data. The analysis unit can also convert speech to text using an HMM (hidden Markov model). For example, the analysis unit inputs speech data into an HMM, extracts speech features, and generates text data. The analysis unit then analyzes the text data using a generation AI to generate an appropriate response. For example, the analysis unit inputs the text "What's the weather like today?" to the generation AI, which then generates the response "It's sunny today." This allows speech to be converted to text and an appropriate response to be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without an AI. For example, the analysis unit can input speech data to the generation AI and have the generation AI perform speech recognition and response generation.

[0077] The providing unit can convert the text response generated by the generation AI into speech using speech synthesis technology and provide it to the user. The providing unit can convert the text response generated by the generation AI into speech using, for example, Text-to-Speech (TTS) technology. For example, the providing unit can convert the text "It's sunny today" into speech using TTS technology and provide it to the user. The providing unit can also convert into speech using a speech sample. For example, the providing unit can convert the text generated by the generation AI into speech using a pre-recorded speech sample. This allows the text response generated by the generation AI to be converted into speech and provided to the user. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can perform speech synthesis using an AI model that receives text data generated by the generation AI as input and outputs speech data.

[0078] Furthermore, the audio platform includes a navigation unit that displays video in accordance with the user's moving speed. The navigation unit, for example, uses GPS to measure the user's moving speed and displays video in accordance with that speed. For example, if the user is walking, the navigation unit displays video in accordance with the walking speed. The navigation unit can also measure the moving speed using an acceleration sensor and display video in accordance with that speed. For example, if the user is traveling by bicycle, the navigation unit displays video in accordance with the bicycle speed. This makes it possible to display video in accordance with the user's moving speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input moving speed data to a generation AI and cause the generation AI to adjust the video display.

[0079] Furthermore, the voice platform includes a terminal unit that improves the accuracy of voice input using a dedicated terminal. The terminal unit improves the accuracy of voice input using, for example, specific hardware. For example, the terminal unit accepts voice input using a high-performance microphone. The terminal unit can also improve the accuracy of voice input using specific software. For example, the terminal unit improves the accuracy of voice input using noise canceling technology. This allows the accuracy of voice input to be improved using a dedicated terminal. Some or all of the above-described processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input voice input data to a generation AI and cause the generation AI to improve the accuracy of the voice input.

[0080] The reception unit can analyze the user's emotions and adjust the timing of receiving the voice input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving the voice input and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of receiving the voice input to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can immediately receive the voice input, enabling a quick response. This allows the timing of receiving the voice input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the reception timing.

[0081] The reception unit can analyze the user's past voice input history and select an appropriate reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also predict and receive commands to be used in a specific time period based on the user's past voice input history. Furthermore, the reception unit can also suggest an optimal voice input reception method based on the user's past voice input history. This makes it possible to select an optimal reception method based on the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice input history data to a generation AI and have the generation AI select an optimal reception method.

[0082] The reception unit can filter the user's current environmental sound to remove noise when receiving voice input. For example, when the user is in a noisy environment, the reception unit can filter the environmental sound to remove noise and improve the accuracy of the voice input. Furthermore, when the user is in a quiet environment, the reception unit can also filter the environmental sound to a minimum and receive natural voice input. Furthermore, when the user is moving, the reception unit can also filter the environmental sound in real time to remove noise. This allows the user's current environmental sound to be filtered and noise to be removed. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to perform noise removal.

[0083] When receiving a voice input, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit receives the voice input using voice recognition technology. Furthermore, if the user selects gesture input, the reception unit can also receive the input using gesture recognition technology. Furthermore, if the user selects touch input, the reception unit can also receive the input using a touch screen. This makes it possible to select the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input input method data to a generation AI and cause the generation AI to select the optimal reception means.

[0084] The reception unit can analyze the user's emotions and determine the priority of the voice inputs to be received based on the analyzed user's emotions. For example, when the user is nervous, the reception unit can prioritize important voice inputs. Furthermore, when the user is relaxed, the reception unit can equally receive all voice inputs. Furthermore, when the user is in a hurry, the reception unit can prioritize urgent voice inputs. This allows the priority of the voice inputs to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation and priority determination.

[0085] When receiving a voice input, the reception unit can prioritize receiving relevant inputs by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving voice inputs related to that location. Furthermore, when the user is moving, the reception unit can prioritize receiving highly relevant voice inputs based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can prioritize receiving voice inputs related to that area. This makes it possible to prioritize receiving highly relevant inputs by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant inputs.

[0086] When receiving a voice input, the reception unit can analyze the user's online activity and receive related voice input. For example, the reception unit can preferentially receive voice input 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 preferentially receive related voice input. Furthermore, the reception unit can also refer to the activities of the user's friends on social media to preferentially receive related voice input. In this way, the user's social media activity can be analyzed and related voice input can be received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related voice input.

[0087] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving voice input. For example, the reception unit can suggest an optimal voice input reception method based on feedback provided by the user in the past. The reception unit can also preferentially receive a specific voice input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the voice input reception method. This makes it possible to customize the reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input feedback data to a generation AI and cause the generation AI to adjust the reception method.

[0088] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows the way the analysis is presented to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the way the emotion is presented.

[0089] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the voice input. For example, the analysis unit performs a detailed analysis on important voice input. The analysis unit can also perform a standard analysis on general voice input. Furthermore, the analysis unit can also perform a simplified analysis on voice input with low urgency. This makes it possible to adjust the level of detail of the analysis based on the importance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice input data to a generation AI and cause the generation AI to adjust the analysis based on the importance.

[0090] During analysis, the analysis unit can apply multiple analysis algorithms depending on the category of the voice input. For example, the analysis unit can apply a news analysis algorithm to a voice input related to news. The analysis unit can also apply a weather analysis algorithm to a voice input related to weather. Furthermore, the analysis unit can apply an entertainment analysis algorithm to a voice input related to entertainment. This makes it possible to apply different analysis algorithms depending on the category of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice input data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result 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 accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0092] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the length of the analysis.

[0093] During analysis, the analysis unit can determine the order of analysis based on the submission time of the voice input. For example, the analysis unit prioritizes analysis of voice inputs with high urgency. The analysis unit can also analyze general voice inputs with standard priority. Furthermore, the analysis unit can postpone analysis of voice inputs with low urgency. This makes it possible to determine the priority of analysis based on the submission time of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to a generation AI and have the generation AI execute the analysis order.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the voice input. For example, the analysis unit prioritizes analysis of highly relevant voice input. The analysis unit can also analyze voice input of general relevance in a standard order. Furthermore, the analysis unit can also postpone analysis of voice input of low relevance. This makes it possible to adjust the order of analysis based on the relevance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI and have the generation AI execute the order of analysis.

[0095] During analysis, the analysis unit can determine the use of technical terminology 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 terminology. Furthermore, if the user has general knowledge, the analysis unit can provide analysis results that use standard terminology. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that use simple terminology. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input technical expertise level data into a generation AI and have the generation AI execute the use of technical terminology.

[0096] The providing unit can analyze the user's emotions and adjust the tone and speed of the provided voice based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide voice in a calm tone at a slow speed. Furthermore, if the user is relaxed, the providing unit can provide voice in a bright tone at a standard speed. Furthermore, if the user is in a hurry, the providing unit can provide voice in a quick and concise tone. This allows the tone and speed of the provided voice to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the tone and speed of the voice.

[0097] The providing unit can select an appropriate delivery method by referring to the user's past response history when providing the message. The providing unit can select the optimal delivery method based on, for example, the user's preferred voice tone in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and customize the delivery method. This makes it possible to select the optimal delivery method based on the user's past response history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input response history data to a generation AI and cause the generation AI to select the optimal delivery method.

[0098] The providing unit can adjust the content to be provided according to the user's current situation when providing the content. For example, when the user is on the move, the providing unit selects concise content to be provided. Furthermore, when the user is relaxed, the providing unit can select detailed content to be provided. Furthermore, when the user is in a hurry, the providing unit can select quick and to-the-point content to be provided. This makes it possible to customize the content to be provided according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data to the generating AI and cause the generating AI to adjust the content to be provided.

[0099] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, if a user provides feedback on the content provided, the providing unit improves the delivery method based on that feedback. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can analyze the user's feedback and customize the delivery method. This makes it possible to improve the delivery method by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data into a generating AI and cause the generating AI to improve the delivery method.

[0100] The providing unit can analyze the user's emotions and determine the priority of the audio to be provided based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can prioritize providing important information. Furthermore, if the user is relaxed, the providing unit can also provide all information equally. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information with high urgency. This makes it possible to determine the priority of the audio to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of the audio.

[0101] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, when the user is in a specific location, the providing unit can prioritize providing information related to that location. Furthermore, when the user is moving, the providing unit can also provide highly relevant information based on the user's current location. Furthermore, when the user is in a specific area, the providing unit can also provide information related to that area. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0102] The providing unit can adjust the content of the provision by analyzing the user's social media activity at the time of provision. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This makes it possible to customize the content of the provision by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and cause the generation AI to adjust the content of the provision.

[0103] The providing unit can adjust the delivery method by reflecting the user's past feedback when providing the information. The providing unit can, for example, suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and customize the delivery method. This makes it possible to customize the delivery method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input feedback data to a generation AI and cause the generation AI to adjust the delivery method.

[0104] The navigation unit can analyze the user's emotions and adjust the navigation display method based on the analyzed user's emotions. For example, if the user is nervous, the navigation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the navigation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. This allows the navigation display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the display method.

[0105] During navigation, the navigation unit can provide an appropriate route by referring to the user's past travel history. The navigation unit, for example, can propose an optimal route based on routes the user has used in the past. The navigation unit can also propose a route that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and propose the most efficient route. This makes it possible to provide an optimal route based on the user's past travel history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input travel history data into a generation AI and have the generation AI propose an optimal route.

[0106] During navigation, the navigation unit can adjust the display content according to the user's current moving speed. For example, the navigation unit can provide detailed navigation information when the user is walking slowly. Furthermore, the navigation unit can provide concise navigation information when the user is walking fast. Furthermore, the navigation unit can provide appropriate navigation information when the user is traveling by bicycle. This makes it possible to customize the display content according to the user's current moving speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input moving speed data to the generation AI and have the generation AI adjust the display content.

[0107] The navigation unit can improve the navigation method by reflecting user feedback during navigation. For example, when a user provides feedback on navigation, the navigation unit improves the navigation method based on that feedback. The navigation unit can also preferentially select a specific navigation method based on the user's past feedback. Furthermore, the navigation unit can analyze the user's feedback and customize the navigation method. This allows the navigation method to be improved by reflecting the user's feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input feedback data to a generation AI and cause the generation AI to improve the navigation method.

[0108] The navigation unit can analyze the user's emotions and determine navigation priorities based on the analyzed user emotions. For example, if the user is nervous, the navigation unit can prioritize providing important navigation information. Furthermore, if the user is relaxed, the navigation unit can also provide all navigation information equally. Furthermore, if the user is in a hurry, the navigation unit can prioritize providing navigation information with high urgency. This allows navigation priorities to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the navigation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and navigation priority determination.

[0109] During navigation, the navigation unit can provide an appropriate route by taking into account the user's geographical location information. For example, if the user is in a specific location, the navigation unit can preferentially provide a route related to that location. In addition, if the user is moving, the navigation unit can also provide an optimal route based on the user's current location. Furthermore, if the user is in a specific area, the navigation unit can also provide a route related to that area. This makes it possible to provide an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or may be performed without using AI. For example, the navigation unit can input geographical location information data to a generation AI and cause the generation AI to provide an optimal route.

[0110] During navigation, the navigation unit can analyze the user's social media activity and adjust the navigation content. For example, the navigation unit provides navigation information related to places where the user has checked in on social media. The navigation unit can also analyze the user's social media posts and provide related navigation information. Furthermore, the navigation unit can provide related navigation information by referring to the activities of the user's friends on social media. This makes it possible to customize the navigation content by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input social media activity data into a generation AI and have the generation AI adjust the navigation content.

[0111] During navigation, the navigation unit can adjust the navigation method by reflecting the user's past feedback. For example, the navigation unit can suggest an optimal navigation method based on feedback provided by the user in the past. The navigation unit can also preferentially select a specific navigation method based on the user's past feedback. Furthermore, the navigation unit can analyze the user's past feedback and customize the navigation method. This makes it possible to customize the navigation method by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input feedback data to a generation AI and have the generation AI adjust the navigation method.

[0112] The terminal unit can analyze the user's emotions and adjust the device operation method based on the analyzed user's emotions. For example, if the user is nervous, the terminal unit can provide a simple and intuitive operation method. Furthermore, if the user is relaxed, the terminal unit can also provide detailed operation options. Furthermore, if the user is in a hurry, the terminal unit can also provide a quick operation method. This allows the device operation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the terminal unit can be performed using, for example, AI, or without AI. For example, the terminal unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the operation method.

[0113] When operating the terminal, the terminal unit can provide an appropriate operation method by referring to the user's past operation history. The terminal unit can provide an optimal operation method based on, for example, operation methods that the user has preferred in the past. The terminal unit can also preferentially provide a specific operation method based on the user's past operation history. Furthermore, the terminal unit can analyze the user's past operation history and customize the operation method. This makes it possible to provide an optimal operation method based on the user's past operation history. Some or all of the above-mentioned processing in the terminal unit can be performed using, for example, AI, or can be performed without using AI. For example, the terminal unit can input operation history data to a generation AI and cause the generation AI to provide an optimal operation method.

[0114] The terminal unit can adjust the operation content according to the user's current situation when operating the terminal. For example, when the user is on the move, the terminal unit can provide simple operation content. Furthermore, when the user is relaxed, the terminal unit can also provide detailed operation content. Furthermore, when the user is in a hurry, the terminal unit can also provide quick and to the point operation content. This makes it possible to customize the operation content according to the user's current situation. Some or all of the above-mentioned processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input current situation data to the generation AI and have the generation AI adjust the operation content.

[0115] The terminal unit can improve the operation method by reflecting user feedback when operating the terminal. For example, when the user provides feedback on the operation content, the terminal unit improves the operation method based on the feedback. The terminal unit can also preferentially provide a specific operation method based on the user's past feedback. Furthermore, the terminal unit can analyze the user's feedback and customize the operation method. This makes it possible to improve the operation method by reflecting the user's feedback. Some or all of the above-mentioned processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input feedback data into a generation AI and cause the generation AI to improve the operation method.

[0116] The terminal unit can analyze the user's emotions and determine the priority of device operations based on the analyzed user emotions. For example, if the user is nervous, the terminal unit can prioritize important operations. Furthermore, if the user is relaxed, the terminal unit can also provide all operations equally. Furthermore, if the user is in a hurry, the terminal unit can prioritize urgent operations. This allows the priority of device operations to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the terminal unit can be performed using, for example, an AI, or without an AI. For example, the terminal unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation and operation priority determination.

[0117] When operating the terminal, the terminal unit can provide an appropriate operation method by taking into account the user's geographical location information. For example, when the user is in a specific location, the terminal unit can provide an operation method related to that location. Furthermore, when the user is moving, the terminal unit can also provide an optimal operation method based on the user's current location. Furthermore, when the user is in a specific area, the terminal unit can also provide an operation method related to that area. This makes it possible to provide an optimal operation method by taking into account the user's geographical location information. Some or all of the above-described processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input geographical location information data to a generation AI and cause the generation AI to provide an optimal operation method.

[0118] When operating the terminal, the terminal unit can analyze the user's social media activity and adjust the operation content. For example, the terminal unit provides operation content related to places where the user has checked in on social media. The terminal unit can also analyze the user's social media posts and provide related operation content. Furthermore, the terminal unit can provide related operation content with reference to the activities of the user's friends on social media. This makes it possible to analyze the user's social media activity and customize the operation content. Some or all of the above-mentioned processing in the terminal unit may be performed using, for example, AI, or may be performed without using AI. For example, the terminal unit can input social media activity data into a generation AI and have the generation AI adjust the operation content.

[0119] The terminal unit can adjust the operation method by reflecting the user's past feedback when operating the terminal. For example, the terminal unit can suggest an optimal operation method based on feedback provided by the user in the past. The terminal unit can also preferentially provide a specific operation method based on the user's past feedback. Furthermore, the terminal unit can analyze the user's past feedback and customize the operation method. This makes it possible to customize the operation method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the terminal unit may be performed using AI, for example, or may be performed without using AI. For example, the terminal unit can input feedback data to a generation AI and cause the generation AI to adjust the operation method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, provision unit, navigation unit, and terminal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the voice using a generation AI, and generates an appropriate response. The provision unit provides the generated voice response to the user using the speaker 40B of the smart device 14. The navigation unit measures the user's moving speed using the GPS and acceleration sensor of the smart device 14 and displays a video according to that speed. The terminal unit improves the accuracy of the voice input using the smart device 14's high-performance microphone and noise-canceling technology. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and terminal 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 accepts a user's voice input 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, analyzes the voice using a generation AI, and generates an appropriate response. The provision unit provides the generated voice response to the user using the speaker 240 of the smart glasses 214. The navigation unit measures the user's moving speed using the GPS and acceleration sensor of the smart glasses 214 and displays a video according to that speed. The terminal unit improves the accuracy of the voice input using the high-performance microphone and noise-canceling technology of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and terminal unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a user's voice input 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, analyzes the voice using a generation AI, and generates an appropriate response. The provision unit provides the generated voice response to the user using the speaker 240 of the headset-type terminal 314. The navigation unit measures the user's moving speed using the GPS and acceleration sensor of the headset-type terminal 314 and displays a video in accordance with that speed. The terminal unit improves the accuracy of the voice input using the headset-type terminal 314's high-performance microphone and noise-canceling technology. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and terminal 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 voice input from the 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, analyzes the voice using a generation AI, and generates an appropriate response. The provision unit provides the generated voice response to the user using the speaker 240 of the robot 414. The navigation unit measures the user's moving speed using the GPS and acceleration sensor of the robot 414 and displays a video in accordance with that speed. The terminal unit improves the accuracy of voice input using the robot 414's high-performance microphone and noise-canceling technology.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can analyze the tone and speed of the user's voice input to estimate the user's emotions. For example, if the user speaks quickly and in a high tone, the analysis unit can estimate that the user is excited. Alternatively, if the user speaks slowly and in a low tone, the analysis unit can estimate that the user is relaxed. Furthermore, the analysis unit can combine the content of the user's voice input with changes in tone to perform more accurate emotion estimation. This allows the analysis unit to estimate the user's emotions and generate a response based on those emotions.

[0122] The providing unit can learn the user's preferred response style based on the user's past voice input history and provide an appropriate response. For example, if the user has preferred short and concise responses in the past, the providing unit generates a response that matches that style. Also, if the user prefers detailed explanations, the providing unit can provide a detailed response. Furthermore, the providing unit can preferentially provide responses related to specific topics based on the user's past voice input history. This allows the providing unit to provide more personalized responses based on the user's past voice input history.

[0123] The analysis unit can analyze background sounds included in the user's voice input and estimate the user's current environment. For example, the analysis unit can detect the sound of a car included in the voice input and estimate that the user is moving. The analysis unit can also detect human voices included in the voice input and estimate that the user is in a public place. Furthermore, the analysis unit can detect natural sounds included in the voice input and estimate that the user is outdoors. This allows the analysis unit to estimate the user's current environment and generate an appropriate response according to that environment.

[0124] The providing unit can estimate the user's emotions and adjust the tone and speed of the response based on the estimated emotions. For example, if the user is nervous, the providing unit can provide a response in a calm tone at a slow speed. If the user is relaxed, the providing unit can provide a response in a bright tone at a standard speed. Furthermore, if the user is in a hurry, the providing unit can provide a response in a quick and concise tone. This allows the providing unit to adjust the tone and speed of the response based on the user's emotions.

[0125] The navigation unit can display videos taking into consideration not only the user's moving speed but also the user's moving direction. For example, if the user is moving north, the navigation unit can display videos related to the north direction. Also, if the user is moving east, the navigation unit can display videos related to the east direction. Furthermore, if the user is moving toward a specific destination, the navigation unit can display videos related to that destination. This allows the navigation unit to display videos according to the user's moving direction.

[0126] The terminal unit may collect voice using multiple microphones to improve the accuracy of the user's voice input. For example, the terminal unit may use microphones arranged at the front and rear to collect the user's voice more accurately. Alternatively, the terminal unit may use microphones arranged on the left and right to collect the user's voice in stereo. Furthermore, the terminal unit may use noise canceling technology to remove ambient noise and improve the accuracy of the voice input. This allows the terminal unit to improve the accuracy of the voice input by using multiple microphones.

[0127] The reception unit can analyze the user's emotions and adjust the method of receiving voice input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can simplify the method of receiving voice input and wait until the user is relaxed. Also, if the user is relaxed, the reception unit can also receive detailed voice input. Furthermore, if the user is in a hurry, the reception unit can provide a method of receiving quick voice input. In this way, the reception unit can adjust the method of receiving voice input according to the user's emotions.

[0128] The analysis unit can analyze the emotional nuances of the user's voice input and adjust the content of the response. For example, if the user is angry, the analysis unit can adjust the content of the response to be calm and collected. If the user is sad, the analysis unit can also adjust the content of the response to words of encouragement or comfort. Furthermore, if the user is happy, the analysis unit can also adjust the content of the response to words of sympathy or congratulations. In this way, the analysis unit can adjust the content of the response based on the user's emotions.

[0129] The providing unit can analyze the user's current environmental sound and select an appropriate audio output method. For example, if the user is in a noisy environment, the providing unit can provide audio at an increased volume. Also, if the user is in a quiet environment, the providing unit can provide audio at a decreased volume. Furthermore, if the user is moving, the providing unit can provide audio using noise canceling technology. This allows the providing unit to select an appropriate audio output method according to the user's current environmental sound.

[0130] The analysis unit can analyze the emotional intensity of the voice input by the user and adjust the intensity of the response. For example, if the user speaks with strong emotion, the analysis unit can increase the intensity of the response. Also, if the user speaks with mild emotion, the analysis unit can decrease the intensity of the response. Furthermore, if the user speaks with neutral emotion, the analysis unit can adjust the intensity of the response to a medium level. In this way, the analysis unit can adjust the intensity of the response based on the emotional intensity of the user.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives voice input from the user. For example, when the user asks a question by voice, such as "What's the weather like today?", the reception unit receives the voice. Step 2: The analysis unit uses a generation AI to analyze the speech received by the reception unit and generate an appropriate response. For example, the analysis unit converts the speech into text using speech recognition technology, analyzes the text, and generates a response such as "It's sunny today." The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The providing unit converts the text response generated by the generation AI into speech using speech synthesis technology and provides it to the user. For example, the providing unit converts the text generated by the generation AI, "It's sunny today," into speech and provides it to the user.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 voice input from a user; an analysis unit that analyzes the voice received by the reception unit and generates a response; a providing unit that provides the response generated by the analysis unit by voice; Equipped with A system characterized by:

2. The reception unit Accepts user voice input 2. The system of claim 1.

3. The analysis unit Uses speech recognition technology to convert speech into text, then analyzes the text to generate a response 2. The system of claim 1.

4. The providing unit The text response generated by the AI ​​is converted into speech using speech synthesis technology and provided to the user.

2. The system of claim 1.

5. The providing unit Equipped with a navigation section that displays videos according to the user's movement speed 2. The system of claim 1.

6. The reception unit Equipped with a terminal unit that uses a dedicated terminal to improve the accuracy of voice input 2. The system of claim 1.

7. The reception unit Analyzes user emotions and adjusts the timing of voice input acceptance based on the analyzed user emotions.

2. The system of claim 1.

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

9. The reception unit When accepting voice input, filters the user's current ambient sounds to reduce noise 2. The system of claim 1.

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