system

Abstract

The system according to this embodiment aims to reproduce the words, actions, and values ​​of a specific person and provide the user with a realistic conversational experience. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to realistically reproduce the actions and values of a specific person and provide a user with an interactive experience.

[0005] The system according to the embodiment aims to reproduce the actions and values of a specific person and provide a user with a realistic interactive experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can reproduce the words, actions, and values ​​of a specific person, providing the user with a realistic dialogue experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that reproduces the words, actions, values, and personality of a specific CEO using multimodal AI. This system allows users to experience the CEO's thoughts and advice through interaction with the agent. Specifically, it uses multimodal AI that combines visual elements (video), auditory elements (voice), and text (speech) and has the ability to provide appropriate responses in real time according to the user's statements. It also has a personalization function that provides customized responses based on the user's interests and past conversations. This provides a realistic interaction as if the CEO were there, from trivial conversations to important discussions. This system is used as an educational tool to deepen insights into business and leadership for business leaders, entrepreneurs, educational institutions, and general users. For example, when a user asks a business question, the AI ​​agent reproduces the CEO's thought process and provides specific advice. This allows users to simulate realistic business scenarios and promote learning and growth. Furthermore, the AI ​​agent brings about concrete quantifiable effects such as improved user understanding, increased engagement, and educational effectiveness. For example, by understanding the CEO's thought process, users can deepen their insights into business and leadership. Furthermore, the realistic dialogue experience enhances user engagement and satisfaction. Additionally, the system offers valuable lessons from the experiences of specific CEOs, making it a highly promising educational tool. This system, through a combination of AI and multimodal technology, achieves both a deeper user experience and a pursuit of realism. For example, users can experience the multifaceted personality of a specific CEO by interacting with an AI agent that replicates their words and actions. This provides users with a new educational tool for business leadership and personal growth, helping more people achieve success. As a result, the AI ​​agent system can deliver concrete quantifiable benefits such as improved user understanding, increased engagement, and educational effectiveness.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. The analysis unit analyzes the information received by the reception unit. Analysis is performed by, for example, natural language processing, image analysis, and voice analysis, but is not limited to, such methods. For example, the analysis unit may use natural language processing technology to analyze the user's text input. The analysis unit may also use image analysis technology to analyze the user's image input. Furthermore, the analysis unit may use voice analysis technology to analyze the user's voice input. The generation unit generates a response based on the information analyzed by the analysis unit. The response is generated in the form of, for example, a text response, a voice response, or an image response, but is not limited to, such forms. For example, the generation unit uses natural language generation technology to generate text responses to user text input. The generation unit can also use speech synthesis technology to generate voice responses to user voice input. Furthermore, the generation unit can use image generation technology to generate image responses to user image input. The providing unit provides the responses generated by the generation unit. Providing is done by methods such as text display, audio playback, and image display, but is not limited to these examples. For example, the providing unit displays text responses on a display. The providing unit can also play voice responses through a speaker. Furthermore, the providing unit can also display image responses on a display. In this way, the AI ​​agent system according to the embodiment realizes realistic interaction by receiving user input, analyzing it, generating responses, and providing them.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may, for example, be equipped with a keyboard or touchscreen for receiving text input. This allows users to easily input text, and the system can quickly receive that input. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. Speech recognition technology can convert the user's voice into text with high accuracy, improving the convenience of voice input. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. Image recognition technology can analyze images provided by the user and extract the necessary information. For example, if a user uploads a product image, the system can extract product information from the image and generate an appropriate response. This allows the reception unit to support diverse input formats and improve user convenience. Furthermore, the reception unit can utilize the latest hardware and software technologies to receive user input quickly and accurately. For example, using high-performance microphones and cameras can improve the quality of voice and images and increase recognition accuracy. Also, optimizing the speech and image recognition algorithms can improve the input processing speed. This allows the reception desk to respond quickly and accurately to diverse user inputs, thereby improving the overall system performance.

[0031] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as natural language processing, image analysis, and speech analysis, but is not limited to these examples. For instance, the analysis unit can analyze user text input using natural language processing technology. Natural language processing technology can grammatically and semantically analyze user input text, accurately understanding the user's intent. This allows the system to respond appropriately to user requests. The analysis unit can also analyze user image input using image analysis technology. Image analysis technology can extract features from user-provided images and recognize objects and text within the images. For example, if a user uploads a product image, the system can extract information such as the product name, price, and features from the image and generate an appropriate response. Furthermore, the analysis unit can analyze user voice input using speech analysis technology. Speech analysis technology can convert user speech into text and analyze its content. This allows the system to respond accurately to voice input as well. By combining these technologies, the analysis unit can handle diverse user inputs and perform accurate analysis. Furthermore, the analysis unit can improve its analysis accuracy by utilizing machine learning and deep learning technologies. For example, by learning from past data, it can predict user input patterns and perform more accurate analysis. This allows the analysis unit to perform rapid and accurate analysis of diverse user inputs, thereby improving the overall system performance.

[0032] The generation unit generates a response based on the information analyzed by the analysis unit. The response may be generated in various forms, such as text responses, voice responses, or image responses, but is not limited to these examples. For instance, the generation unit can use natural language generation technology to generate text responses to user text input. Natural language generation technology can generate text with appropriate grammar and meaning based on user input. This allows the system to provide users with natural conversation. The generation unit can also use speech synthesis technology to generate voice responses to user voice input. Speech synthesis technology can convert text into speech and generate speech with natural pronunciation and intonation. This allows the system to provide users with voice responses. Furthermore, the generation unit can use image generation technology to generate image responses to user image input. Image generation technology can generate appropriate images based on user input. For example, if a user requests an image of a product, the system can generate an image related to that product and provide it to the user. The generation unit can combine these technologies to respond to diverse user requests and generate appropriate responses. Furthermore, the generation unit can utilize machine learning and deep learning technologies to improve the quality of responses. For example, by learning from past response data, it becomes possible to generate more natural and appropriate responses. This allows the generation unit to quickly and accurately generate responses to a variety of user requests, thereby improving the overall system performance.

[0033] The providing unit provides responses generated by the generating unit. These responses may be provided in various ways, such as text display, audio playback, or image display. For example, the providing unit may display text responses on a display. The display is capable of high resolution and clear display, providing users with visually understandable information. The providing unit may also play audio responses through a speaker. The speaker can reproduce high-quality audio, providing users with auditory understandable information. Furthermore, the providing unit may display image responses on a display. Images are capable of high resolution and clear display, providing users with visually understandable information. The providing unit can combine these methods to provide information to users in various formats. Furthermore, the providing unit can collect user feedback to improve the quality of the provided content. For example, by recording how users reacted to the provided information and analyzing the data, areas for improvement in the content can be identified. This allows the providing unit to provide information to users quickly and accurately, improving the overall system performance. Additionally, the providing unit can optimize the interface design and usability to improve user convenience. For example, an intuitive interface can be provided so that users can easily access information. This allows the service provider to deliver high-quality information to users and improve the overall user experience of the system.

[0034] The generation unit can generate responses that combine visual, auditory, and text elements. For example, the generation unit can generate images or videos as visual elements. For instance, the generation unit can present answers to user questions using images or videos. The generation unit can also generate audio as an auditory element. For example, the generation unit can provide answers to user questions in audio format. Furthermore, the generation unit can also generate text. For example, the generation unit can provide answers to user questions in text format. By combining visual, auditory, and text elements, a more realistic response can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input into a generation AI, which can then generate a response that combines visual, auditory, and text elements.

[0035] The service provider can provide customized responses based on the user's interests and past interactions. For example, based on the user's interests, the service provider can provide responses that include relevant topics. For example, if the user is interested in business, the service provider can provide responses that include information about business. The service provider can also provide customized responses based on the user's past interactions. For example, based on questions and conversations the user has had in the past, the service provider can provide responses that include relevant information. This allows for a more personalized experience by providing customized responses based on the user's interests and past interactions. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input the user's interests and past conversations into a generative AI, which can then generate customized responses.

[0036] The analysis unit can provide an appropriate response in real time in response to the user's statements. For example, the analysis unit can analyze the user's statements using natural language processing technology and generate an appropriate response. For example, the analysis unit can generate a response containing relevant information in response to a user's question. The analysis unit can also analyze the emotion in the user's statements and generate a response that corresponds to that emotion. For example, if the user is excited, the analysis unit will generate a calm response. This enables smooth dialogue by providing an appropriate response in real time in response to the user's statements. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's statements into a generative AI, which can then generate an appropriate response.

[0037] The generation unit can reproduce the words, actions, values, and personality of a specific CEO. For example, the generation unit can search a database for a specific CEO's past statements and actions and generate responses based on them. For example, the generation unit can quote the CEO's past statements to generate responses to user questions. The generation unit can also generate responses that reflect the CEO's values. For example, the generation unit can generate responses based on the CEO's management philosophy and social beliefs. Furthermore, the generation unit can generate responses that incorporate the CEO's personal anecdotes in order to reproduce the CEO's personality. This allows for a realistic experience for the user by reproducing the words, actions, values, and personality of a specific CEO. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on a specific CEO's past statements and actions into a generation AI, which can then generate responses based on that data.

[0038] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest relevant input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal reception method.

[0039] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving relevant topics based on the user's current situation. For example, the reception unit can filter and receive relevant input based on the user's areas of interest. The reception unit can also eliminate unnecessary input based on the user's current situation and areas of interest. This allows the reception unit to receive highly relevant input by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and areas of interest into the AI, which can then perform the filtering.

[0040] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inputs related to that region. For example, the reception unit will prioritize receiving relevant topics based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also prioritize receiving relevant inputs based on their current location. In this way, by considering the user's geographical location, highly relevant inputs can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into the AI, which can then prioritize receiving highly relevant inputs.

[0041] The reception unit can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception unit can prioritize receiving relevant topics from the user's social media activity. For example, the reception unit can filter and accept relevant input based on the user's social media activity. The reception unit can also analyze the user's social media activity and eliminate unnecessary input. In this way, by analyzing the user's social media activity, it can accept relevant input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can accept relevant input.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, the analysis unit performs a detailed analysis on important input content. For example, the analysis unit performs a normal analysis on normal input content. The analysis unit can also perform a simplified analysis on unnecessary input content. This allows for detailed analysis of important inputs by adjusting the level of detail of the analysis based on the importance of the input content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the input content to the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related input content. For example, it can apply an education analysis algorithm to education-related input content. The analysis unit can also apply a general analysis algorithm to general input content. By applying different analysis algorithms depending on the category of the input content, more appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the input content into the AI, and the AI ​​can apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the input content during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted input content. For example, the analysis unit may analyze previously submitted input content with normal priority. The analysis unit can also prioritize the analysis of urgent input content. This allows for prioritizing the analysis of recently submitted input content by determining the priority of analysis based on the submission date of the input content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the input content into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the input content during analysis. For example, the analysis unit may prioritize the analysis of highly relevant input content. For example, the analysis unit may analyze less relevant input content in the normal order. The analysis unit can also postpone the analysis of unnecessary input content. In this way, by adjusting the order of analysis based on the relevance of the input content, highly relevant input content can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the input content into the AI, and the AI ​​can adjust the order of analysis.

[0046] The generation unit can adjust the level of detail to reproduce a specific CEO's words, actions, values, and personality when generating responses. For example, the generation unit may refer to past statements and actions to reproduce the CEO's words in detail. For example, the generation unit may refer to the CEO's books and interviews to reflect the CEO's values. The generation unit may also incorporate personal anecdotes to reproduce the CEO's personality. By adjusting the level of detail to reproduce a specific CEO's words, actions, values, and personality, it is possible to provide more realistic responses. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on a specific CEO's past statements and actions into a generation AI, which can then generate a response based on that data.

[0047] The generation unit can optimize the combination of visual, auditory, and text elements when generating a response. For example, the generation unit might use a video clip of the CEO as a visual element. For example, the generation unit might use the CEO's voice as an auditory element. The generation unit can also quote the CEO's statements as text. By optimizing the combination of visual, auditory, and text elements, a more effective response can be provided. Some or all of the processing described above in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can input visual, auditory, and text data into a generative AI, which can then generate a response combining them.

[0048] The generation unit can improve the accuracy of responses by referring to a specific CEO's past statements during response generation. For example, the generation unit can search a database for the CEO's past statements and incorporate them into the response. For example, the generation unit can analyze the CEO's past statements to maintain consistency in responses. The generation unit can also quote the CEO's past statements to increase the reliability of responses. In this way, the accuracy of responses can be improved by referring to a specific CEO's past statements. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a specific CEO's past statements into a generation AI, and the generation AI can generate a response based on that.

[0049] The generation unit can enrich the content of a response by referring to relevant literature on a specific CEO during response generation. For example, the generation unit can refer to the CEO's books and reflect them in the response. For example, the generation unit can refer to articles about the CEO and enrich the content of the response. The generation unit can also refer to interviews with the CEO to increase the specificity of the response. In this way, the content of the response can be enriched by referring to relevant literature on a specific CEO. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevant literature on a specific CEO into a generation AI, and the generation AI can generate a response based on that.

[0050] The service provider can select the optimal service delivery method by referring to the user's past dialogue history when providing a response. For example, the service provider can select the optimal response delivery method from the user's past dialogue history. For example, the service provider can analyze the user's past dialogue history and provide a relevant response. The service provider can also provide a customized response by referring to the user's past dialogue history. This allows the service provider to select the optimal service delivery method by referring to the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dialogue history into AI, and the AI ​​can select the optimal service delivery method.

[0051] The service provider can provide customized responses based on the user's interests when providing responses. For example, the service provider can provide responses that include relevant topics based on the user's interests. For example, the service provider can provide customized responses based on the user's interests. The service provider can also provide responses based on the user's past conversation history. This allows for a more personalized experience by providing customized responses based on the user's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's interests into AI, and the AI ​​can generate customized responses.

[0052] The service provider can select the optimal service delivery method by considering the user's device information when providing a response. For example, if the user is using a smartphone, the service provider will provide a response that matches the screen size. For example, if the user is using a tablet, the service provider will provide a response optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible response. In this way, the service provider can select the optimal service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal service delivery method.

[0053] The service provider can collect user feedback when providing responses and use it to improve the service delivery method. For example, the service provider can collect user feedback and use it to improve the response delivery method. For example, the service provider can analyze user feedback and optimize the delivery method. The service provider can also propose a customized response delivery method based on user feedback. In this way, collecting user feedback can be used to improve the service delivery method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then analyze the feedback and use it to improve the service delivery method.

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

[0055] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). It can also predict and suggest input methods that the user will use at specific times based on their past input history. Furthermore, it can suggest relevant input methods based on content the user has entered in the past. In this way, the reception desk can select the most suitable reception method by analyzing the user's past input history.

[0056] The reception unit can filter input based on the user's current situation and areas of interest. For example, it can prioritize receiving topics relevant to the user's current situation. It can also filter and receive relevant input based on the user's areas of interest. Furthermore, it can eliminate unnecessary input based on the user's current situation and areas of interest. In this way, by filtering based on the user's current situation and areas of interest, it can receive highly relevant input.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, it can perform a detailed analysis on important input content. It can also perform a standard analysis on ordinary input content. Furthermore, it can perform a simplified analysis on unnecessary input content. In this way, by adjusting the level of detail of the analysis based on the importance of the input content, it is possible to perform a detailed analysis on important input.

[0058] The generation unit can improve the accuracy of responses by referencing the past statements of a specific CEO during response generation. For example, it can search a database for the CEO's past statements and incorporate them into the response. It can also analyze the CEO's past statements to maintain consistency in responses. Furthermore, it can quote the CEO's past statements to enhance the reliability of responses. In this way, the accuracy of responses can be improved by referencing the past statements of a specific CEO.

[0059] The response system can select the optimal response method by considering the user's device information when providing a response. For example, if the user is using a smartphone, a response tailored to the screen size can be provided. If the user is using a tablet, a response optimized for a larger screen can be provided. Furthermore, if the user is using a smartwatch, a concise and highly visible response can be provided. In this way, the system can select the optimal response method by considering the user's device information.

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

[0061] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and speech analysis. For example, the analysis unit analyzes the user's text input using natural language processing technology, analyzes the user's image input using image analysis technology, and analyzes the user's voice input using speech analysis technology. Step 3: The generation unit generates a response based on the information analyzed by the analysis unit. The response is generated in the form of a text response, a voice response, an image response, etc. For example, the generation unit generates a text response to the user's text input using natural language generation technology, a voice response to the user's voice input using speech synthesis technology, and an image response to the user's image input using image generation technology. Step 4: The providing unit provides the response generated by the generating unit. The provision is done by methods such as text display, audio playback, and image display. For example, the providing unit displays the text response on a display, plays the audio response through a speaker, and displays the image response on a display.

[0062] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that reproduces the words, actions, values, and personality of a specific CEO using multimodal AI. This system allows users to experience the CEO's thoughts and advice through interaction with the agent. Specifically, it uses multimodal AI that combines visual elements (video), auditory elements (voice), and text (speech) and has the ability to provide appropriate responses in real time according to the user's statements. It also has a personalization function that provides customized responses based on the user's interests and past conversations. This provides a realistic interaction as if the CEO were there, from trivial conversations to important discussions. This system is used as an educational tool to deepen insights into business and leadership for business leaders, entrepreneurs, educational institutions, and general users. For example, when a user asks a business question, the AI ​​agent reproduces the CEO's thought process and provides specific advice. This allows users to simulate realistic business scenarios and promote learning and growth. Furthermore, the AI ​​agent brings about concrete quantifiable effects such as improved user understanding, increased engagement, and educational effectiveness. For example, by understanding the CEO's thought process, users can deepen their insights into business and leadership. Furthermore, the realistic dialogue experience enhances user engagement and satisfaction. Additionally, the system offers valuable lessons from the experiences of specific CEOs, making it a highly promising educational tool. This system, through a combination of AI and multimodal technology, achieves both a deeper user experience and a pursuit of realism. For example, users can experience the multifaceted personality of a specific CEO by interacting with an AI agent that replicates their words and actions. This provides users with a new educational tool for business leadership and personal growth, helping more people achieve success. As a result, the AI ​​agent system can deliver concrete quantifiable benefits such as improved user understanding, increased engagement, and educational effectiveness.

[0063] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. The analysis unit analyzes the information received by the reception unit. Analysis is performed by, for example, natural language processing, image analysis, and voice analysis, but is not limited to, such methods. For example, the analysis unit may use natural language processing technology to analyze the user's text input. The analysis unit may also use image analysis technology to analyze the user's image input. Furthermore, the analysis unit may use voice analysis technology to analyze the user's voice input. The generation unit generates a response based on the information analyzed by the analysis unit. The response is generated in the form of, for example, a text response, a voice response, or an image response, but is not limited to, such forms. For example, the generation unit uses natural language generation technology to generate text responses to user text input. The generation unit can also use speech synthesis technology to generate voice responses to user voice input. Furthermore, the generation unit can use image generation technology to generate image responses to user image input. The providing unit provides the responses generated by the generation unit. Providing is done by methods such as text display, audio playback, and image display, but is not limited to these examples. For example, the providing unit displays text responses on a display. The providing unit can also play voice responses through a speaker. Furthermore, the providing unit can also display image responses on a display. In this way, the AI ​​agent system according to the embodiment realizes realistic interaction by receiving user input, analyzing it, generating responses, and providing them.

[0064] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may, for example, be equipped with a keyboard or touchscreen for receiving text input. This allows users to easily input text, and the system can quickly receive that input. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. Speech recognition technology can convert the user's voice into text with high accuracy, improving the convenience of voice input. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. Image recognition technology can analyze images provided by the user and extract the necessary information. For example, if a user uploads a product image, the system can extract product information from the image and generate an appropriate response. This allows the reception unit to support diverse input formats and improve user convenience. Furthermore, the reception unit can utilize the latest hardware and software technologies to receive user input quickly and accurately. For example, using high-performance microphones and cameras can improve the quality of voice and images and increase recognition accuracy. Also, optimizing the speech and image recognition algorithms can improve the input processing speed. This allows the reception desk to respond quickly and accurately to diverse user inputs, thereby improving the overall system performance.

[0065] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as natural language processing, image analysis, and speech analysis, but is not limited to these examples. For instance, the analysis unit can analyze user text input using natural language processing technology. Natural language processing technology can grammatically and semantically analyze user input text, accurately understanding the user's intent. This allows the system to respond appropriately to user requests. The analysis unit can also analyze user image input using image analysis technology. Image analysis technology can extract features from user-provided images and recognize objects and text within the images. For example, if a user uploads a product image, the system can extract information such as the product name, price, and features from the image and generate an appropriate response. Furthermore, the analysis unit can analyze user voice input using speech analysis technology. Speech analysis technology can convert user speech into text and analyze its content. This allows the system to respond accurately to voice input as well. By combining these technologies, the analysis unit can handle diverse user inputs and perform accurate analysis. Furthermore, the analysis unit can improve its analysis accuracy by utilizing machine learning and deep learning technologies. For example, by learning from past data, it can predict user input patterns and perform more accurate analysis. This allows the analysis unit to perform rapid and accurate analysis of diverse user inputs, thereby improving the overall system performance.

[0066] The generation unit generates a response based on the information analyzed by the analysis unit. The response may be generated in various forms, such as text responses, voice responses, or image responses, but is not limited to these examples. For instance, the generation unit can use natural language generation technology to generate text responses to user text input. Natural language generation technology can generate text with appropriate grammar and meaning based on user input. This allows the system to provide users with natural conversation. The generation unit can also use speech synthesis technology to generate voice responses to user voice input. Speech synthesis technology can convert text into speech and generate speech with natural pronunciation and intonation. This allows the system to provide users with voice responses. Furthermore, the generation unit can use image generation technology to generate image responses to user image input. Image generation technology can generate appropriate images based on user input. For example, if a user requests an image of a product, the system can generate an image related to that product and provide it to the user. The generation unit can combine these technologies to respond to diverse user requests and generate appropriate responses. Furthermore, the generation unit can utilize machine learning and deep learning technologies to improve the quality of responses. For example, by learning from past response data, it becomes possible to generate more natural and appropriate responses. This allows the generation unit to quickly and accurately generate responses to a variety of user requests, thereby improving the overall system performance.

[0067] The providing unit provides responses generated by the generating unit. These responses may be provided in various ways, such as text display, audio playback, or image display. For example, the providing unit may display text responses on a display. The display is capable of high resolution and clear display, providing users with visually understandable information. The providing unit may also play audio responses through a speaker. The speaker can reproduce high-quality audio, providing users with auditory understandable information. Furthermore, the providing unit may display image responses on a display. Images are capable of high resolution and clear display, providing users with visually understandable information. The providing unit can combine these methods to provide information to users in various formats. Furthermore, the providing unit can collect user feedback to improve the quality of the provided content. For example, by recording how users reacted to the provided information and analyzing the data, areas for improvement in the content can be identified. This allows the providing unit to provide information to users quickly and accurately, improving the overall system performance. Additionally, the providing unit can optimize the interface design and usability to improve user convenience. For example, an intuitive interface can be provided so that users can easily access information. This allows the service provider to deliver high-quality information to users and improve the overall user experience of the system.

[0068] The generation unit can generate responses that combine visual, auditory, and text elements. For example, the generation unit can generate images or videos as visual elements. For instance, the generation unit can present answers to user questions using images or videos. The generation unit can also generate audio as an auditory element. For example, the generation unit can provide answers to user questions in audio format. Furthermore, the generation unit can also generate text. For example, the generation unit can provide answers to user questions in text format. By combining visual, auditory, and text elements, a more realistic response can be generated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input into a generation AI, which can then generate a response that combines visual, auditory, and text elements.

[0069] The service provider can provide customized responses based on the user's interests and past interactions. For example, based on the user's interests, the service provider can provide responses that include relevant topics. For example, if the user is interested in business, the service provider can provide responses that include information about business. The service provider can also provide customized responses based on the user's past interactions. For example, based on questions and conversations the user has had in the past, the service provider can provide responses that include relevant information. This allows for a more personalized experience by providing customized responses based on the user's interests and past interactions. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input the user's interests and past conversations into a generative AI, which can then generate customized responses.

[0070] The analysis unit can provide an appropriate response in real time in response to the user's statements. For example, the analysis unit can analyze the user's statements using natural language processing technology and generate an appropriate response. For example, the analysis unit can generate a response containing relevant information in response to a user's question. The analysis unit can also analyze the emotion in the user's statements and generate a response that corresponds to that emotion. For example, if the user is excited, the analysis unit will generate a calm response. This enables smooth dialogue by providing an appropriate response in real time in response to the user's statements. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's statements into a generative AI, which can then generate an appropriate response.

[0071] The generation unit can reproduce the words, actions, values, and personality of a specific CEO. For example, the generation unit can search a database for a specific CEO's past statements and actions and generate responses based on them. For example, the generation unit can quote the CEO's past statements to generate responses to user questions. The generation unit can also generate responses that reflect the CEO's values. For example, the generation unit can generate responses based on the CEO's management philosophy and social beliefs. Furthermore, the generation unit can generate responses that incorporate the CEO's personal anecdotes in order to reproduce the CEO's personality. This allows for a realistic experience for the user by reproducing the words, actions, values, and personality of a specific CEO. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on a specific CEO's past statements and actions into a generation AI, which can then generate responses based on that data.

[0072] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is excited, the reception unit can delay the timing of input acceptance to allow the user to calm down. For example, if the user is relaxed, the reception unit can speed up the timing of input acceptance to facilitate smooth conversation. The reception unit can also adjust the timing of input acceptance to allow the user to relax if the user is stressed. In this way, input can be accepted at a more appropriate time by adjusting the timing of input acceptance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the timing of input acceptance can be adjusted based on the result.

[0073] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest relevant input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal reception method.

[0074] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving relevant topics based on the user's current situation. For example, the reception unit can filter and receive relevant input based on the user's areas of interest. The reception unit can also eliminate unnecessary input based on the user's current situation and areas of interest. This allows the reception unit to receive highly relevant input by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and areas of interest into the AI, which can then perform the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is excited, the reception unit will prioritize important inputs. For example, if the user is relaxed, the reception unit will prioritize normal inputs. The reception unit can also prioritize urgent inputs if the user is stressed. In this way, important inputs can be received preferentially by determining the priority of inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the reception unit can determine the priority of inputs based on the results.

[0076] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inputs related to that region. For example, the reception unit will prioritize receiving relevant topics based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also prioritize receiving relevant inputs based on their current location. In this way, by considering the user's geographical location, highly relevant inputs can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into the AI, which can then prioritize receiving highly relevant inputs.

[0077] The reception unit can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception unit can prioritize receiving relevant topics from the user's social media activity. For example, the reception unit can filter and accept relevant input based on the user's social media activity. The reception unit can also analyze the user's social media activity and eliminate unnecessary input. In this way, by analyzing the user's social media activity, it can accept relevant input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can accept relevant input.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is excited, the analysis unit performs a detailed analysis. For example, if the user is relaxed, the analysis unit performs a normal analysis. The analysis unit can also perform a simplified analysis if the user is stressed. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the analysis method can be adjusted based on the results.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, the analysis unit performs a detailed analysis on important input content. For example, the analysis unit performs a normal analysis on normal input content. The analysis unit can also perform a simplified analysis on unnecessary input content. This allows for detailed analysis of important inputs by adjusting the level of detail of the analysis based on the importance of the input content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the input content to the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related input content. For example, it can apply an education analysis algorithm to education-related input content. The analysis unit can also apply a general analysis algorithm to general input content. By applying different analysis algorithms depending on the category of the input content, more appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the input content into the AI, and the AI ​​can apply different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize important analyses. For example, if the user is relaxed, the analysis unit will prioritize normal analyses. The analysis unit can also prioritize urgent analyses if the user is stressed. In this way, by determining the priority of analysis based on the user's emotions, important analyses can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the analysis priority can be determined based on the results.

[0082] The analysis unit can determine the priority of analysis based on the submission date of the input content during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted input content. For example, the analysis unit may analyze previously submitted input content with normal priority. The analysis unit can also prioritize the analysis of urgent input content. This allows for prioritizing the analysis of recently submitted input content by determining the priority of analysis based on the submission date of the input content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the input content into the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the input content during analysis. For example, the analysis unit may prioritize the analysis of highly relevant input content. For example, the analysis unit may analyze less relevant input content in the normal order. The analysis unit can also postpone the analysis of unnecessary input content. In this way, by adjusting the order of analysis based on the relevance of the input content, highly relevant input content can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the input content into the AI, and the AI ​​can adjust the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is excited, the generation unit will respond in a calm manner. For example, if the user is relaxed, the generation unit will respond in a normal manner. The generation unit can also respond in a concise manner if the user is stressed. This allows for the provision of more appropriate responses by adjusting the way the response is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the way it expresses its response can be adjusted based on the result.

[0085] The generation unit can adjust the level of detail to reproduce a specific CEO's words, actions, values, and personality when generating responses. For example, the generation unit may refer to past statements and actions to reproduce the CEO's words in detail. For example, the generation unit may refer to the CEO's books and interviews to reflect the CEO's values. The generation unit may also incorporate personal anecdotes to reproduce the CEO's personality. By adjusting the level of detail to reproduce a specific CEO's words, actions, values, and personality, it is possible to provide more realistic responses. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on a specific CEO's past statements and actions into a generation AI, which can then generate a response based on that data.

[0086] The generation unit can optimize the combination of visual, auditory, and text elements when generating a response. For example, the generation unit might use a video clip of the CEO as a visual element. For example, the generation unit might use the CEO's voice as an auditory element. The generation unit can also quote the CEO's statements as text. By optimizing the combination of visual, auditory, and text elements, a more effective response can be provided. Some or all of the processing described above in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can input visual, auditory, and text data into a generative AI, which can then generate a response combining them.

[0087] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is excited, the generation unit will generate a short response. For example, if the user is relaxed, the generation unit will generate a response of normal length. The generation unit can also generate a concise response if the user is stressed. By adjusting the length of the response based on the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, 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 processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotion, and adjust the length of the response based on the result.

[0088] The generation unit can improve the accuracy of responses by referring to a specific CEO's past statements during response generation. For example, the generation unit can search a database for the CEO's past statements and incorporate them into the response. For example, the generation unit can analyze the CEO's past statements to maintain consistency in responses. The generation unit can also quote the CEO's past statements to increase the reliability of responses. In this way, the accuracy of responses can be improved by referring to a specific CEO's past statements. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a specific CEO's past statements into a generation AI, and the generation AI can generate a response based on that.

[0089] The generation unit can enrich the content of a response by referring to relevant literature on a specific CEO during response generation. For example, the generation unit can refer to the CEO's books and reflect them in the response. For example, the generation unit can refer to articles about the CEO and enrich the content of the response. The generation unit can also refer to interviews with the CEO to increase the specificity of the response. In this way, the content of the response can be enriched by referring to relevant literature on a specific CEO. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevant literature on a specific CEO into a generation AI, and the generation AI can generate a response based on that.

[0090] The service provider can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, if the user is excited, the service provider will provide a calm response. For example, if the user is relaxed, the service provider will provide a normal response. The service provider can also provide a concise response if the user is stressed. This allows for the provision of more appropriate responses by adjusting the method of providing responses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, the generative AI will estimate the emotions, and the method of providing responses will be adjusted based on the results.

[0091] The service provider can select the optimal service delivery method by referring to the user's past dialogue history when providing a response. For example, the service provider can select the optimal response delivery method from the user's past dialogue history. For example, the service provider can analyze the user's past dialogue history and provide a relevant response. The service provider can also provide a customized response by referring to the user's past dialogue history. This allows the service provider to select the optimal service delivery method by referring to the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dialogue history into AI, and the AI ​​can select the optimal service delivery method.

[0092] The service provider can provide customized responses based on the user's interests when providing responses. For example, the service provider can provide responses that include relevant topics based on the user's interests. For example, the service provider can provide customized responses based on the user's interests. The service provider can also provide responses based on the user's past conversation history. This allows for a more personalized experience by providing customized responses based on the user's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's interests into AI, and the AI ​​can generate customized responses.

[0093] The service provider can estimate the user's emotions and adjust the timing of response delivery based on the estimated emotions. For example, if the user is excited, the service provider can delay the timing of response delivery to allow the user to calm down. For example, if the user is relaxed, the service provider can speed up the timing of response delivery to facilitate smooth conversation. The service provider can also adjust the timing of response delivery to allow the user to relax if the user is stressed. In this way, by adjusting the timing of response delivery based on the user's emotions, responses can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the timing of response delivery can be adjusted based on the result.

[0094] The service provider can select the optimal service delivery method by considering the user's device information when providing a response. For example, if the user is using a smartphone, the service provider will provide a response that matches the screen size. For example, if the user is using a tablet, the service provider will provide a response optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible response. In this way, the service provider can select the optimal service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal service delivery method.

[0095] The service provider can collect user feedback when providing responses and use it to improve the service delivery method. For example, the service provider can collect user feedback and use it to improve the response delivery method. For example, the service provider can analyze user feedback and optimize the delivery method. The service provider can also propose a customized response delivery method based on user feedback. In this way, collecting user feedback can be used to improve the service delivery method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then analyze the feedback and use it to improve the service delivery method.

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

[0097] The analysis unit can estimate the emotion behind the user's statements and adjust the response based on that estimation. For example, if the user is excited, the analysis unit can generate a calm response. If the user is sad, the analysis unit can generate a response that includes encouragement. Furthermore, if the user is angry, the analysis unit can generate a response in a calm tone. This allows for the provision of appropriate responses that match the user's emotions.

[0098] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is excited, the generation unit can generate a response using a calm expression. If the user is relaxed, the generation unit can generate a response using a normal expression. Furthermore, if the user is stressed, the generation unit can generate a response using a concise expression. This allows for the provision of responses using an appropriate expression according to the user's emotions.

[0099] The system can estimate the user's emotions and adjust the timing of its response based on those estimates. For example, if the user is excited, the system can delay its response to allow the user to calm down. If the user is relaxed, the system can speed up its response to facilitate a smoother conversation. Furthermore, if the user is stressed, the system can adjust its response to help the user relax. This allows for responses to be provided at the appropriate time according to the user's emotions.

[0100] The reception system can estimate the user's emotions and adjust the timing of input acceptance based on those estimates. For example, if the user is excited, the reception system can delay input acceptance to allow the user to calm down. Conversely, if the user is relaxed, the reception system can speed up input acceptance to facilitate smoother conversation. Furthermore, if the user is stressed, the reception system can adjust the timing of input acceptance to help the user relax. This allows for input acceptance at the appropriate time according to the user's emotions.

[0101] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is excited, the analysis unit can perform a detailed analysis. If the user is relaxed, the analysis unit can perform a normal analysis. Furthermore, if the user is stressed, the analysis unit can perform a simplified analysis. This allows the system to provide an appropriate analysis method tailored to the user's emotions.

[0102] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). It can also predict and suggest input methods that the user will use at specific times based on their past input history. Furthermore, it can suggest relevant input methods based on content the user has entered in the past. In this way, the reception desk can select the most suitable reception method by analyzing the user's past input history.

[0103] The reception unit can filter input based on the user's current situation and areas of interest. For example, it can prioritize receiving topics relevant to the user's current situation. It can also filter and receive relevant input based on the user's areas of interest. Furthermore, it can eliminate unnecessary input based on the user's current situation and areas of interest. In this way, by filtering based on the user's current situation and areas of interest, it can receive highly relevant input.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, it can perform a detailed analysis on important input content. It can also perform a standard analysis on ordinary input content. Furthermore, it can perform a simplified analysis on unnecessary input content. In this way, by adjusting the level of detail of the analysis based on the importance of the input content, it is possible to perform a detailed analysis on important input.

[0105] The generation unit can improve the accuracy of responses by referencing the past statements of a specific CEO during response generation. For example, it can search a database for the CEO's past statements and incorporate them into the response. It can also analyze the CEO's past statements to maintain consistency in responses. Furthermore, it can quote the CEO's past statements to enhance the reliability of responses. In this way, the accuracy of responses can be improved by referencing the past statements of a specific CEO.

[0106] The response system can select the optimal response method by considering the user's device information when providing a response. For example, if the user is using a smartphone, a response tailored to the screen size can be provided. If the user is using a tablet, a response optimized for a larger screen can be provided. Furthermore, if the user is using a smartwatch, a concise and highly visible response can be provided. In this way, the system can select the optimal response method by considering the user's device information.

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

[0108] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and speech analysis. For example, the analysis unit analyzes the user's text input using natural language processing technology, analyzes the user's image input using image analysis technology, and analyzes the user's voice input using speech analysis technology. Step 3: The generation unit generates a response based on the information analyzed by the analysis unit. The response is generated in the form of a text response, a voice response, an image response, etc. For example, the generation unit generates a text response to the user's text input using natural language generation technology, a voice response to the user's voice input using speech synthesis technology, and an image response to the user's image input using image generation technology. Step 4: The providing unit provides the response generated by the generating unit. The provision is done by methods such as text display, audio playback, and image display. For example, the providing unit displays the text response on a display, plays the audio response through a speaker, and displays the image response on a display.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the touch panel 38A and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing, image analysis, and speech analysis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit provides the generated response using the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing, image analysis, and speech analysis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit provides the generated response using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing, image analysis, and speech analysis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit provides the generated response using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing, image analysis, and speech analysis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit provides the generated response using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a response based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the response generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generates responses that combine visual, auditory, and text elements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides customized responses based on the user's interests and past interactions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It provides appropriate responses in real time based on the user's comments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Recreating the words, actions, values, and personality of a specific CEO. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the input data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the input data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating responses, adjust the level of detail to accurately reproduce the words, actions, values, and personality of a specific CEO. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating responses, optimize the combination of visual, auditory, and text elements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating responses, we improve the accuracy of responses by referencing the past statements of specific CEOs. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a response, enrich the content of the response by referring to relevant literature on a specific CEO. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way responses are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a response, the system selects the optimal method of delivery by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing responses, we offer customized responses based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of response delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing a response, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing responses, we collect user feedback to help improve the delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a response based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the response generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generates responses that combine visual, auditory, and text elements. The system according to feature 1.

3. The aforementioned supply unit is, Provides customized responses based on the user's interests and past interactions. The system according to feature 1.

4. The aforementioned analysis unit, It provides appropriate responses in real time based on the user's comments. The system according to feature 1.

5. The generating unit is Recreating the words, actions, values, and personality of a specific CEO. The system according to feature 1.

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

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

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

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