System

Abstract

To provide a system that enables the easy realization of an experience in which the user engages in dialogue with an ideal conversation partner.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The dialogue unit allows the user to engage in dialogue with the conversation partner generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult for users to easily realize the experience of conversing with their ideal conversation partner.

[0005] The system according to the embodiment aims to allow a user to easily experience a conversation with an ideal conversation partner. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The dialogue unit allows the user to dialogue with the conversation partner generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can easily realize the experience of a user having a conversation with an ideal conversation partner. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) Unlike a normal smart speaker, the experience providing system according to the embodiment of the present invention is a system that allows users to easily experience things that are not easily experienced. This experience providing system provides conversations with close relatives that the user cannot normally meet, experiences that make celebrities feel close to the user, and English conversation practice. For example, when a user inputs the voice and personality of a close relative that the user cannot normally meet, the user can experience as if he or she is talking to that close relative. In addition, by inputting the voice and expression method of a celebrity, the user can experience as if he or she is talking to that celebrity. Furthermore, the system can be used as a practice partner for English conversation, and provides conversations that match the user's level. This system can be used not only for entertainment, but also for education and business. For example, in the field of education, learning can be deepened through conversations with historical figures and experts. In the field of business, communication skills can be improved through conversations with virtual clients and superiors. In addition, this system is provided on a subscription model and is designed to be able to handle a wide range of things. Users can enjoy various experiences by paying a monthly fee. This allows the experience providing system to easily allow users to experience things that they cannot normally experience.

[0029] The experience providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives input from a user. The input from the user includes, for example, voice data, text data, image data, etc., but is not limited to such examples. For example, the reception unit receives voice data through a microphone and text data through a keyboard or a touch screen. The reception unit can also receive image data through a camera. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the voice data or text data using natural language processing technology to identify the user's request. The analysis unit can also analyze the image data using image analysis technology to identify the user's request. The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. The dialogue unit allows the user to dialogue with the conversation partner generated by the generation unit. The dialogue unit performs dialogue in the form of, for example, voice dialogue, text chat, video call, or the like. The dialogue unit provides an interface that allows the user to have a natural dialogue with the generated conversation partner. In this way, the experience providing system according to the embodiment can accept and analyze the user's input, generate an ideal conversation partner, and conduct a dialogue.

[0030] The reception unit receives input from the user. The input from the user includes, but is not limited to, voice data, text data, image data, and the like, for example. For example, the reception unit receives voice data through a microphone, and text data through a keyboard or a touch screen. The reception unit can also receive image data through a camera. Specifically, the voice data is collected clearly using a high-sensitivity microphone, and background sound is removed by applying noise canceling technology. The text data provides an auto-complete function and a spell check function when the user inputs using a keyboard or a touch screen, improving the efficiency and accuracy of input. The image data is obtained by using a high-resolution camera to obtain clear images, and face recognition and object recognition technology are used to more accurately grasp the user's intention. Furthermore, the reception unit has a data buffering function for processing these data in real time and quickly transmitting them to the analysis unit. This allows the user's input to be processed without delay, providing a smooth experience. The reception unit performs data encryption and access control to protect the user's privacy and ensure security. This allows the user to provide input with peace of mind.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes voice data and text data using natural language processing technology to identify the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. Specifically, the voice data is converted into text using voice recognition technology, and then the context and intention are analyzed using natural language processing technology. The text data clarifies the user's request and intention through morphological analysis and grammatical analysis. The image data is subjected to face recognition and object recognition by an image recognition algorithm using deep learning, and emotions and intentions are read from the user's facial expressions and gestures. The analysis unit integrates these analysis results and comprehensively judges the user's request. Furthermore, the analysis unit performs more accurate analysis by referring to past user data and history and learning the user's preferences and patterns. For example, the analysis unit takes into account what requests the user has made in the past and what kind of conversations the user prefers, and more accurately identifies the current request. The analysis unit transmits these analysis results to the generation unit, providing basic information for generating an ideal conversation partner.

[0032] The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. Specifically, the text generation AI generates natural dialogue sentences based on the user's request and customizes the conversation partner's personality and tone of voice. The multimodal generation AI generates the conversation partner's voice using speech synthesis technology and further generates an avatar including facial expressions and gestures. This allows the user to enjoy a realistic conversation experience both visually and aurally. The generation unit can dynamically change the characteristics of the conversation partner according to the user's request. For example, if the user desires a relaxed conversation, the conversation partner's voice is made gentle and the facial expression is made soft. Also, if the user wants to obtain information quickly, the conversation partner's speaking speed is increased and information is provided concisely. The generation unit performs these generation processes in real time and can immediately respond to the user's request.

[0033] The dialogue unit allows the user to dialogue with the conversation partner generated by the generation unit. The dialogue unit dialogues in the form of, for example, voice dialogue, text chat, video call, etc. The dialogue unit provides an interface for the user to have a natural dialogue with the generated conversation partner. Specifically, in voice dialogue, high-quality voice synthesis and voice recognition technology are combined to provide an environment in which the user and the conversation partner can converse smoothly. In text chat, messages are sent and received in real time, allowing the user to continue the dialogue comfortably. In video calls, the generated avatar is displayed on the user's screen and provides visual feedback. The dialogue unit collects the user's reactions and feedback in real time and dynamically adjusts the conversation partner's response by sending the feedback to the generation unit. For example, if the user shows a confused expression, the dialogue unit conveys that information to the generation unit and instructs the conversation partner to repeat the explanation or try a different approach. In this way, the dialogue unit can provide a dialogue experience that satisfies the user and maximize the effectiveness of the entire system.

[0034] The reception unit can receive voice data or text data as input. The voice data or text data includes, but is not limited to, for example, a WAV file, an MP3 file, and a TXT file. For example, the reception unit receives voice data through a microphone and stores the voice data as a WAV file or an MP3 file. The reception unit can also receive text data through a keyboard or a touch screen and store the text data as a TXT file. This allows the reception unit to receive voice data or text data as input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input voice data to a generation AI and cause the generation AI to analyze the voice data.

[0035] The analysis unit can analyze the input information and identify the characteristics of an ideal conversation partner based on the user's request. The analysis unit can, for example, use natural language processing technology to analyze voice data or text data and identify the user's request. For example, the analysis unit converts voice data into text and analyzes the text data to extract the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. For example, the analysis unit recognizes a specific object or scene from the image data and identifies the user's request based on the recognition. This allows the analysis unit to identify the characteristics of an ideal conversation partner based on the user's request. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input voice data or text data to the generation AI and cause the generation AI to identify the user's request.

[0036] The generation unit can generate an ideal conversation partner based on the specified characteristics. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. For example, the generation unit generates an ideal conversation partner using the generation AI based on the voice and personality information input by the user. The generation unit can also adjust the expression method of the conversation partner based on the user's request. For example, the generation unit uses the generation AI to adjust the tone of the conversation partner's voice and speaking style to generate a conversation partner according to the user's request. This allows the generation unit to generate an ideal conversation partner based on the specified characteristics. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause the generation AI to execute the generation of a conversation partner based on the user's request.

[0037] The dialogue unit allows the user to have a dialogue with the generated conversation partner. The dialogue unit performs dialogue in the form of, for example, voice dialogue, text chat, video call, or the like. The dialogue unit provides an interface for the user to have a natural dialogue with the generated conversation partner. For example, in the case of voice dialogue, the dialogue unit exchanges voice between the user and the conversation partner using a microphone and a speaker. In addition, in the case of text chat, the dialogue unit can exchange text messages between the user and the conversation partner using a keyboard or a touch screen. Furthermore, in the case of video calling, the dialogue unit can also exchange images of the user and the conversation partner using a camera and a display. This allows the dialogue unit to have a dialogue between the generated conversation partner and the user. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the dialogue content of the generated conversation partner to the generation AI and cause the generation AI to execute the progress of the dialogue.

[0038] The dialogue unit can provide a concrete means for deepening learning through dialogue with historical figures in the field of education. The dialogue unit, for example, reproduces the voice, personality, and expression method of a historical figure using a generation AI, and provides an experience of a user having a dialogue with the person. For example, the dialogue unit reproduces the voice and personality of a historical figure such as Napoleon, Cleopatra, or Gandhi using a generation AI, and the user can have a dialogue with the person. The dialogue unit can also use a generation AI to provide background information on historical events and figures, and the user can have a dialogue based on the information. For example, the dialogue unit can use a generation AI to answer questions about historical events or provide background information on figures. This allows the dialogue unit to deepen learning through dialogue with historical figures in the field of education. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input information on a historical figure into the generation AI and have the generation AI execute the progress of the dialogue.

[0039] The dialogue unit can provide a concrete means for improving presentation skills through conversation with a virtual client in the business field. The dialogue unit, for example, reproduces the voice, personality, and expression method of a virtual client using a generation AI, and provides an experience of a user interacting with the client. For example, the dialogue unit can use the generation AI to reproduce the voice and personality of a client in a specific industry or position, and the user can interact with the client. The dialogue unit can also use the generation AI to provide a dialogue based on a business scenario, and the user can improve presentation skills. For example, the dialogue unit can use the generation AI to answer questions from the virtual client or provide feedback on the presentation. In this way, the dialogue unit can improve presentation skills through conversation with a virtual client in the business field. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input information about the virtual client to the generation AI, and cause the generation AI to execute the progress of the dialogue.

[0040] The reception unit can analyze the user's past input history and select an appropriate reception method. The reception unit, for example, stores the user's past input history in a database and analyzes the data. For example, the reception unit preferentially receives voice input for a user who has used voice input a lot in the past. The reception unit can also preferentially receive text input for a user who has used text input a lot in the past. Furthermore, the reception unit can suggest an optimal reception method for a specific time period from the user's past input history. For example, if the user has used voice input a lot in a specific time period in the past, the reception unit preferentially receives voice input in that time period. In this way, the reception unit can select an optimal reception method by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and cause the generation AI to select an optimal reception method.

[0041] The reception unit can perform filtering based on the user's current situation and field of interest when receiving the input. The reception unit, for example, acquires the user's current situation from a sensor or device, and filters the input based on the information. For example, when the user is at work, the reception unit preferentially receives business-related input. In addition, when the user is relaxing, the reception unit can also preferentially receive entertainment-related input. Furthermore, when the user is studying, the reception unit can also preferentially receive education-related input. For example, the reception unit acquires the user's current situation from time, place, activity status, etc., and filters the input based on the information. As a result, the reception unit can accept more relevant input by filtering based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the user's current situation and field of interest to the generation AI, and cause the generation AI to execute filtering of the input.

[0042] When receiving an input, the reception unit can preferentially receive an input that is highly relevant based on the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user from GPS data or address information, and filters the input based on the information. For example, when the user is in a specific area, the reception unit preferentially receives an input related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive an input related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially receive an input related to the home. For example, the reception unit acquires the geographical location information of the user from GPS data or address information, and filters the input based on the information. This allows the reception unit to preferentially receive an input that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute filtering of the input.

[0043] The reception unit can analyze the user's social media activity and receive related inputs when receiving the input. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. For example, the reception unit preferentially receives inputs related to topics in which the user has shown interest on social media. The reception unit can also preferentially receive inputs related to people the user follows on social media. Furthermore, the reception unit can also preferentially receive inputs related to groups in which the user participates on social media. For example, the reception unit stores the user's social media activity in a database and analyzes the data to receive related inputs. In this way, the reception unit can receive related inputs by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI and cause the generation AI to filter the inputs.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. The analysis unit evaluates the importance of the input based on criteria such as user evaluation, frequency, and relevance. For example, the analysis unit performs a detailed analysis on important inputs and a brief analysis on general inputs. The analysis unit can also perform a quick analysis on inputs with high urgency. For example, the analysis unit performs a detailed analysis on inputs that the user has given a high rating, and a brief analysis on inputs that the user has given a low rating. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the input to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit classifies the input categories into questions, requests, feedback, and the like, and applies an appropriate analysis algorithm to each of them. For example, the analysis unit applies an analysis algorithm dedicated to education to education-related inputs, and applies an analysis algorithm dedicated to business to business-related inputs. The analysis unit can also apply an analysis algorithm dedicated to entertainment to entertainment-related inputs. For example, the analysis unit refers to an education database for education-related inputs and provides related information. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the category of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the input to the generation AI and cause the generation AI to apply the analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the submission time of the input during the analysis. The analysis unit evaluates the submission time of the input based on criteria such as the submission date and time and the frequency of submission. For example, the analysis unit prioritizes analysis of the most recently submitted input and prioritizes analysis of the input with high urgency. The analysis unit can also determine the priority of the analysis based on a deadline specified by the user. For example, the analysis unit prioritizes analysis of the input submitted earlier based on the deadline specified by the user. In this way, the analysis unit can provide a more appropriate analysis result by determining the priority of the analysis based on the submission time of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the input to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit evaluates the relevance of the input based on criteria such as the degree of content agreement and related topics. For example, the analysis unit preferentially analyzes inputs related to the user's current field of interest, and preferentially analyzes inputs with high relevance based on the user's past input history. The analysis unit can also preferentially analyze inputs with high relevance based on the user's social media activities. For example, the analysis unit preferentially analyzes inputs related to the user's current field of interest, and preferentially analyzes inputs with high relevance based on the user's past input history. This allows the analysis unit to provide more appropriate analysis results by adjusting the order of analysis based on the relevance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the input to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The generation unit can generate an optimal conversation partner by analyzing the user's past dialogue history at the time of generation. The generation unit, for example, stores the user's past dialogue history in a database and analyzes the data. For example, the generation unit generates a conversation partner who has a speaking style that the user has previously preferred, and generates a conversation partner based on a topic that the user has become interested in from the user's past dialogue history. The generation unit can also analyze the user's past dialogue history and identify characteristics of an optimal conversation partner. For example, the generation unit stores the user's past dialogue history in a database and analyzes the data to generate an optimal conversation partner. In this way, the generation unit can generate an optimal conversation partner by analyzing the user's past dialogue history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past dialogue history to the generation AI and cause the generation AI to generate an optimal conversation partner.

[0049] The generation unit can customize the characteristics of the conversation partner based on the user's current situation at the time of generation. The generation unit, for example, acquires the user's current situation from a sensor or device, and customizes the characteristics of the conversation partner based on the information. For example, when the user is at work, the generation unit generates a business-related conversation partner, and when the user is relaxing, generates an entertainment-related conversation partner. The generation unit can also generate an education-related conversation partner when the user is studying. For example, the generation unit acquires the user's current situation from time, place, activity status, etc., and customizes the characteristics of the conversation partner based on the information. In this way, the generation unit can generate a more appropriate conversation partner by customizing the characteristics of the conversation partner based on the user's current situation. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the user's current situation to the generation AI and cause the generation AI to customize the characteristics of the conversation partner.

[0050] The generation unit can generate an appropriate conversation partner by considering the geographical location information of the user at the time of generation. The generation unit, for example, acquires the geographical location information of the user from GPS data or address information, and adjusts the characteristics of the conversation partner based on the information. For example, when the user is in a specific area, the generation unit generates a conversation partner related to the area, and when the user is traveling, the generation unit generates a conversation partner related to the travel destination. In addition, when the user is at home, the generation unit can also generate a conversation partner related to the home. For example, the generation unit acquires the geographical location information of the user from GPS data or address information, and adjusts the characteristics of the conversation partner based on the information. In this way, the generation unit can generate a more appropriate conversation partner by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the characteristics of the conversation partner.

[0051] The generation unit can adjust the characteristics of the conversation partner by analyzing the social media activity of the user at the time of generation. The generation unit, for example, stores the social media activity of the user in a database and analyzes the data. For example, the generation unit generates a conversation partner related to a topic in which the user has shown interest on social media, and generates a conversation partner related to a person the user follows on social media. The generation unit can also generate a conversation partner related to a group in which the user participates on social media. For example, the generation unit stores the social media activity of the user in a database and adjusts the characteristics of the conversation partner by analyzing the data. In this way, the generation unit can generate a more appropriate conversation partner by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the social media activity of the user to the generation AI and cause the generation AI to adjust the characteristics of the conversation partner.

[0052] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit stores the user's past dialogue history in a database and refers to the data. For example, the dialogue unit selects the optimal dialogue method based on the dialogue style that the user has preferred in the past. The dialogue unit can also conduct dialogue based on a topic that the user has become interested in from the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and identify the optimal dialogue method. For example, the dialogue unit stores the user's past dialogue history in a database and refers to the data to select the optimal dialogue method. As a result, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue history to the generation AI and cause the generation AI to select the optimal dialogue method.

[0053] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. The dialogue unit, for example, acquires the user's current situation from a sensor or device, and customizes the content of the dialogue based on the information. For example, the dialogue unit performs business-related dialogue when the user is at work, and performs entertainment-related dialogue when the user is relaxing. The dialogue unit can also perform education-related dialogue when the user is studying. For example, the dialogue unit acquires the user's current situation from time, place, activity status, etc., and customizes the content of the dialogue based on the information. As a result, the dialogue unit can perform a more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input information on the user's current situation to the generation AI and cause the generation AI to customize the content of the dialogue.

[0054] The dialogue unit can select an optimal dialogue method in consideration of the geographical location information of the user during dialogue. The dialogue unit, for example, acquires the geographical location information of the user from GPS data or address information, and adjusts the content of the dialogue based on the information. For example, when the user is in a specific area, the dialogue unit performs a dialogue related to the area, and when the user is traveling, the dialogue unit performs a dialogue related to the travel destination. In addition, when the user is at home, the dialogue unit can also perform a dialogue related to the home. For example, the dialogue unit acquires the geographical location information of the user from GPS data or address information, and adjusts the content of the dialogue based on the information. As a result, the dialogue unit can perform a more appropriate dialogue by considering the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the content of the dialogue.

[0055] The dialogue unit can adjust the content of the dialogue by analyzing the social media activity of the user during the dialogue. For example, the dialogue unit stores the social media activity of the user in a database and analyzes the data. For example, the dialogue unit performs dialogue related to topics in which the user has shown interest on social media and performs dialogue related to people the user follows on social media. The dialogue unit can also perform dialogue related to groups in which the user participates on social media. For example, the dialogue unit stores the social media activity of the user in a database and analyzes the data to adjust the content of the dialogue. This allows the dialogue unit to perform more appropriate dialogue by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the social media activity of the user to the generation AI and cause the generation AI to adjust the content of the dialogue.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When accepting input from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input format. For example, voice input is recommended for a user who has used voice input a lot in the past, and text input is recommended for a user who has used text input a lot. In addition, if a user frequently uses a specific form of input during a specific time period, the acceptance unit can also suggest the optimal input format for that time period. In this way, the acceptance unit can provide a smoother input experience by utilizing the user's past input history.

[0058] When analyzing the user's input, the analysis unit can adjust the analysis method based on the user's current situation and environment. For example, if the user is in a noisy environment, noise reduction of the voice data can be strengthened. Also, if the user is in a quiet environment, more detailed voice analysis can be performed. Furthermore, if the user is on the move, the speed of analysis can be prioritized and results can be provided quickly. This allows the analysis unit to perform flexible analysis according to the user's situation.

[0059] When generating a conversation partner based on the user's input, the generation unit can customize the characteristics of the conversation partner by referring to the user's past dialogue history. For example, the conversation partner's personality and topics can be adjusted based on the user's preferred speaking style and topics in the past. The conversation partner can also be set to avoid topics that the user has avoided in the past. In this way, the generation unit can provide a more personalized conversation partner by utilizing the user's past dialogue history.

[0060] When accepting input from a user, the accepting unit can preferentially accept highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, information related to that area can be preferentially accepted. Also, when the user is traveling, information related to the travel destination can be preferentially accepted. In this way, the accepting unit can provide more relevant input by utilizing the user's geographical location information.

[0061] When analyzing the user's input, the analysis unit can analyze the user's social media activity and provide related information. For example, the analysis unit can prioritize information related to topics in which the user has shown interest on social media. The analysis unit can also provide information related to people the user follows. This allows the analysis unit to utilize the user's social media activity to provide more personalized information.

[0062] When generating a conversation partner based on the user's input, the generation unit can adjust the characteristics of the conversation partner based on the user's current situation or environment. For example, when the user is at work, a business-related conversation partner is generated, and when the user is relaxing, an entertainment-related conversation partner is generated. Also, when the user is studying, an education-related conversation partner can be generated. In this way, the generation unit can provide an appropriate conversation partner according to the user's current situation.

[0063] When a dialogue unit engages in dialogue between a user and a conversation partner, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. For example, the optimal dialogue method can be selected based on the dialogue style that the user has preferred in the past. The dialogue unit can also engage in dialogue based on topics of interest from the user's past dialogue history. This allows the dialogue unit to provide a more personalized dialogue by utilizing the user's past dialogue history.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit receives input from a user. The input from a user includes voice data, text data, image data, etc. For example, voice data is received through a microphone, text data through a keyboard or touch screen, and image data through a camera. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the voice data and text data using natural language processing technology to identify the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. Step 3: The generator generates an ideal conversation partner based on the information analyzed by the analyzer. The generator uses a generation AI to generate a conversation partner based on the user's request. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the voice, personality, and manner of expression of the conversation partner according to the user's request. Step 4: In the dialogue unit, the user dialogues with the conversation partner generated by the generation unit. The dialogue unit conducts dialogue in the form of voice dialogue, text chat, video call, etc. The dialogue unit provides an interface that allows the user to have a natural dialogue with the generated conversation partner.

[0066] (Example 2) Unlike a normal smart speaker, the experience providing system according to the embodiment of the present invention is a system that allows users to easily experience things that are not easily experienced. This experience providing system provides conversations with close relatives that the user cannot normally meet, experiences that make celebrities feel close to the user, and English conversation practice. For example, when a user inputs the voice and personality of a close relative that the user cannot normally meet, the user can experience as if he or she is talking to that close relative. In addition, by inputting the voice and expression method of a celebrity, the user can experience as if he or she is talking to that celebrity. Furthermore, the system can be used as a practice partner for English conversation, and provides conversations that match the user's level. This system can be used not only for entertainment, but also for education and business. For example, in the field of education, learning can be deepened through conversations with historical figures and experts. In the field of business, communication skills can be improved through conversations with virtual clients and superiors. In addition, this system is provided on a subscription model and is designed to be able to handle a wide range of things. Users can enjoy various experiences by paying a monthly fee. This allows the experience providing system to easily allow users to experience things that they cannot normally experience.

[0067] The experience providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives input from a user. The input from the user includes, for example, voice data, text data, image data, etc., but is not limited to such examples. For example, the reception unit receives voice data through a microphone and text data through a keyboard or a touch screen. The reception unit can also receive image data through a camera. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the voice data or text data using natural language processing technology to identify the user's request. The analysis unit can also analyze the image data using image analysis technology to identify the user's request. The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. The dialogue unit allows the user to dialogue with the conversation partner generated by the generation unit. The dialogue unit performs dialogue in the form of, for example, voice dialogue, text chat, video call, or the like. The dialogue unit provides an interface that allows the user to have a natural dialogue with the generated conversation partner. In this way, the experience providing system according to the embodiment can accept and analyze the user's input, generate an ideal conversation partner, and conduct a dialogue.

[0068] The reception unit receives input from the user. The input from the user includes, but is not limited to, voice data, text data, image data, and the like, for example. For example, the reception unit receives voice data through a microphone, and text data through a keyboard or a touch screen. The reception unit can also receive image data through a camera. Specifically, the voice data is collected clearly using a high-sensitivity microphone, and background sound is removed by applying noise canceling technology. The text data provides an auto-complete function and a spell check function when the user inputs using a keyboard or a touch screen, improving the efficiency and accuracy of input. The image data is obtained by using a high-resolution camera to obtain clear images, and face recognition and object recognition technology are used to more accurately grasp the user's intention. Furthermore, the reception unit has a data buffering function for processing these data in real time and quickly transmitting them to the analysis unit. This allows the user's input to be processed without delay, providing a smooth experience. The reception unit performs data encryption and access control to protect the user's privacy and ensure security. This allows the user to provide input with peace of mind.

[0069] The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes voice data and text data using natural language processing technology to identify the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. Specifically, the voice data is converted into text using voice recognition technology, and then the context and intention are analyzed using natural language processing technology. The text data clarifies the user's request and intention through morphological analysis and grammatical analysis. The image data is subjected to face recognition and object recognition by an image recognition algorithm using deep learning, and emotions and intentions are read from the user's facial expressions and gestures. The analysis unit integrates these analysis results and comprehensively judges the user's request. Furthermore, the analysis unit performs more accurate analysis by referring to past user data and history and learning the user's preferences and patterns. For example, the analysis unit takes into account what requests the user has made in the past and what kind of conversations the user prefers, and more accurately identifies the current request. The analysis unit transmits these analysis results to the generation unit, providing basic information for generating an ideal conversation partner.

[0070] The generation unit generates an ideal conversation partner based on the information analyzed by the analysis unit. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. Specifically, the text generation AI generates natural dialogue sentences based on the user's request and customizes the conversation partner's personality and tone of voice. The multimodal generation AI generates the conversation partner's voice using speech synthesis technology and further generates an avatar including facial expressions and gestures. This allows the user to enjoy a realistic conversation experience both visually and aurally. The generation unit can dynamically change the characteristics of the conversation partner according to the user's request. For example, if the user desires a relaxed conversation, the conversation partner's voice is made gentle and the facial expression is made soft. Also, if the user wants to obtain information quickly, the conversation partner's speaking speed is increased and information is provided concisely. The generation unit performs these generation processes in real time and can immediately respond to the user's request.

[0071] The dialogue unit allows the user to dialogue with the conversation partner generated by the generation unit. The dialogue unit dialogues in the form of, for example, voice dialogue, text chat, video call, etc. The dialogue unit provides an interface for the user to have a natural dialogue with the generated conversation partner. Specifically, in voice dialogue, high-quality voice synthesis and voice recognition technology are combined to provide an environment in which the user and the conversation partner can converse smoothly. In text chat, messages are sent and received in real time, allowing the user to continue the dialogue comfortably. In video calls, the generated avatar is displayed on the user's screen and provides visual feedback. The dialogue unit collects the user's reactions and feedback in real time and dynamically adjusts the conversation partner's response by sending the feedback to the generation unit. For example, if the user shows a confused expression, the dialogue unit conveys that information to the generation unit and instructs the conversation partner to repeat the explanation or try a different approach. In this way, the dialogue unit can provide a dialogue experience that satisfies the user and maximize the effectiveness of the entire system.

[0072] The reception unit can receive voice data or text data as input. The voice data or text data includes, but is not limited to, for example, a WAV file, an MP3 file, and a TXT file. For example, the reception unit receives voice data through a microphone and stores the voice data as a WAV file or an MP3 file. The reception unit can also receive text data through a keyboard or a touch screen and store the text data as a TXT file. This allows the reception unit to receive voice data or text data as input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input voice data to a generation AI and cause the generation AI to analyze the voice data.

[0073] The analysis unit can analyze the input information and identify the characteristics of an ideal conversation partner based on the user's request. The analysis unit can, for example, use natural language processing technology to analyze voice data or text data and identify the user's request. For example, the analysis unit converts voice data into text and analyzes the text data to extract the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. For example, the analysis unit recognizes a specific object or scene from the image data and identifies the user's request based on the recognition. This allows the analysis unit to identify the characteristics of an ideal conversation partner based on the user's request. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input voice data or text data to the generation AI and cause the generation AI to identify the user's request.

[0074] The generation unit can generate an ideal conversation partner based on the specified characteristics. The generation unit generates a conversation partner based on the user's request using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and generates the voice, personality, and expression method of the conversation partner according to the user's request. For example, the generation unit generates an ideal conversation partner using the generation AI based on the voice and personality information input by the user. The generation unit can also adjust the expression method of the conversation partner based on the user's request. For example, the generation unit uses the generation AI to adjust the tone of the conversation partner's voice and speaking style to generate a conversation partner according to the user's request. This allows the generation unit to generate an ideal conversation partner based on the specified characteristics. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can cause the generation AI to execute the generation of a conversation partner based on the user's request.

[0075] The dialogue unit allows the user to have a dialogue with the generated conversation partner. The dialogue unit performs dialogue in the form of, for example, voice dialogue, text chat, video call, or the like. The dialogue unit provides an interface for the user to have a natural dialogue with the generated conversation partner. For example, in the case of voice dialogue, the dialogue unit exchanges voice between the user and the conversation partner using a microphone and a speaker. In addition, in the case of text chat, the dialogue unit can exchange text messages between the user and the conversation partner using a keyboard or a touch screen. Furthermore, in the case of video calling, the dialogue unit can also exchange images of the user and the conversation partner using a camera and a display. This allows the dialogue unit to have a dialogue between the generated conversation partner and the user. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the dialogue content of the generated conversation partner to the generation AI and cause the generation AI to execute the progress of the dialogue.

[0076] The dialogue unit can provide a concrete means for deepening learning through dialogue with historical figures in the field of education. The dialogue unit, for example, reproduces the voice, personality, and expression method of a historical figure using a generation AI, and provides an experience of a user having a dialogue with the person. For example, the dialogue unit reproduces the voice and personality of a historical figure such as Napoleon, Cleopatra, or Gandhi using a generation AI, and the user can have a dialogue with the person. The dialogue unit can also use a generation AI to provide background information on historical events and figures, and the user can have a dialogue based on the information. For example, the dialogue unit can use a generation AI to answer questions about historical events or provide background information on figures. This allows the dialogue unit to deepen learning through dialogue with historical figures in the field of education. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input information on a historical figure into the generation AI and have the generation AI execute the progress of the dialogue.

[0077] The dialogue unit can provide a concrete means for improving presentation skills through conversation with a virtual client in the business field. The dialogue unit, for example, reproduces the voice, personality, and expression method of a virtual client using a generation AI, and provides an experience of a user interacting with the client. For example, the dialogue unit can use the generation AI to reproduce the voice and personality of a client in a specific industry or position, and the user can interact with the client. The dialogue unit can also use the generation AI to provide a dialogue based on a business scenario, and the user can improve presentation skills. For example, the dialogue unit can use the generation AI to answer questions from the virtual client or provide feedback on the presentation. In this way, the dialogue unit can improve presentation skills through conversation with a virtual client in the business field. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input information about the virtual client to the generation AI, and cause the generation AI to execute the progress of the dialogue.

[0078] The reception unit can estimate the user's emotion and adjust the timing of receiving the input based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on a change in facial expression and adjusts the timing of receiving the input. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of receiving the input. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of receiving the input. As a result, the reception unit can accept the input at a more appropriate timing by adjusting the timing of receiving the input based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0079] The reception unit can analyze the user's past input history and select an appropriate reception method. The reception unit, for example, stores the user's past input history in a database and analyzes the data. For example, the reception unit preferentially receives voice input for a user who has used voice input a lot in the past. The reception unit can also preferentially receive text input for a user who has used text input a lot in the past. Furthermore, the reception unit can suggest an optimal reception method for a specific time period from the user's past input history. For example, if the user has used voice input a lot in a specific time period in the past, the reception unit preferentially receives voice input in that time period. In this way, the reception unit can select an optimal reception method by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and cause the generation AI to select an optimal reception method.

[0080] The reception unit can perform filtering based on the user's current situation and field of interest when receiving the input. The reception unit, for example, acquires the user's current situation from a sensor or device, and filters the input based on the information. For example, when the user is at work, the reception unit preferentially receives business-related input. In addition, when the user is relaxing, the reception unit can also preferentially receive entertainment-related input. Furthermore, when the user is studying, the reception unit can also preferentially receive education-related input. For example, the reception unit acquires the user's current situation from time, place, activity status, etc., and filters the input based on the information. As a result, the reception unit can accept more relevant input by filtering based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the user's current situation and field of interest to the generation AI, and cause the generation AI to execute filtering of the input.

[0081] The reception unit can estimate the user's emotion and determine the priority order of inputs to be received based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and determines the priority order of inputs. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority order of inputs. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority order of inputs. As a result, the reception unit can preferentially accept more appropriate inputs by determining the priority order of inputs based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0082] When receiving an input, the reception unit can preferentially receive an input that is highly relevant based on the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user from GPS data or address information, and filters the input based on the information. For example, when the user is in a specific area, the reception unit preferentially receives an input related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive an input related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially receive an input related to the home. For example, the reception unit acquires the geographical location information of the user from GPS data or address information, and filters the input based on the information. This allows the reception unit to preferentially receive an input that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute filtering of the input.

[0083] The reception unit can analyze the user's social media activity and receive related inputs when receiving the input. The reception unit, for example, stores the user's social media activity in a database and analyzes the data. For example, the reception unit preferentially receives inputs related to topics in which the user has shown interest on social media. The reception unit can also preferentially receive inputs related to people the user follows on social media. Furthermore, the reception unit can also preferentially receive inputs related to groups in which the user participates on social media. For example, the reception unit stores the user's social media activity in a database and analyzes the data to receive related inputs. In this way, the reception unit can receive related inputs by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI and cause the generation AI to filter the inputs.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the analysis method. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the analysis method. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the analysis method. As a result, the analysis unit can provide a more appropriate analysis result by adjusting the analysis method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. The analysis unit evaluates the importance of the input based on criteria such as user evaluation, frequency, and relevance. For example, the analysis unit performs a detailed analysis on important inputs and a brief analysis on general inputs. The analysis unit can also perform a quick analysis on inputs with high urgency. For example, the analysis unit performs a detailed analysis on inputs that the user has given a high rating, and a brief analysis on inputs that the user has given a low rating. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the input to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit classifies the input categories into questions, requests, feedback, and the like, and applies an appropriate analysis algorithm to each of them. For example, the analysis unit applies an analysis algorithm dedicated to education to education-related inputs, and applies an analysis algorithm dedicated to business to business-related inputs. The analysis unit can also apply an analysis algorithm dedicated to entertainment to entertainment-related inputs. For example, the analysis unit refers to an education database for education-related inputs and provides related information. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the category of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the input to the generation AI and cause the generation AI to apply the analysis algorithm.

[0087] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and determines the analysis priority. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the analysis priority. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and determines the analysis priority. As a result, the analysis unit can provide a more appropriate analysis result by determining the analysis priority based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The analysis unit can determine the priority of the analysis based on the submission time of the input during the analysis. The analysis unit evaluates the submission time of the input based on criteria such as the submission date and time and the frequency of submission. For example, the analysis unit prioritizes analysis of the most recently submitted input and prioritizes analysis of the input with high urgency. The analysis unit can also determine the priority of the analysis based on a deadline specified by the user. For example, the analysis unit prioritizes analysis of the input submitted earlier based on the deadline specified by the user. In this way, the analysis unit can provide a more appropriate analysis result by determining the priority of the analysis based on the submission time of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the input to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit evaluates the relevance of the input based on criteria such as the degree of content agreement and related topics. For example, the analysis unit preferentially analyzes inputs related to the user's current field of interest, and preferentially analyzes inputs with high relevance based on the user's past input history. The analysis unit can also preferentially analyze inputs with high relevance based on the user's social media activities. For example, the analysis unit preferentially analyzes inputs related to the user's current field of interest, and preferentially analyzes inputs with high relevance based on the user's past input history. This allows the analysis unit to provide more appropriate analysis results by adjusting the order of analysis based on the relevance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the input to the generation AI and cause the generation AI to adjust the order of analysis.

[0090] The generation unit can estimate the user's emotion and adjust the characteristics of the conversation partner to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression and adjusts the characteristics of the conversation partner. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the characteristics of the conversation partner. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the characteristics of the conversation partner. As a result, the generation unit can generate a more appropriate conversation partner by adjusting the characteristics of the conversation partner based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0091] The generation unit can generate an optimal conversation partner by analyzing the user's past dialogue history at the time of generation. The generation unit, for example, stores the user's past dialogue history in a database and analyzes the data. For example, the generation unit generates a conversation partner who has a speaking style that the user has previously preferred, and generates a conversation partner based on a topic that the user has become interested in from the user's past dialogue history. The generation unit can also analyze the user's past dialogue history and identify characteristics of an optimal conversation partner. For example, the generation unit stores the user's past dialogue history in a database and analyzes the data to generate an optimal conversation partner. In this way, the generation unit can generate an optimal conversation partner by analyzing the user's past dialogue history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past dialogue history to the generation AI and cause the generation AI to generate an optimal conversation partner.

[0092] The generation unit can customize the characteristics of the conversation partner based on the user's current situation at the time of generation. The generation unit, for example, acquires the user's current situation from a sensor or device, and customizes the characteristics of the conversation partner based on the information. For example, when the user is at work, the generation unit generates a business-related conversation partner, and when the user is relaxing, generates an entertainment-related conversation partner. The generation unit can also generate an education-related conversation partner when the user is studying. For example, the generation unit acquires the user's current situation from time, place, activity status, etc., and customizes the characteristics of the conversation partner based on the information. In this way, the generation unit can generate a more appropriate conversation partner by customizing the characteristics of the conversation partner based on the user's current situation. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input information on the user's current situation to the generation AI and cause the generation AI to customize the characteristics of the conversation partner.

[0093] The generation unit can estimate the user's emotion and determine the priority order of the conversation partners to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression and determines the priority order of the conversation partners. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority order of the conversation partners. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority order of the conversation partners. As a result, the generation unit can prioritize the generation of more appropriate conversation partners by determining the priority order of the conversation partners based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0094] The generation unit can generate an appropriate conversation partner by considering the geographical location information of the user at the time of generation. The generation unit, for example, acquires the geographical location information of the user from GPS data or address information, and adjusts the characteristics of the conversation partner based on the information. For example, when the user is in a specific area, the generation unit generates a conversation partner related to the area, and when the user is traveling, the generation unit generates a conversation partner related to the travel destination. In addition, when the user is at home, the generation unit can also generate a conversation partner related to the home. For example, the generation unit acquires the geographical location information of the user from GPS data or address information, and adjusts the characteristics of the conversation partner based on the information. In this way, the generation unit can generate a more appropriate conversation partner by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the characteristics of the conversation partner.

[0095] The generation unit can adjust the characteristics of the conversation partner by analyzing the social media activity of the user at the time of generation. The generation unit, for example, stores the social media activity of the user in a database and analyzes the data. For example, the generation unit generates a conversation partner related to a topic in which the user has shown interest on social media, and generates a conversation partner related to a person the user follows on social media. The generation unit can also generate a conversation partner related to a group in which the user participates on social media. For example, the generation unit stores the social media activity of the user in a database and adjusts the characteristics of the conversation partner by analyzing the data. In this way, the generation unit can generate a more appropriate conversation partner by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the social media activity of the user to the generation AI and cause the generation AI to adjust the characteristics of the conversation partner.

[0096] The dialogue unit can estimate the user's emotion and adjust the dialogue method based on the estimated user's emotion. For example, the dialogue unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the change in facial expression and adjusts the dialogue method. The dialogue unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the dialogue method. The dialogue unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the dialogue method. As a result, the dialogue unit can adjust the dialogue method based on the user's emotion to perform a more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0097] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during dialogue. For example, the dialogue unit stores the user's past dialogue history in a database and refers to the data. For example, the dialogue unit selects the optimal dialogue method based on the dialogue style that the user has preferred in the past. The dialogue unit can also conduct dialogue based on a topic that the user has become interested in from the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and identify the optimal dialogue method. For example, the dialogue unit stores the user's past dialogue history in a database and refers to the data to select the optimal dialogue method. As a result, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue history to the generation AI and cause the generation AI to select the optimal dialogue method.

[0098] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. The dialogue unit, for example, acquires the user's current situation from a sensor or device, and customizes the content of the dialogue based on the information. For example, the dialogue unit performs business-related dialogue when the user is at work, and performs entertainment-related dialogue when the user is relaxing. The dialogue unit can also perform education-related dialogue when the user is studying. For example, the dialogue unit acquires the user's current situation from time, place, activity status, etc., and customizes the content of the dialogue based on the information. As a result, the dialogue unit can perform a more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input information on the user's current situation to the generation AI and cause the generation AI to customize the content of the dialogue.

[0099] The dialogue unit can estimate the user's emotion and determine the priority of the dialogue based on the estimated user's emotion. For example, the dialogue unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the change in facial expression and determines the priority of the dialogue. The dialogue unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the dialogue unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the dialogue. The dialogue unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority of the dialogue. As a result, the dialogue unit can prioritize more appropriate dialogues by determining the priority of the dialogue based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0100] The dialogue unit can select an optimal dialogue method in consideration of the geographical location information of the user during dialogue. The dialogue unit, for example, acquires the geographical location information of the user from GPS data or address information, and adjusts the content of the dialogue based on the information. For example, when the user is in a specific area, the dialogue unit performs a dialogue related to the area, and when the user is traveling, the dialogue unit performs a dialogue related to the travel destination. In addition, when the user is at home, the dialogue unit can also perform a dialogue related to the home. For example, the dialogue unit acquires the geographical location information of the user from GPS data or address information, and adjusts the content of the dialogue based on the information. As a result, the dialogue unit can perform a more appropriate dialogue by considering the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the content of the dialogue.

[0101] The dialogue unit can adjust the content of the dialogue by analyzing the social media activity of the user during the dialogue. For example, the dialogue unit stores the social media activity of the user in a database and analyzes the data. For example, the dialogue unit performs dialogue related to topics in which the user has shown interest on social media and performs dialogue related to people the user follows on social media. The dialogue unit can also perform dialogue related to groups in which the user participates on social media. For example, the dialogue unit stores the social media activity of the user in a database and analyzes the data to adjust the content of the dialogue. This allows the dialogue unit to perform more appropriate dialogue by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the social media activity of the user to the generation AI and cause the generation AI to adjust the content of the dialogue.

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

[0103] When accepting input from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input format. For example, voice input is recommended for a user who has used voice input a lot in the past, and text input is recommended for a user who has used text input a lot. In addition, if a user frequently uses a specific form of input during a specific time period, the acceptance unit can also suggest the optimal input format for that time period. In this way, the acceptance unit can provide a smoother input experience by utilizing the user's past input history.

[0104] When analyzing the user's input, the analysis unit can adjust the analysis method based on the user's current situation and environment. For example, if the user is in a noisy environment, noise reduction of the voice data can be strengthened. Also, if the user is in a quiet environment, more detailed voice analysis can be performed. Furthermore, if the user is on the move, the speed of analysis can be prioritized and results can be provided quickly. This allows the analysis unit to perform flexible analysis according to the user's situation.

[0105] When generating a conversation partner based on the user's input, the generation unit can customize the characteristics of the conversation partner by referring to the user's past dialogue history. For example, the conversation partner's personality and topics can be adjusted based on the user's preferred speaking style and topics in the past. The conversation partner can also be set to avoid topics that the user has avoided in the past. In this way, the generation unit can provide a more personalized conversation partner by utilizing the user's past dialogue history.

[0106] When a dialogue is conducted between a user and a conversation partner, the dialogue unit can estimate the user's current emotional state and adjust the tone and content of the dialogue based on that emotion. For example, if the user is feeling stressed, the conversation partner can speak to the user in a relaxing tone. Also, if the user is excited, the conversation partner can respond calmly to balance things out. This allows the dialogue unit to provide an appropriate dialogue according to the user's emotions.

[0107] When accepting input from a user, the accepting unit can preferentially accept highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, information related to that area can be preferentially accepted. Also, when the user is traveling, information related to the travel destination can be preferentially accepted. In this way, the accepting unit can provide more relevant input by utilizing the user's geographical location information.

[0108] When analyzing the user's input, the analysis unit can analyze the user's social media activity and provide related information. For example, the analysis unit can prioritize information related to topics in which the user has shown interest on social media. The analysis unit can also provide information related to people the user follows. This allows the analysis unit to utilize the user's social media activity to provide more personalized information.

[0109] When generating a conversation partner based on the user's input, the generation unit can adjust the characteristics of the conversation partner based on the user's current situation or environment. For example, when the user is at work, a business-related conversation partner is generated, and when the user is relaxing, an entertainment-related conversation partner is generated. Also, when the user is studying, an education-related conversation partner can be generated. In this way, the generation unit can provide an appropriate conversation partner according to the user's current situation.

[0110] When a dialogue unit engages in dialogue between a user and a conversation partner, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. For example, the optimal dialogue method can be selected based on the dialogue style that the user has preferred in the past. The dialogue unit can also engage in dialogue based on topics of interest from the user's past dialogue history. This allows the dialogue unit to provide a more personalized dialogue by utilizing the user's past dialogue history.

[0111] When accepting input from a user, the accepting unit can estimate the user's emotion and adjust the timing of accepting the input based on the emotion. For example, if the user is feeling stressed, the accepting unit can temporarily delay the acceptance of the input. Also, if the user is relaxed, the accepting unit can quickly accept the input. This allows the accepting unit to provide appropriate input acceptance according to the user's emotion.

[0112] When analyzing the user's input, the analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is excited, the speed of the analysis is prioritized and results are provided quickly. On the other hand, if the user is relaxed, a more detailed analysis can be performed. This allows the analysis unit to provide a flexible analysis according to the user's emotions.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The reception unit receives input from a user. The input from a user includes voice data, text data, image data, etc. For example, voice data is received through a microphone, text data through a keyboard or touch screen, and image data through a camera. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the voice data and text data using natural language processing technology to identify the user's request. The analysis unit can also analyze image data using image analysis technology to identify the user's request. Step 3: The generator generates an ideal conversation partner based on the information analyzed by the analyzer. The generator uses a generation AI to generate a conversation partner based on the user's request. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the voice, personality, and manner of expression of the conversation partner according to the user's request. Step 4: In the dialogue unit, the user dialogues with the conversation partner generated by the generation unit. The dialogue unit conducts dialogue in the form of voice dialogue, text chat, video call, etc. The dialogue unit provides an interface that allows the user to have a natural dialogue with the generated conversation partner.

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

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

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

[0118] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice data and image data from the user using the microphone 38B and the camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's request using natural language processing technology and image analysis technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an ideal conversation partner using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart device 14, and provides an interface for the user to have a voice dialogue or a text chat with the generated conversation partner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

[0129] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice data and image data from the user using the microphone 238 and the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's request using natural language processing technology and image analysis technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an ideal conversation partner using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface for the user to have a voice dialogue or a text chat with the generated conversation partner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

[0145] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0150] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice data and image data from the user using the microphone 238 and the camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's request using natural language processing technology and image analysis technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an ideal conversation partner using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides an interface for the user to have a voice dialogue or a text chat with the generated conversation partner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0167] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice data and image data from the user using the microphone 238 and the camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's request using natural language processing technology and image analysis technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an ideal conversation partner using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface for the user to have a voice dialogue or a text chat with the generated conversation partner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0169] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0177] In addition, 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 upon request from the data processing device 12.

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

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

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

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0184] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0186] (Appendix 1) a reception unit for receiving input from a user; an analysis unit that analyzes the information received by the reception unit; A generation unit that generates an ideal conversation partner based on the information analyzed by the analysis unit; a dialogue unit in which the user dialogues with the conversation partner generated by the generation unit; A system characterized by: (Appendix 2) The reception unit is Accepts audio or text data as input 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzes input information and identifies the characteristics of an ideal conversation partner based on the user's requirements 2. The system of claim 1, (Appendix 4) The generation unit is Generate ideal conversation partners based on identified characteristics 2. The system of claim 1, (Appendix 5) The dialogue unit includes: The user interacts with the generated partner 2. The system of claim 1, (Appendix 6) The dialogue unit includes: To provide a concrete means for educational purposes to deepen learning through dialogue with historical figures 2. The system of claim 1, (Appendix 7) The dialogue unit includes: Provide concrete steps to improve presentation skills in business through conversations with virtual clients 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1, (Appendix 10) The reception unit is As input is received, it is filtered based on the user's current context and interests. 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting input, prioritize relevant input based on the user's geographic location. 2. The system of claim 1, (Appendix 13) The reception unit is Upon receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the input 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, apply different parsing algorithms depending on the input category 2. The system of claim 1, (Appendix 17) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when inputs were submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During parsing, adjust the order of parsing based on the relevance of the inputs 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the user's emotions and adjust the characteristics of the conversation partner to be generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The generation unit is During generation, the system analyzes the user's past conversation history to generate the most suitable conversation partner. 2. The system of claim 1, (Appendix 22) The generation unit is At creation time, customize the characteristics of the conversation partner based on the user's current situation 2. The system of claim 1, (Appendix 23) The generation unit is Estimate the user's emotions and determine the priority of conversation partners to be generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 24) The generation unit is At the time of generation, appropriate conversation partners are generated taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 25) The generation unit is At the time of generation, the characteristics of the conversation partner are adjusted by analyzing the user's social media activity. 2. The system of claim 1, (Appendix 26) The dialogue unit includes: To estimate a user's emotion and adjust a method of interaction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The dialogue unit includes: When interacting with a user, the system selects the optimal interaction method by referring to the user's past interaction history. 2. The system of claim 1, (Appendix 28) The dialogue unit includes: During a conversation, customize the conversation content based on the user's current situation 2. The system of claim 1, (Appendix 29) The dialogue unit includes: Estimate user emotions and prioritize interactions based on the estimated user emotions 2. The system of claim 1, (Appendix 30) The dialogue unit includes: During dialogue, the optimal dialogue method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The dialogue unit includes: During a conversation, analyze the user's social media activity to tailor the conversation 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. a reception unit for receiving input from a user; an analysis unit that analyzes the information received by the reception unit; A generation unit that generates an ideal conversation partner based on the information analyzed by the analysis unit; A dialogue unit in which a user dialogues with a conversation partner generated by the generation unit. A system characterized by:

2. The reception unit is Accepts audio or text data as input 2. The system of claim 1 .

3. The analysis unit is Analyzes input information and identifies the characteristics of an ideal conversation partner based on the user's requirements 2. The system of claim 1 .

4. The generation unit is Generate ideal conversation partners based on the characteristics identified by the analysis unit.

4. The system of claim 3.

5. The dialogue unit includes: To provide a concrete means for educational purposes to deepen learning through dialogue with historical figures 2. The system of claim 1 .

6. The dialogue unit includes: Provide concrete steps to improve presentation skills in business through conversations with virtual clients 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1 .

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