System

Abstract

An object of a system according to an embodiment is to realize natural communication with a user.SOLUTION: A system according to an embodiment includes a user information acquisition unit, a dialogue generation unit, and an avatar or an android. The user information acquisition unit acquires basic information of a user. The dialogue generation unit generates a dialogue on the basis of the basic information acquired by the user information acquisition unit. The avatar or android provides the dialog generated by the dialog generator to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, dialogue with AI is one-sided, requiring the user to actively speak to the AI, making natural communication difficult.

[0005] The system according to the embodiment aims to realize natural communication with the user. [Means for solving the problem]

[0006] A system according to an embodiment includes a user information acquisition unit, a dialogue generation unit, and an avatar or an android. The user information acquisition unit acquires basic information about a user. The dialogue generation unit generates a dialogue based on the basic information acquired by the user information acquisition unit. The avatar or android provides the dialogue generated by the dialogue generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can realize natural communication with the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication system according to an embodiment of the present invention is a system that uses generative AI to realize human-like communication, thereby enabling communication with users that aims for mutual understanding.

[0029] A communication system according to an embodiment includes a user information acquisition unit, a dialogue generation unit, and an avatar or an android. The user information acquisition unit acquires basic information about a user. For example, the user information acquisition unit acquires basic information such as the user's name, age, gender, and address. The user information acquisition unit can also acquire information about the user's hobbies and interests. The dialogue generation unit generates a dialogue based on the basic information acquired by the user information acquisition unit. For example, the dialogue generation unit recognizes that the user likes music and provides topics related to music. The dialogue generation unit can also analyze the user's past dialogue history to generate more personalized dialogue. The avatar or android provides the user with the dialogue generated by the dialogue generation unit. For example, the avatar can ask, "What music did you listen to today?" to promote communication with the user. The android can also estimate the user's emotional state in real time and generate a dialogue based on that emotion. This enables the communication system according to an embodiment to achieve communication aimed at mutual understanding with the user.

[0030] The dialogue generation unit can analyze the user's past behavioral history and dialogue history based on the basic information acquired by the user information acquisition unit, and generate more personalized questions. The dialogue generation unit, for example, analyzes the user's past dialogue history to identify topics of interest. For example, if the user has talked about music in the past, it generates questions related to music. The dialogue generation unit also generates questions appropriate to the current situation based on the user's behavioral history. For example, it generates questions related to travel based on recent travel history. The dialogue generation unit also analyzes the user's dialogue history, finds specific patterns, and generates questions based on those patterns. For example, for a user who plays sports every weekend, it generates questions related to sports. In this way, more personalized questions can be generated by analyzing the user's past behavioral history and dialogue history.

[0031] The user information acquisition unit can input user information using multimodal methods such as voice input and gesture input. For example, the user information acquisition unit allows the user to input information by voice and converts it into text using voice recognition technology. For example, the user inputs "My name is Tanaka" by voice. The user information acquisition unit also allows the user to input information by gesture and analyzes the input content using gesture recognition technology. For example, the user inputs "Yes" by raising their hand. The user information acquisition unit also allows the user to input information using a touch screen and analyzes the touch gesture. For example, the user inputs "Next" by swiping the screen. This improves user convenience by allowing user information to be input using multimodal methods such as voice input and gesture input.

[0032] The user information acquisition unit synchronizes user information between different devices, enabling a consistent interaction experience to be provided from any device. The user information acquisition unit, for example, stores user information in the cloud and synchronizes it between different devices. For example, the same user information is used on a smartphone and a tablet. The user information acquisition unit also synchronizes user information between different devices in real time, providing a consistent interaction experience. For example, information entered on a smartphone is instantly reflected on a computer. The user information acquisition unit also automatically synchronizes user information between devices, enabling the same information to be used from any device. For example, the same user information is used on a smartwatch and a smart speaker. This allows a consistent interaction experience to be provided by synchronizing user information between different devices.

[0033] The dialogue generation unit can analyze the user's dialogue history based on the basic information acquired by the user information acquisition unit, and generate a deeper dialogue based on the content of the past dialogue. For example, the dialogue generation unit analyzes the user's past dialogue history through an avatar to identify topics of interest. For example, if the user has talked about music in the past, the dialogue generation unit generates a deep dialogue about music. The dialogue generation unit also generates a deep dialogue appropriate to the current situation based on the user's dialogue history through an android. For example, the dialogue generation unit generates a deep dialogue about travel based on recent travel history. The dialogue generation unit also analyzes the user's dialogue history through an avatar or android, finds specific patterns, and generates a deep dialogue based on those patterns. For example, for a user who plays sports every weekend, the dialogue generation unit generates a deep dialogue about sports. In this way, a deeper dialogue can be provided by analyzing the user's dialogue history.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The user information acquisition unit can also acquire health data of the user. For example, the user information acquisition unit acquires health data such as the user's heart rate, blood pressure, and sleep patterns. The user information acquisition unit can also acquire information about the user's diet and exercise. This allows the dialogue generation unit to provide health advice and reminders based on the user's health condition. For example, the dialogue generation unit can recognize that the user has not been exercising recently and generate a dialogue encouraging the user to exercise. Furthermore, if the user is feeling stressed, the dialogue generation unit can generate a dialogue suggesting relaxation methods. This makes it possible to provide a personalized dialogue according to the user's health condition.

[0036] When analyzing the user's past dialogue history, the dialogue generation unit can also take into account information about the user's learning history and skills. For example, if the user has previously studied a specific skill, the dialogue generation unit can generate a dialogue related to that skill. Also, if the user wants to learn a new skill, the dialogue generation unit can generate a dialogue that provides advice and resources related to that skill. Furthermore, the dialogue generation unit can generate a dialogue that suggests the optimal learning method based on the user's learning style and preferences. This makes it possible to provide more personalized dialogue based on the user's learning history and skills.

[0037] The user information acquisition unit can also acquire environmental data about the user. For example, the user information acquisition unit can acquire environmental data such as the temperature, humidity, and noise level around the user. The user information acquisition unit can also acquire location information and movement history of the user. This allows the dialogue generation unit to generate appropriate dialogue based on the user's environment. For example, if the user is in a noisy environment, the dialogue generation unit can generate dialogue that suggests a quiet place. Also, if the user is in a specific location, the dialogue generation unit can generate dialogue that provides information related to that location. This allows for more personalized dialogue to be provided based on the user's environmental data.

[0038] The user information acquisition unit can also acquire the user's purchasing history. For example, the user information acquisition unit acquires information about products and services that the user has purchased in the past. The user information acquisition unit can also acquire information about the user's purchasing patterns and preferences. This allows the dialogue generation unit to generate a dialogue that suggests related products and services based on the user's purchasing history. For example, if the user has purchased products from a specific brand in the past, a dialogue that suggests new products related to that brand can be generated. Also, if the user prefers products from a specific category, a dialogue that provides information about that category can be generated. This makes it possible to provide more personalized dialogue based on the user's purchasing history.

[0039] When analyzing the user's past interaction history, the interaction generation unit can also take into account information about the user's travel history and travel preferences. For example, if the user has visited a particular place in the past, the interaction generation unit can generate an interaction about that place. Also, if the user is looking for a new travel destination, the interaction generation unit can generate an interaction that provides information about the travel destination. Furthermore, the interaction generation unit can generate an interaction that suggests the most suitable travel destination based on the user's travel preferences. This makes it possible to provide more personalized interactions based on the user's travel history and travel preferences.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The user information acquisition unit acquires basic information about the user. For example, the user information acquisition unit acquires basic information such as the user's name, age, gender, and address. The user information acquisition unit can also acquire information about the user's hobbies and interests. Step 2: The dialogue generation unit generates a dialogue based on the basic information acquired by the user information acquisition unit. For example, the dialogue generation unit recognizes that the user likes music and provides topics related to music. The dialogue generation unit can also analyze the user's past dialogue history to generate more personalized dialogue. Step 3: The avatar or android provides the user with the dialogue generated by the dialogue generator. For example, the avatar can encourage communication with the user by asking, "What music did you listen to today?" The android can also estimate the user's emotional state in real time and generate dialogue based on that emotion.

[0042] (Example 2) A communication system according to an embodiment of the present invention is a system that uses generative AI to realize human-like communication, thereby enabling communication with users that aims for mutual understanding.

[0043] A communication system according to an embodiment includes a user information acquisition unit, a dialogue generation unit, and an avatar or an android. The user information acquisition unit acquires basic information about a user. For example, the user information acquisition unit acquires basic information such as the user's name, age, gender, and address. The user information acquisition unit can also acquire information about the user's hobbies and interests. The dialogue generation unit generates a dialogue based on the basic information acquired by the user information acquisition unit. For example, the dialogue generation unit recognizes that the user likes music and provides topics related to music. The dialogue generation unit can also analyze the user's past dialogue history to generate more personalized dialogue. The avatar or android provides the user with the dialogue generated by the dialogue generation unit. For example, the avatar can ask, "What music did you listen to today?" to promote communication with the user. The android can also estimate the user's emotional state in real time and generate a dialogue based on that emotion. This enables the communication system according to an embodiment to achieve communication aimed at mutual understanding with the user.

[0044] The dialogue generation unit can estimate the user's emotional state in real time based on the basic information acquired by the user information acquisition unit, and generate questions corresponding to that emotion. The dialogue generation unit, for example, analyzes the user's facial expression and voice tone to estimate the emotional state in real time. For example, if the user is smiling, a positive question is generated. The dialogue generation unit also analyzes the user's input content to estimate the emotional state. For example, if the user inputs "tired," a topic that will help them relax is provided. The dialogue generation unit also estimates the user's current emotional state based on the user's past emotional data and generates appropriate questions. For example, if the user has felt stressed in the past, a topic that will help them relax is provided. This makes it possible to provide a more personalized dialogue by generating questions according to the user's emotional state.

[0045] The dialogue generation unit can analyze the user's past behavioral history and dialogue history based on the basic information acquired by the user information acquisition unit, and generate more personalized questions. The dialogue generation unit, for example, analyzes the user's past dialogue history to identify topics of interest. For example, if the user has talked about music in the past, it generates questions related to music. The dialogue generation unit also generates questions appropriate to the current situation based on the user's behavioral history. For example, it generates questions related to travel based on recent travel history. The dialogue generation unit also analyzes the user's dialogue history, finds specific patterns, and generates questions based on those patterns. For example, for a user who plays sports every weekend, it generates questions related to sports. In this way, more personalized questions can be generated by analyzing the user's past behavioral history and dialogue history.

[0046] The dialogue generation unit can use the emotion estimation function to analyze the emotional response to information input by the user and generate questions that elicit positive emotions. The dialogue generation unit, for example, analyzes the emotional response to information input by the user in real time and generates questions that elicit positive emotions. For example, if the user inputs "I'm tired," the dialogue generation unit asks, "What have you enjoyed recently?" The dialogue generation unit also uses the emotion estimation function to analyze the emotional response to the user's input content and generates questions that elicit positive emotions. For example, if the user inputs "My work is tough," the dialogue generation unit asks, "What are your recent hobbies?" The dialogue generation unit also generates questions that elicit positive emotions based on the user's emotional response data. For example, if the user inputs "I'm stressed," the dialogue generation unit asks, "What are some ways you've relaxed recently?" In this way, the quality of the dialogue is improved by analyzing the user's emotional response and generating questions that elicit positive emotions.

[0047] The user information acquisition unit can input user information using multimodal methods such as voice input and gesture input. For example, the user information acquisition unit allows the user to input information by voice and converts it into text using voice recognition technology. For example, the user inputs "My name is Tanaka" by voice. The user information acquisition unit also allows the user to input information by gesture and analyzes the input content using gesture recognition technology. For example, the user inputs "Yes" by raising their hand. The user information acquisition unit also allows the user to input information using a touch screen and analyzes the touch gesture. For example, the user inputs "Next" by swiping the screen. This improves user convenience by allowing user information to be input using multimodal methods such as voice input and gesture input.

[0048] The user information acquisition unit synchronizes user information between different devices, enabling a consistent interaction experience to be provided from any device. The user information acquisition unit, for example, stores user information in the cloud and synchronizes it between different devices. For example, the same user information is used on a smartphone and a tablet. The user information acquisition unit also synchronizes user information between different devices in real time, providing a consistent interaction experience. For example, information entered on a smartphone is instantly reflected on a computer. The user information acquisition unit also automatically synchronizes user information between devices, enabling the same information to be used from any device. For example, the same user information is used on a smartwatch and a smart speaker. This allows a consistent interaction experience to be provided by synchronizing user information between different devices.

[0049] The user information acquisition unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and optimize the input content. The user information acquisition unit, for example, analyzes the facial expression and tone of voice when the user is entering input and estimates the emotion in real time. For example, if the user is smiling, positive input content is suggested. The user information acquisition unit also analyzes the user's input content in real time and optimizes the input content according to the user's emotional state. For example, if the user enters "tired," it suggests a topic that will help them relax. The user information acquisition unit also uses the emotion estimation function to analyze the emotion of the user when entering input in real time and optimize the input content. For example, if the user enters "stressed," it suggests a topic that will help them relax. In this way, the emotion of the user when entering input is analyzed in real time and the input content is optimized, thereby improving user convenience.

[0050] The dialogue generation unit can estimate the user's emotional state in real time based on the basic information acquired by the user information acquisition unit, and generate a dialogue corresponding to that emotion. For example, the dialogue generation unit has an avatar analyze the user's facial expression and estimate the emotional state in real time. For example, if the user is smiling, a positive dialogue is generated. The dialogue generation unit also has an android analyze the user's tone of voice and estimate the emotional state in real time. For example, if the user speaks in a tired voice, a relaxing dialogue is generated. The dialogue generation unit also has an avatar or android estimate the user's current emotional state based on the user's past emotional data and generate an appropriate dialogue. For example, if the user has felt stressed in the past, a relaxing dialogue is generated. This allows for the provision of a more personalized dialogue by generating a dialogue corresponding to the user's emotional state.

[0051] The dialogue generation unit can analyze the user's dialogue history based on the basic information acquired by the user information acquisition unit, and generate a deeper dialogue based on the content of the past dialogue. For example, the dialogue generation unit analyzes the user's past dialogue history through an avatar to identify topics of interest. For example, if the user has talked about music in the past, the dialogue generation unit generates a deep dialogue about music. The dialogue generation unit also generates a deep dialogue appropriate to the current situation based on the user's dialogue history through an android. For example, the dialogue generation unit generates a deep dialogue about travel based on recent travel history. The dialogue generation unit also analyzes the user's dialogue history through an avatar or android, finds specific patterns, and generates a deep dialogue based on those patterns. For example, for a user who plays sports every weekend, the dialogue generation unit generates a deep dialogue about sports. In this way, a deeper dialogue can be provided by analyzing the user's dialogue history.

[0052] The dialogue generation unit uses an emotion estimation function to generate dialogue according to the user's emotions and elicit positive emotions. For example, the dialogue generation unit uses an avatar to analyze the user's facial expressions, estimate the user's emotional state in real time, and generate positive dialogue. For example, if the user is smiling, the dialogue generation unit provides a pleasant topic. Furthermore, the dialogue generation unit uses an android to analyze the user's tone of voice, estimate the user's emotional state in real time, and generate positive dialogue. For example, if the user speaks in a tired voice, the dialogue generation unit provides a relaxing topic. Furthermore, the dialogue generation unit uses an avatar or android to estimate the user's current emotional state based on the user's past emotional data, and generate positive dialogue. For example, if the user has felt stressed in the past, the dialogue generation unit provides a relaxing topic. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional elements in the evaluation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The user information acquisition unit can also acquire health data of the user. For example, the user information acquisition unit acquires health data such as the user's heart rate, blood pressure, and sleep patterns. The user information acquisition unit can also acquire information about the user's diet and exercise. This allows the dialogue generation unit to provide health advice and reminders based on the user's health condition. For example, the dialogue generation unit can recognize that the user has not been exercising recently and generate a dialogue encouraging the user to exercise. Furthermore, if the user is feeling stressed, the dialogue generation unit can generate a dialogue suggesting relaxation methods. This makes it possible to provide a personalized dialogue according to the user's health condition.

[0055] The dialogue generation unit can also analyze the content of the user's social media posts when estimating the user's emotional state. For example, if the user posts positive things on social media, it can generate positive dialogue. On the other hand, if the user posts negative things, it can generate encouraging dialogue. Furthermore, the dialogue generation unit can analyze the user's social media friendships and topics of interest and generate dialogue based on that. For example, if the user is interested in a particular event, it can generate dialogue related to that event. This makes it possible to provide more personalized dialogue by utilizing the user's social media information.

[0056] When analyzing the user's past dialogue history, the dialogue generation unit can also take into account information about the user's learning history and skills. For example, if the user has previously studied a specific skill, the dialogue generation unit can generate a dialogue related to that skill. Also, if the user wants to learn a new skill, the dialogue generation unit can generate a dialogue that provides advice and resources related to that skill. Furthermore, the dialogue generation unit can generate a dialogue that suggests the optimal learning method based on the user's learning style and preferences. This makes it possible to provide more personalized dialogue based on the user's learning history and skills.

[0057] The dialogue generation unit can also analyze the user's music playback history when estimating the user's emotional state using the emotion estimation function. For example, if the user is listening to positive music, it can generate positive dialogue. Also, if the user is listening to relaxing music, it can generate relaxing dialogue. Furthermore, the dialogue generation unit can generate dialogue that provides topics related to music based on the user's music preferences. For example, if the user likes a particular artist, it can generate dialogue related to that artist. This makes it possible to provide more personalized dialogue by utilizing the user's music playback history.

[0058] The user information acquisition unit can also acquire environmental data about the user. For example, the user information acquisition unit can acquire environmental data such as the temperature, humidity, and noise level around the user. The user information acquisition unit can also acquire location information and movement history of the user. This allows the dialogue generation unit to generate appropriate dialogue based on the user's environment. For example, if the user is in a noisy environment, the dialogue generation unit can generate dialogue that suggests a quiet place. Also, if the user is in a specific location, the dialogue generation unit can generate dialogue that provides information related to that location. This allows for more personalized dialogue to be provided based on the user's environmental data.

[0059] The user information acquisition unit can also acquire the user's purchasing history. For example, the user information acquisition unit acquires information about products and services that the user has purchased in the past. The user information acquisition unit can also acquire information about the user's purchasing patterns and preferences. This allows the dialogue generation unit to generate a dialogue that suggests related products and services based on the user's purchasing history. For example, if the user has purchased products from a specific brand in the past, a dialogue that suggests new products related to that brand can be generated. Also, if the user prefers products from a specific category, a dialogue that provides information about that category can be generated. This makes it possible to provide more personalized dialogue based on the user's purchasing history.

[0060] The user information acquisition unit can also analyze the user's exercise data when estimating the user's emotional state using the emotion estimation function. For example, if the user feels positive after exercising, it can generate a dialogue about exercise. Also, if the user feels negative because of a lack of exercise, it can generate a dialogue encouraging exercise. Furthermore, the dialogue generation unit can generate a dialogue that provides exercise advice or reminders based on the user's exercise data. For example, if the user has not exercised recently, it can generate a dialogue encouraging exercise. This makes it possible to provide more personalized dialogue by utilizing the user's exercise data.

[0061] The dialogue generation unit can also analyze the user's reading history when estimating the user's emotional state. For example, if the user has positive emotions, it can generate dialogue about positive reading. Also, if the user feels like relaxing, it can generate dialogue about relaxing reading. Furthermore, the dialogue generation unit can generate dialogue that provides topics related to reading based on the user's reading preferences. For example, if the user likes books of a particular genre, it can generate dialogue about that genre. This makes it possible to utilize the user's reading history to provide more personalized dialogue.

[0062] When analyzing the user's past interaction history, the interaction generation unit can also take into account information about the user's travel history and travel preferences. For example, if the user has visited a particular place in the past, the interaction generation unit can generate an interaction about that place. Also, if the user is looking for a new travel destination, the interaction generation unit can generate an interaction that provides information about the travel destination. Furthermore, the interaction generation unit can generate an interaction that suggests the most suitable travel destination based on the user's travel preferences. This makes it possible to provide more personalized interactions based on the user's travel history and travel preferences.

[0063] When estimating the user's emotional state using the emotion estimation function, the dialogue generation unit can also analyze information about the user's hobbies and interests. For example, if the user has positive feelings about a particular hobby, the dialogue generation unit can generate a dialogue about that hobby. Also, if the user wants to find a new hobby, the dialogue generation unit can generate a dialogue that provides information about that hobby. Furthermore, the dialogue generation unit can generate a dialogue that provides related topics based on the user's interests. For example, if the user is interested in a particular sport, the dialogue generation unit can generate a dialogue about that sport. This makes it possible to provide more personalized dialogue based on the user's hobbies and interests.

[0064] The processing flow of the second embodiment will be briefly explained below.

[0065] Step 1: The user information acquisition unit acquires basic information about the user. For example, the user information acquisition unit acquires basic information such as the user's name, age, gender, and address. The user information acquisition unit can also acquire information about the user's hobbies and interests. Step 2: The dialogue generation unit generates a dialogue based on the basic information acquired by the user information acquisition unit. For example, the dialogue generation unit recognizes that the user likes music and provides topics related to music. The dialogue generation unit can also analyze the user's past dialogue history to generate more personalized dialogue. Step 3: The avatar or android provides the user with the dialogue generated by the dialogue generator. For example, the avatar can encourage communication with the user by asking, "What music did you listen to today?" The android can also estimate the user's emotional state in real time and generate dialogue based on that emotion.

[0066] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0067] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0068] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0069] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0070] 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.

[0071] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0072] 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.

[0073] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0074] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0075] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0076] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0077] 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.

[0078] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0079] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0080] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0084] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] 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.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0100] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The 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.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0116] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0117] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0118] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0119] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0120] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0121] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0122] 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.

[0123] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0124] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0125] 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.

[0126] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0127] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0128] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0129] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0130] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0131] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0132] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0133] 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 user information acquisition unit that acquires basic information of a user; a dialogue generation unit that generates a dialogue based on the basic information acquired by the user information acquisition unit; an avatar or android that provides the user with the dialogue generated by the dialogue generation unit. A system characterized by:

2. The dialogue generation unit The emotional state of the user is estimated in real time based on the basic information acquired by the user information acquisition unit, and a question is generated according to the emotional state.

2. The system of claim 1.

3. The dialogue generation unit Based on the basic information acquired by the user information acquisition unit, the user's past behavior history and conversation history are analyzed to generate more personalized questions.

2. The system of claim 1.

4. The dialogue generation unit Based on the basic information acquired by the user information acquisition unit, an emotional response to the information input by the user is analyzed, and questions that elicit positive emotions are generated.

2. The system of claim 1.

5. The user information acquisition unit Input user information through multimodal methods such as voice and gesture input 2. The system of claim 1.

6. The user information acquisition unit Synchronize user information across devices to provide a consistent interaction experience from any device 2. The system of claim 1.

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