System

Abstract

To provide a system for reducing a user's feeling of loneliness by listening to the user's speech, continuing a conversation and recognizing the user's feeling.SOLUTION: A specific processing part 290 of a data processing device 12 in the system executes a talk listening process for listening to the talk of the user, a conversation continuation process for continuing the conversation on the basis of the talk of the user received by the talk listening process, and an emotion recognition part process for recognizing the emotion of the user in the conversation continued by the conversation continuation part.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that users often feel lonely because they cannot find someone to talk to about what they want to talk about.

[0005] The system according to the embodiment aims to reduce the user's sense of loneliness by listening to the user, continuing the conversation, and recognizing the user's emotions. [Means for solving the problem]

[0006] A system according to an embodiment includes a listening unit, a conversation continuation unit, and an emotion recognition unit. The listening unit listens to a user's speech. The conversation continuation unit continues the conversation based on the user's speech received by the listening unit. The emotion recognition unit recognizes the user's emotion in the conversation continued by the conversation continuation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the user's sense of loneliness by listening to the user, continuing the conversation, and recognizing the user's emotions. [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) The AI ​​system according to the embodiment of the present invention provides a compatible AI for each user, thereby eliminating the loneliness of people who have no one to talk to and making their daily lives more lively. As a result, the AI ​​system can listen to the user and hold a conversation, becoming an important presence for those who are single or feel lonely.

[0029] The AI ​​system according to the embodiment includes a listening unit, a conversation continuation unit, and an emotion recognition unit. The listening unit listens to a user's speech. For example, it converts the user's speech into text data using voice input. The listening unit can also receive the user's speech through text chat. The listening unit can also analyze the user's movements using gesture recognition technology to understand the content of the speech. The conversation continuation unit continues the conversation based on the user's speech received by the listening unit. For example, the generation AI generates an appropriate response to the user's speech. The conversation continuation unit can also suggest related topics based on the user's interests and concerns to deepen the conversation. The conversation continuation unit can also learn the user's past conversation history and generate personalized responses. The emotion recognition unit recognizes the user's emotions in the conversation continued by the conversation continuation unit. For example, it uses an emotion estimation function to analyze the user's tone of voice and speaking patterns to estimate emotions. The emotion recognition unit can also analyze the user's facial expressions and body movements to recognize emotions. The emotion recognition unit can also generate responses according to the user's emotions to provide emotional support to the user. As a result, the AI ​​system according to the embodiment can listen to the user, continue the conversation, and recognize emotions, thereby reducing the user's sense of loneliness and invigorating their daily life.

[0030] The listening unit can learn the user's past conversation history and generate personalized responses that reflect the user's preferences and interests. For example, the listening unit analyzes the user's past conversation history to identify interests in specific topics. For example, if there are many discussions about movies, it will prioritize questions about movies. The listening unit also learns the user's preferences based on the conversation history and personalizes responses. For example, it identifies the user's favorite music genre and provides topics related to that genre. The listening unit also uses the past conversation history to generate responses that reflect the user's interests. For example, it provides new information about hobbies that the user has previously mentioned. In this way, personalized responses are possible by learning the user's past conversation history.

[0031] The listening unit can suggest related music and videos based on what the user is saying, enriching the conversation. For example, the listening unit analyzes what the user is saying and suggests related music. For example, if the user says that they want to relax, it suggests relaxing music. The listening unit also suggests videos related to the user's topic. For example, if the user is talking about traveling, it suggests videos of travel destinations. The listening unit also suggests a combination of music and videos based on what the user is saying. For example, if the user is talking about a movie, it suggests the soundtrack and trailer for that movie. In this way, it is possible to enrich the conversation by suggesting related music and videos based on what the user is saying.

[0032] The listening unit can support conversations in different languages ​​and accommodate international users. For example, the listening unit automatically detects the language spoken by the user and generates a response in that language. For example, if spoken to in English, it will respond in English. The listening unit also has a real-time translation function to support conversations in different languages. For example, if spoken to in Japanese, it will translate the content into English and respond. The listening unit also builds a multilingual conversation database to smoothly carry out conversations in different languages. For example, it supports multiple languages ​​such as French, Spanish, and Chinese. This allows international users to be accommodated by supporting conversations in different languages.

[0033] The conversation continuation unit can automatically generate related topics and questions to dig deeper into the user's interests and concerns, thereby continuing the conversation. For example, the conversation continuation unit analyzes the user's utterances and automatically generates related topics. For example, if the user is talking about sports, it provides the latest sports news. The conversation continuation unit also automatically generates questions to dig deeper based on the user's interests. For example, if the user is interested in cooking, it asks about new recipes. The conversation continuation unit also analyzes the flow of the conversation and generates related topics and questions in real time. For example, if the user is talking about traveling, it asks about the next travel destination. In this way, the conversation can be continued by digging deeper into the user's interests and concerns.

[0034] The conversation continuation unit allows the generative AI to provide relevant knowledge and information in response to what the user says, thereby increasing the depth of the conversation. For example, the conversation continuation unit analyzes what the user says and provides relevant knowledge. For example, if the user is talking about history, it will provide relevant historical facts. The conversation continuation unit also provides relevant information based on what the user says. For example, if the user is talking about science, it will provide the latest science news. The conversation continuation unit also analyzes the flow of the conversation and provides relevant knowledge and information in real time. For example, if the user is talking about music, it will provide the history of that music genre. In this way, by providing relevant knowledge and information in response to what the user says, it is possible to increase the depth of the conversation.

[0035] The conversation continuation unit can suggest related events and communities based on the user's interests, and promote actual interactions. For example, the conversation continuation unit analyzes the user's interests and suggests related events. For example, if the user is interested in music, it suggests music festivals. The conversation continuation unit also suggests related communities based on the user's interests. For example, if the user is interested in reading, it suggests book clubs. The conversation continuation unit also suggests events and communities in real time based on the user's interests. For example, if the user is interested in sports, it suggests sporting events and clubs. In this way, actual interactions can be promoted by suggesting related events and communities based on the user's interests.

[0036] The conversation continuation unit can recommend books and articles that are appropriate for the user based on the content of the conversation, thereby expanding the user's knowledge. For example, the conversation continuation unit analyzes the user's statements and recommends related books. For example, if the user is talking about history, it recommends history books. The conversation continuation unit also recommends related articles based on the user's interests. For example, if the user is talking about science, it recommends the latest science articles. The conversation continuation unit also analyzes the content of the conversation and recommends books and articles that are appropriate for the user in real time. For example, if the user is talking about cooking, it recommends cookbooks and recipe articles. This allows the user's knowledge to be expanded by recommending books and articles that are appropriate for the user based on the content of the conversation.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The AI ​​system can further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure and analyzes their health condition. For example, if the heart rate is high, it provides advice on how to relax. The health management unit also manages the user's diet and exercise records to support healthy lifestyle habits. For example, if the user's diet is unbalanced, it suggests a nutritionally balanced diet. The health management unit also suggests regular health checks based on the user's health condition. For example, if the user's blood pressure is high, it recommends a doctor's consultation. This makes it possible to monitor the user's health condition and support a healthy lifestyle.

[0039] The AI ​​system can further include a learning support unit based on the user's hobbies and interests. The learning support unit provides learning resources related to areas of interest to the user. For example, if the user is interested in programming, it can suggest online programming courses. The learning support unit also manages the user's learning progress and provides appropriate feedback. For example, if the user is struggling with a particular task, it can provide additional learning resources. The learning support unit can also create a customized learning plan based on the user's learning style. For example, it can suggest video learning materials to a user who prefers visual learning. This allows for effective learning support based on the user's hobbies and interests.

[0040] The AI ​​system can further include a lifestyle rhythm management unit that manages the user's lifestyle rhythm. The lifestyle rhythm management unit, for example, analyzes the user's sleep patterns and provides appropriate sleep advice. For example, if the user's sleep time is short, it may advise the user to go to bed earlier. The lifestyle rhythm management unit also monitors the user's activity level and supports a balanced lifestyle. For example, if the user is not getting enough exercise, it may suggest moderate exercise. The lifestyle rhythm management unit also manages the user's meal and rest timings and provides advice to maintain a healthy lifestyle. For example, if the user's meal timings are irregular, it may advise the user to eat regularly. This allows the system to manage the user's lifestyle rhythm and support a healthy lifestyle.

[0041] The AI ​​system can further include a hobby support unit based on the user's hobbies and interests. The hobby support unit, for example, provides information about hobbies that the user is interested in. For example, if the user is interested in gardening, it suggests gardening tips and how to grow plants. The hobby support unit also suggests events and communities related to the user's hobbies. For example, if the user is interested in photography, it provides information about photo exhibitions and photo walks. The hobby support unit also suggests new ideas and projects related to the user's hobbies. For example, if the user is interested in cooking, it suggests new recipes and cooking techniques. This supports the user's hobbies and interests and helps them live a fulfilling life.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The listening unit listens to what the user says. For example, it converts what the user says into text data using voice input. The listening unit can also receive what the user says through text chat. Furthermore, the listening unit can analyze the user's movements using gesture recognition technology and understand the content of what is being said. Step 2: The conversation continuation unit continues the conversation based on the user's speech received by the listening unit. For example, the generation AI generates an appropriate response to the user's speech. The conversation continuation unit can also suggest related topics based on the user's interests and concerns to deepen the conversation. Furthermore, the conversation continuation unit can learn the user's past conversation history and generate personalized responses. Step 3: The emotion recognition unit recognizes the user's emotions during the conversation continued by the conversation continuation unit. For example, the emotion estimation function is used to analyze the user's tone of voice and speaking patterns to estimate emotions. The emotion recognition unit can also recognize emotions by analyzing the user's facial expressions and body movements. Furthermore, the emotion recognition unit can generate a response according to the user's emotions to provide emotional support to the user.

[0044] (Example 2) The AI ​​system according to the embodiment of the present invention provides a compatible AI for each user, thereby eliminating the loneliness of people who have no one to talk to and making their daily lives more lively. As a result, the AI ​​system can listen to the user and hold a conversation, becoming an important presence for those who are single or feel lonely.

[0045] The AI ​​system according to the embodiment includes a listening unit, a conversation continuation unit, and an emotion recognition unit. The listening unit listens to a user's speech. For example, it converts the user's speech into text data using voice input. The listening unit can also receive the user's speech through text chat. The listening unit can also analyze the user's movements using gesture recognition technology to understand the content of the speech. The conversation continuation unit continues the conversation based on the user's speech received by the listening unit. For example, the generation AI generates an appropriate response to the user's speech. The conversation continuation unit can also suggest related topics based on the user's interests and concerns to deepen the conversation. The conversation continuation unit can also learn the user's past conversation history and generate personalized responses. The emotion recognition unit recognizes the user's emotions in the conversation continued by the conversation continuation unit. For example, it uses an emotion estimation function to analyze the user's tone of voice and speaking patterns to estimate emotions. The emotion recognition unit can also analyze the user's facial expressions and body movements to recognize emotions. The emotion recognition unit can also generate responses according to the user's emotions to provide emotional support to the user. As a result, the AI ​​system according to the embodiment can listen to the user, continue the conversation, and recognize emotions, thereby reducing the user's sense of loneliness and invigorating their daily life.

[0046] The listening unit can analyze the user's tone of voice and speaking patterns to estimate the user's mood and emotions and adjust the response accordingly. The listening unit, for example, analyzes the user's tone of voice to detect changes in emotions. For example, when the user's voice gets lower, it determines that the user is likely tired and adjusts the response accordingly. The listening unit also analyzes speaking patterns to estimate the user's mood. For example, when the user's speaking speed slows, it determines that the user is likely depressed and adjusts the response accordingly. The listening unit also analyzes a combination of the tone of voice and speaking patterns to more accurately estimate the user's emotions. For example, when the user's voice is high and fast, it determines that the user is excited and adjusts the response accordingly. In this way, by analyzing the user's tone of voice and speaking patterns, it is possible to provide a response that matches the user's mood and emotions.

[0047] The listening unit can learn the user's past conversation history and generate personalized responses that reflect the user's preferences and interests. For example, the listening unit analyzes the user's past conversation history to identify interests in specific topics. For example, if there are many discussions about movies, it will prioritize questions about movies. The listening unit also learns the user's preferences based on the conversation history and personalizes responses. For example, it identifies the user's favorite music genre and provides topics related to that genre. The listening unit also uses the past conversation history to generate responses that reflect the user's interests. For example, it provides new information about hobbies that the user has previously mentioned. In this way, personalized responses are possible by learning the user's past conversation history.

[0048] The emotion recognition unit uses the emotion estimation function to analyze the emotion a user is feeling when speaking in real time and can respond according to that emotion. For example, the emotion recognition unit uses the emotion estimation function to analyze the user's tone of voice and facial expression in real time and estimate the emotion. For example, if the user sounds sad, the emotion recognition unit will offer words of comfort. The emotion recognition unit also analyzes the user's emotion in real time and adjusts the response based on the results. For example, if the user is angry, the emotion recognition unit will offer a calm response. The emotion recognition unit also uses the emotion estimation function to generate a response according to the user's emotion in real time. For example, if the user is happy, the emotion recognition unit will offer words of sympathy. This makes it possible to analyze the user's emotion in real time and respond according to the emotion.

[0049] The listening unit can suggest related music and videos based on what the user is saying, enriching the conversation. For example, the listening unit analyzes what the user is saying and suggests related music. For example, if the user says that they want to relax, it suggests relaxing music. The listening unit also suggests videos related to the user's topic. For example, if the user is talking about traveling, it suggests videos of travel destinations. The listening unit also suggests a combination of music and videos based on what the user is saying. For example, if the user is talking about a movie, it suggests the soundtrack and trailer for that movie. In this way, it is possible to enrich the conversation by suggesting related music and videos based on what the user is saying.

[0050] The listening unit can support conversations in different languages ​​and accommodate international users. For example, the listening unit automatically detects the language spoken by the user and generates a response in that language. For example, if spoken to in English, it will respond in English. The listening unit also has a real-time translation function to support conversations in different languages. For example, if spoken to in Japanese, it will translate the content into English and respond. The listening unit also builds a multilingual conversation database to smoothly carry out conversations in different languages. For example, it supports multiple languages ​​such as French, Spanish, and Chinese. This allows international users to be accommodated by supporting conversations in different languages.

[0051] The emotion recognition unit can use the emotion estimation function to suggest relaxation techniques (e.g., breathing techniques or meditation guides) according to the emotions expressed by the user when speaking. For example, the emotion recognition unit can use the emotion estimation function to suggest relaxation techniques when the user is feeling stressed. For example, it can teach the user how to take deep breaths. The emotion recognition unit can also provide meditation guidance according to the user's emotions. For example, if the user is feeling anxious, it can suggest a meditation method. The emotion recognition unit can also use the emotion estimation function to suggest relaxation techniques according to the user's emotions in real time. For example, if the user is tense, it can suggest music to help them relax. In this way, by suggesting relaxation techniques according to the user's emotions, it is possible to support the user's physical and mental health.

[0052] The conversation continuation unit can automatically generate related topics and questions to dig deeper into the user's interests and concerns, thereby continuing the conversation. For example, the conversation continuation unit analyzes the user's utterances and automatically generates related topics. For example, if the user is talking about sports, it provides the latest sports news. The conversation continuation unit also automatically generates questions to dig deeper based on the user's interests. For example, if the user is interested in cooking, it asks about new recipes. The conversation continuation unit also analyzes the flow of the conversation and generates related topics and questions in real time. For example, if the user is talking about traveling, it asks about the next travel destination. In this way, the conversation can be continued by digging deeper into the user's interests and concerns.

[0053] The conversation continuation unit allows the generative AI to provide relevant knowledge and information in response to what the user says, thereby increasing the depth of the conversation. For example, the conversation continuation unit analyzes what the user says and provides relevant knowledge. For example, if the user is talking about history, it will provide relevant historical facts. The conversation continuation unit also provides relevant information based on what the user says. For example, if the user is talking about science, it will provide the latest science news. The conversation continuation unit also analyzes the flow of the conversation and provides relevant knowledge and information in real time. For example, if the user is talking about music, it will provide the history of that music genre. In this way, by providing relevant knowledge and information in response to what the user says, it is possible to increase the depth of the conversation.

[0054] The conversation continuation unit uses the emotion estimation function to generate questions according to the user's emotions, thereby creating an environment in which the user feels comfortable talking. The conversation continuation unit, for example, uses the emotion estimation function to generate questions according to the user's emotions. For example, if the user is relaxed, it provides relaxing topics. The conversation continuation unit also analyzes the user's emotions in real time and generates questions based on the results. For example, if the user is nervous, it asks questions to relieve the tension. The conversation continuation unit also uses the emotion estimation function to generate questions in real time to create an environment in which the user feels comfortable talking. For example, if the user is sad, it asks comforting questions. In this way, by generating questions according to the user's emotions, it is possible to create an environment in which the user feels comfortable talking.

[0055] The conversation continuation unit can suggest related events and communities based on the user's interests, and promote actual interactions. For example, the conversation continuation unit analyzes the user's interests and suggests related events. For example, if the user is interested in music, it suggests music festivals. The conversation continuation unit also suggests related communities based on the user's interests. For example, if the user is interested in reading, it suggests book clubs. The conversation continuation unit also suggests events and communities in real time based on the user's interests. For example, if the user is interested in sports, it suggests sporting events and clubs. In this way, actual interactions can be promoted by suggesting related events and communities based on the user's interests.

[0056] The conversation continuation unit can recommend books and articles that are appropriate for the user based on the content of the conversation, thereby expanding the user's knowledge. For example, the conversation continuation unit analyzes the user's statements and recommends related books. For example, if the user is talking about history, it recommends history books. The conversation continuation unit also recommends related articles based on the user's interests. For example, if the user is talking about science, it recommends the latest science articles. The conversation continuation unit also analyzes the content of the conversation and recommends books and articles that are appropriate for the user in real time. For example, if the user is talking about cooking, it recommends cookbooks and recipe articles. This allows the user's knowledge to be expanded by recommending books and articles that are appropriate for the user based on the content of the conversation.

[0057] The conversation continuation unit can use the emotion estimation function to suggest exercises and activities that correspond to the user's emotions, thereby supporting physical and mental health. For example, the conversation continuation unit uses the emotion estimation function to suggest exercises that correspond to the user's emotions. For example, if the user is feeling stressed, yoga is suggested. The conversation continuation unit also analyzes the user's emotions in real time and suggests activities based on the results. For example, if the user is tired, a relaxing activity is suggested. The conversation continuation unit also uses the emotion estimation function to suggest exercises and activities that correspond to the user's emotions in real time. For example, if the user is feeling down, an activity that will help them change their mood is suggested. In this way, physical and mental health can be supported by suggesting exercises and activities that correspond to the user's emotions.

[0058] The emotion recognition unit analyzes facial expressions and body movements to recognize the user's emotions, enabling more accurate emotion estimation. The emotion recognition unit, for example, analyzes the user's facial expressions to estimate emotions. For example, if the user is smiling, it is determined that the user is happy. The emotion recognition unit also analyzes the user's body movements to estimate emotions. For example, if the user's shoulders are slumped, it is determined that the user is depressed. The emotion recognition unit also analyzes a combination of facial expressions and body movements to estimate emotions more accurately. For example, if the user's face is smiling but the body is tense, it is determined that the user has complex emotions. This makes it possible to more accurately estimate emotions by analyzing the user's facial expressions and body movements.

[0059] The emotion recognition unit learns past emotion data to generate a response that corresponds to the user's emotion, thereby improving the accuracy of the response. The emotion recognition unit, for example, learns past emotion data to generate a response that corresponds to the user's emotion. For example, if the user is sad, the emotion recognition unit offers words of comfort. The emotion recognition unit also learns response patterns that correspond to the user's emotion based on the emotion data. For example, if the user is angry, the emotion recognition unit provides a calm response. The emotion recognition unit also uses past emotion data to improve the accuracy of the response. For example, if the user is happy, the emotion recognition unit offers words of sympathy. In this way, by learning past emotion data, the accuracy of the response that corresponds to the user's emotion can be improved.

[0060] The emotion recognition unit uses the emotion estimation function to automatically generate words of encouragement or comfort according to the user's emotions, thereby providing emotional support to the user. For example, the emotion recognition unit uses the emotion estimation function to automatically generate words of comfort when the user is sad. For example, the emotion recognition unit responds with, "That must have been hard. Thank you for telling me." The emotion recognition unit also analyzes the user's emotions in real time and automatically generates words of encouragement based on the results. For example, when the user is feeling down, the emotion recognition unit responds with, "I'm sure you can overcome this." The emotion recognition unit also uses the emotion estimation function to automatically generate words of encouragement or comfort according to the user's emotions in real time, thereby providing emotional support to the user. For example, when the user is feeling anxious, the emotion recognition unit responds with, "Let's do our best together." In this way, the emotion estimation function can be used to automatically generate words of encouragement or comfort according to the user's emotions, thereby providing emotional support to the user.

[0061] The emotion recognition unit can suggest music and videos that correspond to the user's emotions and support emotional changes. For example, the emotion recognition unit uses an emotion estimation function to suggest relaxing music when the user wants to relax. For example, classical music or natural sounds are provided. The emotion recognition unit also analyzes the user's emotions in real time and suggests videos based on the results. For example, if the user is feeling stressed, a relaxing landscape video is provided. The emotion recognition unit also uses the emotion estimation function to suggest music and videos that correspond to the user's emotions in real time and support emotional changes. For example, if the user wants to cheer up, up-tempo music is provided. In this way, emotional changes can be supported by suggesting music and videos that correspond to the user's emotions.

[0062] The emotion recognition unit can use the emotion recognition function to provide feedback according to the user's emotions and help deepen self-understanding. For example, the emotion recognition unit uses the emotion recognition function to analyze the user's emotions and provide feedback based on the results. For example, it can respond, "You seem a little tired today. Don't push yourself." The emotion recognition unit can also analyze the user's emotions in real time and provide feedback to deepen self-understanding based on the results. For example, it can respond, "You seem to be under a lot of stress lately. Make time to relax." The emotion recognition unit can also use the emotion recognition function to provide feedback according to the user's emotions in real time and help deepen self-understanding. For example, it can respond, "You seem to be in good spirits today. Keep it up." In this way, it is possible to use the emotion recognition function to provide feedback according to the user's emotions and help deepen self-understanding.

[0063] The emotion recognition unit can use the emotion estimation function to suggest relaxation techniques (e.g., breathing techniques or meditation guides) according to the user's emotions. For example, the emotion recognition unit can use the emotion estimation function to suggest relaxation techniques when the user is feeling stressed. For example, it can teach the user how to take deep breaths. The emotion recognition unit can also analyze the user's emotions in real time and provide meditation guidance based on the results. For example, if the user is feeling anxious, it can suggest a meditation method. The emotion recognition unit can also use the emotion estimation function to suggest relaxation techniques according to the user's emotions in real time. For example, if the user is feeling tense, it can suggest music to help them relax. In this way, the emotion estimation function can be used to suggest relaxation techniques according to the user's emotions, thereby supporting the user's physical and mental health.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The AI ​​system can further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure and analyzes their health condition. For example, if the heart rate is high, it provides advice on how to relax. The health management unit also manages the user's diet and exercise records to support healthy lifestyle habits. For example, if the user's diet is unbalanced, it suggests a nutritionally balanced diet. The health management unit also suggests regular health checks based on the user's health condition. For example, if the user's blood pressure is high, it recommends a doctor's consultation. This makes it possible to monitor the user's health condition and support a healthy lifestyle.

[0066] The AI ​​system can further include a learning support unit based on the user's hobbies and interests. The learning support unit provides learning resources related to areas of interest to the user. For example, if the user is interested in programming, it can suggest online programming courses. The learning support unit also manages the user's learning progress and provides appropriate feedback. For example, if the user is struggling with a particular task, it can provide additional learning resources. The learning support unit can also create a customized learning plan based on the user's learning style. For example, it can suggest video learning materials to a user who prefers visual learning. This allows for effective learning support based on the user's hobbies and interests.

[0067] The AI ​​system can further include a lifestyle rhythm management unit that manages the user's lifestyle rhythm. The lifestyle rhythm management unit, for example, analyzes the user's sleep patterns and provides appropriate sleep advice. For example, if the user's sleep time is short, it may advise the user to go to bed earlier. The lifestyle rhythm management unit also monitors the user's activity level and supports a balanced lifestyle. For example, if the user is not getting enough exercise, it may suggest moderate exercise. The lifestyle rhythm management unit also manages the user's meal and rest timings and provides advice to maintain a healthy lifestyle. For example, if the user's meal timings are irregular, it may advise the user to eat regularly. This allows the system to manage the user's lifestyle rhythm and support a healthy lifestyle.

[0068] The AI ​​system can further include a hobby support unit based on the user's hobbies and interests. The hobby support unit, for example, provides information about hobbies that the user is interested in. For example, if the user is interested in gardening, it suggests gardening tips and how to grow plants. The hobby support unit also suggests events and communities related to the user's hobbies. For example, if the user is interested in photography, it provides information about photo exhibitions and photo walks. The hobby support unit also suggests new ideas and projects related to the user's hobbies. For example, if the user is interested in cooking, it suggests new recipes and cooking techniques. This supports the user's hobbies and interests and helps them live a fulfilling life.

[0069] The AI ​​system can also suggest relaxation techniques (e.g., breathing techniques or meditation guides) according to the user's emotions. For example, the emotion recognition unit uses an emotion estimation function to suggest relaxation techniques if the user is feeling stressed. For example, it may teach the user how to take deep breaths. The emotion recognition unit also analyzes the user's emotions in real time and provides meditation guidance based on the results. For example, if the user is feeling anxious, it may suggest a meditation method. The emotion recognition unit also uses the emotion estimation function to suggest relaxation techniques according to the user's emotions in real time. For example, if the user is feeling tense, it may suggest music to help them relax. In this way, by suggesting relaxation techniques according to the user's emotions, it is possible to support the user's physical and mental health.

[0070] The AI ​​system can also suggest exercises and activities according to the user's emotions. For example, the emotion recognition unit uses the emotion estimation function to suggest exercises if the user is feeling stressed. For example, it can teach yoga or stretching techniques. The emotion recognition unit also analyzes the user's emotions in real time and suggests activities based on the results. For example, if the user is tired, it can suggest activities that will help them relax. The emotion recognition unit also uses the emotion estimation function to suggest exercises and activities according to the user's emotions in real time. For example, if the user is feeling down, it can suggest activities that will help them change their mood. This makes it possible to support physical and mental health by suggesting exercises and activities according to the user's emotions.

[0071] The AI ​​system can also suggest music and videos that correspond to the user's emotions. For example, the emotion recognition unit uses an emotion estimation function to suggest relaxing music when the user wants to relax. For example, it can provide classical music or natural sounds. The emotion recognition unit also analyzes the user's emotions in real time and suggests videos based on the results. For example, if the user is feeling stressed, it can provide relaxing landscape videos. The emotion recognition unit also uses the emotion estimation function to suggest music and videos that correspond to the user's emotions in real time, supporting emotional changes. For example, if the user wants to cheer up, it can provide up-tempo music. This makes it possible to support emotional changes by suggesting music and videos that correspond to the user's emotions.

[0072] The AI ​​system can also provide feedback according to the user's emotions, helping to deepen self-understanding. The emotion recognition unit, for example, uses the emotion recognition function to analyze the user's emotions and provide feedback based on the results. For example, it may respond, "You seem a little tired today. Don't push yourself." The emotion recognition unit can also analyze the user's emotions in real time and provide feedback to deepen self-understanding based on the results. For example, it may respond, "You seem to be under a lot of stress lately. Make time to relax." The emotion recognition unit can also use the emotion recognition function to provide feedback according to the user's emotions in real time, helping to deepen self-understanding. For example, it may respond, "You seem to be in good spirits today. Keep it up." In this way, it is possible to use the emotion recognition function to provide feedback according to the user's emotions, helping to deepen self-understanding.

[0073] The AI ​​system can also automatically generate words of encouragement or comfort based on the user's emotions, providing emotional support to the user. For example, the emotion recognition unit uses an emotion estimation function to automatically generate words of comfort when the user is sad. For example, it responds with, "That must have been tough. Thank you for telling me." The emotion recognition unit also analyzes the user's emotions in real time and automatically generates words of encouragement based on the results. For example, if the user is feeling down, it responds with, "I'm sure you can overcome this." The emotion recognition unit also uses the emotion estimation function to automatically generate words of encouragement or comfort based on the user's emotions, providing emotional support to the user. For example, if the user is feeling anxious, it responds with, "Let's do our best together." This allows the emotion estimation function to automatically generate words of encouragement or comfort based on the user's emotions, providing emotional support to the user.

[0074] The AI ​​system can also suggest relaxation techniques (e.g., breathing techniques or meditation guides) according to the user's emotions. For example, the emotion recognition unit uses an emotion estimation function to suggest relaxation techniques if the user is feeling stressed. For example, it may teach the user how to take deep breaths. The emotion recognition unit also analyzes the user's emotions in real time and provides meditation guidance based on the results. For example, if the user is feeling anxious, it may suggest a meditation method. The emotion recognition unit also uses the emotion estimation function to suggest relaxation techniques according to the user's emotions in real time. For example, if the user is feeling tense, it may suggest music to help them relax. In this way, by suggesting relaxation techniques according to the user's emotions, it is possible to support the user's physical and mental health.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The listening unit listens to what the user says. For example, it converts what the user says into text data using voice input. The listening unit can also receive what the user says through text chat. Furthermore, the listening unit can analyze the user's movements using gesture recognition technology and understand the content of what is being said. Step 2: The conversation continuation unit continues the conversation based on the user's speech received by the listening unit. For example, the generation AI generates an appropriate response to the user's speech. The conversation continuation unit can also suggest related topics based on the user's interests and concerns to deepen the conversation. Furthermore, the conversation continuation unit can learn the user's past conversation history and generate personalized responses. Step 3: The emotion recognition unit recognizes the user's emotions during the conversation continued by the conversation continuation unit. For example, the emotion estimation function is used to analyze the user's tone of voice and speaking patterns to estimate emotions. The emotion recognition unit can also recognize emotions by analyzing the user's facial expressions and body movements. Furthermore, the emotion recognition unit can generate a response according to the user's emotions to provide emotional support to the user.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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]

[0144] 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 department that listens to users' stories, a conversation continuation unit that continues the conversation based on the user's speech received by the speech listening unit; an emotion recognition unit that recognizes the emotion of the user in the conversation continued by the conversation continuation unit. A system characterized by:

2. The section that will hear the story is: Analyzing the user's tone of voice and speaking patterns to estimate the user's mood and emotions and adjust responses accordingly 2. The system of claim 1.

3. The section that will hear the story is: Learns the user's past conversation history and generates personalized responses that reflect the user's preferences and interests 2. The system of claim 1.

4. The emotion recognition unit Analyzing the emotion of the user when he / she speaks in real time and responding according to the emotion 2. The system of claim 1.

5. The section that will hear the story is: Based on what the user says, relevant music and videos are suggested to enrich the conversation.

2. The system of claim 1.

Images