System

Abstract

An object of a system according to an embodiment is to detect an emotion of a user and provide appropriate advice on the basis of the detected emotion.SOLUTION: A system according to an embodiment includes a smart emotion detection speaker, an emotion detection AI, and a mentoring generation AI. The smart speaker detects emotions from the user's voice and speech. The emotion detection AI generates words based on emotions. The mentoring generation AI listens to the user's distress and provides advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to properly detect a user's emotions and provide appropriate advice based on those emotions.

[0005] The system according to the embodiment aims to detect the user's emotions and provide appropriate advice based on the emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion-detecting smart speaker, an emotion-detecting AI, and a mentoring generation AI. The emotion-detecting smart speaker detects emotions from a user's voice and speaking style. The emotion-detecting AI generates words based on the emotions. The mentoring generation AI listens to the user's concerns and provides advice. [Effects of the Invention]

[0007] The system according to the embodiment can detect the user's emotions and provide appropriate advice based on the 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) A system according to an embodiment of the present invention utilizes AI technology to deepen connections between people and enhance spiritual enrichment. This system detects a user's emotions and provides appropriate feedback and support by combining three elements: an emotion-detecting smart speaker, an emotion-detecting AI, and a mentoring generation AI. This enables the system to detect a user's emotions and provide appropriate feedback and support.

[0029] A system according to an embodiment includes an emotion-detecting smart speaker, an emotion-detecting AI, and a mentoring generation AI. The emotion-detecting smart speaker detects emotions from a user's voice and speaking style. For example, the emotion-detecting smart speaker analyzes the user's tone of voice and speech rate to grasp the user's emotional state in real time. The emotion-detecting smart speaker also analyzes emotions based on voice data and suggests appropriate advice or relaxation methods when the user is tired or stressed. The emotion-detecting AI generates appropriate words based on the user's emotions. For example, the emotion-detecting AI has the function of conveying things the user finds difficult to say on their behalf and generates appropriate words based on data indicating the user's emotional state. The mentoring generation AI listens to the user's concerns and provides appropriate advice and support based on the content of those concerns. For example, the mentoring generation AI analyzes the user's speech and generates solutions or encouraging words. This allows the system to detect the user's emotions and provide appropriate feedback and support.

[0030] Emotion-detecting smart speakers analyze not only the user's voice tone and speech rate, but also background and environmental sounds, enabling more accurate emotion detection. For example, while the user is speaking, the emotion-detecting smart speaker simultaneously analyzes the surrounding background and environmental sounds, improving the accuracy of emotion detection. For example, it considers the difference in voice tone between quiet and noisy environments. In addition to the user's voice tone and speech rate, the emotion-detecting smart speaker also analyzes environmental sounds such as surrounding music and television sounds to more accurately grasp the user's emotional state. For example, it can detect the difference between relaxing music and tense music. The emotion-detecting smart speaker also monitors changes in environmental sounds in real time to identify factors affecting the user's emotional state. For example, it analyzes the impact of sudden noise or silence on the user's emotions. This improves the accuracy of emotion detection by analyzing background and environmental sounds.

[0031] Emotion-detecting smart speakers can learn from a user's past emotional data and generate an emotion detection model specialized for each individual user. For example, emotion-detecting smart speakers collect a user's past emotional data and generate an emotion detection model specialized for each individual user. For example, they learn what emotions a user has shown in what situations in the past. Emotion-detecting smart speakers also identify individual emotional patterns based on the user's emotional history and improve the accuracy of emotion detection. For example, they learn emotional reactions to specific words and phrases. Emotion-detecting smart speakers also continuously update the user's emotional data and maintain an emotion detection model based on the latest emotional state. For example, they periodically collect emotional data and retrain the model. This generates an emotion detection model specialized for each individual user, improving the accuracy of emotion detection.

[0032] Emotion-detecting smart speakers can automatically generate personalized music playlists and podcasts that correspond to the user's emotional state based on the emotion detection results. For example, emotion-detecting smart speakers detect the user's emotional state and automatically generate personalized music playlists based on the results. For example, they can suggest calm music when the user wants to relax and upbeat music when the user wants to cheer up. Emotion-detecting smart speakers can also automatically select and play podcasts that match the user's mood based on the emotion detection results. For example, they can suggest podcasts with relaxing content when the user is feeling stressed. Emotion-detecting smart speakers can also update music and podcast playlists in real time based on the user's emotional state. For example, they can switch the content being played in response to changes in emotion. This improves user satisfaction by providing personalized content that corresponds to the user's emotional state.

[0033] Emotion-sensing smart speakers can link with other smart devices in the home and automatically adjust environmental settings such as lighting and temperature. For example, emotion-sensing smart speakers can detect a user's emotional state and automatically adjust the lighting in the home based on the results. For example, they can change the lighting to warmer colors when the user wants to relax. Emotion-sensing smart speakers can also link with a smart thermostat to automatically adjust the room temperature according to the user's emotional state. For example, they can set the temperature to a comfortable level when the user is feeling stressed. Emotion-sensing smart speakers can also link with smart devices in the home and simultaneously adjust environmental settings according to the user's emotional state. For example, they can adjust music, lighting, and temperature simultaneously. In this way, by linking with smart devices in the home, they can automatically provide a comfortable environment for the user.

[0034] Emotion-detecting smart speakers can be introduced into educational settings to grasp students' emotional states in real time and provide appropriate learning support. Emotion-detecting smart speakers can be installed, for example, in classrooms to monitor students' emotional states in real time. For example, they can detect whether students are concentrating during class. Emotion-detecting smart speakers can also provide appropriate feedback to teachers and support learning based on students' emotional states. For example, if a student is feeling stressed, they can suggest activities that will help them relax. Emotion-detecting smart speakers can also collect students' emotional data and create individual learning plans. For example, they can adjust the learning content and pace according to their emotional states. This allows educational settings to grasp students' emotional states and provide appropriate learning support, thereby improving learning effectiveness.

[0035] Emotion detection AI also analyzes the user's facial expressions and gestures, and by combining this with voice data, can detect emotions more accurately. For example, in addition to the user's voice data, emotion detection AI can analyze facial expressions using a camera to detect emotions more accurately. For example, it detects changes in facial expressions such as smiling or furrowing the brow. Emotion detection AI can also analyze the user's gestures and combine this with voice data to detect emotions. For example, it analyzes hand movements and changes in posture. Emotion detection AI can also integrate facial expression recognition technology and voice analysis technology to detect the user's emotional state from multiple angles. For example, it can simultaneously analyze changes in voice tone and facial expression. This improves the accuracy of emotion detection by analyzing facial expressions and gestures.

[0036] Emotion detection AI can add a scheduling function to provide feedback at the appropriate time when it detects a user's emotions. Emotion detection AI can add a scheduling function to provide feedback at the appropriate time when it detects a user's emotions. For example, it can provide feedback when the user is relaxed. Emotion detection AI can also adjust the timing of feedback according to the user's emotional state. For example, it can provide feedback immediately when the user is feeling stressed. Emotion detection AI can also analyze the user's emotional data and automatically schedule the optimal feedback timing. For example, it can provide feedback according to changes in the user's emotions. This allows for feedback to be provided at the appropriate time, enabling a quick and effective response to the user's emotions.

[0037] Emotion detection AI can detect a user's emotions and then generate appropriate action suggestions and action plans based on those emotions. For example, emotion detection AI detects a user's emotions and generates appropriate action suggestions based on those emotions. For example, if the user is feeling stressed, it will suggest ways to relax. Emotion detection AI can also generate action plans and suggest specific actions based on the user's emotional state. For example, if the user is feeling depressed, it will suggest taking a walk or exercising. Emotion detection AI can also analyze the user's emotional data and generate individual action plans. For example, it can suggest optimal actions based on the user's past emotional data. This makes user behavior more effective by providing action suggestions and action plans based on emotions.

[0038] Emotion detection AI can be integrated into a company's customer support system to automate responses according to the customer's emotional state. Emotion detection AI can be integrated into a customer support system, for example, to automate responses according to the customer's emotional state. For example, if a customer is dissatisfied, a quick response can be provided. Emotion detection AI can also analyze customer emotional data in real time and automatically provide an appropriate response. For example, if a customer is feeling stressed, a response that helps them relax can be provided. Emotion detection AI can also automatically generate customer support scripts based on the customer's emotional state. For example, it can provide appropriate words according to the customer's emotions. This improves the efficiency of customer support by automating responses according to the customer's emotional state.

[0039] Emotion detection AI can be introduced into medical settings to grasp a patient's emotional state in real time and provide appropriate care. Emotion detection AI can be introduced into medical settings to monitor a patient's emotional state in real time. For example, if a patient is feeling anxious, it can provide a relaxing environment. Emotion detection AI can also analyze a patient's emotional data and automatically generate an appropriate care plan. For example, it can suggest a rehabilitation plan based on the patient's emotional state. Emotion detection AI can also provide feedback to medical staff based on the patient's emotional state. For example, if a patient is feeling stressed, it can instruct medical staff on how to respond appropriately. This allows medical settings to grasp a patient's emotional state and provide appropriate care, thereby improving patient satisfaction.

[0040] The mentoring generation AI can learn from a user's past consultation content and advice history to provide more personalized mentoring. For example, the mentoring generation AI can provide more personalized mentoring by collecting and learning from a user's past consultation content and advice history. For example, it can provide specific advice based on past consultation content. The mentoring generation AI can also analyze a user's advice history to provide mentoring tailored to individual needs. For example, it can provide feedback on how helpful past advice was. The mentoring generation AI can also predict future consultations based on the user's past consultation content and prepare appropriate advice in advance. For example, it can learn from past patterns and provide advice for predicted problems. In this way, by learning from past consultation content and advice history, it can provide more individually tailored mentoring.

[0041] The mentoring generation AI can listen to a user's concerns and then propose multiple solutions to those concerns, allowing the user to choose one. For example, after listening to a user's concerns, the mentoring generation AI can propose multiple solutions to those concerns. For example, it can suggest exercise, meditation, or hobby time as ways to relieve stress. The mentoring generation AI can also present multiple solutions to a user's concerns, allowing the user to select the most appropriate method. For example, for career concerns, it can suggest changing jobs, improving skills, or measures to improve in the current job. The mentoring generation AI can also analyze a user's concerns and generate multiple solutions. For example, it can suggest dietary improvements, exercise, or medical consultations for health issues. By proposing multiple solutions, the user can select the best solution.

[0042] Mentoring generation AI can be introduced into a company's human resources department to support employee mental healthcare. Mentoring generation AI can be introduced into a company's human resources department to support employee mental healthcare. For example, it can provide appropriate advice if an employee is feeling stressed. Mentoring generation AI can also monitor employees' emotional state in real time and provide feedback for mental healthcare. For example, it can suggest that an employee take a rest if they are tired. Mentoring generation AI can also automate employee mental healthcare programs. For example, it can provide regular mental health checks and advice. In this way, supporting employee mental healthcare in a company's human resources department improves employee health and productivity.

[0043] Mentoring generation AI can be introduced into educational settings to provide appropriate advice to students in response to their concerns and worries. Mentoring generation AI can be introduced into educational settings to provide appropriate advice to students in response to their concerns and worries. For example, it can provide advice on study methods and time management in response to academic worries. Mentoring generation AI can also monitor students' emotional states in real time and provide advice at the appropriate time. For example, it can suggest relaxation methods to students who are feeling anxious before an exam. Mentoring generation AI can also provide personalized advice to students' individual concerns. For example, it can suggest specific career paths in response to career counseling. In this way, by providing appropriate advice in response to students' concerns and worries in educational settings, students' learning effectiveness and psychological stability can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The system can not only detect the user's emotions, but also monitor the user's health status. For example, it can measure heart rate and blood pressure and evaluate stress levels. It can also analyze the user's sleep patterns and provide advice on how to improve sleep quality. It can also record the user's diet and exercise habits and make suggestions to support a healthy lifestyle. This allows for comprehensive management of both emotions and health status, improving the user's overall well-being.

[0046] The system can not only detect a user's emotions, but also provide content based on the user's hobbies and interests. For example, if the user is a movie lover, the system can provide the latest movie information and recommended movies. If the user is a book lover, the system can suggest a list of books that will interest the user. Furthermore, if the user is a travel lover, the system can provide information on travel destinations and recommended tourist spots. This makes it possible to provide personalized content based on the user's emotions and hobbies, thereby improving user satisfaction.

[0047] The system not only detects the user's emotions, but also provides functions to strengthen the user's social connections. For example, if the user feels lonely, it will make suggestions to promote communication with friends and family. If the user wants to make new friends, it will match the user with people who share common hobbies and interests. It can also provide information for the user to participate in local community activities. This will strengthen the user's social connections and improve their mental well-being.

[0048] The system can not only detect the user's emotions, but also provide learning support. For example, if the user wants to learn a new skill, it can suggest appropriate online courses and learning materials. If the user is studying for an exam, it can provide advice on effective study methods and time management. It can also suggest relaxation methods to reduce the stress the user feels while studying. This can improve the user's learning effectiveness and support their personal growth.

[0049] The system can not only detect a user's emotions, but also provide features to stimulate the user's creativity. For example, if a user likes drawing, it can suggest ideas for a new art project. If a user likes to compose music, it can provide hints and inspiration for composing. Furthermore, if a user likes writing, it can suggest themes and story ideas for creative writing. This can stimulate the user's creativity and provide opportunities for self-expression.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: Emotion detection: The smart speaker detects emotions from the user's voice and speaking style. For example, an emotion-detecting smart speaker analyzes the user's tone of voice and speaking speed to grasp their emotional state in real time. It also analyzes emotions based on voice data and suggests appropriate advice and relaxation methods if the user is tired or stressed. Step 2: Emotion detection AI generates appropriate words based on the user's emotions. For example, emotion detection AI has the ability to convey things that the user finds difficult to say, and generates appropriate words based on data that indicates the user's emotional state. Step 3: The mentoring generation AI listens to the user's concerns and provides appropriate advice and support based on the content. For example, the mentoring generation AI analyzes the user's story and generates solutions and words of encouragement.

[0052] (Example 2) A system according to an embodiment of the present invention utilizes AI technology to deepen connections between people and enhance spiritual enrichment. This system detects a user's emotions and provides appropriate feedback and support by combining three elements: an emotion-detecting smart speaker, an emotion-detecting AI, and a mentoring generation AI. This enables the system to detect a user's emotions and provide appropriate feedback and support.

[0053] A system according to an embodiment includes an emotion-detecting smart speaker, an emotion-detecting AI, and a mentoring generation AI. The emotion-detecting smart speaker detects emotions from a user's voice and speaking style. For example, the emotion-detecting smart speaker analyzes the user's tone of voice and speech rate to grasp the user's emotional state in real time. The emotion-detecting smart speaker also analyzes emotions based on voice data and suggests appropriate advice or relaxation methods when the user is tired or stressed. The emotion-detecting AI generates appropriate words based on the user's emotions. For example, the emotion-detecting AI has the function of conveying things the user finds difficult to say on their behalf and generates appropriate words based on data indicating the user's emotional state. The mentoring generation AI listens to the user's concerns and provides appropriate advice and support based on the content of those concerns. For example, the mentoring generation AI analyzes the user's speech and generates solutions or encouraging words. This allows the system to detect the user's emotions and provide appropriate feedback and support.

[0054] Emotion-detecting smart speakers analyze not only the user's voice tone and speech rate, but also background and environmental sounds, enabling more accurate emotion detection. For example, while the user is speaking, the emotion-detecting smart speaker simultaneously analyzes the surrounding background and environmental sounds, improving the accuracy of emotion detection. For example, it considers the difference in voice tone between quiet and noisy environments. In addition to the user's voice tone and speech rate, the emotion-detecting smart speaker also analyzes environmental sounds such as surrounding music and television sounds to more accurately grasp the user's emotional state. For example, it can detect the difference between relaxing music and tense music. The emotion-detecting smart speaker also monitors changes in environmental sounds in real time to identify factors affecting the user's emotional state. For example, it analyzes the impact of sudden noise or silence on the user's emotions. This improves the accuracy of emotion detection by analyzing background and environmental sounds.

[0055] Emotion-detecting smart speakers can learn from a user's past emotional data and generate an emotion detection model specialized for each individual user. For example, emotion-detecting smart speakers collect a user's past emotional data and generate an emotion detection model specialized for each individual user. For example, they learn what emotions a user has shown in what situations in the past. Emotion-detecting smart speakers also identify individual emotional patterns based on the user's emotional history and improve the accuracy of emotion detection. For example, they learn emotional reactions to specific words and phrases. Emotion-detecting smart speakers also continuously update the user's emotional data and maintain an emotion detection model based on the latest emotional state. For example, they periodically collect emotional data and retrain the model. This generates an emotion detection model specialized for each individual user, improving the accuracy of emotion detection.

[0056] Emotion-detecting smart speakers can automatically generate personalized music playlists and podcasts that correspond to the user's emotional state based on the emotion detection results. For example, emotion-detecting smart speakers detect the user's emotional state and automatically generate personalized music playlists based on the results. For example, they can suggest calm music when the user wants to relax and upbeat music when the user wants to cheer up. Emotion-detecting smart speakers can also automatically select and play podcasts that match the user's mood based on the emotion detection results. For example, they can suggest podcasts with relaxing content when the user is feeling stressed. Emotion-detecting smart speakers can also update music and podcast playlists in real time based on the user's emotional state. For example, they can switch the content being played in response to changes in emotion. This improves user satisfaction by providing personalized content that corresponds to the user's emotional state.

[0057] Emotion-sensing smart speakers can link with other smart devices in the home and automatically adjust environmental settings such as lighting and temperature. For example, emotion-sensing smart speakers can detect a user's emotional state and automatically adjust the lighting in the home based on the results. For example, they can change the lighting to warmer colors when the user wants to relax. Emotion-sensing smart speakers can also link with a smart thermostat to automatically adjust the room temperature according to the user's emotional state. For example, they can set the temperature to a comfortable level when the user is feeling stressed. Emotion-sensing smart speakers can also link with smart devices in the home and simultaneously adjust environmental settings according to the user's emotional state. For example, they can adjust music, lighting, and temperature simultaneously. In this way, by linking with smart devices in the home, they can automatically provide a comfortable environment for the user.

[0058] Emotion-detecting smart speakers can be introduced into educational settings to grasp students' emotional states in real time and provide appropriate learning support. Emotion-detecting smart speakers can be installed, for example, in classrooms to monitor students' emotional states in real time. For example, they can detect whether students are concentrating during class. Emotion-detecting smart speakers can also provide appropriate feedback to teachers and support learning based on students' emotional states. For example, if a student is feeling stressed, they can suggest activities that will help them relax. Emotion-detecting smart speakers can also collect students' emotional data and create individual learning plans. For example, they can adjust the learning content and pace according to their emotional states. This allows educational settings to grasp students' emotional states and provide appropriate learning support, thereby improving learning effectiveness.

[0059] Emotion-detecting smart speakers can use their emotion estimation function to suggest encouraging messages and relaxation techniques when a user is feeling emotionally down. For example, emotion-detecting smart speakers can detect a user's emotional state and provide encouraging messages when the user is feeling down. For example, they can play positive messages such as "You are wonderful." Emotion-detecting smart speakers can also suggest relaxation techniques when the user is feeling emotionally down. For example, they can provide deep breathing or meditation guidance. Emotion-detecting smart speakers can also monitor a user's emotional state in real time and suggest encouraging messages and relaxation techniques at appropriate times. For example, they can update messages according to changes in emotions. This helps support the user's mental health by providing appropriate support when the user is feeling emotionally down.

[0060] Emotion detection AI also analyzes the user's facial expressions and gestures, and by combining this with voice data, can detect emotions more accurately. For example, in addition to the user's voice data, emotion detection AI can analyze facial expressions using a camera to detect emotions more accurately. For example, it detects changes in facial expressions such as smiling or furrowing the brow. Emotion detection AI can also analyze the user's gestures and combine this with voice data to detect emotions. For example, it analyzes hand movements and changes in posture. Emotion detection AI can also integrate facial expression recognition technology and voice analysis technology to detect the user's emotional state from multiple angles. For example, it can simultaneously analyze changes in voice tone and facial expression. This improves the accuracy of emotion detection by analyzing facial expressions and gestures.

[0061] Emotion detection AI can add a scheduling function to provide feedback at the appropriate time when it detects a user's emotions. Emotion detection AI can add a scheduling function to provide feedback at the appropriate time when it detects a user's emotions. For example, it can provide feedback when the user is relaxed. Emotion detection AI can also adjust the timing of feedback according to the user's emotional state. For example, it can provide feedback immediately when the user is feeling stressed. Emotion detection AI can also analyze the user's emotional data and automatically schedule the optimal feedback timing. For example, it can provide feedback according to changes in the user's emotions. This allows for feedback to be provided at the appropriate time, enabling a quick and effective response to the user's emotions.

[0062] Emotion detection AI can detect a user's emotions and then generate appropriate action suggestions and action plans based on those emotions. For example, emotion detection AI detects a user's emotions and generates appropriate action suggestions based on those emotions. For example, if the user is feeling stressed, it will suggest ways to relax. Emotion detection AI can also generate action plans and suggest specific actions based on the user's emotional state. For example, if the user is feeling depressed, it will suggest taking a walk or exercising. Emotion detection AI can also analyze the user's emotional data and generate individual action plans. For example, it can suggest optimal actions based on the user's past emotional data. This makes user behavior more effective by providing action suggestions and action plans based on emotions.

[0063] Emotion detection AI can be integrated into a company's customer support system to automate responses according to the customer's emotional state. Emotion detection AI can be integrated into a customer support system, for example, to automate responses according to the customer's emotional state. For example, if a customer is dissatisfied, a quick response can be provided. Emotion detection AI can also analyze customer emotional data in real time and automatically provide an appropriate response. For example, if a customer is feeling stressed, a response that helps them relax can be provided. Emotion detection AI can also automatically generate customer support scripts based on the customer's emotional state. For example, it can provide appropriate words according to the customer's emotions. This improves the efficiency of customer support by automating responses according to the customer's emotional state.

[0064] Emotion detection AI can be introduced into medical settings to grasp a patient's emotional state in real time and provide appropriate care. Emotion detection AI can be introduced into medical settings to monitor a patient's emotional state in real time. For example, if a patient is feeling anxious, it can provide a relaxing environment. Emotion detection AI can also analyze a patient's emotional data and automatically generate an appropriate care plan. For example, it can suggest a rehabilitation plan based on the patient's emotional state. Emotion detection AI can also provide feedback to medical staff based on the patient's emotional state. For example, if a patient is feeling stressed, it can instruct medical staff on how to respond appropriately. This allows medical settings to grasp a patient's emotional state and provide appropriate care, thereby improving patient satisfaction.

[0065] Emotion detection AI uses its emotion estimation function to generate appropriate words based on the user's emotions when communicating something that is difficult for the user to say, thereby reducing the user's stress. Emotion detection AI, for example, uses its emotion estimation function to generate appropriate words based on the user's emotions when communicating something that is difficult for the user to say. For example, it provides gentle words that correspond to the user's emotional state. Emotion detection AI also analyzes the user's emotional data and automatically generates appropriate words to reduce stress. For example, it provides calm words when the user is emotionally charged. Emotion detection AI also uses its emotion estimation function to build a system that reduces stress when the user communicates something that is difficult for the user to say. For example, it provides feedback based on emotions in real time. This reduces stress when the user communicates something that is difficult for the user to say, thereby facilitating smoother communication.

[0066] The mentoring generation AI can learn from a user's past consultation content and advice history to provide more personalized mentoring. For example, the mentoring generation AI can provide more personalized mentoring by collecting and learning from a user's past consultation content and advice history. For example, it can provide specific advice based on past consultation content. The mentoring generation AI can also analyze a user's advice history to provide mentoring tailored to individual needs. For example, it can provide feedback on how helpful past advice was. The mentoring generation AI can also predict future consultations based on the user's past consultation content and prepare appropriate advice in advance. For example, it can learn from past patterns and provide advice for predicted problems. In this way, by learning from past consultation content and advice history, it can provide more individually tailored mentoring.

[0067] When listening to a user, the mentoring generation AI can monitor emotional changes in real time and provide feedback at the appropriate time. For example, when listening to a user, the mentoring generation AI can monitor emotional changes in real time and provide feedback at the appropriate time. For example, it can provide calm advice when the user becomes emotional. The mentoring generation AI can also analyze the user's emotional state in real time and provide feedback according to emotional changes. For example, it can provide specific advice when the user is calm. The mentoring generation AI can also monitor emotional changes and provide feedback at the time when the user is most receptive. For example, it can provide words of encouragement when the user is relaxed. In this way, by monitoring emotional changes in real time, it can provide feedback at the appropriate time.

[0068] The mentoring generation AI can listen to a user's concerns and then propose multiple solutions to those concerns, allowing the user to choose one. For example, after listening to a user's concerns, the mentoring generation AI can propose multiple solutions to those concerns. For example, it can suggest exercise, meditation, or hobby time as ways to relieve stress. The mentoring generation AI can also present multiple solutions to a user's concerns, allowing the user to select the most appropriate method. For example, for career concerns, it can suggest changing jobs, improving skills, or measures to improve in the current job. The mentoring generation AI can also analyze a user's concerns and generate multiple solutions. For example, it can suggest dietary improvements, exercise, or medical consultations for health issues. By proposing multiple solutions, the user can select the best solution.

[0069] Mentoring generation AI can be introduced into a company's human resources department to support employee mental healthcare. Mentoring generation AI can be introduced into a company's human resources department to support employee mental healthcare. For example, it can provide appropriate advice if an employee is feeling stressed. Mentoring generation AI can also monitor employees' emotional state in real time and provide feedback for mental healthcare. For example, it can suggest that an employee take a rest if they are tired. Mentoring generation AI can also automate employee mental healthcare programs. For example, it can provide regular mental health checks and advice. In this way, supporting employee mental healthcare in a company's human resources department improves employee health and productivity.

[0070] Mentoring generation AI can be introduced into educational settings to provide appropriate advice to students in response to their concerns and worries. Mentoring generation AI can be introduced into educational settings to provide appropriate advice to students in response to their concerns and worries. For example, it can provide advice on study methods and time management in response to academic worries. Mentoring generation AI can also monitor students' emotional states in real time and provide advice at the appropriate time. For example, it can suggest relaxation methods to students who are feeling anxious before an exam. Mentoring generation AI can also provide personalized advice to students' individual concerns. For example, it can suggest specific career paths in response to career counseling. In this way, by providing appropriate advice in response to students' concerns and worries in educational settings, students' learning effectiveness and psychological stability can be improved.

[0071] The mentoring generation AI uses the emotion estimation function to provide an environment where the user feels comfortable talking and can generate appropriate advice based on the user's emotions. The mentoring generation AI, for example, uses the emotion estimation function to provide an environment where the user feels comfortable talking. For example, it may set music and lighting that will relax the user. The mentoring generation AI also analyzes the user's emotional state in real time to provide an environment where the user feels comfortable talking. For example, if the user feels nervous, it may create an environment where the user can relax. The mentoring generation AI also generates appropriate advice based on the user's emotions based on the emotion estimation data. For example, it may provide specific advice when the user feels calm. This provides an environment where the user feels comfortable talking and generates appropriate advice based on their emotions, thereby reducing the user's psychological burden.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The system can not only detect the user's emotions, but also monitor the user's health status. For example, it can measure heart rate and blood pressure and evaluate stress levels. It can also analyze the user's sleep patterns and provide advice on how to improve sleep quality. It can also record the user's diet and exercise habits and make suggestions to support a healthy lifestyle. This allows for comprehensive management of both emotions and health status, improving the user's overall well-being.

[0074] The system can not only detect a user's emotions, but also provide content based on the user's hobbies and interests. For example, if the user is a movie lover, the system can provide the latest movie information and recommended movies. If the user is a book lover, the system can suggest a list of books that will interest the user. Furthermore, if the user is a travel lover, the system can provide information on travel destinations and recommended tourist spots. This makes it possible to provide personalized content based on the user's emotions and hobbies, thereby improving user satisfaction.

[0075] The system not only detects the user's emotions, but also provides functions to strengthen the user's social connections. For example, if the user feels lonely, it will make suggestions to promote communication with friends and family. If the user wants to make new friends, it will match the user with people who share common hobbies and interests. It can also provide information for the user to participate in local community activities. This will strengthen the user's social connections and improve their mental well-being.

[0076] The system can not only detect the user's emotions, but also provide learning support. For example, if the user wants to learn a new skill, it can suggest appropriate online courses and learning materials. If the user is studying for an exam, it can provide advice on effective study methods and time management. It can also suggest relaxation methods to reduce the stress the user feels while studying. This can improve the user's learning effectiveness and support their personal growth.

[0077] The system can not only detect a user's emotions, but also provide features to stimulate the user's creativity. For example, if a user likes drawing, it can suggest ideas for a new art project. If a user likes to compose music, it can provide hints and inspiration for composing. Furthermore, if a user likes writing, it can suggest themes and story ideas for creative writing. This can stimulate the user's creativity and provide opportunities for self-expression.

[0078] The system can detect the user's emotions, assess the user's stress level based on the estimated emotions, and suggest appropriate stress management methods. For example, if the user is feeling high stress, it can provide deep breathing or meditation guidance. If the user wants to relax, it can play relaxing music or nature sounds. It can also identify the causes of the user's stress and suggest specific measures to address them. This supports the user's stress management and helps maintain mental health.

[0079] The system can detect the user's emotions and provide feedback to improve the user's motivation based on the estimated emotions. For example, if the user is feeling unmotivated, the system can send encouraging messages or share success stories. If the user achieves a goal, the system can offer praise or rewards. It can also provide specific advice and support for the task the user is trying to complete. This can improve the user's motivation and help them achieve their goals.

[0080] The system can detect a user's emotions and provide training to improve the user's communication skills based on the estimated emotions. For example, if the user is nervous, the system can teach them how to relax. Also, if the user is experiencing difficulties in interpersonal relationships, the system can suggest appropriate communication methods. Furthermore, when the user is giving a presentation, the system can provide advice on effective speaking and gestures. This can improve the user's communication skills and smoothen interpersonal relationships.

[0081] The system can detect a user's emotions and provide a personalized exercise plan based on the user's emotional state. For example, if the user is feeling stressed, the system can suggest a relaxing yoga or stretching plan. If the user is feeling energetic, the system can suggest a high-intensity workout. Furthermore, the system can adjust the intensity and type of exercise in real time according to the user's emotional state. This allows the system to provide an exercise plan that is optimal for the user's emotional state and support health maintenance.

[0082] The system can detect the user's emotions and provide a personalized relaxation plan based on the estimated emotions. For example, if the user is tired, it can provide relaxing music or guided meditation. If the user is feeling anxious, it can suggest deep breathing or mindfulness exercises. Furthermore, it can adjust relaxation methods in real time according to the user's emotional state. This allows the system to provide a relaxation plan that is optimal for the user's emotional state and support mental health.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: Emotion detection: The smart speaker detects emotions from the user's voice and speaking style. For example, an emotion-detecting smart speaker analyzes the user's tone of voice and speaking speed to grasp their emotional state in real time. It also analyzes emotions based on voice data and suggests appropriate advice and relaxation methods if the user is tired or stressed. Step 2: Emotion detection AI generates appropriate words based on the user's emotions. For example, emotion detection AI has the ability to convey things that the user finds difficult to say, and generates appropriate words based on data that indicates the user's emotional state. Step 3: The mentoring generation AI listens to the user's concerns and provides appropriate advice and support based on the content. For example, the mentoring generation AI analyzes the user's story and generates solutions and words of encouragement.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

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

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. Emotion-detecting smart speakers and Emotion detection AI and Mentoring generation AI, The emotion-sensing smart speaker is Detects emotions from the user's voice and speaking style, The emotion detection AI is generating words based on the emotions; The mentoring generation AI is Listen to the user's concerns and provide advice A system characterized by:

2. The emotion-sensing smart speaker is Not only the tone and speech rate of the user's voice, but also background and environmental sounds are analyzed to perform more accurate emotion detection.

2. The system of claim 1.

3. The emotion-sensing smart speaker is The past emotion data of the user is learned, and an emotion detection model specialized for each user is generated.

2. The system of claim 1.

4. The emotion-sensing smart speaker is Based on the emotion detection results, a personalized music playlist or podcast is automatically generated according to the user's emotional state.

2. The system of claim 1.

5. The emotion-sensing smart speaker is Works with other smart devices in the home to automatically adjust lighting, temperature, and other environmental settings 2. The system of claim 1.

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