System

Abstract

An object of a system according to an embodiment is to grasp a user's mental health condition and take an appropriate action.SOLUTION: A system includes an emotion recognition unit, a response unit, and a proposal unit. The emotion recognition unit grasps an emotional state of the user. The response unit performs an appropriate response based on the emotional state grasped by the emotion recognition unit. The proposal unit makes a proposal to promote a social connection on the basis of the response made by the response unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 do not adequately understand the mental health status of users and provide appropriate responses, and there is room for improvement.

[0005] The system according to the embodiment aims to understand the mental health state of the user and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion recognition unit, a response unit, and a suggestion unit. The emotion recognition unit grasps the emotional state of a user. The response unit takes an appropriate action based on the emotional state grasped by the emotion recognition unit. The suggestion unit makes a suggestion to promote social connections based on the action taken by the response unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the mental health state of the user and take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The emotion recognition unit can perform emotion estimation by combining at least one vital data of the user, either the heart rate or the skin temperature. The emotion recognition unit, for example, collects vital data such as the user's heart rate and skin temperature, and performs emotion estimation by combining it with emotion recognition technology. For example, an elevated heart rate may be estimated to indicate stress. The emotion recognition unit also develops a system that analyzes vital data in real time and integrates it with emotion recognition technology. For example, the emotional state may be estimated based on changes in skin temperature. The emotion recognition unit also monitors the user's vital data over the long term and improves the accuracy of emotion estimation based on that data. For example, the emotional state may be estimated by comparing past vital data with current data. This improves the accuracy of emotion estimation.

[0029] The emotion recognition unit can work in conjunction with other devices in the home to provide multiple pieces of content according to the user's emotional state. For example, the emotion recognition unit may link the emotion recognition technology with a smart speaker to build a system that automatically plays music or podcasts according to the user's emotional state. For example, relaxing music may be played when the user feels like relaxing. The emotion recognition unit may also link the emotion recognition technology with a television to develop a system that recommends movies and dramas according to the user's emotional state. For example, comedy movies may be recommended when the user is sad. The emotion recognition unit may also link the emotion recognition technology with multiple devices in the home to build a system that provides integrated content according to the user's emotional state. For example, a smart speaker and a television may work in conjunction to provide entertainment tailored to the user's emotions. This makes it possible to provide content according to the user's emotional state.

[0030] The emotion recognition unit is installed in a pet robot or a care robot, and can take action according to the user's emotions. For example, the emotion recognition unit is installed in a pet robot to develop a system in which the robot changes its behavior according to the user's emotional state. For example, if the user is feeling lonely, the robot will take a comforting action. The emotion recognition unit is also installed in a care robot to build a system that provides care services according to the user's emotional state. For example, if the user is feeling stressed, the robot will take a relaxing action. The emotion recognition unit is also used to develop an algorithm that allows the pet robot or care robot to interact and take action according to the user's emotions. For example, if the user is happy, the robot will take a playful action. This makes it possible to take action according to the user's emotions.

[0031] The emotion recognition unit can analyze daily activity logs based on the user's emotional state and monitor the user's mental health over the long term. For example, the emotion recognition unit builds a system that collects the user's daily activity logs and analyzes them based on the user's emotional state. For example, the activity logs and emotional data are combined to monitor the user's mental health. The emotion recognition unit also develops an algorithm that analyzes the user's activity logs over the long term based on the user's emotional state and tracks changes in the user's mental health. For example, the emotion recognition unit analyzes trends in the user's mental health based on data from the past few months. The emotion recognition unit also integrates the user's emotional state and activity logs to develop a system that monitors the user's mental health in real time. For example, the system detects abnormalities based on the emotional data and activity data. This allows the user's mental health to be understood over the long term.

[0032] The emotion recognition unit can provide a personalized mental health program according to the user's emotional state. For example, the emotion recognition unit uses emotion recognition technology to develop a system that automatically generates a personalized mental health program according to the user's emotional state. For example, it provides a meditation program for stress reduction. The emotion recognition unit also builds an algorithm that provides an individually customized mental health program based on the user's emotional state. For example, it suggests relaxation exercises based on emotional data. The emotion recognition unit also uses emotion recognition technology to develop a system that adjusts the mental health program in real time according to the user's emotional state. For example, it adjusts the program content every time emotions change. This makes it possible to provide a mental health program that is suitable for the user.

[0033] The emotion recognition unit can share the mental health support function with family members and caregivers to provide support based on the user's emotional state. The emotion recognition unit, for example, builds a system that shares the mental health support function with family members and caregivers to provide support based on the user's emotional state. For example, it notifies family members and caregivers of emotional data. The emotion recognition unit also develops an algorithm that shares the user's emotional state with family members and caregivers in real time to provide appropriate support. For example, the family members and caregivers suggest countermeasures based on the emotional data. The emotion recognition unit also develops a system that links the mental health support function with family members and caregivers to provide support according to the user's emotional state. For example, emotional data can be shared to provide joint support. This allows the user to be supported in cooperation with family members and caregivers.

[0034] The emotion recognition unit can link the mental health support function with an online counseling service, allowing users to receive expert support as needed. For example, the emotion recognition unit links the mental health support function with an online counseling service to build a system that allows users to receive expert support as needed. For example, emotional data is shared with a counselor. The emotion recognition unit also develops an algorithm that automatically recommends online counseling services based on the user's emotional state. For example, counseling is suggested when stress is high. The emotion recognition unit also integrates the mental health support function with an online counseling service to develop a system that allows users to easily receive expert support. For example, counseling appointments are automated based on emotional data. This allows users to receive expert support.

[0035] The emotion recognition unit can personalize and suggest community events and online social gatherings that the user would like to participate in. For example, the emotion recognition unit uses emotion recognition technology to develop a system that automatically suggests community events and online social gatherings that the user would like to participate in based on the user's emotional state. For example, it recommends events based on emotional data. The emotion recognition unit also builds an algorithm that suggests personalized community events and online social gatherings according to the user's emotional state. For example, it suggests events when the emotion is positive. The emotion recognition unit also uses emotion recognition technology to develop a system that suggests events and social gatherings that the user would like to participate in in real time. For example, it adjusts the content of suggestions every time the emotion changes. This makes it possible to suggest events and social gatherings that the user would like to participate in.

[0036] The emotion recognition unit can suggest virtual reality (VR) events based on the user's emotional state to promote social connections. For example, the emotion recognition unit develops a system that analyzes the user's emotional state and automatically suggests emotion-based virtual reality (VR) events. For example, it suggests relaxing VR events based on emotional data. The emotion recognition unit also uses emotion recognition technology to build an algorithm that suggests personalized VR events according to the user's emotional state. For example, it suggests fun VR events when the emotion is positive. The emotion recognition unit also develops a system that suggests VR events that the user would like to participate in in real time based on emotion estimation data. For example, it adjusts the suggestions each time the emotion changes. This makes it possible to suggest VR events based on the user's emotional state.

[0037] The emotion recognition unit can provide online games and collaborative work platforms that correspond to the user's emotional state in order to promote social connections. The emotion recognition unit, for example, develops a system that analyzes the user's emotional state and automatically suggests online games and collaborative work platforms based on emotions. For example, it suggests cooperative online games based on emotional data. The emotion recognition unit also uses emotion recognition technology to build an algorithm that suggests personalized online games and collaborative work platforms that correspond to the user's emotional state. For example, it suggests fun games when the emotion is positive. The emotion recognition unit also develops a system that suggests online games and collaborative work platforms that the user would like to participate in in real time based on emotion estimation data. For example, it adjusts the suggestions each time the emotion changes. This makes it possible to provide online games and collaborative work platforms that correspond to the user's emotional state.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The AI ​​assistant system can further include a health management unit. The health management unit collects data on the user's diet and exercise and monitors their health condition. For example, the health management unit records the calories and nutrients in the meals the user consumes and suggests balanced meals. The health management unit can also analyze the user's exercise data and provide an appropriate exercise plan. For example, if the user is not getting enough exercise, it can suggest simple exercises. The health management unit can also remind the user when to see a doctor based on their health condition. This enables comprehensive health management for the user.

[0040] The AI ​​assistant system may further include a learning support unit. The learning support unit provides learning content based on the user's interests. For example, the learning support unit may suggest online courses in areas of interest to the user. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, if the user is struggling with a particular task, the learning support unit may provide additional learning resources. The learning support unit may also create a customized learning plan tailored to the user's learning style. This improves the user's learning effectiveness.

[0041] The AI ​​assistant system can further include a safety monitoring unit. The safety monitoring unit monitors home security to ensure the user's safety. For example, the safety monitoring unit uses sensors on doors and windows to detect suspicious activity and issue an alarm. The safety monitoring unit can also build a system that automatically notifies emergency contacts if the user falls. For example, when the user falls, a notification is sent to family members or caregivers. The safety monitoring unit can also use GPS to track the user's location when they are out and ensure their safety. This ensures the user's safety.

[0042] The AI ​​assistant system can further include a hobby support unit. The hobby support unit suggests new activities based on the user's hobbies and interests. For example, the hobby support unit suggests workshops and events in areas that interest the user. The hobby support unit can also introduce online communities related to the user's hobbies. For example, if the user is interested in gardening, the hobby support unit can introduce online gardening forums. The hobby support unit can also create a customized activity plan tailored to the user's hobbies. This will enrich the user's hobby activities.

[0043] The AI ​​assistant system can further include a travel support unit. The travel support unit supports the user's travel plans. For example, the travel support unit provides information on travel destinations based on the user's preferences. The travel support unit can also manage the user's travel schedule and send reminders at appropriate times. For example, it can remind the user to remember their flight boarding time. The travel support unit can also provide the user with information needed during their trip in real time. For example, it can provide local weather and traffic information. This helps the user's trip proceed smoothly.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The emotion recognition unit grasps the user's emotional state. For example, the emotion recognition unit analyzes the user's facial expressions using a camera and estimates the user's emotional state. The emotion recognition unit can also analyze the user's tone of voice and estimate the user's emotional state. Furthermore, the emotion recognition unit can analyze the user's choice of words and estimate the user's emotional state. Step 2: The response unit responds appropriately based on the emotional state grasped by the emotion recognition unit. For example, if the user is feeling lonely, the response unit may ask, "What did you do today?". It may also suggest relaxing music if the user is feeling stressed. It may also teach deep breathing techniques if the user is feeling anxious. Step 3: The suggestion unit makes suggestions to promote social connections based on the responses made by the response unit. For example, the suggestion unit can suggest nearby community events if the user feels lonely. It can also suggest online meetups if the user feels lonely. It can also remind the user to send periodic messages to encourage contact with family and friends.

[0046] (Example 2) The AI ​​assistant system according to the embodiment of the present invention is a system that uses emotion recognition technology to support the mental health of elderly people, reduce loneliness, and promote social connections. As a result, the AI ​​assistant system can support the mental health of elderly people, reduce loneliness, and promote social connections.

[0047] An AI assistant system according to an embodiment includes an emotion recognition unit, a response unit, and a suggestion unit. The emotion recognition unit grasps the user's emotional state. For example, the emotion recognition unit analyzes the user's facial expressions using a camera and estimates the emotional state. The emotion recognition unit can also analyze the user's tone of voice and estimate the emotional state. The emotion recognition unit can also analyze the user's choice of words and estimate the emotional state. The response unit takes an appropriate action based on the emotional state grasped by the emotion recognition unit. For example, if the user is feeling lonely, the response unit asks, "What did you do today?" The response unit can also suggest relaxing music if the user is feeling stressed. The response unit can also teach the user how to take deep breaths if the user is feeling anxious. The suggestion unit makes suggestions to promote social connections based on the response performed by the response unit. For example, if the user is feeling lonely, the suggestion unit can suggest nearby community events. The suggestion unit can also suggest online social gatherings if the user is feeling lonely. The suggestion unit can also remind the user to periodically send messages to encourage contact with family and friends. This allows the AI ​​assistant system according to the embodiment to support the mental health of elderly people, reduce loneliness, and promote social connections.

[0048] The emotion recognition unit can compare a user's past emotional history with their current emotional state and analyze patterns of emotional change. For example, the emotion recognition unit stores the user's past emotional history in a database and compares it with their current emotional state to analyze patterns of emotional change. For example, it analyzes how the current emotional state is changing based on emotional data from the past week. The emotion recognition unit also develops an algorithm that compares the emotional history with the current emotional state and automatically analyzes patterns of emotional change. For example, it detects a tendency for a user's emotions to change during specific time periods. The emotion recognition unit also builds a system that predicts patterns of emotional change for a user based on their past emotional history and displays the prediction results in real time. For example, it predicts a tendency for a user's emotions to change after a specific event. This allows for a detailed understanding of changes in the user's emotions.

[0049] The emotion recognition unit can perform emotion estimation by combining at least one vital data of the user, either the heart rate or the skin temperature. The emotion recognition unit, for example, collects vital data such as the user's heart rate and skin temperature, and performs emotion estimation by combining it with emotion recognition technology. For example, an elevated heart rate may be estimated to indicate stress. The emotion recognition unit also develops a system that analyzes vital data in real time and integrates it with emotion recognition technology. For example, the emotional state may be estimated based on changes in skin temperature. The emotion recognition unit also monitors the user's vital data over the long term and improves the accuracy of emotion estimation based on that data. For example, the emotional state may be estimated by comparing past vital data with current data. This improves the accuracy of emotion estimation.

[0050] The emotion recognition unit can generate a personalized voice tone or speaking style based on the user's emotional state. For example, the emotion recognition unit will use the emotion estimation function to develop a system that automatically generates a voice tone or speaking style according to the user's emotional state. For example, if the user is sad, the emotion recognition unit will speak in a gentle tone. The emotion recognition unit will also build an algorithm that personalizes the AI ​​assistant's speaking style based on the user's emotional state. For example, if the user is excited, the emotion recognition unit will speak in a calm tone. The emotion recognition unit will also develop a system that adjusts the voice tone or speaking style in real time to match the user's emotional state based on the emotion estimation data. For example, the voice tone will be adjusted every time the user's emotion changes. This will enable more natural conversations.

[0051] The emotion recognition unit can work in conjunction with other devices in the home to provide multiple pieces of content according to the user's emotional state. For example, the emotion recognition unit may link the emotion recognition technology with a smart speaker to build a system that automatically plays music or podcasts according to the user's emotional state. For example, relaxing music may be played when the user feels like relaxing. The emotion recognition unit may also link the emotion recognition technology with a television to develop a system that recommends movies and dramas according to the user's emotional state. For example, comedy movies may be recommended when the user is sad. The emotion recognition unit may also link the emotion recognition technology with multiple devices in the home to build a system that provides integrated content according to the user's emotional state. For example, a smart speaker and a television may work in conjunction to provide entertainment tailored to the user's emotions. This makes it possible to provide content according to the user's emotional state.

[0052] The emotion recognition unit is installed in a pet robot or a care robot, and can take action according to the user's emotions. For example, the emotion recognition unit is installed in a pet robot to develop a system in which the robot changes its behavior according to the user's emotional state. For example, if the user is feeling lonely, the robot will take a comforting action. The emotion recognition unit is also installed in a care robot to build a system that provides care services according to the user's emotional state. For example, if the user is feeling stressed, the robot will take a relaxing action. The emotion recognition unit is also used to develop an algorithm that allows the pet robot or care robot to interact and take action according to the user's emotions. For example, if the user is happy, the robot will take a playful action. This makes it possible to take action according to the user's emotions.

[0053] The emotion recognition unit can automatically provide relaxing environmental sounds and scents when the user is feeling emotionally down. The emotion recognition unit, for example, uses an emotion estimation function to develop a system that automatically plays relaxing environmental sounds when the user is feeling emotionally down. For example, the emotion recognition unit plays sounds of nature or the sound of waves. The emotion recognition unit also builds a system that automatically provides relaxing scents according to the user's emotional state. For example, if the user is feeling stressed, the scent of lavender is provided. The emotion recognition unit also develops a system that adjusts the relaxing environmental sounds and scents in real time based on the emotion estimation data. For example, the environmental sounds and scents are adjusted every time the user's emotions change. This makes it possible to provide a relaxing environment for the user.

[0054] The emotion recognition unit can analyze daily activity logs based on the user's emotional state and monitor the user's mental health over the long term. For example, the emotion recognition unit builds a system that collects the user's daily activity logs and analyzes them based on the user's emotional state. For example, the activity logs and emotional data are combined to monitor the user's mental health. The emotion recognition unit also develops an algorithm that analyzes the user's activity logs over the long term based on the user's emotional state and tracks changes in the user's mental health. For example, the emotion recognition unit analyzes trends in the user's mental health based on data from the past few months. The emotion recognition unit also integrates the user's emotional state and activity logs to develop a system that monitors the user's mental health in real time. For example, the system detects abnormalities based on the emotional data and activity data. This allows the user's mental health to be understood over the long term.

[0055] The emotion recognition unit can provide a personalized mental health program according to the user's emotional state. For example, the emotion recognition unit uses emotion recognition technology to develop a system that automatically generates a personalized mental health program according to the user's emotional state. For example, it provides a meditation program for stress reduction. The emotion recognition unit also builds an algorithm that provides an individually customized mental health program based on the user's emotional state. For example, it suggests relaxation exercises based on emotional data. The emotion recognition unit also uses emotion recognition technology to develop a system that adjusts the mental health program in real time according to the user's emotional state. For example, it adjusts the program content every time emotions change. This makes it possible to provide a mental health program that is suitable for the user.

[0056] The emotion recognition unit can share the mental health support function with family members and caregivers to provide support based on the user's emotional state. The emotion recognition unit, for example, builds a system that shares the mental health support function with family members and caregivers to provide support based on the user's emotional state. For example, it notifies family members and caregivers of emotional data. The emotion recognition unit also develops an algorithm that shares the user's emotional state with family members and caregivers in real time to provide appropriate support. For example, the family members and caregivers suggest countermeasures based on the emotional data. The emotion recognition unit also develops a system that links the mental health support function with family members and caregivers to provide support according to the user's emotional state. For example, emotional data can be shared to provide joint support. This allows the user to be supported in cooperation with family members and caregivers.

[0057] The emotion recognition unit can link the mental health support function with an online counseling service, allowing users to receive expert support as needed. For example, the emotion recognition unit links the mental health support function with an online counseling service to build a system that allows users to receive expert support as needed. For example, emotional data is shared with a counselor. The emotion recognition unit also develops an algorithm that automatically recommends online counseling services based on the user's emotional state. For example, counseling is suggested when stress is high. The emotion recognition unit also integrates the mental health support function with an online counseling service to develop a system that allows users to easily receive expert support. For example, counseling appointments are automated based on emotional data. This allows users to receive expert support.

[0058] The emotion recognition unit can suggest hobbies and activities that will help the user relax and promote mental health. The emotion recognition unit, for example, uses the emotion estimation function to develop a system that automatically suggests hobbies and activities that will help the user relax. For example, hobbies such as gardening and reading are suggested. The emotion recognition unit also builds an algorithm that individually customizes relaxing activities based on the user's emotional state. For example, it suggests relaxation methods such as yoga and meditation based on the emotion data. The emotion recognition unit also develops a system that suggests hobbies and activities that will help the user relax in real time based on the emotion estimation data. For example, it suggests appropriate activities every time the user's emotions change. This can promote the user's mental health.

[0059] The emotion recognition unit can provide reminders to encourage contact with family and friends at the optimal timing based on the user's emotional state. The emotion recognition unit, for example, develops a system that analyzes the user's emotional state and automatically generates reminders to encourage contact with family and friends at the optimal timing. For example, the emotion recognition unit encourages contact when the user is feeling lonely. The emotion recognition unit also builds an algorithm based on emotional data to predict the optimal timing for the user to contact family and friends. For example, the emotion recognition unit encourages contact when the user is feeling down. The emotion recognition unit also develops a system that provides reminders to encourage contact with family and friends in real time according to the user's emotional state. For example, the reminder is adjusted each time the user's emotions change. This makes it easier for the user to contact their family and friends.

[0060] The emotion recognition unit can personalize and suggest community events and online social gatherings that the user would like to participate in. For example, the emotion recognition unit uses emotion recognition technology to develop a system that automatically suggests community events and online social gatherings that the user would like to participate in based on the user's emotional state. For example, it recommends events based on emotional data. The emotion recognition unit also builds an algorithm that suggests personalized community events and online social gatherings according to the user's emotional state. For example, it suggests events when the emotion is positive. The emotion recognition unit also uses emotion recognition technology to develop a system that suggests events and social gatherings that the user would like to participate in in real time. For example, it adjusts the content of suggestions every time the emotion changes. This makes it possible to suggest events and social gatherings that the user would like to participate in.

[0061] The emotion recognition unit can provide empathetic topics and stories when a user is feeling lonely, thereby promoting conversation. The emotion recognition unit, for example, uses an emotion estimation function to develop a system that automatically provides empathetic topics and stories when a user is feeling lonely. For example, it suggests empathetic topics based on emotion data. The emotion recognition unit also builds an algorithm that individually customizes empathetic topics and stories based on the user's emotional state. For example, it provides an encouraging story when the user is feeling down. The emotion recognition unit also develops a system that provides empathetic topics and stories in real time when a user is feeling lonely, based on emotion estimation data. For example, it adjusts the topic each time the emotion changes. This makes it possible to promote conversation when a user feels lonely.

[0062] The emotion recognition unit can suggest virtual reality (VR) events based on the user's emotional state to promote social connections. For example, the emotion recognition unit develops a system that analyzes the user's emotional state and automatically suggests emotion-based virtual reality (VR) events. For example, it suggests relaxing VR events based on emotional data. The emotion recognition unit also uses emotion recognition technology to build an algorithm that suggests personalized VR events according to the user's emotional state. For example, it suggests fun VR events when the emotion is positive. The emotion recognition unit also develops a system that suggests VR events that the user would like to participate in in real time based on emotion estimation data. For example, it adjusts the suggestions each time the emotion changes. This makes it possible to suggest VR events based on the user's emotional state.

[0063] The emotion recognition unit can provide online games and collaborative work platforms that correspond to the user's emotional state in order to promote social connections. The emotion recognition unit, for example, develops a system that analyzes the user's emotional state and automatically suggests online games and collaborative work platforms based on emotions. For example, it suggests cooperative online games based on emotional data. The emotion recognition unit also uses emotion recognition technology to build an algorithm that suggests personalized online games and collaborative work platforms that correspond to the user's emotional state. For example, it suggests fun games when the emotion is positive. The emotion recognition unit also develops a system that suggests online games and collaborative work platforms that the user would like to participate in in real time based on emotion estimation data. For example, it adjusts the suggestions each time the emotion changes. This makes it possible to provide online games and collaborative work platforms that correspond to the user's emotional state.

[0064] The emotion recognition unit can suggest exercises and activities to elicit positive emotions when the user is feeling emotionally down. The emotion recognition unit, for example, uses an emotion estimation function to develop a system that automatically suggests exercises and activities to elicit positive emotions when the user is feeling emotionally down. For example, yoga or meditation is suggested based on emotion data. The emotion recognition unit also builds an algorithm that individually customizes exercises and activities to elicit positive emotions based on the user's emotional state. For example, exercises to relax when feeling down are suggested. The emotion recognition unit also develops a system that, based on emotion estimation data, suggests exercises and activities in real time to elicit positive emotions when the user is feeling emotionally down. For example, the suggestions are adjusted each time the user's emotions change. This makes it possible to suggest exercises and activities to elicit positive emotions when the user is feeling emotionally down.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The AI ​​assistant system can further include a health management unit. The health management unit collects data on the user's diet and exercise and monitors their health condition. For example, the health management unit records the calories and nutrients in the meals the user consumes and suggests balanced meals. The health management unit can also analyze the user's exercise data and provide an appropriate exercise plan. For example, if the user is not getting enough exercise, it can suggest simple exercises. The health management unit can also remind the user when to see a doctor based on their health condition. This enables comprehensive health management for the user.

[0067] The AI ​​assistant system may further include a learning support unit. The learning support unit provides learning content based on the user's interests. For example, the learning support unit may suggest online courses in areas of interest to the user. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, if the user is struggling with a particular task, the learning support unit may provide additional learning resources. The learning support unit may also create a customized learning plan tailored to the user's learning style. This improves the user's learning effectiveness.

[0068] The AI ​​assistant system can further include a safety monitoring unit. The safety monitoring unit monitors home security to ensure the user's safety. For example, the safety monitoring unit uses sensors on doors and windows to detect suspicious activity and issue an alarm. The safety monitoring unit can also build a system that automatically notifies emergency contacts if the user falls. For example, when the user falls, a notification is sent to family members or caregivers. The safety monitoring unit can also use GPS to track the user's location when they are out and ensure their safety. This ensures the user's safety.

[0069] The AI ​​assistant system can further include a hobby support unit. The hobby support unit suggests new activities based on the user's hobbies and interests. For example, the hobby support unit suggests workshops and events in areas that interest the user. The hobby support unit can also introduce online communities related to the user's hobbies. For example, if the user is interested in gardening, the hobby support unit can introduce online gardening forums. The hobby support unit can also create a customized activity plan tailored to the user's hobbies. This will enrich the user's hobby activities.

[0070] The AI ​​assistant system can further include a travel support unit. The travel support unit supports the user's travel plans. For example, the travel support unit provides information on travel destinations based on the user's preferences. The travel support unit can also manage the user's travel schedule and send reminders at appropriate times. For example, it can remind the user to remember their flight boarding time. The travel support unit can also provide the user with information needed during their trip in real time. For example, it can provide local weather and traffic information. This helps the user's trip proceed smoothly.

[0071] The AI ​​assistant system can also use its emotion estimation function to provide relaxation programs based on the user's emotional state. For example, if the user is feeling stressed, the system can suggest a relaxation meditation program. If the user is feeling anxious, the system can also provide deep breathing exercises. If the user feels like relaxing, the system can play relaxation music. This allows for relaxation tailored to the user's emotional state.

[0072] The AI ​​assistant system can further use emotion estimation to provide personalized entertainment content based on the user's emotional state. For example, if the user is sad, the emotion estimation function can be used to suggest a comedy movie to lighten the mood. If the user feels like relaxing, the emotion estimation function can be used to suggest relaxing music or podcasts. If the user is excited, the emotion estimation function can be used to suggest an audiobook with a calming tone. In this way, entertainment can be provided according to the user's emotional state.

[0073] The AI ​​assistant system can further use the emotion estimation function to provide a personalized fitness program based on the user's emotional state. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest a yoga program to help them relax. If the user is feeling energetic, the emotion estimation function can be used to suggest high-intensity interval training (HIIT). If the user is feeling depressed, the emotion estimation function can be used to suggest a dance exercise to lift their spirits. This allows the system to provide a fitness program tailored to the user's emotional state.

[0074] The AI ​​assistant system can further use its emotion estimation function to provide a personalized learning program based on the user's emotional state. For example, if the user feels that they want to improve their concentration, the system can use the emotion estimation function to suggest learning techniques to improve their concentration. If the user feels that they want to relax, the system can also use the emotion estimation function to provide content that allows them to learn while relaxing. If the user feels that they want to increase their motivation, the system can also use the emotion estimation function to suggest a learning plan to increase their motivation. This makes it possible to provide a learning program that suits the user's emotional state.

[0075] The AI ​​assistant system can further use the emotion estimation function to provide personalized mental health support based on the user's emotional state. For example, if the user is feeling anxious, the emotion estimation function can be used to suggest a counseling session to reduce anxiety. If the user is feeling stressed, the emotion estimation function can be used to provide techniques for stress management. If the user is feeling depressed, the emotion estimation function can be used to suggest mental health resources to improve their mood. In this way, mental health support can be provided according to the user's emotional state.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The emotion recognition unit grasps the user's emotional state. For example, the emotion recognition unit analyzes the user's facial expressions using a camera and estimates the user's emotional state. The emotion recognition unit can also analyze the user's tone of voice and estimate the user's emotional state. Furthermore, the emotion recognition unit can analyze the user's choice of words and estimate the user's emotional state. Step 2: The response unit responds appropriately based on the emotional state grasped by the emotion recognition unit. For example, if the user is feeling lonely, the response unit may ask, "What did you do today?". It may also suggest relaxing music if the user is feeling stressed. It may also teach deep breathing techniques if the user is feeling anxious. Step 3: The suggestion unit makes suggestions to promote social connections based on the responses made by the response unit. For example, the suggestion unit can suggest nearby community events if the user feels lonely. It can also suggest online meetups if the user feels lonely. It can also remind the user to send periodic messages to encourage contact with family and friends.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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. an emotion recognition unit for grasping the emotional state of a user; a response unit that takes an appropriate response based on the emotional state grasped by the emotion recognition unit; a suggestion unit that makes a suggestion to promote social connections based on the response made by the response unit. A system characterized by:

2. The emotion recognition unit Compare the user's past emotional history with their current emotional state and analyze patterns of emotional change.

2. The system of claim 1.

3. The emotion recognition unit Emotion estimation is performed by combining at least one vital data of the user, either heart rate or skin temperature.

2. The system of claim 1.

4. The emotion recognition unit Generate a personalized voice tone or speaking style based on the user's emotional state 2. The system of claim 1.

5. The emotion recognition unit Links with other devices in the home to provide multiple content options based on the user's emotional state 2. The system of claim 1.

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