System

Abstract

An object of a system according to an embodiment is to realize a natural conversation and smoothly cooperate with an external service.SOLUTION: A system includes a facial expression generation part, a prompt setting part, a communication part, and a cooperation part. The facial expression generation unit performs a conversation with the user using the tablet application having the facial expression. The prompt setting unit sets the response generated by the facial expression generation unit as a prompt. The communication unit communicates the content set by the prompt setting unit using the cellular model. The linkage unit links the content communicated by the communication unit with the external service.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 technology has had issues with the fact that conversations with users using tablet apps are not natural and that integration with external services is not smooth.

[0005] The system according to the embodiment aims to realize natural conversation and smoothly link with external services. [Means for solving the problem]

[0006] The system according to the embodiment includes a facial expression generation unit, a prompt setting unit, a communication unit, and a linking unit. The facial expression generation unit converses with a user using a tablet app with facial expressions. The prompt setting unit sets a response generated by the facial expression generation unit as a prompt. The communication unit communicates the content set by the prompt setting unit using a cellular model. The linking unit links the content communicated by the communication unit with an external service. [Effects of the Invention]

[0007] The system according to the embodiment can realize natural conversation and smoothly cooperate with external services. [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 dialogue support system according to an embodiment of the present invention uses an app with facial expressions on a tablet to become a conversation partner for elderly people living alone, allowing them to easily exchange news, questions, etc. by voice. This allows the dialogue support system to provide an environment in which elderly people can live with peace of mind and reduce their sense of loneliness.

[0029] A dialogue support system according to an embodiment includes a facial expression generation unit, a prompt setting unit, a communication unit, and a linking unit. The facial expression generation unit uses an app that provides facial expressions on a tablet to converse with a user. For example, the facial expression generation unit uses a generation AI to understand what the user is saying and generate an appropriate response. The facial expression generation unit can also change the response speed and tone depending on the tone and speed of the user's voice. The prompt setting unit sets the response generated by the generation AI as a prompt. For example, if a user requests "set casual settings," the prompt setting unit changes the settings so that the generation AI responds in a casual tone. The communication unit communicates the content set by the prompt setting unit using a cellular model. For example, the communication unit allows the user to listen to the news or ask questions through the app even while on the go. The linking unit links the content communicated by the communication unit with external services. For example, the linking unit can link with Demae-can or Yahoo! Shopping to arrange meals or shopping. As a result, the dialogue support system according to an embodiment enables natural dialogue with users and improves quality of life by linking with external services.

[0030] The facial expression generation unit can change the response speed and tone according to the tone and speed of the user's voice. The facial expression generation unit, for example, analyzes the tone and speed of the user's speaking voice in real time and adjusts the response speed and tone of the app accordingly. For example, if the user speaks slowly, the app will also respond slowly. Furthermore, if the voice tone is high, the app's facial expression will be brighter, and if the voice tone is low, the app's facial expression will be calmer. This makes the conversation with the user more natural. Furthermore, if the user's voice tone is fast, the app's response will also be faster, and conversely, if the voice tone is slow, the response will be slower. This makes it possible to provide a response that matches the user's speaking style. This makes it possible to provide a natural response that matches the user's speaking style.

[0031] The facial expression generation unit can analyze a user's past conversation history and generate responses optimized for each individual user. For example, the facial expression generation unit stores the user's past conversation history in a database, and the generation AI generates optimal responses based on that data. For example, it prioritizes providing information about topics that the user talks about frequently. It also analyzes the conversation history to understand the user's preferences and interests. For example, if a user likes a particular news genre, it provides the latest information on that genre. It also learns the user's emotional tendencies from past conversations and generates responses accordingly. For example, if the user is feeling stressed, it provides topics that will help them relax. This makes it possible to provide responses that match the user's preferences and interests.

[0032] The prompt setting unit can learn the user's past setting history and automatically suggest optimal prompt settings. For example, the prompt setting unit stores the user's past setting history in a database, and the generation AI automatically suggests optimal prompt settings based on that data. For example, it prioritizes suggestions of settings that the user uses frequently. It also analyzes the setting history to understand the user's preferences and tendencies. For example, it suggests formal settings to a user who prefers formal settings. Furthermore, it learns from the past setting history which settings the user prefers in specific situations and automatically suggests settings that suit those situations. For example, it suggests casual settings at night. This makes it possible to provide optimal settings according to the user's preferences.

[0033] The prompt setting unit can automatically change the interface design of the app according to the prompt setting. The prompt setting unit automatically changes the interface design of the app according to, for example, the prompt setting. For example, in a formal setting, the design is made simple and subdued. In addition, in a casual setting, the design is changed to a bright and colorful design. This provides an interface that matches the user's preferences. Furthermore, in a child-like setting, the design is changed to one that makes extensive use of characters and animations. This provides an interface that children can enjoy. This makes it possible to design an interface that matches the user's preferences.

[0034] The communication unit can automatically compress and optimize data depending on the communication status, thereby improving communication stability. The communication unit, for example, monitors the communication status in real time, and automatically compresses data when communication is unstable. For example, it reduces the resolution of images and videos. It also optimizes data depending on the communication status, improving communication stability. For example, it temporarily suspends the transmission of unnecessary data. Furthermore, when communication is stable, it decompresses data and transmits high-quality data. This maintains communication quality. This improves communication stability and allows users to use the service comfortably.

[0035] The communication unit can utilize the user's location information to provide information and services specialized for the area. For example, the communication unit obtains the user's location information in real time and provides information and services specialized for the area. For example, it provides information about nearby restaurants and events. It also provides local news and weather forecasts based on the location information. For example, it provides audio updates on the latest news for the area where the user is located. It also utilizes the location information to provide services specialized for the area. For example, it provides information about nearby medical institutions and public facilities. This makes it possible to provide information and services tailored to the user's location.

[0036] The linking unit can learn the user's lifestyle patterns and provide reminders and suggestions at the optimal time. For example, the linking unit stores the user's lifestyle patterns in a database, and the generation AI uses that data to provide reminders and suggestions at the optimal time. For example, it may remind the user to take their medicine every morning. It may also analyze lifestyle patterns to understand the user's preferences and habits. For example, it may provide news at specific times. Furthermore, it may learn from past data what kind of suggestions the user prefers in specific situations and provide suggestions that suit those situations. For example, it may suggest recipes at dinner time. This makes it possible to provide reminders and suggestions that suit the user's lifestyle patterns.

[0037] The linking unit can link with home appliances and smart home devices to support all aspects of a user's life. For example, the linking unit can link with home appliances and smart home devices to support all aspects of a user's life. For example, it can automatically adjust the temperature of an air conditioner. It can also automate tasks based on the user's lifestyle through smart home devices, for example, turning on lights at specific times. It can also monitor the status of home appliances in real time and notify the user if an abnormality occurs. For example, it can issue a warning if the refrigerator temperature rises. This allows it to link with home appliances and smart home devices to support all aspects of a user's life.

[0038] The linking unit can link with health management apps and fitness devices to monitor the user's health status. The linking unit, for example, links with health management apps and fitness devices to monitor the user's health status in real time. For example, it records heart rate and number of steps. It also makes suggestions based on the health data according to the user's health status. For example, it sends a notification encouraging exercise if the user is not getting enough exercise. It also analyzes data from the fitness device to build a system that evaluates the user's health status. For example, it evaluates the quality of sleep and makes suggestions for improvement. This allows the user's health status to be monitored in real time.

[0039] The linking unit can link with travel and event reservation services to make suggestions that will enrich the user's life. The linking unit, for example, links with travel and event reservation services to make suggestions that will enrich the user's life. For example, it can provide information about events being held nearby. It can also make suggestions about trips and events based on the user's preferences and interests. For example, it can provide information about concerts by the user's favorite artists. It can also link with reservation services to enable users to easily make reservations for trips and events. For example, it can perform reservation procedures using voice operations. This makes it possible to make suggestions that will enrich the user's life.

[0040] The communication unit can learn the characteristics of a user's voice and individually optimize it to improve the accuracy of voice recognition. For example, the communication unit stores the characteristics of a user's voice in a database, and the generation AI improves the accuracy of voice recognition based on that data. For example, the communication unit learns the tone and accent of the user's voice. In addition, to improve the accuracy of voice recognition, the communication unit analyzes the characteristics of the user's voice in real time. For example, the recognition accuracy is adjusted according to changes in the user's voice. Furthermore, the speech recognition algorithm is individually optimized based on the characteristics of the user's voice. For example, a specific speech recognition model is used for a specific user. This makes it possible to improve the accuracy of speech recognition according to the characteristics of the user's voice.

[0041] The communication unit can automatically filter background sounds and noise during voice operations to achieve clear voice communication. The communication unit, for example, builds a system that filters background sounds and noise in real time during voice operations. For example, it uses noise canceling technology. It also automatically detects background sounds and noise to improve the quality of voice communication. For example, it reduces ambient noise. Furthermore, to achieve clear voice communication during voice operations, it uses a noise filtering algorithm. For example, it removes noise in a specific frequency band. This enables clear voice communication.

[0042] The communication unit can realize multimodal operations that combine gesture operations and gaze tracking in addition to voice operations. The communication unit realizes multimodal operations that combine gesture operations in addition to voice operations, for example. For example, scrolling the screen with hand movements. Also, gaze tracking technology is used to perform operations according to the user's gaze. For example, an icon that the gaze is aligned with is selected. Furthermore, a multimodal operation system that combines voice operations, gesture operations, and gaze tracking is constructed. For example, instructions are given by voice and confirmed by gestures. This makes it possible to perform operations that combine gesture operations and gaze tracking in addition to voice operations.

[0043] The communication unit can utilize context information to more accurately understand the user's intention when performing voice operations. The communication unit, for example, analyzes context information to more accurately understand the user's intention when performing voice operations. For example, it generates an appropriate response by taking into account the content of the conversation before and after. Furthermore, a system is constructed that infers the user's intention based on the context information and generates an appropriate response. For example, when a user says "that," it infers what "that" refers to based on the content of the previous conversation. Furthermore, it analyzes the user's past conversation history and behavior history and utilizes context information to improve the accuracy of voice operations. For example, it learns phrases and words that the user frequently uses. This makes it possible to more accurately understand the user's intention.

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

[0045] The dialogue support system can further include a health management unit that monitors the user's health condition. For example, the health management unit periodically measures the user's heart rate and blood pressure and issues an alert if an abnormality is detected. The health management unit can also manage the user's diet and exercise records and make suggestions to support healthy lifestyle habits. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise menu. Furthermore, the health management unit has a function for linking with medical institutions and can share the user's health data with doctors. This makes it possible to provide comprehensive support for the user's health condition.

[0046] The dialogue support system can further include a content providing unit based on the user's hobbies and interests. The content providing unit can, for example, recommend music or movies that the user likes. It can also provide news and articles that match the user's interests. For example, if the user is interested in sports, it can provide the latest sports news. Furthermore, the content providing unit can analyze the user's past viewing history and suggest content that is optimized for each individual user. This makes it possible to provide content that matches the user's interests and preferences.

[0047] The dialogue support system can further include a schedule management unit that supports the user's lifestyle. The schedule management unit, for example, manages the user's schedule and sets reminders. It can also make schedule suggestions that suit the user's lifestyle. For example, if the user is a nocturnal person, it can suggest reducing morning schedules. Furthermore, the schedule management unit can analyze the user's past behavior history and automatically generate an optimal schedule. This makes it possible to manage the user's schedule according to their lifestyle.

[0048] The dialogue support system can further include an education support unit that supports the user's learning. The education support unit, for example, provides learning content in areas of interest to the user. It can also suggest assignments and tests according to the user's learning progress. For example, if the user is learning a language, it can provide practice questions at an appropriate level. Furthermore, the education support unit can analyze the user's learning history and suggest a learning plan optimized for each individual user. This makes it possible to effectively support the user's learning.

[0049] The dialogue support system may further include a hobby support unit that supports the user's hobby activities. The hobby support unit, for example, provides information about hobbies that interest the user. It can also record the user's hobby activities and manage their progress. For example, if the user's hobby is gardening, it can provide information on how to grow plants and how to care for them according to the season. Furthermore, the hobby support unit can introduce events and communities related to the user's hobby. This makes it possible to support the user's hobby activities and provide a fulfilling life.

[0050] The dialogue support system can further include a smart home linkage unit that supports the user's life in all aspects. The smart home linkage unit, for example, links with home appliances and smart home devices to automate the user's life. It can also suggest how to operate home appliances according to the user's lifestyle patterns. For example, it can automatically turn on the air conditioner when the user returns home. Furthermore, the smart home linkage unit can monitor the status of home appliances in real time and notify the user if an abnormality occurs. This makes it possible to support the user's life in all aspects and provide a comfortable living environment.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The facial expression generator uses an app that gives the tablet facial expressions to converse with the user. For example, the facial expression generator uses generative AI to understand what the user is saying and generate an appropriate response. The facial expression generator can also change the response speed and tone depending on the tone and speed of the user's voice. Step 2: The prompt setting unit sets the response generated by the generation AI as a prompt. For example, if the user says "set it to casual," the prompt setting unit changes the setting so that the generation AI responds in a casual tone. Step 3: The communication unit communicates the content set by the prompt setting unit using a cellular model. For example, the communication unit can listen to the news or ask questions through an app even when you are out and about. Step 4: The linking unit links the content communicated by the communication unit with an external service. For example, the linking unit can link with Demae-can or Yahoo Shopping to arrange meals or shopping.

[0053] (Example 2) The dialogue support system according to an embodiment of the present invention uses an app with facial expressions on a tablet to become a conversation partner for elderly people living alone, allowing them to easily exchange news, questions, etc. by voice. This allows the dialogue support system to provide an environment in which elderly people can live with peace of mind and reduce their sense of loneliness.

[0054] A dialogue support system according to an embodiment includes a facial expression generation unit, a prompt setting unit, a communication unit, and a linking unit. The facial expression generation unit uses an app that provides facial expressions on a tablet to converse with a user. For example, the facial expression generation unit uses a generation AI to understand what the user is saying and generate an appropriate response. The facial expression generation unit can also change the response speed and tone depending on the tone and speed of the user's voice. The prompt setting unit sets the response generated by the generation AI as a prompt. For example, if a user requests "set casual settings," the prompt setting unit changes the settings so that the generation AI responds in a casual tone. The communication unit communicates the content set by the prompt setting unit using a cellular model. For example, the communication unit allows the user to listen to the news or ask questions through the app even while on the go. The linking unit links the content communicated by the communication unit with external services. For example, the linking unit can link with Demae-can or Yahoo! Shopping to arrange meals or shopping. As a result, the dialogue support system according to an embodiment enables natural dialogue with users and improves quality of life by linking with external services.

[0055] The facial expression generation unit can change the response speed and tone according to the tone and speed of the user's voice. The facial expression generation unit, for example, analyzes the tone and speed of the user's speaking voice in real time and adjusts the response speed and tone of the app accordingly. For example, if the user speaks slowly, the app will also respond slowly. Furthermore, if the voice tone is high, the app's facial expression will be brighter, and if the voice tone is low, the app's facial expression will be calmer. This makes the conversation with the user more natural. Furthermore, if the user's voice tone is fast, the app's response will also be faster, and conversely, if the voice tone is slow, the response will be slower. This makes it possible to provide a response that matches the user's speaking style. This makes it possible to provide a natural response that matches the user's speaking style.

[0056] The facial expression generation unit can analyze a user's past conversation history and generate responses optimized for each individual user. For example, the facial expression generation unit stores the user's past conversation history in a database, and the generation AI generates optimal responses based on that data. For example, it prioritizes providing information about topics that the user talks about frequently. It also analyzes the conversation history to understand the user's preferences and interests. For example, if a user likes a particular news genre, it provides the latest information on that genre. It also learns the user's emotional tendencies from past conversations and generates responses accordingly. For example, if the user is feeling stressed, it provides topics that will help them relax. This makes it possible to provide responses that match the user's preferences and interests.

[0057] The facial expression generation unit can use the emotion estimation function to generate facial expressions and responses according to the user's emotional state. The facial expression generation unit, for example, analyzes the user's facial expressions and voice and uses the emotion estimation function to grasp the user's emotional state in real time. For example, if the user is smiling, the app also displays a smiling expression. Furthermore, based on the emotion estimation data, the app generates a response according to the user's emotional state. For example, if the user is sad, the app offers words of encouragement. Furthermore, the emotion estimation function is used to automatically generate background music and sound effects according to the user's emotions and adjust the atmosphere of the conversation. For example, if the user is relaxed, calm music is played. This makes it possible to provide an appropriate response according to the user's emotions.

[0058] The prompt setting unit can learn the user's past setting history and automatically suggest optimal prompt settings. For example, the prompt setting unit stores the user's past setting history in a database, and the generation AI automatically suggests optimal prompt settings based on that data. For example, it prioritizes suggestions of settings that the user uses frequently. It also analyzes the setting history to understand the user's preferences and tendencies. For example, it suggests formal settings to a user who prefers formal settings. Furthermore, it learns from the past setting history which settings the user prefers in specific situations and automatically suggests settings that suit those situations. For example, it suggests casual settings at night. This makes it possible to provide optimal settings according to the user's preferences.

[0059] The prompt setting unit can automatically change the interface design of the app according to the prompt setting. The prompt setting unit automatically changes the interface design of the app according to, for example, the prompt setting. For example, in a formal setting, the design is made simple and subdued. In addition, in a casual setting, the design is changed to a bright and colorful design. This provides an interface that matches the user's preferences. Furthermore, in a child-like setting, the design is changed to one that makes extensive use of characters and animations. This provides an interface that children can enjoy. This makes it possible to design an interface that matches the user's preferences.

[0060] The prompt setting unit can use the emotion estimation function to automatically adjust prompt settings according to the user's emotional state. The prompt setting unit, for example, analyzes the user's emotional state in real time and automatically adjusts prompt settings accordingly. For example, if the user is relaxed, the prompt setting is changed to a casual setting. Furthermore, based on the emotion estimation data, the prompt setting optimal for the user's emotional state is suggested. For example, if the user is feeling stressed, the prompt setting is suggested to be a formal setting. Furthermore, a system is constructed that dynamically changes prompt settings according to the user's emotions. For example, the settings are adjusted every time the user's emotions change. This makes it possible to set optimal prompt settings according to the user's emotions.

[0061] The communication unit can automatically compress and optimize data depending on the communication status, thereby improving communication stability. The communication unit, for example, monitors the communication status in real time, and automatically compresses data when communication is unstable. For example, it reduces the resolution of images and videos. It also optimizes data depending on the communication status, improving communication stability. For example, it temporarily suspends the transmission of unnecessary data. Furthermore, when communication is stable, it decompresses data and transmits high-quality data. This maintains communication quality. This improves communication stability and allows users to use the service comfortably.

[0062] The communication unit can utilize the user's location information to provide information and services specialized for the area. For example, the communication unit obtains the user's location information in real time and provides information and services specialized for the area. For example, it provides information about nearby restaurants and events. It also provides local news and weather forecasts based on the location information. For example, it provides audio updates on the latest news for the area where the user is located. It also utilizes the location information to provide services specialized for the area. For example, it provides information about nearby medical institutions and public facilities. This makes it possible to provide information and services tailored to the user's location.

[0063] The communication unit uses the emotion estimation function to set communication priorities according to the user's emotional state and prioritize the transmission of important information. The communication unit, for example, analyzes the user's emotional state in real time and prioritizes the transmission of important information when the user is emotionally excited. For example, breaking news and important notifications are immediately transmitted. The communication unit also sets communication priorities according to the user's emotional state based on the emotion estimation data. For example, when the user is relaxed, regular information is prioritized. Furthermore, a system is constructed that dynamically changes communication priorities according to the user's emotions. For example, the communication priority is adjusted every time the user's emotions change. This makes it possible to prioritize the transmission of important information according to the user's emotions.

[0064] The linking unit can learn the user's lifestyle patterns and provide reminders and suggestions at the optimal time. For example, the linking unit stores the user's lifestyle patterns in a database, and the generation AI uses that data to provide reminders and suggestions at the optimal time. For example, it may remind the user to take their medicine every morning. It may also analyze lifestyle patterns to understand the user's preferences and habits. For example, it may provide news at specific times. Furthermore, it may learn from past data what kind of suggestions the user prefers in specific situations and provide suggestions that suit those situations. For example, it may suggest recipes at dinner time. This makes it possible to provide reminders and suggestions that suit the user's lifestyle patterns.

[0065] The linking unit can link with home appliances and smart home devices to support all aspects of a user's life. For example, the linking unit can link with home appliances and smart home devices to support all aspects of a user's life. For example, it can automatically adjust the temperature of an air conditioner. It can also automate tasks based on the user's lifestyle through smart home devices, for example, turning on lights at specific times. It can also monitor the status of home appliances in real time and notify the user if an abnormality occurs. For example, it can issue a warning if the refrigerator temperature rises. This allows it to link with home appliances and smart home devices to support all aspects of a user's life.

[0066] The linking unit can use the emotion estimation function to make lifestyle suggestions based on the user's emotional state. For example, the linking unit analyzes the user's emotional state in real time and makes lifestyle suggestions based on that. For example, if the user is feeling stressed, it suggests relaxing music. Furthermore, based on the emotion estimation data, it makes lifestyle suggestions that are optimal for the user's emotional state. For example, if the user is relaxed, it suggests relaxing activities. Furthermore, a system is constructed that dynamically changes lifestyle suggestions based on the user's emotions. For example, the suggestions are adjusted every time the user's emotions change. This makes it possible to make lifestyle suggestions based on the user's emotions.

[0067] The linking unit can link with health management apps and fitness devices to monitor the user's health status. The linking unit, for example, links with health management apps and fitness devices to monitor the user's health status in real time. For example, it records heart rate and number of steps. It also makes suggestions based on the health data according to the user's health status. For example, it sends a notification encouraging exercise if the user is not getting enough exercise. It also analyzes data from the fitness device to build a system that evaluates the user's health status. For example, it evaluates the quality of sleep and makes suggestions for improvement. This allows the user's health status to be monitored in real time.

[0068] The linking unit can link with travel and event reservation services to make suggestions that will enrich the user's life. The linking unit, for example, links with travel and event reservation services to make suggestions that will enrich the user's life. For example, it can provide information about events being held nearby. It can also make suggestions about trips and events based on the user's preferences and interests. For example, it can provide information about concerts by the user's favorite artists. It can also link with reservation services to enable users to easily make reservations for trips and events. For example, it can perform reservation procedures using voice operations. This makes it possible to make suggestions that will enrich the user's life.

[0069] The collaboration unit can use the emotion estimation function to suggest entertainment content according to the user's emotions. The collaboration unit, for example, analyzes the user's emotional state in real time and suggests entertainment content according to that. For example, if the user is relaxed, it suggests a relaxing movie. Furthermore, based on the emotion estimation data, it suggests entertainment content that is optimal for the user's emotional state. For example, if the user is feeling stressed, it suggests relaxing music. Furthermore, a system is constructed that dynamically changes the entertainment content suggestions according to the user's emotions. For example, the suggestions are adjusted every time the user's emotions change. This makes it possible to suggest entertainment content according to the user's emotions.

[0070] The communication unit can learn the characteristics of a user's voice and individually optimize it to improve the accuracy of voice recognition. For example, the communication unit stores the characteristics of a user's voice in a database, and the generation AI improves the accuracy of voice recognition based on that data. For example, the communication unit learns the tone and accent of the user's voice. In addition, to improve the accuracy of voice recognition, the communication unit analyzes the characteristics of the user's voice in real time. For example, the recognition accuracy is adjusted according to changes in the user's voice. Furthermore, the speech recognition algorithm is individually optimized based on the characteristics of the user's voice. For example, a specific speech recognition model is used for a specific user. This makes it possible to improve the accuracy of speech recognition according to the characteristics of the user's voice.

[0071] The communication unit can automatically filter background sounds and noise during voice operations to achieve clear voice communication. The communication unit, for example, builds a system that filters background sounds and noise in real time during voice operations. For example, it uses noise canceling technology. It also automatically detects background sounds and noise to improve the quality of voice communication. For example, it reduces ambient noise. Furthermore, to achieve clear voice communication during voice operations, it uses a noise filtering algorithm. For example, it removes noise in a specific frequency band. This enables clear voice communication.

[0072] The communication unit can use the emotion estimation function to generate a voice response according to the user's emotional state. The communication unit, for example, analyzes the user's emotional state in real time and generates a voice response according to the user's emotional state. For example, if the user is relaxed, the communication unit responds in a gentle tone. Furthermore, based on the emotion estimation data, the communication unit generates a voice response that is optimal for the user's emotional state. For example, if the user is feeling stressed, the communication unit offers words of encouragement. Furthermore, a system is constructed that dynamically changes the voice response according to the user's emotions. For example, the response is adjusted every time the user's emotions change. This makes it possible to provide a voice response according to the user's emotions.

[0073] The communication unit can realize multimodal operations that combine gesture operations and gaze tracking in addition to voice operations. The communication unit realizes multimodal operations that combine gesture operations in addition to voice operations, for example. For example, scrolling the screen with hand movements. Also, gaze tracking technology is used to perform operations according to the user's gaze. For example, an icon that the gaze is aligned with is selected. Furthermore, a multimodal operation system that combines voice operations, gesture operations, and gaze tracking is constructed. For example, instructions are given by voice and confirmed by gestures. This makes it possible to perform operations that combine gesture operations and gaze tracking in addition to voice operations.

[0074] The communication unit can utilize context information to more accurately understand the user's intention when performing voice operations. The communication unit, for example, analyzes context information to more accurately understand the user's intention when performing voice operations. For example, it generates an appropriate response by taking into account the content of the conversation before and after. Furthermore, a system is constructed that infers the user's intention based on the context information and generates an appropriate response. For example, when a user says "that," it infers what "that" refers to based on the content of the previous conversation. Furthermore, it analyzes the user's past conversation history and behavior history and utilizes context information to improve the accuracy of voice operations. For example, it learns phrases and words that the user frequently uses. This makes it possible to more accurately understand the user's intention.

[0075] The communication unit uses the emotion estimation function to provide voice operation feedback according to the user's emotion, thereby improving satisfaction with the operation. The communication unit, for example, analyzes the user's emotional state in real time and provides voice operation feedback according to the analysis. For example, if the user is relaxed, feedback is provided in a gentle tone. Furthermore, a system is constructed that provides optimal feedback for the user's emotional state based on the emotion estimation data. For example, if the user is feeling stressed, words of encouragement are given. Furthermore, the voice operation feedback is dynamically changed according to the user's emotion. For example, the feedback is adjusted each time the user's emotion changes. This makes it possible to provide feedback according to the user's emotion and improve satisfaction with the operation.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The dialogue support system can further include a health management unit that monitors the user's health condition. For example, the health management unit periodically measures the user's heart rate and blood pressure and issues an alert if an abnormality is detected. The health management unit can also manage the user's diet and exercise records and make suggestions to support healthy lifestyle habits. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise menu. Furthermore, the health management unit has a function for linking with medical institutions and can share the user's health data with doctors. This makes it possible to provide comprehensive support for the user's health condition.

[0078] The dialogue support system can further include a content providing unit based on the user's hobbies and interests. The content providing unit can, for example, recommend music or movies that the user likes. It can also provide news and articles that match the user's interests. For example, if the user is interested in sports, it can provide the latest sports news. Furthermore, the content providing unit can analyze the user's past viewing history and suggest content that is optimized for each individual user. This makes it possible to provide content that matches the user's interests and preferences.

[0079] The dialogue support system can also be equipped with a relaxation function that responds to the user's emotional state. For example, if the user is feeling stressed, the relaxation function can play relaxing music or natural sounds. It can also provide breathing and meditation guidance that responds to the user's emotional state. For example, if the user is tense, the function can provide deep breathing guidance. Furthermore, the relaxation function can monitor the user's emotional state in real time and suggest appropriate relaxation methods. This makes it possible to provide relaxation that responds to the user's emotional state.

[0080] The dialogue support system can further include a schedule management unit that supports the user's lifestyle. The schedule management unit, for example, manages the user's schedule and sets reminders. It can also make schedule suggestions that suit the user's lifestyle. For example, if the user is a nocturnal person, it can suggest reducing morning schedules. Furthermore, the schedule management unit can analyze the user's past behavior history and automatically generate an optimal schedule. This makes it possible to manage the user's schedule according to their lifestyle.

[0081] The dialogue support system can further include an entertainment suggestion function that corresponds to the user's emotional state. For example, if the user is relaxed, the entertainment suggestion function can suggest relaxing movies or music. Also, if the user is feeling stressed, the entertainment suggestion function can suggest activities that will help change the user's mood. For example, if the user is tired, the entertainment suggestion function can suggest light exercise or a walk. Furthermore, the entertainment suggestion function can monitor the user's emotional state in real time and suggest appropriate entertainment. This makes it possible to provide entertainment that corresponds to the user's emotional state.

[0082] The dialogue support system can further include an education support unit that supports the user's learning. The education support unit, for example, provides learning content in areas of interest to the user. It can also suggest assignments and tests according to the user's learning progress. For example, if the user is learning a language, it can provide practice questions at an appropriate level. Furthermore, the education support unit can analyze the user's learning history and suggest a learning plan optimized for each individual user. This makes it possible to effectively support the user's learning.

[0083] The dialogue support system can further include a communication support function that corresponds to the user's emotional state. For example, if the user feels lonely, the communication support function can encourage the user to contact friends or family. Also, if the user feels stressed, the communication support function can suggest counseling services. For example, if the user feels anxious, the communication support function can suggest consulting with a specialist. Furthermore, the communication support function can monitor the user's emotional state in real time and suggest an appropriate communication method. This makes it possible to provide communication support that corresponds to the user's emotional state.

[0084] The dialogue support system may further include a hobby support unit that supports the user's hobby activities. The hobby support unit, for example, provides information about hobbies that interest the user. It can also record the user's hobby activities and manage their progress. For example, if the user's hobby is gardening, it can provide information on how to grow plants and how to care for them according to the season. Furthermore, the hobby support unit can introduce events and communities related to the user's hobby. This makes it possible to support the user's hobby activities and provide a fulfilling life.

[0085] The dialogue support system can further include a feedback function that responds to the user's emotional state. For example, if the user is relaxed, the feedback function can provide feedback in a gentle tone. Also, if the user is feeling stressed, the feedback function can provide encouraging words. For example, if the user is feeling anxious, the feedback function can provide reassuring words. Furthermore, the feedback function can monitor the user's emotional state in real time and provide appropriate feedback. This makes it possible to provide feedback that responds to the user's emotional state and improve satisfaction with the operation.

[0086] The dialogue support system can further include a smart home linkage unit that supports the user's life in all aspects. The smart home linkage unit, for example, links with home appliances and smart home devices to automate the user's life. It can also suggest how to operate home appliances according to the user's lifestyle patterns. For example, it can automatically turn on the air conditioner when the user returns home. Furthermore, the smart home linkage unit can monitor the status of home appliances in real time and notify the user if an abnormality occurs. This makes it possible to support the user's life in all aspects and provide a comfortable living environment.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The facial expression generator uses an app that gives the tablet facial expressions to converse with the user. For example, the facial expression generator uses generative AI to understand what the user is saying and generate an appropriate response. The facial expression generator can also change the response speed and tone depending on the tone and speed of the user's voice. Step 2: The prompt setting unit sets the response generated by the generation AI as a prompt. For example, if the user says "set it to casual," the prompt setting unit changes the setting so that the generation AI responds in a casual tone. Step 3: The communication unit communicates the content set by the prompt setting unit using a cellular model. For example, the communication unit can listen to the news or ask questions through an app even when you are out and about. Step 4: The linking unit links the content communicated by the communication unit with an external service. For example, the linking unit can link with Demae-can or Yahoo Shopping to arrange meals or shopping.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a facial expression generating unit that uses a tablet application with facial expressions to converse with a user; a prompt setting unit that sets the response generated by the facial expression generating unit as a prompt; a communication unit that communicates the content set by the prompt setting unit using a cellular model; a linking unit that links the content communicated by the communication unit with an external service. A system characterized by:

2. The facial expression generation unit Changing the response speed and tone according to the tone and speed of the user's voice 2. The system of claim 1.

3. The facial expression generation unit Analyzing the user's past conversation history and generating responses optimized for the individual user 2. The system of claim 1.

4. The facial expression generation unit generating the facial expression or response according to the emotional state of the user; 2. The system of claim 1.

5. The prompt setting unit Learn the user's past setting history and automatically suggest optimal prompt settings 2. The system of claim 1.

6. The prompt setting unit Automatically change app interface design according to prompt settings 2. The system of claim 1.

7. The prompt setting unit Automatically adjust prompt settings according to the user's emotional state.

2. The system of claim 1.

8. The communication unit Automatically compresses and optimizes data according to communication conditions, improving the stability of said communication.

2. The system of claim 1.

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