System

Abstract

An object of a system according to an embodiment is to provide optimal advice to a user.SOLUTION: A system according to an embodiment includes a general-purpose AI, an individual-specialized AI, and a cooperation processing unit. The AI having general-purpose intelligence has general-purpose intelligence. The individual-specific AI learns a conversation record or an action history of the user. The cooperation processing unit generates an optimal advice for the user through cooperation between a AI having general-purpose intelligence and the individual-specialized AI.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 techniques can only provide general advice to users, and there is room for improvement in providing optimal advice to individual users.

[0005] The system according to the embodiment aims to provide optimal advice to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI with general knowledge, a personalized AI, and a collaborative processing unit. The general knowledge AI has general knowledge. The personalized AI learns the user's conversation records and behavioral history. The collaborative processing unit generates optimal advice for the user through collaboration between the AI ​​with general knowledge and the personalized AI. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal advice to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​recommendation system according to an embodiment of the present invention is a system that generates optimal advice for users by cooperating an AI with general knowledge and a personalized AI, thereby enabling the AI ​​recommendation system to provide more effective and accurate advice to users.

[0029] An AI recommendation system according to an embodiment includes an AI with general knowledge, a personalized AI, and a collaborative processing unit. The general AI has a wide range of information and provides appropriate answers to general questions and problems. For example, if a user asks, "What is a healthy diet?", the general AI provides an answer such as, "A balanced diet is important. Try to eat a diet that includes vegetables, fruits, whole grains, and protein." The personalized AI learns the user's conversation records, behavioral history, preferences, etc., and provides advice tailored to the user. For example, if a user asks, "I've been feeling tired lately. What should I do?", the personalized AI provides specific advice such as, "You seem to be busy with work lately and not getting enough sleep. First, I recommend getting enough sleep. You should also spend more time on your hobbies to relax." The collaborative processing unit generates optimal advice for the user through collaboration between the general AI and the personalized AI. For example, if a user asks, "What is a healthy diet?", the AI ​​with general knowledge provides general health information, and the personalized AI proposes a specific diet plan based on the user's diet history and preferences. This allows the AI ​​recommendation system according to the embodiment to provide more effective and accurate advice to users. For example, if a user says, "I want to go on a diet," the personalized AI considers the user's diet history and exercise habits and proposes a specific diet plan. This allows users to receive the most appropriate advice tailored to their own situation.

[0030] An AI with general knowledge can refer to a user's past question history and prioritize providing highly relevant information. For example, an AI with general knowledge can analyze a user's past question history and prioritize providing highly relevant information. For example, if a user has asked many health-related questions in the past, health-related information can be displayed preferentially. An AI with general knowledge can also automatically suggest related topics based on a user's past question history. For example, if a user has asked a question about travel in the past, the latest travel information can be provided. An AI with general knowledge can also develop an algorithm that refers to a user's past question history and prioritizes displaying highly relevant information. For example, if a user has asked a question about technology in the past, the latest technology information can be provided. This makes it possible to provide highly relevant information based on a user's past question history.

[0031] An AI with general knowledge can generate optimal answers by taking into account the user's current situation and environment. For example, an AI with general knowledge can obtain the user's current weather information and generate optimal answers based on that information. For example, it can suggest indoor activities on rainy days. An AI with general knowledge can also provide appropriate advice by taking into account the user's current time of day. For example, if a question is asked late at night, it can suggest ways to relax. An AI with general knowledge can also obtain information about the user's current environment (e.g., location information) and generate optimal answers based on that information. For example, if the user is traveling, it can provide local tourist information. This allows the AI ​​to provide optimal answers based on the user's current situation and environment.

[0032] An AI with general knowledge can provide answers from multiple perspectives to a user's questions, allowing the user to choose. For example, an AI with general knowledge can generate answers from multiple perspectives to a user's question, allowing the user to choose. For example, an AI with general knowledge can provide answers from both a nutritional perspective and a kinesiology perspective to a health-related question. An AI with general knowledge can also generate multiple answers to a user's question based on the opinions of different experts. For example, an AI with general knowledge can provide answers from the perspectives of an engineer and a designer to a technology-related question. An AI with general knowledge can also build a system that provides answers from multiple perspectives to a user's question. For example, an AI with general knowledge can provide information on tourist spots and local cultural information to a travel-related question. This allows the user to select an answer from multiple perspectives.

[0033] An AI with general knowledge can work with AIs with general knowledge in different fields to provide comprehensive answers to complex questions from users. For example, an AI with general knowledge from different fields can work together to build a system that provides comprehensive answers to complex questions. For example, AIs with medical and nutritional knowledge can work together to answer complex questions about health. An AI with general knowledge can also work with AIs with general knowledge from different fields to generate comprehensive answers to complex questions from users. For example, an AI with technical and business knowledge can work together to answer questions about new product development. An AI with general knowledge can also work with AIs with general knowledge from different fields to develop an algorithm that provides comprehensive answers to complex questions. For example, an AI with knowledge of the environment and energy can work together to answer questions about sustainable energy. This allows AIs with knowledge from different fields to work together to provide comprehensive answers.

[0034] Personalized AI can learn not only a user's behavioral history, but also their social media posts and online activities, allowing it to provide more accurate advice. For example, personalized AI can analyze a user's social media posts and provide more accurate advice based on that information. For example, it can understand a user's interests from the content of their posts and generate advice based on that. Personalized AI can also learn a user's online activities and build a system that provides more accurate advice. For example, it can generate advice based on information about websites the user frequently visits. Personalized AI can also learn a user's social media posts and online activities and develop an algorithm that provides more accurate advice based on that data. For example, it can generate advice based on a user's online shopping history. This allows it to learn a user's social media posts and online activities and provide more accurate advice.

[0035] Personalized AI can incorporate a user's health data and provide advice based on their health condition. For example, personalized AI incorporates data from a user's fitness tracker or smartwatch and provides advice based on their health condition based on that data. For example, it analyzes a user's heart rate and sleep data to generate health advice. Personalized AI can also incorporate a user's health data and build a system that provides advice based on their health condition. For example, it can suggest an appropriate exercise plan based on the user's exercise data. Personalized AI can also incorporate data from a user's smartwatch and develop an algorithm that provides advice based on that data based on their health condition. For example, it can suggest healthy lifestyle habits based on the user's activity data. This makes it possible to provide advice tailored to the user's health condition based on their health data.

[0036] Personalized AI can also learn information about a user's family and friends and provide advice that takes social relationships into consideration. For example, personalized AI can learn information about a user's family and friends and provide advice that takes social relationships into consideration based on that data. For example, it can suggest a meal plan that takes the health of the user's family into consideration. Personalized AI can also learn a user's friendships and build a system that provides advice that takes social relationships into consideration. For example, it can suggest activities that the user can do together with their friends. Personalized AI can also learn information about a user's family and friends and develop an algorithm that provides advice that takes social relationships into consideration based on that data. For example, it can provide advice tailored to the user's family events. This makes it possible to learn information about a user's family and friends and provide advice that takes social relationships into consideration.

[0037] Personalized AI can suggest new activities and events based on a user's hobbies and interests. For example, personalized AI learns a user's hobbies and interests and suggests new activities and events based on that data. For example, if a user is interested in music, it can suggest concerts and music festivals. Personalized AI can also build a system that suggests new activities and events based on a user's hobbies and interests. For example, if a user is interested in outdoor activities, it can suggest hiking and camping events. Personalized AI can also learn a user's hobbies and interests and develop an algorithm that suggests new activities and events based on that data. For example, if a user is interested in cooking, it can suggest cooking classes and recipe events. This makes it possible to suggest new activities and events based on a user's hobbies and interests.

[0038] A general-purpose AI and a personalized AI can exchange information in real time and instantly generate the most appropriate advice for a user's question. For example, a general-purpose AI and a personalized AI can exchange information in real time to build a system that instantly generates the most appropriate advice for a user's question. For example, in response to a health question, general health information and advice based on individual health conditions are provided. Furthermore, a general-purpose AI and a personalized AI can exchange information in real time to develop an algorithm that generates the most appropriate advice for a user's question. For example, in response to a travel question, general tourist information and advice based on individual travel history are provided. Furthermore, a general-purpose AI and a personalized AI can exchange information in real time to develop a system that instantly generates the most appropriate advice for a user's question. For example, in response to a career question, general career advice and advice based on individual work history are provided. This allows for the exchange of information in real time and the instant generation of the most appropriate advice.

[0039] Generic AI and personalized AI can continuously learn from user feedback and improve the accuracy of advice. For example, generic AI and personalized AI can continuously learn from user feedback and build a system that improves the accuracy of advice based on that data. For example, they can adjust the content of advice based on user feedback. Generic AI and personalized AI can also continuously learn from user feedback and develop algorithms that improve the accuracy of advice. For example, they can improve the quality of advice based on user evaluation scores. Generic AI and personalized AI can also continuously learn from user feedback and develop systems that improve the accuracy of advice based on that data. For example, they can provide advice that reflects user opinions. This allows them to continuously learn from user feedback and improve the accuracy of advice.

[0040] A generic AI and a personalized AI can work together on different devices to provide advice tailored to the user's situation. A generic AI and a personalized AI can work together on different devices, such as a smartphone and a smart speaker, to build a system that provides advice tailored to the user's situation. For example, if the user is on the move, advice can be provided through a smartphone. A generic AI and a personalized AI can also work together on different devices to develop an algorithm that provides advice tailored to the user's situation. For example, if the user is at home, advice can be provided through a smart speaker. A generic AI and a personalized AI can also work together on different devices to develop a system that provides advice tailored to the user's situation. For example, if the user is in the office, advice can be provided through a desktop computer. This allows different devices to work together to provide advice tailored to the user's situation.

[0041] Generic AI and personalized AI can provide step-by-step advice based on a user's long-term goals. For example, generic AI and personalized AI can build a system that learns a user's long-term goals and provides step-by-step advice based on that data. For example, they can provide advice for improving skills based on a user's career plan. Generic AI and personalized AI can also develop an algorithm that provides step-by-step advice based on a user's long-term goals. For example, they can provide step-by-step advice on diet and exercise based on a user's health goals. Generic AI and personalized AI can also develop a system that learns a user's long-term goals and provides step-by-step advice based on that data. For example, they can provide a step-by-step study plan based on a user's learning goals. This makes it possible to provide step-by-step advice based on a user's long-term goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] AI recommendation systems can also analyze a user's past purchase history and predict future purchases. For example, if a user has frequently purchased products from a particular brand in the past, they can send a notification when a new product from that brand is released. Also, if a user regularly purchases products from a particular category, they can send a notification when products in that category go on sale. Furthermore, they can recommend related products based on a user's purchase history. For example, if a user purchases a camera, they can be suggested camera accessories and related gadgets. This allows them to provide more personalized recommendations based on a user's purchase history.

[0044] AI recommendation systems can also analyze a user's health data and provide advice based on their health status. For example, they can import data from a user's fitness tracker or smartwatch and provide advice based on their health status based on that data. For example, they can analyze a user's heart rate and sleep data to generate health advice. They can also suggest appropriate exercise plans based on the user's exercise data. They can also analyze a user's dietary data and suggest nutritionally balanced meal plans. This allows for more accurate advice to be provided based on the user's health data.

[0045] AI recommendation systems can also suggest new activities and events based on a user's hobbies and interests. For example, if a user is interested in music, they can suggest concerts and music festivals. If a user is interested in outdoor activities, they can suggest hiking and camping events. Furthermore, if a user is interested in cooking, they can suggest cooking classes and recipe events. This allows them to suggest new activities and events based on a user's hobbies and interests.

[0046] The AI ​​recommendation system can also learn information about the user's family and friends and provide advice that takes social relationships into account. For example, it can suggest a meal plan that takes the user's family's health into account. It can also learn about the user's friendships and provide advice that takes social relationships into account. For example, it can suggest activities that the user can do together with their friends. It can also provide advice that is tailored to the user's family events. This makes it possible to learn information about the user's family and friends and provide advice that takes social relationships into account.

[0047] AI recommendation systems can also provide step-by-step advice based on a user's long-term goals. For example, they can provide advice for skill development based on a user's career plan. They can also provide step-by-step advice on diet and exercise based on a user's health goals. They can also provide step-by-step learning plans based on a user's learning goals. This makes it possible to provide step-by-step advice based on a user's long-term goals.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: General knowledge AI has a wide range of information and can provide appropriate answers to common questions and problems. For example, if a user asks, "What is a healthy diet?", it can provide an answer such as, "A balanced diet is important. Try to eat a diet that includes vegetables, fruits, whole grains, and protein." Step 2: The personalized AI learns the user's conversation records, behavioral history, preferences, etc., and provides advice tailored to that user. For example, if a user asks, "I've been feeling tired easily lately. What should I do?", the AI ​​will provide specific advice such as, "You've been busy at work lately, and it seems you're not getting enough sleep. First of all, I recommend that you get enough sleep. It would also be a good idea to spend more time on your hobbies to relax." Step 3: In the collaborative processing unit, the general-purpose AI and the personalized AI work together to generate optimal advice for the user. For example, if a user asks, "What is a healthy diet?", the general-purpose AI will provide general health information, while the personalized AI will suggest a specific meal plan based on the user's dietary history and preferences. This allows for more effective and accurate advice to be provided to the user.

[0050] (Example 2) The AI ​​recommendation system according to an embodiment of the present invention is a system that generates optimal advice for users by cooperating an AI with general knowledge and a personalized AI, thereby enabling the AI ​​recommendation system to provide more effective and accurate advice to users.

[0051] An AI recommendation system according to an embodiment includes an AI with general knowledge, a personalized AI, and a collaborative processing unit. The general AI has a wide range of information and provides appropriate answers to general questions and problems. For example, if a user asks, "What is a healthy diet?", the general AI provides an answer such as, "A balanced diet is important. Try to eat a diet that includes vegetables, fruits, whole grains, and protein." The personalized AI learns the user's conversation records, behavioral history, preferences, etc., and provides advice tailored to the user. For example, if a user asks, "I've been feeling tired lately. What should I do?", the personalized AI provides specific advice such as, "You seem to be busy with work lately and not getting enough sleep. First, I recommend getting enough sleep. You should also spend more time on your hobbies to relax." The collaborative processing unit generates optimal advice for the user through collaboration between the general AI and the personalized AI. For example, if a user asks, "What is a healthy diet?", the AI ​​with general knowledge provides general health information, and the personalized AI proposes a specific diet plan based on the user's diet history and preferences. This allows the AI ​​recommendation system according to the embodiment to provide more effective and accurate advice to users. For example, if a user says, "I want to go on a diet," the personalized AI considers the user's diet history and exercise habits and proposes a specific diet plan. This allows users to receive the most appropriate advice tailored to their own situation.

[0052] An AI with general knowledge can refer to a user's past question history and prioritize providing highly relevant information. For example, an AI with general knowledge can analyze a user's past question history and prioritize providing highly relevant information. For example, if a user has asked many health-related questions in the past, health-related information can be displayed preferentially. An AI with general knowledge can also automatically suggest related topics based on a user's past question history. For example, if a user has asked a question about travel in the past, the latest travel information can be provided. An AI with general knowledge can also develop an algorithm that refers to a user's past question history and prioritizes displaying highly relevant information. For example, if a user has asked a question about technology in the past, the latest technology information can be provided. This makes it possible to provide highly relevant information based on a user's past question history.

[0053] An AI with general knowledge can generate optimal answers by taking into account the user's current situation and environment. For example, an AI with general knowledge can obtain the user's current weather information and generate optimal answers based on that information. For example, it can suggest indoor activities on rainy days. An AI with general knowledge can also provide appropriate advice by taking into account the user's current time of day. For example, if a question is asked late at night, it can suggest ways to relax. An AI with general knowledge can also obtain information about the user's current environment (e.g., location information) and generate optimal answers based on that information. For example, if the user is traveling, it can provide local tourist information. This allows the AI ​​to provide optimal answers based on the user's current situation and environment.

[0054] An AI with general knowledge can use its emotion estimation function to analyze a user's emotions when asking a question and provide an answer with a tone and content that matches the emotion. For example, an AI with general knowledge can use its emotion estimation function to analyze a user's emotions when asking a question in real time and provide an answer with a tone that matches the emotion. For example, if the user is feeling stressed, it can answer in a gentle tone. An AI with general knowledge can also build a system that analyzes a user's emotions when asking a question and provides an answer with content that matches the emotion. For example, if the user is sad, it can provide an answer that includes an encouraging message. An AI with general knowledge can also use its emotion estimation function to develop an algorithm that analyzes a user's emotions when asking a question and provides an answer with a tone and content that matches the emotion. For example, if the user is excited, it can answer in a calm tone. This makes it possible to provide an answer with a tone and content that matches the user's emotions.

[0055] An AI with general knowledge can provide answers from multiple perspectives to a user's questions, allowing the user to choose. For example, an AI with general knowledge can generate answers from multiple perspectives to a user's question, allowing the user to choose. For example, an AI with general knowledge can provide answers from both a nutritional perspective and a kinesiology perspective to a health-related question. An AI with general knowledge can also generate multiple answers to a user's question based on the opinions of different experts. For example, an AI with general knowledge can provide answers from the perspectives of an engineer and a designer to a technology-related question. An AI with general knowledge can also build a system that provides answers from multiple perspectives to a user's question. For example, an AI with general knowledge can provide information on tourist spots and local cultural information to a travel-related question. This allows the user to select an answer from multiple perspectives.

[0056] An AI with general knowledge can work with AIs with general knowledge in different fields to provide comprehensive answers to complex questions from users. For example, an AI with general knowledge from different fields can work together to build a system that provides comprehensive answers to complex questions. For example, AIs with medical and nutritional knowledge can work together to answer complex questions about health. An AI with general knowledge can also work with AIs with general knowledge from different fields to generate comprehensive answers to complex questions from users. For example, an AI with technical and business knowledge can work together to answer questions about new product development. An AI with general knowledge can also work with AIs with general knowledge from different fields to develop an algorithm that provides comprehensive answers to complex questions. For example, an AI with knowledge of the environment and energy can work together to answer questions about sustainable energy. This allows AIs with knowledge from different fields to work together to provide comprehensive answers.

[0057] A generic AI can use its emotion estimation function to collect emotional responses to user questions and continuously improve the quality of answers. For example, a generic AI can use its emotion estimation function to collect emotional responses to user questions in real time and build a system that continuously improves the quality of answers based on that data. For example, it can adjust the tone of the answer based on the user's emotion score. A generic AI can also collect emotional responses to user questions and develop an algorithm that improves the quality of answers based on that data. For example, it can reevaluate answers that receive many negative responses and identify areas for improvement. A generic AI can also use its emotion estimation function to collect emotional responses to user questions and develop a system that continuously improves the quality of answers based on that data. For example, it can prioritize answers that receive many positive responses. This allows a system to collect users' emotional responses and continuously improve the quality of answers.

[0058] Personalized AI can learn not only a user's behavioral history, but also their social media posts and online activities, allowing it to provide more accurate advice. For example, personalized AI can analyze a user's social media posts and provide more accurate advice based on that information. For example, it can understand a user's interests from the content of their posts and generate advice based on that. Personalized AI can also learn a user's online activities and build a system that provides more accurate advice. For example, it can generate advice based on information about websites the user frequently visits. Personalized AI can also learn a user's social media posts and online activities and develop an algorithm that provides more accurate advice based on that data. For example, it can generate advice based on a user's online shopping history. This allows it to learn a user's social media posts and online activities and provide more accurate advice.

[0059] Personalized AI can incorporate a user's health data and provide advice based on their health condition. For example, personalized AI incorporates data from a user's fitness tracker or smartwatch and provides advice based on their health condition based on that data. For example, it analyzes a user's heart rate and sleep data to generate health advice. Personalized AI can also incorporate a user's health data and build a system that provides advice based on their health condition. For example, it can suggest an appropriate exercise plan based on the user's exercise data. Personalized AI can also incorporate data from a user's smartwatch and develop an algorithm that provides advice based on that data based on their health condition. For example, it can suggest healthy lifestyle habits based on the user's activity data. This makes it possible to provide advice tailored to the user's health condition based on their health data.

[0060] Personalized AI can use emotion estimation functions to analyze a user's past emotional fluctuations and provide advice based on emotional patterns. For example, personalized AI can use emotion estimation functions to analyze a user's past emotional fluctuations and build a system that provides advice based on emotional patterns based on that data. For example, it can suggest ways for the user to relax when they are prone to stress. Personalized AI can also develop algorithms that analyze a user's past emotional fluctuations and provide advice based on emotional patterns. For example, it can provide advice to help the user increase activities that produce positive emotions. Personalized AI can also use emotion estimation functions to analyze a user's past emotional fluctuations and develop a system that provides advice based on emotional patterns based on that data. For example, it can provide advice to help the user avoid situations that cause negative emotions. This makes it possible to provide advice based on the user's emotional patterns.

[0061] Personalized AI can also learn information about a user's family and friends and provide advice that takes social relationships into consideration. For example, personalized AI can learn information about a user's family and friends and provide advice that takes social relationships into consideration based on that data. For example, it can suggest a meal plan that takes the health of the user's family into consideration. Personalized AI can also learn a user's friendships and build a system that provides advice that takes social relationships into consideration. For example, it can suggest activities that the user can do together with their friends. Personalized AI can also learn information about a user's family and friends and develop an algorithm that provides advice that takes social relationships into consideration based on that data. For example, it can provide advice tailored to the user's family events. This makes it possible to learn information about a user's family and friends and provide advice that takes social relationships into consideration.

[0062] Personalized AI can suggest new activities and events based on a user's hobbies and interests. For example, personalized AI learns a user's hobbies and interests and suggests new activities and events based on that data. For example, if a user is interested in music, it can suggest concerts and music festivals. Personalized AI can also build a system that suggests new activities and events based on a user's hobbies and interests. For example, if a user is interested in outdoor activities, it can suggest hiking and camping events. Personalized AI can also learn a user's hobbies and interests and develop an algorithm that suggests new activities and events based on that data. For example, if a user is interested in cooking, it can suggest cooking classes and recipe events. This makes it possible to suggest new activities and events based on a user's hobbies and interests.

[0063] Personalized AI can use its emotion estimation function to suggest relaxation and stress relief methods based on the user's emotions. For example, personalized AI can use its emotion estimation function to analyze a user's emotions in real time and build a system that suggests relaxation and stress relief methods based on that data. For example, if a user is feeling stressed, it can suggest meditation or deep breathing. Personalized AI can also develop algorithms that analyze a user's emotions and suggest relaxation and stress relief methods based on their emotions. For example, if a user is feeling anxious, it can suggest relaxing music or aromatherapy. Personalized AI can also use its emotion estimation function to analyze a user's emotions and develop a system that suggests relaxation and stress relief methods based on that data. For example, if a user is feeling tired, it can suggest a massage or a trip to a hot spring. This makes it possible to suggest relaxation and stress relief methods based on the user's emotions.

[0064] A general-purpose AI and a personalized AI can exchange information in real time and instantly generate the most appropriate advice for a user's question. For example, a general-purpose AI and a personalized AI can exchange information in real time to build a system that instantly generates the most appropriate advice for a user's question. For example, in response to a health question, general health information and advice based on individual health conditions are provided. Furthermore, a general-purpose AI and a personalized AI can exchange information in real time to develop an algorithm that generates the most appropriate advice for a user's question. For example, in response to a travel question, general tourist information and advice based on individual travel history are provided. Furthermore, a general-purpose AI and a personalized AI can exchange information in real time to develop a system that instantly generates the most appropriate advice for a user's question. For example, in response to a career question, general career advice and advice based on individual work history are provided. This allows for the exchange of information in real time and the instant generation of the most appropriate advice.

[0065] Generic AI and personalized AI can continuously learn from user feedback and improve the accuracy of advice. For example, generic AI and personalized AI can continuously learn from user feedback and build a system that improves the accuracy of advice based on that data. For example, they can adjust the content of advice based on user feedback. Generic AI and personalized AI can also continuously learn from user feedback and develop algorithms that improve the accuracy of advice. For example, they can improve the quality of advice based on user evaluation scores. Generic AI and personalized AI can also continuously learn from user feedback and develop systems that improve the accuracy of advice based on that data. For example, they can provide advice that reflects user opinions. This allows them to continuously learn from user feedback and improve the accuracy of advice.

[0066] Generic AI and personalized AI can use emotion estimation to adjust the tone and content of advice based on the user's emotions, allowing for more personalized advice. For example, generic AI and personalized AI could use emotion estimation to build a system that analyzes a user's emotions in real time and adjusts the tone and content of advice based on that data. For example, if a user is feeling down, the advice could be provided in an encouraging tone. Generic AI and personalized AI could also develop an algorithm that analyzes a user's emotions and adjusts the tone and content of advice based on their emotions. For example, if a user is excited, the advice could be provided in a calm tone. Generic AI and personalized AI could also use emotion estimation to develop a system that analyzes a user's emotions and adjusts the tone and content of advice based on that data. For example, if a user is feeling anxious, the advice could be provided in a reassuring tone. This allows for the tone and content to be adjusted based on the user's emotions, allowing for more personalized advice.

[0067] A generic AI and a personalized AI can work together on different devices to provide advice tailored to the user's situation. A generic AI and a personalized AI can work together on different devices, such as a smartphone and a smart speaker, to build a system that provides advice tailored to the user's situation. For example, if the user is on the move, advice can be provided through a smartphone. A generic AI and a personalized AI can also work together on different devices to develop an algorithm that provides advice tailored to the user's situation. For example, if the user is at home, advice can be provided through a smart speaker. A generic AI and a personalized AI can also work together on different devices to develop a system that provides advice tailored to the user's situation. For example, if the user is in the office, advice can be provided through a desktop computer. This allows different devices to work together to provide advice tailored to the user's situation.

[0068] Generic AI and personalized AI can provide step-by-step advice based on a user's long-term goals. For example, generic AI and personalized AI can build a system that learns a user's long-term goals and provides step-by-step advice based on that data. For example, they can provide advice for improving skills based on a user's career plan. Generic AI and personalized AI can also develop an algorithm that provides step-by-step advice based on a user's long-term goals. For example, they can provide step-by-step advice on diet and exercise based on a user's health goals. Generic AI and personalized AI can also develop a system that learns a user's long-term goals and provides step-by-step advice based on that data. For example, they can provide a step-by-step study plan based on a user's learning goals. This makes it possible to provide step-by-step advice based on a user's long-term goals.

[0069] The generic AI and the personalized AI can use the emotion estimation function to adjust the frequency and timing of advice in response to the user's emotional fluctuations, thereby providing optimal support. For example, the generic AI and the personalized AI could use the emotion estimation function to analyze the user's emotional fluctuations in real time and build a system that adjusts the frequency and timing of advice based on that data. For example, if the user is feeling stressed, the system could frequently suggest relaxation techniques. The generic AI and the personalized AI could also develop an algorithm that analyzes the user's emotional fluctuations and adjusts the frequency and timing of advice based on that emotion. For example, the system could suggest new challenges when the user is feeling positive. The generic AI and the personalized AI could also use the emotion estimation function to analyze the user's emotional fluctuations and develop a system that adjusts the frequency and timing of advice based on that data. For example, if the user is feeling fatigued, the system could adjust the timing of suggestions to take a rest. This allows the system to adjust the frequency and timing of advice in response to the user's emotional fluctuations, providing optimal support.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] AI recommendation systems can also analyze a user's past purchase history and predict future purchases. For example, if a user has frequently purchased products from a particular brand in the past, they can send a notification when a new product from that brand is released. Also, if a user regularly purchases products from a particular category, they can send a notification when products in that category go on sale. Furthermore, they can recommend related products based on a user's purchase history. For example, if a user purchases a camera, they can be suggested camera accessories and related gadgets. This allows them to provide more personalized recommendations based on a user's purchase history.

[0072] AI recommendation systems can also analyze a user's health data and provide advice based on their health status. For example, they can import data from a user's fitness tracker or smartwatch and provide advice based on their health status based on that data. For example, they can analyze a user's heart rate and sleep data to generate health advice. They can also suggest appropriate exercise plans based on the user's exercise data. They can also analyze a user's dietary data and suggest nutritionally balanced meal plans. This allows for more accurate advice to be provided based on the user's health data.

[0073] AI recommendation systems can also suggest new activities and events based on a user's hobbies and interests. For example, if a user is interested in music, they can suggest concerts and music festivals. If a user is interested in outdoor activities, they can suggest hiking and camping events. Furthermore, if a user is interested in cooking, they can suggest cooking classes and recipe events. This allows them to suggest new activities and events based on a user's hobbies and interests.

[0074] The AI ​​recommendation system can also learn information about the user's family and friends and provide advice that takes social relationships into account. For example, it can suggest a meal plan that takes the user's family's health into account. It can also learn about the user's friendships and provide advice that takes social relationships into account. For example, it can suggest activities that the user can do together with their friends. It can also provide advice that is tailored to the user's family events. This makes it possible to learn information about the user's family and friends and provide advice that takes social relationships into account.

[0075] AI recommendation systems can also provide step-by-step advice based on a user's long-term goals. For example, they can provide advice for skill development based on a user's career plan. They can also provide step-by-step advice on diet and exercise based on a user's health goals. They can also provide step-by-step learning plans based on a user's learning goals. This makes it possible to provide step-by-step advice based on a user's long-term goals.

[0076] Using its emotion estimation function, the AI ​​recommendation system can also suggest relaxation and stress relief methods according to the user's emotions. For example, if the user is feeling stressed, it can suggest meditation or deep breathing. If the user is feeling anxious, it can suggest relaxing music or aromatherapy. Furthermore, if the user is feeling tired, it can suggest a massage or a trip to a hot spring. In this way, it is possible to suggest relaxation and stress relief methods according to the user's emotions.

[0077] The AI ​​recommendation system can also use its emotion estimation function to adjust the frequency and timing of advice in response to fluctuations in the user's emotions, providing optimal support. For example, if the user is feeling stressed, it can frequently suggest ways to relax. It can also suggest new challenges when the user is feeling positive. Furthermore, if the user is feeling tired, it can adjust the timing of suggestions for rest. This allows the system to adjust the frequency and timing of advice in response to fluctuations in the user's emotions, providing optimal support.

[0078] The AI ​​recommendation system can also use emotion estimation to analyze a user's past emotional fluctuations and provide advice based on their emotional patterns. For example, it can suggest ways to relax when the user is prone to stress. It can also provide advice to help the user increase activities that give them positive emotions. It can also provide advice to help the user avoid situations that give them negative emotions. This makes it possible to provide advice based on the user's emotional patterns.

[0079] The AI ​​recommendation system can use its emotion estimation function to analyze the emotions expressed by the user when asking a question, and provide answers in a tone and content that matches the emotion. For example, if the user is feeling stressed, the system can respond in a gentle tone. If the user is sad, the system can provide an answer that includes an encouraging message. Furthermore, if the user is excited, the system can respond in a calm tone. This allows the system to provide answers in a tone and content that matches the user's emotions.

[0080] Using emotion estimation capabilities, AI recommendation systems can adjust the tone and content of advice based on the user's emotions, allowing for more personalized advice. For example, if a user is feeling down, advice can be provided in an encouraging tone. If the user is excited, advice can be provided in a calm tone. Furthermore, if the user is feeling anxious, advice can be provided in a reassuring tone. This allows for the adjustment of tone and content based on the user's emotions, allowing for more personalized advice.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: General knowledge AI has a wide range of information and can provide appropriate answers to common questions and problems. For example, if a user asks, "What is a healthy diet?", it can provide an answer such as, "A balanced diet is important. Try to eat a diet that includes vegetables, fruits, whole grains, and protein." Step 2: The personalized AI learns the user's conversation records, behavioral history, preferences, etc., and provides advice tailored to that user. For example, if a user asks, "I've been feeling tired easily lately. What should I do?", the AI ​​will provide specific advice such as, "You've been busy at work lately, and it seems you're not getting enough sleep. First of all, I recommend that you get enough sleep. It would also be a good idea to spend more time on your hobbies to relax." Step 3: In the collaborative processing unit, the general-purpose AI and the personalized AI work together to generate optimal advice for the user. For example, if a user asks, "What is a healthy diet?", the general-purpose AI will provide general health information, while the personalized AI will suggest a specific meal plan based on the user's dietary history and preferences. This allows for more effective and accurate advice to be provided to the user.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. AI with general knowledge and Personalized AI that learns from users' conversation records and behavioral history, a collaborative processing unit in which the AI ​​with general knowledge and the personalized AI collaborate to generate optimal advice for the user. A system characterized by:

2. The AI ​​with general knowledge is: Refer to the user's past question history and provide relevant information preferentially.

2. The system of claim 1.

3. The AI ​​with general knowledge is: Generate optimal answers taking into account the user's current situation and environment 2. The system of claim 1.

4. The AI ​​with general knowledge is: Analyze the user's emotions when asking a question and provide an answer with a tone and content that matches the emotions.

2. The system of claim 1.

5. The AI ​​with general knowledge is: Providing answers from multiple perspectives to the user's question, allowing the user to choose 2. The system of claim 1.

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