System

Abstract

An object of a system according to an embodiment is to enable an elderly person to answer a question by voice without performing an Internet search.SOLUTION: A system includes a speech recognition unit, a generation AI unit, an answer generation unit, and a reporting unit. The voice recognition unit recognizes a voice of a user. The generation AI unit analyzes the voice recognized by the voice recognizing unit. The answer generation unit generates an answer on the basis of the voice analyzed by the generation AI unit. The notification unit notifies the user of the answer generated by the answer generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, elderly people may find it difficult to operate the internet when searching, so there is room for improvement.

[0005] The system according to the embodiment aims to enable elderly people to answer questions by voice without having to search the Internet. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a generation AI unit, an answer generation unit, and a notification unit. The speech recognition unit recognizes the user's speech. The generation AI unit analyzes the speech recognized by the speech recognition unit. The answer generation unit generates an answer based on the speech analyzed by the generation AI unit. The notification unit notifies the user of the answer generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows seniors to answer questions by voice without having to search the internet. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 new type of smartphone according to the embodiment of the present invention utilizes conversation-based generation AI that allows users to answer questions simply by talking to the smartphone, and is a system that returns answers as if having a conversation, without the need for searching the internet. This makes the new type of smartphone easy to use for the elderly, and can provide a comfortable lifestyle.

[0029] A novel type of smartphone according to an embodiment includes a speech recognition unit, a generation AI unit, a response generation unit, and a notification unit. The speech recognition unit recognizes a user's speech. For example, the speech recognition unit can recognize what the user says with high accuracy. The speech recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the user's speech. The speech recognition unit supports multiple languages ​​and can recognize speech in different languages. The generation AI unit analyzes the speech recognized by the speech recognition unit. For example, the generation AI unit converts speech into text using a text generation AI (e.g., LLM) and analyzes the text. The generation AI unit can also analyze multiple modalities, such as speech and images, using a multimodal generation AI. The generation AI unit can also understand the meaning of speech and generate an appropriate response using natural language processing technology. The response generation unit generates an appropriate response based on the speech analyzed by the generation AI unit. For example, the response generation unit obtains information from the Internet and generates an appropriate response in response to a user's question. The answer generation unit can also learn the user's past question history and generate answers optimized for each individual user. The answer generation unit can also analyze the user's tone and speed of voice and estimate the user's emotional state to generate an appropriate answer. The notification unit notifies the user of the answer generated by the answer generation unit. For example, the notification unit can convey the answer to the user using a voice notification. The notification unit can also display the answer on the screen using a text notification. The notification unit can also notify the user of the answer using a vibration notification. As a result, the new type of smartphone according to the embodiment can provide a system that is easy for the elderly to use by generating and notifying an appropriate answer based on the user's voice.

[0030] The generation AI unit can learn the user's past question history and provide answers that are appropriate for the user. For example, the generation AI unit analyzes the user's past question history and provides optimal answers to frequently asked questions. For example, if a user asks about the weather every morning, the generation AI unit learns that pattern and provides weather information before the user even speaks. The generation AI unit can also understand the user's interests and concerns based on the user's past question history and provide answers based on that. The generation AI unit can also save the user's past question history in the cloud and share it with other devices. This allows the unit to learn the user's past question history and provide answers that are optimized for each individual user.

[0031] The generation AI unit can support multiple languages ​​and answer questions in different languages. The generation AI unit, for example, can support multiple languages ​​and provide instant answers even when users ask questions in different languages. For example, the generation AI unit supports major languages ​​such as English, Spanish, and Chinese. The generation AI unit can also generate answers in an appropriate language based on the user's language settings. The generation AI unit can also store the user's language history in the cloud and share it with other devices. This allows the system to support multiple languages ​​and provide instant answers to questions in different languages.

[0032] The generation AI unit can learn the user's hobbies and interests and provide related information. For example, the generation AI unit can learn the user's hobbies and interests and automatically provide related information. For example, if the user is a movie lover, the generation AI unit can provide the latest movie information. The generation AI unit can also provide event information and news articles based on the user's hobbies and interests. The generation AI unit can also save the user's hobbies and interests in the cloud and share them with other devices. This allows the generation AI unit to automatically provide related information by learning the user's hobbies and interests.

[0033] The generation AI unit can learn the user's past search history and provide search results. The generation AI unit, for example, learns the user's past search history and provides predicted search results. For example, it can automatically display the latest information on topics that the user frequently searches for. The generation AI unit can also prioritize displaying related search results based on the user's search history. The generation AI unit can also save the user's search history in the cloud and share it with other devices. This allows it to provide predicted search results by learning the user's past search history.

[0034] The generation AI unit can provide information in real time based on the user's location information. The generation AI unit provides optimal information in real time based on the user's location information, for example. For example, if a user asks for restaurant information around their current location, the generation AI unit will display nearby restaurants. The generation AI unit can also provide traffic information and weather information based on the user's location information. The generation AI unit can also store the user's location information in the cloud and share it with other devices. This allows the generation AI unit to provide optimal information in real time based on the user's location information.

[0035] The generation AI unit can refer to the user's calendar information and provide information based on the schedule. For example, if the user asks about a meeting schedule, the generation AI unit displays the meeting details from the calendar. The generation AI unit can also set reminders based on the user's schedule. The generation AI unit can also store the user's calendar information in the cloud and share it with other devices. This allows the generation AI unit to refer to the user's calendar information and provide information based on the schedule.

[0036] The generation AI unit can analyze a user's social media account and provide information. For example, the generation AI unit can analyze a user's social media account and provide relevant information. For example, if a user asks about their friends' latest posts, the generation AI unit can display that information. The generation AI unit can also provide information that matches the user's interests and concerns based on the user's social media account. The generation AI unit can also store the user's social media account in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's social media account and provide relevant information.

[0037] The generation AI unit can learn the characteristics of a user's voice and provide voice recognition for each individual user. The generation AI unit, for example, learns the characteristics of a user's voice and provides voice recognition optimized for each individual user. For example, it learns the tone and accent of a user's voice to achieve accurate voice recognition. The generation AI unit can also improve the accuracy of voice recognition based on the characteristics of a user's voice. The generation AI unit can also store the characteristics of a user's voice in the cloud and share them with other devices. This allows the generation AI unit to learn the characteristics of a user's voice and provide voice recognition optimized for each individual user.

[0038] The generation AI unit can learn the user's vision and hearing status and adjust the screen display and audio output. The generation AI unit can, for example, learn the user's vision and hearing status and adjust the screen display and audio output. For example, it can display larger text size for a user with poor vision. It can also increase the volume for a user with poor hearing. The generation AI unit can also save the user's vision and hearing status in the cloud and share it with other devices. This allows it to adjust the screen display and audio output by learning the user's vision and hearing status.

[0039] The generation AI unit can monitor the user's physical condition and provide advice based on the health condition. The generation AI unit, for example, monitors the user's physical condition and provides advice according to the health condition. For example, it measures the user's heart rate and blood pressure and provides health management advice. The generation AI unit can also provide advice on exercise and diet based on the user's physical condition. The generation AI unit can also store the user's physical condition in the cloud and share it with other devices. This allows the user's physical condition to be monitored and advice according to the health condition to be provided.

[0040] The generation AI unit can work with the user's family and caregivers to share support information. The generation AI unit can, for example, work with the user's family and caregivers to share support information. For example, it can notify the family and caregivers of the user's health condition and schedule. The generation AI unit can also work with the user's family and caregivers to respond quickly in emergencies. The generation AI unit can also store the user's support information in the cloud and share it with other devices. This allows the system to work with the user's family and caregivers to share support information.

[0041] The generation AI unit can learn the user's schedule and set reminders. For example, the generation AI unit can learn the user's schedule and automatically set reminders. For example, if the user takes their medicine at the same time every day, the generation AI unit can set a reminder at that time. The generation AI unit can also remind the user of important appointments based on the user's schedule. The generation AI unit can also save the user's schedule in the cloud and share it with other devices. This allows the generation AI unit to learn the user's schedule and automatically set reminders.

[0042] The generation AI unit can learn from the user's diet and exercise records and provide health management advice. For example, when a user records their meals, the generation AI unit can provide advice on nutritional balance. The generation AI unit can also suggest an appropriate exercise plan based on the user's exercise records. The generation AI unit can also save the user's diet and exercise records in the cloud and share them with other devices. This allows the generation AI unit to learn from the user's diet and exercise records and provide health management advice.

[0043] The generation AI unit works in conjunction with the user's home appliances and allows them to be operated by voice. The generation AI unit works in conjunction with the user's home appliances and allows them to be operated by voice. For example, if the user says, "Turn on the TV," the generation AI unit will turn on the TV. The generation AI unit can also monitor the status of the user's home appliances and perform appropriate operations. The generation AI unit can also store information about the user's home appliances in the cloud and share it with other devices. This allows the unit to work in conjunction with the user's home appliances and allow them to be operated by voice.

[0044] The generation AI unit can provide information based on the user's hobbies and interests. For example, the generation AI unit can provide event information based on the user's hobbies and interests. For example, if the user is interested in music, the generation AI unit can provide concert information. The generation AI unit can also provide related news articles and product information based on the user's hobbies and interests. The generation AI unit can also store the user's hobbies and interests in the cloud and share them with other devices. This makes it possible to provide information based on the user's hobbies and interests.

[0045] The generation AI unit can monitor the user's location information and transmit the location information in an emergency. For example, the generation AI unit can constantly monitor the user's location information and automatically transmit the location information in an emergency. For example, when the user says "help," the generation AI unit transmits the location information to an emergency contact. The generation AI unit can also contact emergency services based on the user's location information. The generation AI unit can also store the user's location information in the cloud and share it with other devices. This allows the user's location information to be constantly monitored and automatically transmitted in an emergency.

[0046] The generation AI unit can work with the user's family and friends to contact them in an emergency. The generation AI unit can, for example, work with the user's family and friends to automatically contact them in an emergency. For example, when the user says "help," the generation AI unit automatically contacts family and friends. The generation AI unit can also take appropriate action based on the user's emergency contact information. The generation AI unit can also save the user's emergency contact information in the cloud and share it with other devices. This allows the system to work with the user's family and friends to automatically contact them in an emergency.

[0047] The generation AI unit can manage the user's medical information and provide it to medical institutions in the event of an emergency. The generation AI unit can, for example, manage the user's medical information and provide it to medical institutions in the event of an emergency. For example, when the user says "help," the generation AI unit sends the user's medical information to a medical institution. The generation AI unit can also take appropriate action based on the user's medical information. The generation AI unit can also store the user's medical information in the cloud and share it with other devices. This allows the user's medical information to be managed and provided to medical institutions in the event of an emergency.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] New types of smartphones can monitor users' health status and provide health management advice. For example, they can measure heart rate and blood pressure and, if abnormalities are detected, prompt users to contact a medical institution. They can also record daily exercise volume and suggest appropriate exercise plans. They can also record meals and provide advice on nutritional balance. This can support users' health management and improve their quality of life.

[0050] New types of smartphones can provide information about nearby tourist spots and events based on the user's location information. For example, if the user is in a tourist spot, information about nearby tourist spots and restaurants can be provided. Also, if the user is at an event venue, detailed information about the event and its schedule can be provided. Furthermore, it is possible to provide real-time traffic and weather information based on the user's location information. This makes it possible to provide useful information based on the user's location information.

[0051] New types of smartphones can provide news articles based on a user's hobbies and interests. For example, if a user is interested in sports, they can provide the latest sports news. Or, if a user is interested in technology, they can provide the latest technology news. Furthermore, they can recommend related blog articles and videos based on the user's hobbies and interests. This makes it possible to provide information that matches the user's interests.

[0052] New types of smartphones can support schedule management based on the user's calendar information. For example, they can set reminders for meetings and appointments and notify the user. They can also update the schedule in real time and notify the user if there are any changes. They can also suggest optimal schedules based on the user's calendar information. This allows for efficient support of the user's schedule management.

[0053] New types of smartphones can connect with users' social media accounts and provide relevant information. For example, they can notify users of their friends' latest posts and event information. They can also recommend related groups and pages based on the user's interests. They can also analyze users' social media activity and provide appropriate content. This can improve users' social media experience.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit can recognize what the user says with high accuracy. The voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the user's voice. The voice recognition unit also supports multiple languages ​​and can recognize voices in different languages. Step 2: The generation AI unit analyzes the speech recognized by the speech recognition unit. For example, the generation AI unit converts the speech into text using a text generation AI (e.g., LLM) and analyzes its content. The generation AI unit can also use a multimodal generation AI to analyze multiple modalities, such as speech and images. The generation AI unit can also use natural language processing technology to understand the meaning of the speech and generate an appropriate response. Step 3: The answer generation unit generates an appropriate answer based on the voice analyzed by the generation AI unit. For example, the answer generation unit obtains information from the Internet and generates an appropriate answer to the user's question. The answer generation unit can also learn the user's past question history and generate an answer optimized for each individual user. The answer generation unit can also analyze the user's tone and speed of voice and estimate their emotional state to generate an appropriate answer. Step 4: The notification unit notifies the user of the answer generated by the answer generation unit. For example, the notification unit may notify the user of the answer using a voice notification. The notification unit may also display the answer on the screen using a text notification. The notification unit may also notify the user of the answer using a vibration notification.

[0056] (Example 2) The new type of smartphone according to the embodiment of the present invention utilizes conversation-based generation AI that allows users to answer questions simply by talking to the smartphone, and is a system that returns answers as if having a conversation, without the need for searching the internet. This makes the new type of smartphone easy to use for the elderly, and can provide a comfortable lifestyle.

[0057] A novel type of smartphone according to an embodiment includes a speech recognition unit, a generation AI unit, a response generation unit, and a notification unit. The speech recognition unit recognizes a user's speech. For example, the speech recognition unit can recognize what the user says with high accuracy. The speech recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the user's speech. The speech recognition unit supports multiple languages ​​and can recognize speech in different languages. The generation AI unit analyzes the speech recognized by the speech recognition unit. For example, the generation AI unit converts speech into text using a text generation AI (e.g., LLM) and analyzes the text. The generation AI unit can also analyze multiple modalities, such as speech and images, using a multimodal generation AI. The generation AI unit can also understand the meaning of speech and generate an appropriate response using natural language processing technology. The response generation unit generates an appropriate response based on the speech analyzed by the generation AI unit. For example, the response generation unit obtains information from the Internet and generates an appropriate response in response to a user's question. The answer generation unit can also learn the user's past question history and generate answers optimized for each individual user. The answer generation unit can also analyze the user's tone and speed of voice and estimate the user's emotional state to generate an appropriate answer. The notification unit notifies the user of the answer generated by the answer generation unit. For example, the notification unit can convey the answer to the user using a voice notification. The notification unit can also display the answer on the screen using a text notification. The notification unit can also notify the user of the answer using a vibration notification. As a result, the new type of smartphone according to the embodiment can provide a system that is easy for the elderly to use by generating and notifying an appropriate answer based on the user's voice.

[0058] The generation AI unit can learn the user's past question history and provide answers that are appropriate for the user. For example, the generation AI unit analyzes the user's past question history and provides optimal answers to frequently asked questions. For example, if a user asks about the weather every morning, the generation AI unit learns that pattern and provides weather information before the user even speaks. The generation AI unit can also understand the user's interests and concerns based on the user's past question history and provide answers based on that. The generation AI unit can also save the user's past question history in the cloud and share it with other devices. This allows the unit to learn the user's past question history and provide answers that are optimized for each individual user.

[0059] The generation AI unit can analyze the user's voice tone and speed, estimate their emotional state, and generate a response. For example, the generation AI unit can analyze the user's voice tone and speed in real time to estimate their emotional state. For example, if the user is in a hurry, the generation AI unit can provide a short, concise response. The generation AI unit can also classify the user's emotional state based on the user's voice tone and speed and generate a response accordingly. The generation AI unit can also store the user's voice tone and speed in the cloud and share it with other devices. This allows the generation AI unit to provide an appropriate response according to the user's emotional state by analyzing the user's voice tone and speed.

[0060] The generation AI unit can use the emotion estimation function to analyze the emotions a user feels when asking a question and generate an answer that elicits those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions a user feels when asking a question in real time and generate an answer that elicits positive emotions. For example, if the user is feeling anxious, the generation AI unit can provide an answer that gives a sense of security. The generation AI unit can also generate an answer that improves the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's emotions and provide an answer that elicits positive emotions.

[0061] The generation AI unit can support multiple languages ​​and answer questions in different languages. The generation AI unit, for example, can support multiple languages ​​and provide instant answers even when users ask questions in different languages. For example, the generation AI unit supports major languages ​​such as English, Spanish, and Chinese. The generation AI unit can also generate answers in an appropriate language based on the user's language settings. The generation AI unit can also store the user's language history in the cloud and share it with other devices. This allows the system to support multiple languages ​​and provide instant answers to questions in different languages.

[0062] The generation AI unit can learn the user's hobbies and interests and provide related information. For example, the generation AI unit can learn the user's hobbies and interests and automatically provide related information. For example, if the user is a movie lover, the generation AI unit can provide the latest movie information. The generation AI unit can also provide event information and news articles based on the user's hobbies and interests. The generation AI unit can also save the user's hobbies and interests in the cloud and share them with other devices. This allows the generation AI unit to automatically provide related information by learning the user's hobbies and interests.

[0063] The generation AI unit can use the emotion estimation function to analyze the emotions of the user when asking a question in real time and provide an answer based on those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of the user when asking a question in real time and provide an answer based on those emotions. For example, if the user is angry, the generation AI unit will respond in a calm tone. The generation AI unit can also provide an answer that soothes the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's emotions in real time and provide an answer based on those emotions.

[0064] The generation AI unit can learn the user's past search history and provide search results. The generation AI unit, for example, learns the user's past search history and provides predicted search results. For example, it can automatically display the latest information on topics that the user frequently searches for. The generation AI unit can also prioritize displaying related search results based on the user's search history. The generation AI unit can also save the user's search history in the cloud and share it with other devices. This allows it to provide predicted search results by learning the user's past search history.

[0065] The generation AI unit can provide information in real time based on the user's location information. The generation AI unit provides optimal information in real time based on the user's location information, for example. For example, if a user asks for restaurant information around their current location, the generation AI unit will display nearby restaurants. The generation AI unit can also provide traffic information and weather information based on the user's location information. The generation AI unit can also store the user's location information in the cloud and share it with other devices. This allows the generation AI unit to provide optimal information in real time based on the user's location information.

[0066] The generation AI unit can use the emotion estimation function to analyze the emotions a user feels when searching and provide search results based on those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions a user feels when searching in real time and provide search results that correspond to those emotions. For example, if a user wants to relax, the generation AI unit can provide information on how to relax. The generation AI unit can also provide search results that match the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's emotions and provide search results that correspond to those emotions.

[0067] The generation AI unit can refer to the user's calendar information and provide information based on the schedule. For example, if the user asks about a meeting schedule, the generation AI unit displays the meeting details from the calendar. The generation AI unit can also set reminders based on the user's schedule. The generation AI unit can also store the user's calendar information in the cloud and share it with other devices. This allows the generation AI unit to refer to the user's calendar information and provide information based on the schedule.

[0068] The generation AI unit can analyze a user's social media account and provide information. For example, the generation AI unit can analyze a user's social media account and provide relevant information. For example, if a user asks about their friends' latest posts, the generation AI unit can display that information. The generation AI unit can also provide information that matches the user's interests and concerns based on the user's social media account. The generation AI unit can also store the user's social media account in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's social media account and provide relevant information.

[0069] The generation AI unit can use the emotion estimation function to analyze the emotions of users when searching in real time and provide search results based on those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of users when searching in real time and provide search results based on those emotions. For example, if a user wants to relax, the generation AI unit can provide information on how to relax. The generation AI unit can also provide search results that match the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's emotions in real time and provide search results based on those emotions.

[0070] The generation AI unit can learn the characteristics of a user's voice and provide voice recognition for each individual user. The generation AI unit, for example, learns the characteristics of a user's voice and provides voice recognition optimized for each individual user. For example, it learns the tone and accent of a user's voice to achieve accurate voice recognition. The generation AI unit can also improve the accuracy of voice recognition based on the characteristics of a user's voice. The generation AI unit can also store the characteristics of a user's voice in the cloud and share them with other devices. This allows the generation AI unit to learn the characteristics of a user's voice and provide voice recognition optimized for each individual user.

[0071] The generation AI unit can learn the user's vision and hearing status and adjust the screen display and audio output. The generation AI unit can, for example, learn the user's vision and hearing status and adjust the screen display and audio output. For example, it can display larger text size for a user with poor vision. It can also increase the volume for a user with poor hearing. The generation AI unit can also save the user's vision and hearing status in the cloud and share it with other devices. This allows it to adjust the screen display and audio output by learning the user's vision and hearing status.

[0072] The generation AI unit can use the emotion estimation function to analyze the emotions of the user when operating the device and provide an interface based on those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of the user when operating the device in real time and provide an interface that corresponds to those emotions. For example, if the user is feeling stressed, it can provide a simple interface. The generation AI unit can also provide an interface that matches the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This makes it possible to analyze the user's emotions and provide an interface that corresponds to those emotions.

[0073] The generation AI unit can monitor the user's physical condition and provide advice based on the health condition. The generation AI unit, for example, monitors the user's physical condition and provides advice according to the health condition. For example, it measures the user's heart rate and blood pressure and provides health management advice. The generation AI unit can also provide advice on exercise and diet based on the user's physical condition. The generation AI unit can also store the user's physical condition in the cloud and share it with other devices. This allows the user's physical condition to be monitored and advice according to the health condition to be provided.

[0074] The generation AI unit can work with the user's family and caregivers to share support information. The generation AI unit can, for example, work with the user's family and caregivers to share support information. For example, it can notify the family and caregivers of the user's health condition and schedule. The generation AI unit can also work with the user's family and caregivers to respond quickly in emergencies. The generation AI unit can also store the user's support information in the cloud and share it with other devices. This allows the system to work with the user's family and caregivers to share support information.

[0075] The generation AI unit can use the emotion estimation function to analyze the emotions of the user when operating the device in real time and provide an interface based on those emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of the user when operating the device in real time and provide an interface that corresponds to those emotions. For example, if the user is feeling stressed, it can provide a simple interface. The generation AI unit can also provide an interface that matches the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the user's emotions in real time and provide an interface that corresponds to those emotions.

[0076] The generation AI unit can learn the user's schedule and set reminders. For example, the generation AI unit can learn the user's schedule and automatically set reminders. For example, if the user takes their medicine at the same time every day, the generation AI unit can set a reminder at that time. The generation AI unit can also remind the user of important appointments based on the user's schedule. The generation AI unit can also save the user's schedule in the cloud and share it with other devices. This allows the generation AI unit to learn the user's schedule and automatically set reminders.

[0077] The generation AI unit can learn from the user's diet and exercise records and provide health management advice. For example, when a user records their meals, the generation AI unit can provide advice on nutritional balance. The generation AI unit can also suggest an appropriate exercise plan based on the user's exercise records. The generation AI unit can also save the user's diet and exercise records in the cloud and share them with other devices. This allows the generation AI unit to learn from the user's diet and exercise records and provide health management advice.

[0078] The generation AI unit can use the emotion estimation function to analyze the stress the user feels in their daily life and suggest relaxation methods. The generation AI unit, for example, uses the emotion estimation function to analyze the stress the user feels in their daily life in real time and suggest relaxation methods. For example, if the user is feeling stressed, the generation AI unit can suggest relaxation methods. The generation AI unit can also suggest relaxation methods to ease the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the stress the user feels in their daily life and suggest relaxation methods.

[0079] The generation AI unit works in conjunction with the user's home appliances and allows them to be operated by voice. The generation AI unit works in conjunction with the user's home appliances and allows them to be operated by voice. For example, if the user says, "Turn on the TV," the generation AI unit will turn on the TV. The generation AI unit can also monitor the status of the user's home appliances and perform appropriate operations. The generation AI unit can also store information about the user's home appliances in the cloud and share it with other devices. This allows the unit to work in conjunction with the user's home appliances and allow them to be operated by voice.

[0080] The generation AI unit can provide information based on the user's hobbies and interests. For example, the generation AI unit can provide event information based on the user's hobbies and interests. For example, if the user is interested in music, the generation AI unit can provide concert information. The generation AI unit can also provide related news articles and product information based on the user's hobbies and interests. The generation AI unit can also store the user's hobbies and interests in the cloud and share them with other devices. This makes it possible to provide information based on the user's hobbies and interests.

[0081] The generation AI unit can use the emotion estimation function to analyze the stress the user feels in their daily life in real time and suggest relaxation methods. The generation AI unit can, for example, use the emotion estimation function to analyze the stress the user feels in their daily life in real time and suggest relaxation methods. For example, if the user is feeling stressed, the generation AI unit can suggest relaxation methods. The generation AI unit can also suggest relaxation methods to ease the user's mood based on the user's emotional state. The generation AI unit can also save the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the stress the user feels in their daily life in real time and suggest relaxation methods.

[0082] The generation AI unit can monitor the user's location information and transmit the location information in an emergency. For example, the generation AI unit can constantly monitor the user's location information and automatically transmit the location information in an emergency. For example, when the user says "help," the generation AI unit transmits the location information to an emergency contact. The generation AI unit can also contact emergency services based on the user's location information. The generation AI unit can also store the user's location information in the cloud and share it with other devices. This allows the user's location information to be constantly monitored and automatically transmitted in an emergency.

[0083] The generation AI unit can use the emotion estimation function to analyze the anxiety a user feels in an emergency and provide a message that gives a sense of security. The generation AI unit, for example, uses the emotion estimation function to analyze the anxiety a user feels in an emergency in real time and provide a message that gives a sense of security. For example, if the user feels anxious, the generation AI unit can provide a message such as "Help will be on its way soon." The generation AI unit can also provide a message that soothes the user's mood based on the user's emotional state. The generation AI unit can also store the user's emotional state in the cloud and share it with other devices. This allows the generation AI unit to analyze the anxiety a user feels in an emergency and provide a message that gives a sense of security.

[0084] The generation AI unit can work with the user's family and friends to contact them in an emergency. The generation AI unit can, for example, work with the user's family and friends to automatically contact them in an emergency. For example, when the user says "help," the generation AI unit automatically contacts family and friends. The generation AI unit can also take appropriate action based on the user's emergency contact information. The generation AI unit can also save the user's emergency contact information in the cloud and share it with other devices. This allows the system to work with the user's family and friends to automatically contact them in an emergency.

[0085] The generation AI unit can manage the user's medical information and provide it to medical institutions in the event of an emergency. The generation AI unit can, for example, manage the user's medical information and provide it to medical institutions in the event of an emergency. For example, when the user says "help," the generation AI unit sends the user's medical information to a medical institution. The generation AI unit can also take appropriate action based on the user's medical information. The generation AI unit can also store the user's medical information in the cloud and share it with other devices. This allows the user's medical information to be managed and provided to medical institutions in the event of an emergency.

[0086] The generation AI unit can use the emotion estimation function to analyze the anxiety a user feels in an emergency in real time and provide a message that gives a sense of security. The generation AI unit can, for example, use the emotion estimation function to analyze the anxiety a user feels in an emergency in real time and provide a message that gives a sense of security. For example, if the user feels anxious, the generation AI unit can provide a message such as "Help will be on its way soon." The generation AI unit can also provide a message that soothes the user's mood based on the user's emotional state. The generation AI unit can also store the user's emotional state in the cloud and share it with other devices. This allows the system to analyze the anxiety a user feels in an emergency in real time and provide a message that gives a sense of security.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] New types of smartphones can monitor users' health status and provide health management advice. For example, they can measure heart rate and blood pressure and, if abnormalities are detected, prompt users to contact a medical institution. They can also record daily exercise volume and suggest appropriate exercise plans. They can also record meals and provide advice on nutritional balance. This can support users' health management and improve their quality of life.

[0089] A new type of smartphone can estimate a user's emotions and recommend music based on those emotions. For example, if a user feels like relaxing, it can recommend relaxing music. If a user feels like cheering up, it can recommend upbeat music. It can also automatically generate a music playlist based on the user's emotional state. This allows the user to enjoy a music experience tailored to their emotions.

[0090] New types of smartphones can provide information about nearby tourist spots and events based on the user's location information. For example, if the user is in a tourist spot, information about nearby tourist spots and restaurants can be provided. Also, if the user is at an event venue, detailed information about the event and its schedule can be provided. Furthermore, it is possible to provide real-time traffic and weather information based on the user's location information. This makes it possible to provide useful information based on the user's location information.

[0091] A new type of smartphone can estimate a user's emotions and set reminders based on the user's emotions. For example, if a user feels stressed, it can set a reminder to relax. If a user feels busy, it can set a reminder to take a break. It can also set reminders based on the user's emotional state to ensure that important appointments are not forgotten. This makes it possible to provide reminders that correspond to the user's emotions.

[0092] New types of smartphones can provide news articles based on a user's hobbies and interests. For example, if a user is interested in sports, they can provide the latest sports news. Or, if a user is interested in technology, they can provide the latest technology news. Furthermore, they can recommend related blog articles and videos based on the user's hobbies and interests. This makes it possible to provide information that matches the user's interests.

[0093] A new type of smartphone can estimate a user's emotions and provide feedback based on those emotions. For example, if the user is feeling anxious, it can provide reassuring feedback. If the user is happy, it can also provide empathetic feedback. It can also provide appropriate advice or encouraging messages based on the user's emotional state. This makes it possible to provide feedback that corresponds to the user's emotions.

[0094] New types of smartphones can support schedule management based on the user's calendar information. For example, they can set reminders for meetings and appointments and notify the user. They can also update the schedule in real time and notify the user if there are any changes. They can also suggest optimal schedules based on the user's calendar information. This allows for efficient support of the user's schedule management.

[0095] A new type of smartphone can estimate a user's emotions and provide an exercise plan based on those emotions. For example, if a user is feeling stressed, a relaxing yoga or stretching plan can be provided. If a user is feeling energetic, a high-intensity exercise plan can be provided. Furthermore, it can also suggest appropriate exercise timing based on the user's emotional state. This makes it possible to provide an exercise plan that matches the user's emotions.

[0096] New types of smartphones can connect with users' social media accounts and provide relevant information. For example, they can notify users of their friends' latest posts and event information. They can also recommend related groups and pages based on the user's interests. They can also analyze users' social media activity and provide appropriate content. This can improve users' social media experience.

[0097] New types of smartphones can estimate a user's emotions and provide emotionally-based mental health support. For example, if a user is feeling down, they can provide encouraging messages and relaxation tips. If a user is feeling stressed, they can provide advice on how to relieve stress. Furthermore, based on the user's emotional state, they can provide mental health resources and contact information for experts. This can support the user's mental health.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit can recognize what the user says with high accuracy. The voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the user's voice. The voice recognition unit also supports multiple languages ​​and can recognize voices in different languages. Step 2: The generation AI unit analyzes the speech recognized by the speech recognition unit. For example, the generation AI unit converts the speech into text using a text generation AI (e.g., LLM) and analyzes its content. The generation AI unit can also use a multimodal generation AI to analyze multiple modalities, such as speech and images. The generation AI unit can also use natural language processing technology to understand the meaning of the speech and generate an appropriate response. Step 3: The answer generation unit generates an appropriate answer based on the voice analyzed by the generation AI unit. For example, the answer generation unit obtains information from the Internet and generates an appropriate answer to the user's question. The answer generation unit can also learn the user's past question history and generate an answer optimized for each individual user. The answer generation unit can also analyze the user's tone and speed of voice and estimate their emotional state to generate an appropriate answer. Step 4: The notification unit notifies the user of the answer generated by the answer generation unit. For example, the notification unit may notify the user of the answer using a voice notification. The notification unit may also display the answer on the screen using a text notification. The notification unit may also notify the user of the answer using a vibration notification.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a speech recognition unit that recognizes a user's speech; a generation AI unit that analyzes the voice recognized by the voice recognition unit; an answer generation unit that generates an answer based on the voice analyzed by the generation AI unit; a notification unit that notifies the user of the answer generated by the answer generation unit. A system characterized by:

2. The generation AI unit Supports multiple languages ​​and can answer questions in different languages 2. The system of claim 1.

3. The generation AI unit Learn the user's past search history and provide search results 2. The system of claim 1.

4. The generation AI unit Learns the characteristics of the user's voice and provides voice recognition for each individual user 2. The system of claim 1.

5. The generation AI unit Learn the user's schedule and set reminders 2. The system of claim 1.

6. The generation AI unit Analyzing the anxiety felt by the user in an emergency and providing a message 2. The system of claim 1.

7. The generation AI unit Analyzing the tone and speed of the user's voice to estimate their emotional state and generate a response 2. The system of claim 1.

8. The generation AI unit Analyzing the user's emotions when searching and providing emotion-based search results 2. The system of claim 1.

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