System

Abstract

An object of a system according to an embodiment is to allow an elderly person to easily ask a question in a natural language and to provide an appropriate answer.SOLUTION: A system includes a question understanding unit, an answer generation unit, an interface unit, a daily life support unit, and a cooperation unit. The question understanding unit understands a question in natural language from the elderly person. The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. The interface unit provides the elderly person with the answer generated by the answer generation unit. The daily life supporting unit supports the daily life of the elderly person. The cooperation unit strengthens cooperation with family members and caregivers.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, it was difficult for elderly people to use smartphones effectively, and there was a lack of systems that could properly understand and answer questions.

[0005] The system according to the embodiment aims to make it easy for elderly people to ask questions in natural language and to provide appropriate answers. [Means for solving the problem]

[0006] The system according to the embodiment includes a question understanding unit, an answer generation unit, an interface unit, a daily life support unit, and a collaboration unit. The question understanding unit understands questions in natural language from the elderly. The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. The interface unit provides the elderly with the answer generated by the answer generation unit. The daily life support unit supports the elderly in their daily lives. The collaboration unit strengthens collaboration with family members and caregivers. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy for elderly people to ask questions in natural language and can provide appropriate answers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A concierge service system according to an embodiment of the present invention is a system that uses generative AI to understand questions and provide appropriate answers to help elderly people master smartphones. As a result, the concierge service system can help elderly people master smartphones and improve the quality of their daily lives.

[0029] A concierge service system according to an embodiment includes a question understanding unit, an answer generation unit, an interface unit, a daily life support unit, and a linking unit. The question understanding unit understands natural language questions from elderly people. For example, the question understanding unit analyzes the questions using natural language processing technology and understands their content. The question understanding unit can also estimate the intent of the questions using machine learning algorithms. The question understanding unit can also analyze questions entered through voice input using speech recognition technology. For example, the question understanding unit converts voice input into text using speech recognition technology and analyzes the text. The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. For example, the answer generation unit searches for related information using a database search and generates an answer. The answer generation unit can also generate an answer to a question using a generative model. The answer generation unit can also automatically generate related video tutorials or interactive guides based on the content of the question to provide answers that are visually easy to understand. For example, the answer generation unit generates video tutorials using video editing software. The interface unit provides the answers generated by the answer generation unit to the elderly people. For example, the interface unit provides answers by voice using a voice interface. The interface unit can also display answers using a touch screen. The interface unit can also dynamically change the design and color tone of the interface according to the user's emotional state using an emotion estimation function. For example, the interface unit changes the color tone to a calmer tone if the user is feeling anxious. The daily life support unit supports the elderly person's daily life. For example, the daily life support unit reminds the elderly person to take their medicine. The daily life support unit can also manage schedules. The daily life support unit can also provide health advice. For example, the daily life support unit monitors health data, analyzes the data, and provides advice according to the elderly person's health condition. The collaboration unit strengthens collaboration with family members and caregivers. For example, the collaboration unit notifies the family members and caregivers of the elderly person's activity status and health condition. The collaboration unit can also provide a function to automatically contact them in an emergency.For example, the linking unit automatically contacts a medical institution in an emergency. As a result, the concierge service system according to the embodiment can help elderly people master smartphone use and improve the quality of their daily lives. For example, the generating AI can provide appropriate answers to questions from elderly people, making it easier to operate a smartphone. The generating AI can also support the elderly in their daily lives and create an environment in which they can live with peace of mind. Furthermore, the generating AI can strengthen collaboration with family members and caregivers to ensure the safety of the elderly.

[0030] The answer generation unit automatically generates a relevant video tutorial or interactive guide based on the content of the question, thereby providing an answer that is visually easy to understand. For example, when an elderly person inputs a question, the answer generation unit uses the generation AI to automatically generate a relevant video tutorial based on the content of the question, thereby providing an answer that is visually easy to understand. For example, it generates a video that explains how to use an app. The answer generation unit also automatically generates an interactive guide based on the content of the question, thereby providing an answer that is visually easy to understand. For example, it generates a guide that shows operation procedures step by step. The answer generation unit also automatically generates a relevant video tutorial or interactive guide based on the content of the elderly person's question, thereby providing an answer that is visually easy to understand. For example, it generates a video that explains how to set up the app. This makes it possible to provide an answer that is visually easy to understand.

[0031] The answer generation unit can generate a more personalized answer by taking into account background information of the question. The answer generation unit, for example, takes into account the elderly person's past question history, allowing the generation AI to generate a more personalized answer. For example, related information is provided based on the content of previous questions. The answer generation unit also takes into account the elderly person's usage status, allowing the generation AI to generate a more personalized answer. For example, detailed operating instructions are provided for questions about frequently used apps. The answer generation unit also takes into account background information of the question, allowing the generation AI to generate a more personalized answer. For example, appropriate advice is provided based on past question history and usage status. This makes it possible to provide a more personalized answer.

[0032] The question understanding unit combines speech recognition technology and can also handle questions entered by voice. For example, when an elderly person inputs a question by voice, the generation AI uses speech recognition technology to analyze the content of the question and generate an appropriate answer. For example, a voice question such as "Please tell me how to use this app." The question understanding unit also combines speech recognition technology to enable elderly people to also handle questions entered by voice. For example, a question can be entered by voice, and the generation AI analyzes the content and provides an answer. The question understanding unit also combines speech recognition technology with the question understanding and answer generation processes to enable elderly people to enter questions by voice. For example, a question can be entered by voice, and the generation AI analyzes the content and generates an answer. This makes it possible to also handle questions entered by voice.

[0033] The question understanding unit can automatically translate the content of the question into other languages ​​and provide a multilingual concierge service. For example, when an elderly person inputs a question, the generation AI automatically translates the content into other languages ​​and provides a multilingual concierge service. For example, a question in Japanese is translated into English and an answer is provided. The question understanding unit can also automatically translate the content of the question into other languages ​​to provide a multilingual concierge service. For example, a question in English is translated into Japanese and an answer is provided. The question understanding unit can also automatically translate the content of the elderly person's question into other languages ​​to provide a multilingual concierge service. For example, a question in French is translated into Spanish and an answer is provided. This makes it possible to provide a multilingual concierge service.

[0034] The interface unit can improve the reliability of operation by adding tactile feedback and providing a physical sensation during operation. The interface unit can improve the reliability of operation by providing tactile feedback when, for example, an elderly person operates the interface. For example, a vibration can be felt when a button is pressed. The interface unit can also add tactile feedback to the interface and provide a physical sensation during operation. For example, a resistance can be felt when operating a slider. The interface unit can also use tactile feedback to improve the reliability of operation by an elderly person operates the interface. For example, a vibration can be felt when operating a touch screen. This can improve the reliability of operation.

[0035] The interface unit can provide customization options according to the user's visual acuity or hearing ability to meet individual needs. The interface unit can provide customization options according to the visual acuity or hearing ability of, for example, elderly people, and individually adjust the interface. For example, it can make it possible to adjust the text size or volume. The interface unit can also add customization options to the interface and make it possible to adjust it according to the user's visual acuity or hearing ability. For example, it can make it possible to adjust the contrast or audio output. The interface unit can also provide customization options according to the user's visual acuity or hearing ability to individually adjust the interface. For example, it can provide color vision correction or audio guidance. This makes it possible to provide customization options according to the user's visual acuity or hearing ability.

[0036] The interface unit can incorporate gesture recognition technology to provide input methods other than touch operation. The interface unit incorporates gesture recognition technology to enable, for example, elderly people to operate the interface by methods other than touch operation. For example, scrolling the screen with hand movements. The interface unit also incorporates gesture recognition technology to provide input methods other than touch operation. For example, launching an app with hand movements. The interface unit also uses gesture recognition technology to enable elderly people to operate the interface by methods other than touch operation. For example, selecting a menu with hand movements. This makes it possible to provide input methods other than touch operation.

[0037] The interface unit can be linked with other devices such as a smartwatch or smartglasses to achieve multi-device compatibility. For example, the interface unit links the interface with a smartwatch, allowing elderly people to operate the interface on devices other than smartphones. For example, notifications can be checked on the smartwatch. The interface unit also links the interface with smartglasses to achieve multi-device compatibility. For example, navigation information can be displayed on the smartglasses. The interface unit also links the interface with other devices so that elderly people can operate the interface on devices other than smartphones. For example, voice input can be performed on the smartwatch. This allows multi-device compatibility to be achieved.

[0038] The daily life support unit can combine monitoring and analysis of health data to provide advice according to the health condition. For example, the daily life support unit monitors the health data of an elderly person and analyzes the data to provide advice according to the health condition. For example, health advice is provided based on blood pressure and heart rate. The daily life support unit also combines the monitoring of health data with the daily life support function to provide advice according to the health condition. For example, exercise advice is provided based on the number of steps taken and the amount of exercise. The daily life support unit also monitors health data and analyzes the data to provide advice according to the health condition of the elderly person. For example, advice on improving sleep is provided based on sleep data. This makes it possible to provide advice according to the health condition.

[0039] The daily life support unit can learn the lifestyle patterns of the elderly person and provide support in advance based on predicted needs. For example, the daily life support unit learns the lifestyle patterns of the elderly person and provides support in advance based on predicted needs. For example, it reminds the elderly person to take their medicine every morning. Furthermore, the daily life support unit uses a generation AI to learn the lifestyle patterns of the elderly person and provide support in advance based on predicted needs. For example, it reminds the elderly person to exercise regularly. Furthermore, the daily life support unit learns the lifestyle patterns of the elderly person and provides support in advance based on predicted needs. For example, it provides nutritional advice based on meal times. This makes it possible to provide support in advance based on predicted needs.

[0040] The daily life support unit can work in conjunction with smart home devices to automatically adjust the home environment. For example, the daily life support unit links the daily life support function with smart home devices to automatically adjust the home environment. For example, automatically adjusting the room temperature. Furthermore, the daily life support unit has the generation AI work in conjunction with smart home devices to automatically adjust the home environment. For example, automatically adjusting the brightness of the lights. Furthermore, the daily life support unit links the daily life support function with smart home devices to automatically adjust the home environment. For example, automating the opening and closing of curtains. In this way, the home environment can be automatically adjusted.

[0041] The daily life support unit can provide comprehensive support in cooperation with local community services and medical institutions. For example, the daily life support unit can link the support function of the generative AI with local community services to provide comprehensive support. For example, it can provide information about local events. The daily life support unit can also link the support function with medical institutions to support health management for the elderly. For example, it can provide reminders for regular health checks. The daily life support unit can also link the support function of the generative AI with local community services and medical institutions to provide comprehensive support. For example, it can automatically contact medical institutions in the event of an emergency. This makes it possible to provide comprehensive support.

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

[0043] The question understanding unit can take into account the context behind the question when analyzing the content of a user's question. For example, the question understanding unit can refer to questions the user has previously asked and the situation at that time to provide a more appropriate answer. The question understanding unit can also take into account the user's current location information to provide a location-based answer. For example, if the user is in a specific location, it can provide information related to that location. The question understanding unit can also take into account the user's device usage history to provide an answer based on past usage patterns. This makes it possible to provide an answer to the user's question that is more contextual.

[0044] The answer generator can provide multiple answer options for a user's question. For example, the answer generator can present multiple answers from different perspectives for the user to choose from. The answer generator can also allow the user to select the format of the answer according to their preferences. For example, the answer can be provided in different formats, such as text, audio, or video. The answer generator can also continuously improve the quality of the answer based on user feedback. This allows the user to be provided with more diverse and flexible answers.

[0045] The interface unit can learn the user's operation history and perform customization to improve operation efficiency. For example, the interface unit can prioritize displaying functions that the user uses frequently. The interface unit can also analyze the user's operation patterns and simplify operations. For example, it can make frequently performed operations possible with one tap. The interface unit can also continuously improve the interface design and functions based on user feedback. This can improve the user's operation experience.

[0046] The daily life support unit can provide reminders and notifications at optimal times based on the user's lifestyle rhythm. For example, the daily life support unit can analyze the user's sleep patterns and provide important reminders after waking up. The daily life support unit can also monitor the user's activity level and provide exercise reminders at appropriate times. For example, if the user has been sitting for a long time, the unit can send a notification encouraging the user to stand up and walk. The daily life support unit can also analyze the user's eating patterns and provide nutritional advice based on meal times. This makes it possible to provide support tailored to the user's lifestyle rhythm.

[0047] The linking unit can provide a function to facilitate communication with the user's family and caregivers. For example, the linking unit periodically reports the user's activity status and health condition to the family and caregivers. The linking unit can also provide a function that allows the user to easily contact the family and caregivers in an emergency. For example, the linking unit can provide a function that allows the user to send an emergency message with one tap. The linking unit can also provide a dashboard that allows the family and caregivers to check the user's status in real time. This can strengthen collaboration to ensure the user's safety and security.

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

[0049] Step 1: The question understanding unit understands natural language questions from the elderly. For example, the question understanding unit uses natural language processing technology to analyze the question and understand its content. The question understanding unit can also use machine learning algorithms to estimate the intent of the question. Furthermore, the question understanding unit can also analyze questions entered via voice input using speech recognition technology. For example, the speech input is converted into text using speech recognition technology, and the text is analyzed. Step 2: The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. For example, the answer generation unit uses a database search to search for relevant information and generate an answer. It can also generate answers to questions using a generative model. Furthermore, it can automatically generate relevant video tutorials or interactive guides based on the content of the question to provide answers that are visually easy to understand. For example, a video tutorial can be generated using video editing software. Step 3: The interface unit provides the answer generated by the answer generation unit to the elderly person. For example, the interface unit provides the answer by voice using a voice interface. The answer can also be displayed using a touch screen. Furthermore, the emotion estimation function can be used to dynamically change the design and color tone of the interface depending on the user's emotional state. For example, if the user is feeling anxious, the color tone can be changed to a calmer tone. Step 4: The daily life support unit supports the elderly person's daily life. For example, it can remind them to take their medicine. It can also manage their schedule. It can also provide health advice. For example, it can monitor health data, analyze that data, and provide advice based on their health condition. Step 5: The collaboration unit strengthens collaboration with family and caregivers. For example, it can notify family and caregivers of the elderly person's activity status and health condition. It can also provide a function to automatically contact them in an emergency. For example, it can automatically contact a medical institution in an emergency.

[0050] (Example 2) A concierge service system according to an embodiment of the present invention is a system that uses generative AI to understand questions and provide appropriate answers to help elderly people master smartphones. As a result, the concierge service system can help elderly people master smartphones and improve the quality of their daily lives.

[0051] A concierge service system according to an embodiment includes a question understanding unit, an answer generation unit, an interface unit, a daily life support unit, and a linking unit. The question understanding unit understands natural language questions from elderly people. For example, the question understanding unit analyzes the questions using natural language processing technology and understands their content. The question understanding unit can also estimate the intent of the questions using machine learning algorithms. The question understanding unit can also analyze questions entered through voice input using speech recognition technology. For example, the question understanding unit converts voice input into text using speech recognition technology and analyzes the text. The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. For example, the answer generation unit searches for related information using a database search and generates an answer. The answer generation unit can also generate an answer to a question using a generative model. The answer generation unit can also automatically generate related video tutorials or interactive guides based on the content of the question to provide answers that are visually easy to understand. For example, the answer generation unit generates video tutorials using video editing software. The interface unit provides the answers generated by the answer generation unit to the elderly people. For example, the interface unit provides answers by voice using a voice interface. The interface unit can also display answers using a touch screen. The interface unit can also dynamically change the design and color tone of the interface according to the user's emotional state using an emotion estimation function. For example, the interface unit changes the color tone to a calmer tone if the user is feeling anxious. The daily life support unit supports the elderly person's daily life. For example, the daily life support unit reminds the elderly person to take their medicine. The daily life support unit can also manage schedules. The daily life support unit can also provide health advice. For example, the daily life support unit monitors health data, analyzes the data, and provides advice according to the elderly person's health condition. The collaboration unit strengthens collaboration with family members and caregivers. For example, the collaboration unit notifies the family members and caregivers of the elderly person's activity status and health condition. The collaboration unit can also provide a function to automatically contact them in an emergency.For example, the linking unit automatically contacts a medical institution in an emergency. As a result, the concierge service system according to the embodiment can help elderly people master smartphone use and improve the quality of their daily lives. For example, the generating AI can provide appropriate answers to questions from elderly people, making it easier to operate a smartphone. The generating AI can also support the elderly in their daily lives and create an environment in which they can live with peace of mind. Furthermore, the generating AI can strengthen collaboration with family members and caregivers to ensure the safety of the elderly.

[0052] The question understanding unit can use the emotion estimation function to analyze the emotions of the elderly when they ask a question and generate an answer that corresponds to their emotions. For example, when an elderly person inputs a question, the question understanding unit uses the generation AI to analyze the emotions in real time and generate an answer that corresponds to their emotions. For example, if the question is accompanied by anxiety, an answer that includes reassuring words is provided. The question understanding unit also uses the emotion estimation function to analyze the emotions of the elderly when they ask a question and generate an answer that corresponds to their emotions. For example, if the question is accompanied by anger, an answer that responds calmly is provided. The question understanding unit also analyzes the emotions of the elderly when they ask a question and generates an answer that corresponds to their emotions. For example, if the question is accompanied by sadness, an answer that includes encouraging words is provided. This makes it possible to provide an appropriate answer that corresponds to the emotions of the elderly.

[0053] The answer generation unit automatically generates a relevant video tutorial or interactive guide based on the content of the question, thereby providing an answer that is visually easy to understand. For example, when an elderly person inputs a question, the answer generation unit uses the generation AI to automatically generate a relevant video tutorial based on the content of the question, thereby providing an answer that is visually easy to understand. For example, it generates a video that explains how to use an app. The answer generation unit also automatically generates an interactive guide based on the content of the question, thereby providing an answer that is visually easy to understand. For example, it generates a guide that shows operation procedures step by step. The answer generation unit also automatically generates a relevant video tutorial or interactive guide based on the content of the elderly person's question, thereby providing an answer that is visually easy to understand. For example, it generates a video that explains how to set up the app. This makes it possible to provide an answer that is visually easy to understand.

[0054] The answer generation unit can generate a more personalized answer by taking into account background information of the question. The answer generation unit, for example, takes into account the elderly person's past question history, allowing the generation AI to generate a more personalized answer. For example, related information is provided based on the content of previous questions. The answer generation unit also takes into account the elderly person's usage status, allowing the generation AI to generate a more personalized answer. For example, detailed operating instructions are provided for questions about frequently used apps. The answer generation unit also takes into account background information of the question, allowing the generation AI to generate a more personalized answer. For example, appropriate advice is provided based on past question history and usage status. This makes it possible to provide a more personalized answer.

[0055] The question understanding unit combines speech recognition technology and can also handle questions entered by voice. For example, when an elderly person inputs a question by voice, the generation AI uses speech recognition technology to analyze the content of the question and generate an appropriate answer. For example, a voice question such as "Please tell me how to use this app." The question understanding unit also combines speech recognition technology to enable elderly people to also handle questions entered by voice. For example, a question can be entered by voice, and the generation AI analyzes the content and provides an answer. The question understanding unit also combines speech recognition technology with the question understanding and answer generation processes to enable elderly people to enter questions by voice. For example, a question can be entered by voice, and the generation AI analyzes the content and generates an answer. This makes it possible to also handle questions entered by voice.

[0056] The question understanding unit can automatically translate the content of the question into other languages ​​and provide a multilingual concierge service. For example, when an elderly person inputs a question, the generation AI automatically translates the content into other languages ​​and provides a multilingual concierge service. For example, a question in Japanese is translated into English and an answer is provided. The question understanding unit can also automatically translate the content of the question into other languages ​​to provide a multilingual concierge service. For example, a question in English is translated into Japanese and an answer is provided. The question understanding unit can also automatically translate the content of the elderly person's question into other languages ​​to provide a multilingual concierge service. For example, a question in French is translated into Spanish and an answer is provided. This makes it possible to provide a multilingual concierge service.

[0057] The question understanding unit can use the emotion estimation function to simultaneously provide relaxing music or a message based on the emotion at the time of asking the question. For example, when an elderly person inputs a question, the generation AI in the question understanding unit uses the emotion estimation function to analyze the emotion and simultaneously provide relaxing music. For example, if the elderly person is feeling anxious, relaxing music is played. The question understanding unit also uses the emotion estimation function to provide a relaxing message based on the emotion at the time of asking the question. For example, an encouraging message is displayed if the question is stressful. The question understanding unit also uses the emotion estimation function to simultaneously provide relaxing music or a message based on the emotion at the time of asking the question. For example, if the question is stressful, relaxing music is played and a reassuring message is displayed. In this way, relaxing music or a message can be provided based on the emotion at the time of asking the question.

[0058] The interface unit can use the emotion estimation function to dynamically change the design and color tone of the interface according to the emotional state of the user. The interface unit, for example, analyzes the emotional state of an elderly person in real time and dynamically changes the design and color tone of the interface. For example, if the user is feeling anxious, the color tone is changed to a calmer tone. The interface unit also uses the emotion estimation function to dynamically change the design of the interface according to the emotional state of the user. For example, if the user is happy, the color tone is changed to a brighter tone. The interface unit also analyzes the emotional state of the user and dynamically changes the color tone of the interface. For example, if the user is feeling stressed, the color tone is changed to a relaxing tone. In this way, the design and color tone of the interface can be dynamically changed according to the emotional state of the user.

[0059] The interface unit can improve the reliability of operation by adding tactile feedback and providing a physical sensation during operation. The interface unit can improve the reliability of operation by providing tactile feedback when, for example, an elderly person operates the interface. For example, a vibration can be felt when a button is pressed. The interface unit can also add tactile feedback to the interface and provide a physical sensation during operation. For example, a resistance can be felt when operating a slider. The interface unit can also use tactile feedback to improve the reliability of operation by an elderly person operates the interface. For example, a vibration can be felt when operating a touch screen. This can improve the reliability of operation.

[0060] The interface unit can provide customization options according to the user's visual acuity or hearing ability to meet individual needs. The interface unit can provide customization options according to the visual acuity or hearing ability of, for example, elderly people, and individually adjust the interface. For example, it can make it possible to adjust the text size or volume. The interface unit can also add customization options to the interface and make it possible to adjust it according to the user's visual acuity or hearing ability. For example, it can make it possible to adjust the contrast or audio output. The interface unit can also provide customization options according to the user's visual acuity or hearing ability to individually adjust the interface. For example, it can provide color vision correction or audio guidance. This makes it possible to provide customization options according to the user's visual acuity or hearing ability.

[0061] The interface unit can incorporate gesture recognition technology to provide input methods other than touch operation. The interface unit incorporates gesture recognition technology to enable, for example, elderly people to operate the interface by methods other than touch operation. For example, scrolling the screen with hand movements. The interface unit also incorporates gesture recognition technology to provide input methods other than touch operation. For example, launching an app with hand movements. The interface unit also uses gesture recognition technology to enable elderly people to operate the interface by methods other than touch operation. For example, selecting a menu with hand movements. This makes it possible to provide input methods other than touch operation.

[0062] The interface unit can be linked with other devices such as a smartwatch or smartglasses to achieve multi-device compatibility. For example, the interface unit links the interface with a smartwatch, allowing elderly people to operate the interface on devices other than smartphones. For example, notifications can be checked on the smartwatch. The interface unit also links the interface with smartglasses to achieve multi-device compatibility. For example, navigation information can be displayed on the smartglasses. The interface unit also links the interface with other devices so that elderly people can operate the interface on devices other than smartphones. For example, voice input can be performed on the smartwatch. This allows multi-device compatibility to be achieved.

[0063] The interface unit can use the emotion estimation function to display guide messages on the interface according to the user's emotions and support operations. The interface unit, for example, analyzes the emotional state of an elderly person and displays guide messages on the interface according to the emotions. For example, if the elderly person is feeling anxious, a reassuring message is displayed. The interface unit also uses the emotion estimation function to display guide messages on the interface according to the user's emotions and support operations. For example, if the user is unsure how to operate the device, an encouraging message is displayed. The interface unit also analyzes the user's emotional state and displays guide messages on the interface according to the emotions. For example, if the user is feeling stressed, a message to help the user relax is displayed. In this way, guide messages on the interface according to the user's emotions can be displayed and operations can be supported.

[0064] The daily life support unit can use the emotion estimation function to adjust the content and timing of reminders and advice based on the emotional state of the elderly person. The daily life support unit, for example, analyzes the emotional state of the elderly person and adjusts the content and timing of reminders. For example, if the elderly person is feeling anxious, the unit sends a gentle reminder. The daily life support unit also uses the emotion estimation function to adjust the content and timing of advice based on the emotional state of the elderly person. For example, if the elderly person is feeling stressed, the unit provides advice on how to relax. The daily life support unit also analyzes the emotional state of the elderly person and adjusts the content and timing of reminders and advice. For example, if the elderly person is emotionally stable, the unit provides health advice. This makes it possible to adjust the content and timing of reminders and advice based on the emotional state of the elderly person.

[0065] The daily life support unit can combine monitoring and analysis of health data to provide advice according to the health condition. For example, the daily life support unit monitors the health data of an elderly person and analyzes the data to provide advice according to the health condition. For example, health advice is provided based on blood pressure and heart rate. The daily life support unit also combines the monitoring of health data with the daily life support function to provide advice according to the health condition. For example, exercise advice is provided based on the number of steps taken and the amount of exercise. The daily life support unit also monitors health data and analyzes the data to provide advice according to the health condition of the elderly person. For example, advice on improving sleep is provided based on sleep data. This makes it possible to provide advice according to the health condition.

[0066] The daily life support unit can learn the lifestyle patterns of the elderly person and provide support in advance based on predicted needs. For example, the daily life support unit learns the lifestyle patterns of the elderly person and provides support in advance based on predicted needs. For example, it reminds the elderly person to take their medicine every morning. Furthermore, the daily life support unit uses a generation AI to learn the lifestyle patterns of the elderly person and provide support in advance based on predicted needs. For example, it reminds the elderly person to exercise regularly. Furthermore, the daily life support unit learns the lifestyle patterns of the elderly person and provides support in advance based on predicted needs. For example, it provides nutritional advice based on meal times. This makes it possible to provide support in advance based on predicted needs.

[0067] The daily life support unit can work in conjunction with smart home devices to automatically adjust the home environment. For example, the daily life support unit links the daily life support function with smart home devices to automatically adjust the home environment. For example, automatically adjusting the room temperature. Furthermore, the daily life support unit has the generation AI work in conjunction with smart home devices to automatically adjust the home environment. For example, automatically adjusting the brightness of the lights. Furthermore, the daily life support unit links the daily life support function with smart home devices to automatically adjust the home environment. For example, automating the opening and closing of curtains. In this way, the home environment can be automatically adjusted.

[0068] The daily life support unit can provide comprehensive support in cooperation with local community services and medical institutions. For example, the daily life support unit can link the support function of the generative AI with local community services to provide comprehensive support. For example, it can provide information about local events. The daily life support unit can also link the support function with medical institutions to support health management for the elderly. For example, it can provide reminders for regular health checks. The daily life support unit can also link the support function of the generative AI with local community services and medical institutions to provide comprehensive support. For example, it can automatically contact medical institutions in the event of an emergency. This makes it possible to provide comprehensive support.

[0069] The daily life support unit can use the emotion estimation function to provide a relaxation method according to the emotional state of the elderly person. The daily life support unit, for example, analyzes the emotional state of the elderly person and provides a relaxation method according to the emotion. For example, if the elderly person is feeling anxious, relaxing music is played. The daily life support unit also uses the emotion estimation function to provide a relaxation method according to the emotional state of the elderly person. For example, if the elderly person is feeling stressed, a meditation guide is provided. The daily life support unit also analyzes the emotional state of the elderly person and provides a relaxation method according to the emotion. For example, if the elderly person is feeling excited, a deep breathing guide is provided. In this way, a relaxation method according to the emotional state of the elderly person can be provided.

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

[0071] The question understanding unit can take into account the context behind the question when analyzing the content of a user's question. For example, the question understanding unit can refer to questions the user has previously asked and the situation at that time to provide a more appropriate answer. The question understanding unit can also take into account the user's current location information to provide a location-based answer. For example, if the user is in a specific location, it can provide information related to that location. The question understanding unit can also take into account the user's device usage history to provide an answer based on past usage patterns. This makes it possible to provide an answer to the user's question that is more contextual.

[0072] The answer generator can provide multiple answer options for a user's question. For example, the answer generator can present multiple answers from different perspectives for the user to choose from. The answer generator can also allow the user to select the format of the answer according to their preferences. For example, the answer can be provided in different formats, such as text, audio, or video. The answer generator can also continuously improve the quality of the answer based on user feedback. This allows the user to be provided with more diverse and flexible answers.

[0073] The interface unit can learn the user's operation history and perform customization to improve operation efficiency. For example, the interface unit can prioritize displaying functions that the user uses frequently. The interface unit can also analyze the user's operation patterns and simplify operations. For example, it can make frequently performed operations possible with one tap. The interface unit can also continuously improve the interface design and functions based on user feedback. This can improve the user's operation experience.

[0074] The daily life support unit can provide reminders and notifications at optimal times based on the user's lifestyle rhythm. For example, the daily life support unit can analyze the user's sleep patterns and provide important reminders after waking up. The daily life support unit can also monitor the user's activity level and provide exercise reminders at appropriate times. For example, if the user has been sitting for a long time, the unit can send a notification encouraging the user to stand up and walk. The daily life support unit can also analyze the user's eating patterns and provide nutritional advice based on meal times. This makes it possible to provide support tailored to the user's lifestyle rhythm.

[0075] The linking unit can provide a function to facilitate communication with the user's family and caregivers. For example, the linking unit periodically reports the user's activity status and health condition to the family and caregivers. The linking unit can also provide a function that allows the user to easily contact the family and caregivers in an emergency. For example, the linking unit can provide a function that allows the user to send an emergency message with one tap. The linking unit can also provide a dashboard that allows the family and caregivers to check the user's status in real time. This can strengthen collaboration to ensure the user's safety and security.

[0076] The question understanding unit can use the emotion estimation function to analyze the emotional state of the user and provide feedback according to the emotion. For example, when the user inputs a question, the question understanding unit can use the emotion estimation function to analyze the emotion and provide feedback according to the emotion. For example, if the user is feeling anxious, the question understanding unit can provide reassuring feedback. The question understanding unit can also use the emotion estimation function to provide advice according to the user's emotional state. For example, if the user is feeling stressed, the question understanding unit can provide advice on how to relax. The question understanding unit can also use the emotion estimation function to provide support according to the user's emotional state. This makes it possible to provide appropriate feedback according to the user's emotion.

[0077] The answer generation unit can use the emotion estimation function to adjust the tone and content of the answer based on the user's emotional state. For example, when the user inputs a question, the answer generation unit uses the emotion estimation function to analyze the emotion and provide an answer in a tone that corresponds to the emotion. For example, if the user is feeling angry, the answer generation unit provides an answer in a calm and collected tone. The answer generation unit can also use the emotion estimation function to provide an answer with content that corresponds to the user's emotional state. For example, if the user is feeling sad, the answer generation unit provides an answer that includes words of encouragement. The answer generation unit can also use the emotion estimation function to provide support that corresponds to the user's emotional state. This makes it possible to provide an appropriate answer that corresponds to the user's emotion.

[0078] The interface unit can use the emotion estimation function to dynamically change the design and color tone of the interface based on the emotional state of the user. For example, the interface unit analyzes the emotional state of the user in real time and dynamically changes the design and color tone of the interface. For example, if the user is feeling anxious, the color tone is changed to a calmer tone. The interface unit can also dynamically change the design of the interface according to the emotional state of the user using the emotion estimation function. For example, if the user is happy, the color tone is changed to a brighter tone. The interface unit can also analyze the emotional state of the user and dynamically change the color tone of the interface. In this way, the design and color tone of the interface can be dynamically changed according to the emotional state of the user.

[0079] The daily life support unit can use the emotion estimation function to adjust the content and timing of reminders and advice based on the user's emotional state. For example, the daily life support unit analyzes the user's emotional state and adjusts the content and timing of reminders. For example, if the user is feeling anxious, the unit may send a gentle reminder. The daily life support unit can also use the emotion estimation function to adjust the content and timing of advice based on the user's emotional state. For example, if the user is feeling stressed, the unit may provide advice on how to relax. The daily life support unit can also analyze the user's emotional state and adjust the content and timing of reminders and advice. This makes it possible to adjust the content and timing of reminders and advice based on the user's emotional state.

[0080] The daily life support unit can use the emotion estimation function to provide a relaxation method based on the user's emotional state. For example, the daily life support unit can analyze the user's emotional state and provide a relaxation method according to the emotion. For example, if the user is feeling anxious, the unit can play relaxing music. The daily life support unit can also use the emotion estimation function to provide a relaxation method according to the user's emotional state. For example, if the user is feeling stressed, the unit can provide a meditation guide. The daily life support unit can also analyze the user's emotional state and provide a relaxation method according to the emotion. This makes it possible to provide a relaxation method according to the user's emotional state.

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

[0082] Step 1: The question understanding unit understands natural language questions from the elderly. For example, the question understanding unit uses natural language processing technology to analyze the question and understand its content. The question understanding unit can also use machine learning algorithms to estimate the intent of the question. Furthermore, the question understanding unit can also analyze questions entered via voice input using speech recognition technology. For example, the speech input is converted into text using speech recognition technology, and the text is analyzed. Step 2: The answer generation unit generates an appropriate answer based on the question understood by the question understanding unit. For example, the answer generation unit uses a database search to search for relevant information and generate an answer. It can also generate answers to questions using a generative model. Furthermore, it can automatically generate relevant video tutorials or interactive guides based on the content of the question to provide answers that are visually easy to understand. For example, a video tutorial can be generated using video editing software. Step 3: The interface unit provides the answer generated by the answer generation unit to the elderly person. For example, the interface unit provides the answer by voice using a voice interface. The answer can also be displayed using a touch screen. Furthermore, the emotion estimation function can be used to dynamically change the design and color tone of the interface depending on the user's emotional state. For example, if the user is feeling anxious, the color tone can be changed to a calmer tone. Step 4: The daily life support unit supports the elderly person's daily life. For example, it can remind them to take their medicine. It can also manage their schedule. It can also provide health advice. For example, it can monitor health data, analyze that data, and provide advice based on their health condition. Step 5: The collaboration unit strengthens collaboration with family and caregivers. For example, it can notify family and caregivers of the elderly person's activity status and health condition. It can also provide a function to automatically contact them in an emergency. For example, it can automatically contact a medical institution in an emergency.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question understanding unit that understands natural language questions from elderly people; an answer generation unit that generates an appropriate answer based on the question understood by the question understanding unit; an interface unit that provides the answer generated by the answer generation unit to the elderly; The Daily Life Support Department provides support for the daily lives of the elderly, A liaison department that strengthens cooperation with families and caregivers. A system characterized by:

2. The question understanding unit Analyzing the emotions of the elderly person when asking a question and generating an answer according to the emotions 2. The system of claim 1.

3. The answer generation unit Automatically generate relevant video tutorials and interactive guides based on your questions to provide visually understandable answers 2. The system of claim 1.

4. The answer generation unit Considers the context of the question to generate a more personalized answer 2. The system of claim 1.

5. The question understanding unit Combined with voice recognition technology, it also responds to the above questions via voice input.

2. The system of claim 1.

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