System

Abstract

An object of a system according to an embodiment is to intuitively assist an elderly person with a video or a sound when the elderly person operates a smartphone.SOLUTION: A system according to an embodiment includes an action pattern learning part, a video assist part, and a voice assist part. The behavior pattern learning unit learns a behavior pattern of the elderly person. The video assist unit assists an operation with a video on the basis of the action pattern learned by the action pattern learning unit. The voice assist unit assists the operation by voice based on the action pattern learned by the action pattern learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, elderly people often find it difficult to operate smartphones, and there is a problem that support is insufficient.

[0005] The system of the embodiment aims to provide intuitive assistance to elderly people using video and audio when operating a smartphone. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior pattern learning unit, a video assist unit, and a voice assist unit. The behavior pattern learning unit learns the behavior patterns of elderly people. The video assist unit provides video assistance for operations based on the behavior patterns learned by the behavior pattern learning unit. The voice assist unit provides voice assistance for operations based on the behavior patterns learned by the behavior pattern learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide intuitive assistance to elderly people using smartphones through video and audio. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The smartphone operation assistance app according to the embodiment of the present invention is a system in which AI, which has previously learned the behavioral patterns of elderly people, assists them in smartphone operation by making full use of video and voice reading functions. As a result, the smartphone operation assistance app enables elderly people to use smartphones more easily and efficiently.

[0029] A smartphone operation assistance app according to an embodiment includes a behavior pattern learning unit, a video assistance unit, and a voice assistance unit. The behavior pattern learning unit learns the behavior patterns of elderly people. For example, the behavior pattern learning unit performs learning based on past smartphone operation history and daily behavior data. The behavior pattern learning unit can also learn tendencies to use specific apps during specific time periods and frequently performed operations (such as making phone calls and sending messages). The behavior pattern learning unit also analyzes the behavior patterns of elderly people using a machine learning algorithm. For example, the behavior pattern learning unit learns tendencies to use specific apps during specific time periods and predicts the next operation that is likely to be performed based on the results. The video assistance unit provides operation assistance using video based on the behavior patterns learned by the behavior pattern learning unit. For example, the video assistance unit monitors the elderly person's operations in real time using the smartphone camera and, as necessary, displays arrows or highlights on the screen to indicate the next tap location. The video assistance unit can also provide video instructions for operation procedures. For example, the video assistance unit displays an arrow on the screen to indicate the next tap location. The video assist unit displays operation procedures in video format, making it easier for elderly people to understand visually. The voice assist unit provides voice assistance for operations based on the behavioral patterns learned by the behavioral pattern learning unit. For example, the voice assist unit reads out on-screen text and button descriptions. The voice assist unit can also provide voice guidance for operation procedures. For example, the voice assist unit may provide guidance such as, "Next, tap the button in the upper right corner." This allows the smartphone operation assist app according to the embodiment to enable elderly people to use smartphones more easily and efficiently. For example, the smartphone operation assist app assists elderly people in understanding operations by providing video and voice assistance when operating a smartphone. The smartphone operation assist app also provides a function to automate operations frequently performed by elderly people, eliminating the need for complex operations.

[0030] The behavioral pattern learning unit can learn the tendency to use a specific app during a specific time period or frequently performed operations. The behavioral pattern learning unit, for example, learns the tendency to use a specific app during a specific time period. For example, the behavioral pattern learning unit learns the tendency to use a news app in the morning. The behavioral pattern learning unit also learns frequently performed operations. For example, the behavioral pattern learning unit learns the operations of making a phone call and sending a message. This makes it possible to learn the behavioral patterns of the elderly in more detail and provide personalized support.

[0031] The video assist unit can display an arrow or highlight on the screen to indicate the next location to tap. The video assist unit, for example, displays an arrow on the screen to indicate the next location to tap. For example, the video assist unit indicates the next button to tap with an arrow. The video assist unit also displays a highlight on the screen to indicate the next location to tap. For example, the video assist unit highlights the next icon to tap. This makes it easier for elderly people to visually understand the operation.

[0032] The voice assist unit can read out aloud the text on the screen or the explanation of the buttons. For example, the voice assist unit reads out the text on the screen. For example, the voice assist unit reads out the explanation on the screen. The voice assist unit also reads out the explanation of the buttons on the screen. For example, the voice assist unit reads out the explanation of the send button. This makes it easier for elderly people to understand the operation by voice.

[0033] The behavioral pattern learning unit can also collect data on daily life activities other than those using a smartphone and analyze comprehensive behavioral patterns. The behavioral pattern learning unit, for example, collects data on daily life activities other than those using a smartphone. For example, the behavioral pattern learning unit collects walking patterns and meal timings. The behavioral pattern learning unit also analyzes comprehensive behavioral patterns. For example, the behavioral pattern learning unit analyzes comprehensive behavioral patterns based on daily life activity data. This makes it possible to analyze the comprehensive behavioral patterns of elderly people and provide more personalized support.

[0034] The behavior pattern learning unit can incorporate feedback from family members and caregivers to provide more personalized support. The behavior pattern learning unit, for example, collects feedback from family members and caregivers. For example, the family members input the preferences and habits of the elderly person into the behavior pattern learning unit. The behavior pattern learning unit also learns the behavior patterns of the elderly person based on that information. For example, the behavior pattern learning unit provides personalized support based on feedback from family members. In this way, by incorporating feedback from family members and caregivers, more personalized support can be provided.

[0035] The behavior pattern learning unit collects data on elderly people from different cultural spheres and regions, and can analyze behavior patterns from a global perspective. The behavior pattern learning unit, for example, collects data on elderly people from different cultural spheres and regions. For example, the behavior pattern learning unit collects data on elderly people from Asia and Europe. The behavior pattern learning unit also analyzes behavior patterns from a global perspective. For example, the behavior pattern learning unit analyzes behavior patterns based on data from different cultural spheres and regions. This makes it possible to collect data from different cultural spheres and regions and analyze behavior patterns from a global perspective.

[0036] The behavior pattern learning unit can cooperate with smart home devices to collect more detailed behavior data. The behavior pattern learning unit cooperates with, for example, smart home devices. For example, the behavior pattern learning unit cooperates with smart speakers and sensors. The behavior pattern learning unit also collects more detailed behavior data. For example, the behavior pattern learning unit collects information on room movements and home appliance usage. In this way, by cooperating with smart home devices, more detailed behavior data can be collected.

[0037] The video assist unit can use augmented reality technology to display an operation guide superimposed on the actual smartphone screen. The video assist unit, for example, uses augmented reality technology to display an operation guide superimposed on the actual smartphone screen. For example, the video assist unit indicates the next tap location with an arrow or highlight. The video assist unit also shows operation procedures using augmented reality technology. For example, the video assist unit displays operation procedures superimposed on the actual smartphone screen. This makes it possible to visually provide operation guides using augmented reality technology.

[0038] The video assist unit can display operation procedures using three-dimensional animation to enable more intuitive understanding. The video assist unit, for example, displays operation procedures using three-dimensional animation. For example, the video assist unit uses a three-dimensional character to demonstrate how to use an app. The video assist unit also visually displays operation procedures using three-dimensional animation. For example, the video assist unit uses three-dimensional animation to indicate the location to tap next. In this way, displaying operation procedures using three-dimensional animation allows for intuitive understanding.

[0039] The video assist unit can provide a video guide that corresponds to different languages ​​or dialects, thereby realizing multilingual support. The video assist unit, for example, provides a video guide that corresponds to different languages ​​or dialects. For example, the video assist unit realizes multilingual support such as English, Spanish, and Chinese. The video assist unit also provides a video guide that corresponds to dialects. For example, the video assist unit supports dialects such as Kansai dialect and Tohoku dialect. This makes it possible to provide a video guide that corresponds to different languages ​​and dialects.

[0040] The video assist unit makes it possible to customize the operation guide and provide a guide that suits the preferences of the elderly. The video assist unit, for example, makes it possible to customize the operation guide. For example, the video assist unit makes it possible to adjust the font size and color. Furthermore, the video assist unit provides a guide that suits the preferences of the elderly. For example, the video assist unit displays the operation guide in a preferred color or design. This makes it possible to customize the operation guide and provide a guide that suits the preferences of the elderly.

[0041] The voice assist unit can use voice synthesis technology to provide guidance in a voice that is friendly to the elderly. The voice assist unit, for example, uses voice synthesis technology to provide guidance in a voice that is friendly to the elderly. For example, the voice assist unit uses the voice of a family member or the voice of a famous voice actor. The voice assist unit also provides operation guidance in a friendly voice. For example, the voice assist unit provides guidance in a gentle tone. This allows the elderly to receive guidance in a friendly voice.

[0042] The voice assist unit can read out the operation procedures to a rhythm or melody, making them easier to remember. For example, the voice assist unit reads out the operation procedures to a rhythm or melody. For example, the voice assist unit sings the operation procedures to a simple melody. The voice assist unit also reads out the operation procedures to a rhythm. For example, the voice assist unit reads out the operation procedures to a rhythm. In this way, reading out the operation procedures to a rhythm or melody makes them easier to remember.

[0043] The voice assist unit can provide voice guidance corresponding to different languages ​​or dialects, thereby realizing multilingual support. The voice assist unit, for example, provides voice guidance corresponding to different languages ​​or dialects. For example, the voice assist unit realizes multilingual support such as English, Spanish, and Chinese. The voice assist unit also provides voice guidance corresponding to dialects. For example, the voice assist unit supports dialects such as Kansai dialect and Tohoku dialect. This makes it possible to provide voice guidance corresponding to different languages ​​and dialects.

[0044] The voice assist unit makes it possible to customize the voice guide and provide a guide tailored to the preferences of the elderly. The voice assist unit, for example, makes it possible to customize the voice guide. For example, the voice assist unit makes it possible to adjust the tone and speed of the voice. Furthermore, the voice assist unit provides a guide tailored to the preferences of the elderly. For example, the voice assist unit provides a voice guide in a preferred voice and tone. This makes it possible to customize the voice guide and provide a guide tailored to the preferences of the elderly.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The smartphone operation assistance app can further include a health management unit. The health management unit collects health data of the elderly and monitors their daily health condition. For example, the health management unit may collect data in conjunction with a pedometer or heart rate monitor. The health management unit can also provide health advice based on the collected data. For example, the health management unit may send a notification encouraging exercise if the number of steps taken is low. This supports the elderly's health management and enables them to live a healthier life.

[0047] The behavioral pattern learning unit may further include a hobby recommendation unit. The hobby recommendation unit analyzes the behavioral patterns and interests of the elderly person and suggests new hobbies and activities. For example, the hobby recommendation unit may recommend hobbies based on past app usage history and search history. The hobby recommendation unit may also provide local event information. For example, the hobby recommendation unit may notify the elderly person of nearby hobby events. This allows the elderly person to find new hobbies and activities and live a fulfilling life.

[0048] The video assist unit can further include an environment adaptation unit, which analyzes the environment around the elderly person and provides optimal operation guidance. For example, the environment adaptation unit detects the surrounding brightness and volume and adjusts the display and audio guidance appropriately. The environment adaptation unit can also increase the volume of audio guidance in noisy places. This allows elderly people to operate their smartphones comfortably in any environment.

[0049] The voice assistant unit can also be equipped with a reminder function. The reminder function notifies the elderly by voice so that they do not forget important appointments or tasks. For example, the voice assistant unit can remind them to take their medicine. The voice assistant unit can also notify them of regular health checks or doctor's appointments. This helps the elderly not forget to perform important tasks in their daily lives.

[0050] The behavioral pattern learning unit can further include a dietary management unit. The dietary management unit collects dietary data of the elderly person and analyzes nutritional balance. For example, the dietary management unit analyzes photos of meals to calculate nutrients. The dietary management unit can also suggest dietary improvements if the nutritional balance is unbalanced. This allows the elderly person to maintain a healthy diet.

[0051] The video assistance unit can further include a feedback collection unit. The feedback collection unit collects feedback from the elderly regarding operation and improves the assistance content. For example, the feedback collection unit collects information in the form of a questionnaire about points in the operation that were difficult to understand and areas for improvement. The feedback collection unit can also customize the assistance content based on the collected feedback. This makes it possible to provide more effective assistance tailored to the needs of the elderly.

[0052] The voice assistant unit can also be equipped with a learning function. The learning function provides voice support when elderly people are learning new operations. For example, the voice assistant unit can provide step-by-step guidance on how to use a new app. The voice assistant unit can also provide additional explanations if an operation is difficult. This allows elderly people to smoothly learn new operations.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The behavioral pattern learning unit learns the behavioral patterns of elderly people. For example, it learns based on past smartphone operation history and daily behavioral data, learning tendencies to use specific apps at specific times of the day and frequently performed operations (making phone calls, sending messages, etc.). It then uses a machine learning algorithm to analyze the elderly person's behavioral patterns and predicts the operation they are likely to perform next. Step 2: The video assistance unit uses video to assist with operations based on the behavioral patterns learned by the behavioral pattern learning unit. For example, it can monitor the elderly person's operations in real time using the smartphone camera, and, if necessary, display arrows or highlights on the screen to indicate the next tap location. Also, video instructions can be used to visually assist the elderly. Step 3: The voice assist unit provides voice assistance based on the behavioral patterns learned by the behavioral pattern learning unit. For example, it reads out on-screen text and button explanations. It also provides voice guidance on the operation procedure, making it easier for the elderly person to understand what to do next.

[0055] (Example 2) The smartphone operation assistance app according to the embodiment of the present invention is a system in which AI, which has previously learned the behavioral patterns of elderly people, assists them in smartphone operation by making full use of video and voice reading functions. As a result, the smartphone operation assistance app enables elderly people to use smartphones more easily and efficiently.

[0056] A smartphone operation assistance app according to an embodiment includes a behavior pattern learning unit, a video assistance unit, and a voice assistance unit. The behavior pattern learning unit learns the behavior patterns of elderly people. For example, the behavior pattern learning unit performs learning based on past smartphone operation history and daily behavior data. The behavior pattern learning unit can also learn tendencies to use specific apps during specific time periods and frequently performed operations (such as making phone calls and sending messages). The behavior pattern learning unit also analyzes the behavior patterns of elderly people using a machine learning algorithm. For example, the behavior pattern learning unit learns tendencies to use specific apps during specific time periods and predicts the next operation that is likely to be performed based on the results. The video assistance unit provides operation assistance using video based on the behavior patterns learned by the behavior pattern learning unit. For example, the video assistance unit monitors the elderly person's operations in real time using the smartphone camera and, as necessary, displays arrows or highlights on the screen to indicate the next tap location. The video assistance unit can also provide video instructions for operation procedures. For example, the video assistance unit displays an arrow on the screen to indicate the next tap location. The video assist unit displays operation procedures in video format, making it easier for elderly people to understand visually. The voice assist unit provides voice assistance for operations based on the behavioral patterns learned by the behavioral pattern learning unit. For example, the voice assist unit reads out on-screen text and button descriptions. The voice assist unit can also provide voice guidance for operation procedures. For example, the voice assist unit may provide guidance such as, "Next, tap the button in the upper right corner." This allows the smartphone operation assist app according to the embodiment to enable elderly people to use smartphones more easily and efficiently. For example, the smartphone operation assist app assists elderly people in understanding operations by providing video and voice assistance when operating a smartphone. The smartphone operation assist app also provides a function to automate operations frequently performed by elderly people, eliminating the need for complex operations.

[0057] The behavioral pattern learning unit can learn the tendency to use a specific app during a specific time period or frequently performed operations. The behavioral pattern learning unit, for example, learns the tendency to use a specific app during a specific time period. For example, the behavioral pattern learning unit learns the tendency to use a news app in the morning. The behavioral pattern learning unit also learns frequently performed operations. For example, the behavioral pattern learning unit learns the operations of making a phone call and sending a message. This makes it possible to learn the behavioral patterns of the elderly in more detail and provide personalized support.

[0058] The video assist unit can display an arrow or highlight on the screen to indicate the next location to tap. The video assist unit, for example, displays an arrow on the screen to indicate the next location to tap. For example, the video assist unit indicates the next button to tap with an arrow. The video assist unit also displays a highlight on the screen to indicate the next location to tap. For example, the video assist unit highlights the next icon to tap. This makes it easier for elderly people to visually understand the operation.

[0059] The voice assist unit can read out aloud the text on the screen or the explanation of the buttons. For example, the voice assist unit reads out the text on the screen. For example, the voice assist unit reads out the explanation on the screen. The voice assist unit also reads out the explanation of the buttons on the screen. For example, the voice assist unit reads out the explanation of the send button. This makes it easier for elderly people to understand the operation by voice.

[0060] The behavioral pattern learning unit can use the emotion estimation function to analyze emotional responses to specific operations and prioritize learning of operations that elicit positive emotions. The behavioral pattern learning unit, for example, uses the emotion estimation function to analyze emotional responses to specific operations. For example, the behavioral pattern learning unit analyzes emotional responses when using a specific app. The behavioral pattern learning unit also prioritizes learning of operations that elicit positive emotions. For example, the behavioral pattern learning unit prioritizes learning of operations that make people smile. This allows elderly people to operate their smartphones with positive emotions.

[0061] The behavioral pattern learning unit can also collect data on daily life activities other than those using a smartphone and analyze comprehensive behavioral patterns. The behavioral pattern learning unit, for example, collects data on daily life activities other than those using a smartphone. For example, the behavioral pattern learning unit collects walking patterns and meal timings. The behavioral pattern learning unit also analyzes comprehensive behavioral patterns. For example, the behavioral pattern learning unit analyzes comprehensive behavioral patterns based on daily life activity data. This makes it possible to analyze the comprehensive behavioral patterns of elderly people and provide more personalized support.

[0062] The behavior pattern learning unit can incorporate feedback from family members and caregivers to provide more personalized support. The behavior pattern learning unit, for example, collects feedback from family members and caregivers. For example, the family members input the preferences and habits of the elderly person into the behavior pattern learning unit. The behavior pattern learning unit also learns the behavior patterns of the elderly person based on that information. For example, the behavior pattern learning unit provides personalized support based on feedback from family members. In this way, by incorporating feedback from family members and caregivers, more personalized support can be provided.

[0063] The behavior pattern learning unit collects data on elderly people from different cultural spheres and regions, and can analyze behavior patterns from a global perspective. The behavior pattern learning unit, for example, collects data on elderly people from different cultural spheres and regions. For example, the behavior pattern learning unit collects data on elderly people from Asia and Europe. The behavior pattern learning unit also analyzes behavior patterns from a global perspective. For example, the behavior pattern learning unit analyzes behavior patterns based on data from different cultural spheres and regions. This makes it possible to collect data from different cultural spheres and regions and analyze behavior patterns from a global perspective.

[0064] The behavior pattern learning unit can cooperate with smart home devices to collect more detailed behavior data. The behavior pattern learning unit cooperates with, for example, smart home devices. For example, the behavior pattern learning unit cooperates with smart speakers and sensors. The behavior pattern learning unit also collects more detailed behavior data. For example, the behavior pattern learning unit collects information on room movements and home appliance usage. In this way, by cooperating with smart home devices, more detailed behavior data can be collected.

[0065] The behavior pattern learning unit can use the emotion estimation function to analyze the stress or anxiety that the elderly feel when performing a specific operation, and provide support to alleviate that emotion. The behavior pattern learning unit, for example, uses the emotion estimation function to analyze the stress or anxiety that the elderly feel when performing a specific operation. For example, the behavior pattern learning unit analyzes the stress felt when the operation is complicated. The behavior pattern learning unit also provides support to alleviate that emotion. For example, the behavior pattern learning unit suggests simplifying an operation when it is complicated. This can alleviate the stress and anxiety that the elderly feel when performing a specific operation.

[0066] The video assist unit can use an emotion estimation function to estimate the elderly person's level of understanding of the operation from their facial expressions or movements, and provide additional video assistance if their understanding is insufficient. The video assist unit, for example, analyzes the elderly person's facial expressions and movements using a camera to estimate their level of understanding of the operation. For example, if the video assist unit detects a confused expression, it provides additional video assistance. The video assist unit also provides additional video assistance if their understanding of the operation is insufficient. For example, the video assist unit shows the operation procedure again. This makes it possible to estimate the elderly person's level of understanding of the operation and provide additional video assistance as needed.

[0067] The video assist unit can use augmented reality technology to display an operation guide superimposed on the actual smartphone screen. The video assist unit, for example, uses augmented reality technology to display an operation guide superimposed on the actual smartphone screen. For example, the video assist unit indicates the next tap location with an arrow or highlight. The video assist unit also shows operation procedures using augmented reality technology. For example, the video assist unit displays operation procedures superimposed on the actual smartphone screen. This makes it possible to visually provide operation guides using augmented reality technology.

[0068] The video assist unit can display operation procedures using three-dimensional animation to enable more intuitive understanding. The video assist unit, for example, displays operation procedures using three-dimensional animation. For example, the video assist unit uses a three-dimensional character to demonstrate how to use an app. The video assist unit also visually displays operation procedures using three-dimensional animation. For example, the video assist unit uses three-dimensional animation to indicate the location to tap next. In this way, displaying operation procedures using three-dimensional animation allows for intuitive understanding.

[0069] The video assist unit can provide a video guide that corresponds to different languages ​​or dialects, thereby realizing multilingual support. The video assist unit, for example, provides a video guide that corresponds to different languages ​​or dialects. For example, the video assist unit realizes multilingual support such as English, Spanish, and Chinese. The video assist unit also provides a video guide that corresponds to dialects. For example, the video assist unit supports dialects such as Kansai dialect and Tohoku dialect. This makes it possible to provide a video guide that corresponds to different languages ​​and dialects.

[0070] The video assist unit makes it possible to customize the operation guide and provide a guide that suits the preferences of the elderly. The video assist unit, for example, makes it possible to customize the operation guide. For example, the video assist unit makes it possible to adjust the font size and color. Furthermore, the video assist unit provides a guide that suits the preferences of the elderly. For example, the video assist unit displays the operation guide in a preferred color or design. This makes it possible to customize the operation guide and provide a guide that suits the preferences of the elderly.

[0071] The video assist unit can use the emotion estimation function to monitor the emotional reactions of the elderly person during video assistance in real time, and provide a video guide that elicits positive emotions. The video assist unit, for example, uses the emotion estimation function to monitor the emotional reactions of the elderly person during video assistance in real time. For example, the video assist unit displays an encouraging message when it detects a smile. The video assist unit also provides a video guide that elicits positive emotions. For example, the video assist unit displays a video guide that elicits positive emotions. This makes it possible to monitor the emotional reactions of the elderly person in real time, and provide a video guide that elicits positive emotions.

[0072] The voice assist unit can estimate the elderly person's level of understanding of the operation from the tone or speed of their voice using the emotion estimation function, and provide additional voice assistance if their understanding is insufficient. The voice assist unit, for example, analyzes the tone or speed of the elderly person's voice to estimate their level of understanding of the operation. For example, the voice assist unit provides additional voice assistance if their voice is unstable or the speed is slow. The voice assist unit also provides additional voice assistance if their understanding of the operation is insufficient. For example, the voice assist unit guides them through the operation procedure again. This makes it possible to estimate the elderly person's level of understanding of the operation and provide additional voice assistance as needed.

[0073] The voice assist unit can use voice synthesis technology to provide guidance in a voice that is friendly to the elderly. The voice assist unit, for example, uses voice synthesis technology to provide guidance in a voice that is friendly to the elderly. For example, the voice assist unit uses the voice of a family member or the voice of a famous voice actor. The voice assist unit also provides operation guidance in a friendly voice. For example, the voice assist unit provides guidance in a gentle tone. This allows the elderly to receive guidance in a friendly voice.

[0074] The voice assist unit can read out the operation procedures to a rhythm or melody, making them easier to remember. For example, the voice assist unit reads out the operation procedures to a rhythm or melody. For example, the voice assist unit sings the operation procedures to a simple melody. The voice assist unit also reads out the operation procedures to a rhythm. For example, the voice assist unit reads out the operation procedures to a rhythm. In this way, reading out the operation procedures to a rhythm or melody makes them easier to remember.

[0075] The voice assist unit can provide voice guidance corresponding to different languages ​​or dialects, thereby realizing multilingual support. The voice assist unit, for example, provides voice guidance corresponding to different languages ​​or dialects. For example, the voice assist unit realizes multilingual support such as English, Spanish, and Chinese. The voice assist unit also provides voice guidance corresponding to dialects. For example, the voice assist unit supports dialects such as Kansai dialect and Tohoku dialect. This makes it possible to provide voice guidance corresponding to different languages ​​and dialects.

[0076] The voice assist unit makes it possible to customize the voice guide and provide a guide tailored to the preferences of the elderly. The voice assist unit, for example, makes it possible to customize the voice guide. For example, the voice assist unit makes it possible to adjust the tone and speed of the voice. Furthermore, the voice assist unit provides a guide tailored to the preferences of the elderly. For example, the voice assist unit provides a voice guide in a preferred voice and tone. This makes it possible to customize the voice guide and provide a guide tailored to the preferences of the elderly.

[0077] The voice assist unit can use the emotion estimation function to monitor the emotional reactions of the elderly person during voice assistance in real time, and provide voice guidance that elicits positive emotions. The voice assist unit, for example, uses the emotion estimation function to monitor the emotional reactions of the elderly person during voice assistance in real time. For example, the voice assist unit adjusts the tone and content of the voice. The voice assist unit also provides voice guidance that elicits positive emotions. For example, the voice assist unit provides voice guidance that elicits positive emotions. This makes it possible to monitor the emotional reactions of the elderly person in real time, and provide voice guidance that elicits positive emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The smartphone operation assistance app can further include a health management unit. The health management unit collects health data of the elderly and monitors their daily health condition. For example, the health management unit may collect data in conjunction with a pedometer or heart rate monitor. The health management unit can also provide health advice based on the collected data. For example, the health management unit may send a notification encouraging exercise if the number of steps taken is low. This supports the elderly's health management and enables them to live a healthier life.

[0080] The behavioral pattern learning unit may further include a hobby recommendation unit. The hobby recommendation unit analyzes the behavioral patterns and interests of the elderly person and suggests new hobbies and activities. For example, the hobby recommendation unit may recommend hobbies based on past app usage history and search history. The hobby recommendation unit may also provide local event information. For example, the hobby recommendation unit may notify the elderly person of nearby hobby events. This allows the elderly person to find new hobbies and activities and live a fulfilling life.

[0081] The video assist unit can further include an environment adaptation unit, which analyzes the environment around the elderly person and provides optimal operation guidance. For example, the environment adaptation unit detects the surrounding brightness and volume and adjusts the display and audio guidance appropriately. The environment adaptation unit can also increase the volume of audio guidance in noisy places. This allows elderly people to operate their smartphones comfortably in any environment.

[0082] The voice assistant unit can also be equipped with a reminder function. The reminder function notifies the elderly by voice so that they do not forget important appointments or tasks. For example, the voice assistant unit can remind them to take their medicine. The voice assistant unit can also notify them of regular health checks or doctor's appointments. This helps the elderly not forget to perform important tasks in their daily lives.

[0083] The behavioral pattern learning unit can use the emotion estimation function to analyze the emotional state of the elderly person and provide relaxation content to reduce stress. For example, if the behavioral pattern learning unit estimates that the elderly person is feeling stressed, it can play relaxation music. The behavioral pattern learning unit can also provide guidance on meditation and deep breathing. This allows the elderly person to relax and reduce stress.

[0084] The behavioral pattern learning unit can further include a dietary management unit. The dietary management unit collects dietary data of the elderly person and analyzes nutritional balance. For example, the dietary management unit analyzes photos of meals to calculate nutrients. The dietary management unit can also suggest dietary improvements if the nutritional balance is unbalanced. This allows the elderly person to maintain a healthy diet.

[0085] The behavioral pattern learning unit can use the emotion estimation function to analyze the emotional state of the elderly person and provide entertainment content that elicits positive emotions. For example, if the behavioral pattern learning unit estimates that the elderly person is feeling depressed, it can suggest fun videos or games. The behavioral pattern learning unit can also provide relaxing content if the elderly person's emotions are stable. This allows the elderly person to spend their daily lives with positive emotions.

[0086] The video assistance unit can further include a feedback collection unit. The feedback collection unit collects feedback from the elderly regarding operation and improves the assistance content. For example, the feedback collection unit collects information in the form of a questionnaire about points in the operation that were difficult to understand and areas for improvement. The feedback collection unit can also customize the assistance content based on the collected feedback. This makes it possible to provide more effective assistance tailored to the needs of the elderly.

[0087] The voice assistant unit can use its emotion estimation function to analyze the emotional state of the elderly person and provide voice guidance that corresponds to their emotions. For example, the voice assistant unit can provide guidance in a gentle tone when the person is emotionally unstable. The voice assistant unit can also provide guidance in a normal tone when the person is emotionally stable. This allows the elderly person to operate their smartphone with peace of mind.

[0088] The voice assistant unit can also be equipped with a learning function. The learning function provides voice support when elderly people are learning new operations. For example, the voice assistant unit can provide step-by-step guidance on how to use a new app. The voice assistant unit can also provide additional explanations if an operation is difficult. This allows elderly people to smoothly learn new operations.

[0089] The video assistance unit uses its emotion estimation function to estimate the elderly person's level of understanding of the operation from their facial expressions and movements, and can provide additional video assistance if their understanding is insufficient. For example, if the video assistance unit detects a confused expression, it can provide additional video assistance. The video assistance unit can also show the operation procedure again if their understanding of the operation is insufficient. This makes it possible to estimate the elderly person's level of understanding of the operation and provide additional video assistance as needed.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The behavioral pattern learning unit learns the behavioral patterns of elderly people. For example, it learns based on past smartphone operation history and daily behavioral data, learning tendencies to use specific apps at specific times of the day and frequently performed operations (making phone calls, sending messages, etc.). It then uses a machine learning algorithm to analyze the elderly person's behavioral patterns and predicts the operation they are likely to perform next. Step 2: The video assistance unit uses video to assist with operations based on the behavioral patterns learned by the behavioral pattern learning unit. For example, it can monitor the elderly person's operations in real time using the smartphone camera, and, if necessary, display arrows or highlights on the screen to indicate the next tap location. Also, video instructions can be used to visually assist the elderly. Step 3: The voice assist unit provides voice assistance based on the behavioral patterns learned by the behavioral pattern learning unit. For example, it reads out on-screen text and button explanations. It also provides voice guidance on the operation procedure, making it easier for the elderly person to understand what to do next.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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, in order to avoid confusion and to 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.

[0158] 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]

[0159] 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 behavior pattern learning unit that learns behavior patterns of elderly people; a video assist unit that provides video assistance for operations based on the behavior pattern learned by the behavior pattern learning unit; a voice assist unit that provides voice assistance for operations based on the behavior pattern learned by the behavior pattern learning unit. A system characterized by:

2. The behavior pattern learning unit Collecting data on elderly people from different cultures and regions to analyze their behavioral patterns from a global perspective 2. The system of claim 1.

3. The image assist unit Augmented reality technology is used to display operation guides overlaid on the actual smartphone screen 2. The system of claim 1.

4. The voice assist unit Using voice synthesis technology, elderly people can receive guidance in a friendly voice.

2. The system of claim 1.

5. The behavior pattern learning unit Using emotion estimation capabilities, the system analyzes emotional responses to specific operations and prioritizes learning operations that elicit positive emotions.

2. The system of claim 1.

6. The image assist unit Using emotion estimation functionality, the system estimates the elderly person's level of understanding of the operation from their facial expressions or movements, and provides additional visual assistance if their understanding is insufficient.

2. The system of claim 1.

7. The voice assist unit Using emotion estimation, the system estimates the elderly person's understanding of the operation from their tone or speed of voice, and provides additional voice assistance if their understanding is insufficient.

2. The system of claim 1.

8. The voice assist unit Using emotion estimation functionality, we monitor the emotional responses of elderly people during voice assistance in real time and provide voice guidance that elicits positive emotions.

2. The system of claim 1.

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