system

Abstract

We provide the system. [Solution] An information processing device acquires user attribute information and provides means for personalizing a learning plan based on said attribute information. An information processing device provides means for presenting instructions on how to operate a smart device in real time using voice and visual hints, The information processing device records the user's operation history and learning progress, and provides means for optimizing the next learning plan. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that beginners of digital devices, including the elderly, have difficulty acquiring operations quickly and accurately. In conventional teaching methods, individual guidance according to the interests and skills of individual users is not sufficiently carried out, and the efficiency of acquiring operations is low. In addition, there is a lack of a mechanism to solve operation problems of learners in real time, so there is a problem that the motivation to learn tends to decrease.

Means for Solving the Problems

[0005] This invention provides a means for an information processing device to acquire user attribute information and personalize a learning plan based on that information. It also has a function to provide instructions on how to operate the device using real-time voice and visual hints, and optimizes the next learning plan by recording the user's operation history and learning progress. This enables an efficient learning experience tailored to the individual needs of the user, supporting rapid acquisition.

[0006] An "information processing device" is a device that receives data from users, processes it, and provides appropriate information or instructions based on that data.

[0007] A "user" is a person who operates an information processing device and receives a learning plan.

[0008] "Attribute information" refers to information about individual characteristics of users, such as their interests, skills, and behavioral history.

[0009] A "learning plan" is a set of learning programs and assignments customized based on the user's attribute information.

[0010] "Personalizing" means adjusting the content to suit each user's individual needs and pace.

[0011] "Audio and visual cues" refer to information that provides instructions for users to refer to while learning, through audio messages and visual displays.

[0012] "Operating instructions" refer to the procedures and methods for using the functions and applications of a smart device.

[0013] "Real-time" is a temporal concept that means operations and information transmission occur instantly.

[0014] "Operation history" refers to a record of a series of operations performed by a user on a device.

[0015] "Learning progress" is an indicator of progress showing the degree to which the user has deepened their understanding of what they are learning.

[0016] "To optimize" means to adjust and improve so as to obtain the best results under the given conditions.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiment for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to an information processing system for elderly people and beginners with digital devices to learn how to operate smart devices more efficiently. This system provides individually personalized learning plans based on the user's attribute information and can demonstrate how to operate the device in real time. The operation of the system is described in detail below.

[0039] First, the user activates the device and enters attribute information using a dedicated application. This information includes the user's age, experience with digital devices, and specific learning goals. The server receives the entered information and generates an optimal learning plan for each user.

[0040] Next, the server sends the generated learning plan to the user's device. Based on this learning plan, the device prepares to provide the user with appropriate audio and visual guidance. For example, if the user wants to learn how to use the camera app, the device will automatically start the camera app and provide the user with audio instructions and visual instructions on the screen for taking photos.

[0041] During a learning session, users can ask any question they like. The device that receives the question will communicate with the server as needed to obtain an answer and provide it to the user in real time. For example, if a user asks, "How do I set aperture priority mode?", the device will explain the method in detail and display the operating procedure on the screen.

[0042] Furthermore, the device records the user's operation history and monitors their learning progress. This information is sent to a server and used to optimize the next learning session. In this way, an environment is provided that supports users in efficiently progressing through their learning and gaining confidence in operating digital devices. This entire process makes digital technology more accessible to beginners, such as the elderly, and enables smoother social interaction. As a concrete example, when a user learns how to share photos using a messaging app, the device automatically provides guidance and shows the steps of operation.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user activates their smart device and enters their personal information using a dedicated application. This includes registering their name, age, smart device usage experience, and learning objectives.

[0046] Step 2:

[0047] The server receives attribute information sent by the user and stores it in a database. Next, it analyzes this information to generate a learning plan optimized for the user.

[0048] Step 3:

[0049] The server sends the generated learning plan to the user's device. The device then prepares for the learning session based on this plan.

[0050] Step 4:

[0051] The user starts a learning session on their device. The device's AI assistant provides voice and visual guidance according to the learning plan, explaining how to operate the smart device. For example, if learning how to use the camera app, it will automatically launch the camera and show specific functions and settings.

[0052] Step 5:

[0053] If a user has questions or concerns, they can submit inquiries via voice or text to their device. The device then communicates with the server to provide solutions and additional explanations in real time.

[0054] Step 6:

[0055] During a learning session, the device records the user's activity history and learning progress. This data is periodically sent to the server and used to optimize the next learning plan to improve the user's learning efficiency.

[0056] Step 7:

[0057] The server analyzes the collected data and provides feedback to further improve the approach tailored to each user. This leads to a continuous improvement in the learning experience.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In recent years, learning how to operate electronic devices efficiently and effectively has become a difficult challenge for the elderly and those new to digital devices. Traditional learning methods rely on general procedures and information, making it difficult to provide a personalized learning experience tailored to the individual user's attributes, interests, and skill levels. Furthermore, real-time instruction and question support are insufficient, sometimes leaving users unable to operate devices with confidence. There is a growing need for information processing systems that can solve these problems and provide a more appropriate learning experience.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for acquiring user attribute information and personalizing a learning plan from prompt sentences using a generative AI model; means for presenting instructions on how to operate electronic devices in real time using voice and visual hints; and means for recording the user's operation history and learning progress and optimizing the next learning plan. This enables elderly people and beginners with digital devices to efficiently learn how to operate devices under an optimal learning plan tailored to their individual attributes and to acquire skills with confidence through real-time question support.

[0063] An "information processing device" is an electronic device that analyzes data collected from users and processes that data according to a specific purpose.

[0064] "User attribute information" refers to data that represents the individual characteristics of a user, such as age, experience using digital devices, interests, and what they want to learn.

[0065] A "generative AI model" is a type of algorithm that uses machine learning techniques to analyze data and make judgments and predictions.

[0066] A "prompt sentence" is initial input data or instruction sentence used to derive specific information or answers from a generative AI model.

[0067] A "learning plan" is a plan that includes procedures and materials to help users efficiently learn how to use digital technologies and devices.

[0068] "Audio and visual cues" refer to auditory and visual support information used to explain how to operate a device to the user.

[0069] "Operation history" refers to data that records past operations and actions performed by a user on a digital device.

[0070] "Learning progress" refers to information indicating the extent to which the user has achieved their set learning goals at this point in time.

[0071] "Presenting in real time" means that information and instructions are provided to users immediately without any time delay.

[0072] This invention relates to an information processing system for elderly people and those new to digital devices to learn how to operate electronic devices efficiently.

[0073] The user first activates their electronic device and enters their attribute information using a dedicated application. This attribute information includes age, experience using digital devices, and what they want to learn. Once this data entry is complete, the server receives the information and uses a generative AI model to create a learning plan tailored to the user. In this process, the generative AI model provides a customized learning plan based on prompts and according to the user's needs. For example, a possible prompt might be, "Please create a learning plan for taking pictures using a camera app."

[0074] Next, the server sends the generated personalized learning plan to the user's device. The device uses this plan to prepare to provide the user with audio and visual guidance. Audio guidance includes voice output through a microphone and speaker, while visual guidance includes step-by-step instructions on the display. This combination allows the user to learn the operation procedures in real time. For example, if the user wants to learn how to use the camera app, the device will automatically launch the camera app and guide them through the shooting process.

[0075] Furthermore, if a user has a question during their learning process, they can send it to the server via their device. The server uses a generative AI model to generate an appropriate response to the question and provides it to the user in real time. In this way, users can progress with their learning while resolving any questions they have about how to use the system.

[0076] As learning progresses, the device records the user's operation history and learning progress. This information is sent to a server and used to optimize the next learning session. As a result, users can effectively acquire digital skills at their own pace and gain greater confidence in operating the device.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user activates their electronic device and opens a dedicated application. The user then enters attribute information such as age, experience with digital devices, and what they wish to learn. This entered information is then sent to the server.

[0080] Step 2:

[0081] The server analyzes the attribute information received from the user. Using a generative AI model, the server creates a personalized learning plan based on the prompt "Create the optimal learning plan for the user based on this information." This prompt prompt allows the generative AI model to determine the learning steps and content that best suit the user's needs. As a result, a specific learning plan is generated.

[0082] Step 3:

[0083] The server sends the generated learning plan to the user's device. The device receives this learning plan and prepares to provide the user with audio instruction and visual guidance. The device configures its speaker and display to present the learning content clearly.

[0084] Step 4:

[0085] The user begins learning through their device. The device automatically launches the appropriate app based on what the user wants to learn, such as how to use the camera app. If necessary, it provides real-time audio and visual guidance to help users resolve any questions they may have while learning how to use the device.

[0086] Step 5:

[0087] When a user has a question during the learning process, they send the question from their device to the server. The server uses a generative AI model to analyze the question and generate the best possible answer. The generated answer is sent to the device and presented to the user in real time. This allows the user to operate with confidence.

[0088] Step 6:

[0089] The device records the user's operation history and learning progress as the learning process progresses. This recorded data is sent to a server and used for analysis to make the next learning session more effective. Based on this historical information, the server optimizes the content of the next learning session to better meet the individual needs of the user.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern society, the difficulties elderly and those new to digital technology face in accessing technology pose a problem that hinders their social interactions and daily lives. In particular, learning the digital technologies necessary for elderly people to communicate with their families is often complex and stressful. Therefore, there is a need for technology that provides personalized, real-time learning support.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for an information processing device to collect user characteristic data and personalize a learning plan based on said characteristic data; means for the information processing device to immediately show how to operate an electronic device using voice and visual instructions; means for the information processing device to save the user's operation behavior and learning progress and optimize the next learning plan; and means for the information processing device to guide the user's interface operation on the digital screen using visual effects. This makes it possible for elderly people and digital device beginners to receive guidance tailored to their individual learning needs and to operate digital devices with confidence.

[0095] An "information processing device" is a device that collects user characteristic data, creates individualized learning plans based on that data, and provides real-time instruction in cooperation with servers and client devices.

[0096] "Characteristic data" refers to attribute information of individual users, such as their age, experience with digital devices, interests, and motivation to learn, and is used to develop personalized learning plans.

[0097] A "learning plan" or "study schedule" is a personalized instructional program created based on the individual user's characteristics data to teach them how to use digital devices.

[0098] "Audio and visual instructions" refers to technologies that provide audio guidance and on-screen visual instructions to help users understand how to operate digital devices.

[0099] "Operational behavior" refers to a series of actions and interactions that users perform when using digital devices, and is used to evaluate learning progress and optimize future learning plans.

[0100] "Progress" is a measure used to evaluate the user's progress and understanding in the process of learning how to operate digital devices.

[0101] "Visual effects" refer to visual techniques such as animations and highlighting used on the user's screen to assist in navigating the interface.

[0102] An information processing system is essential to realize this invention. This system provides a personalized learning experience tailored to the user's attributes, targeting elderly people and beginners who use digital devices.

[0103] The server receives characteristic data from the user and generates a learning plan based on it. This characteristic data includes age and experience using digital devices. The generated learning plan is sent to the user's device, and instructions on how to operate the digital device are provided through voice and visual guidance.

[0104] The device uses visual effects to assist the user with interface operations on the screen. For example, when a user makes a video call, in addition to voice guidance such as "Please tap the call button," the button is highlighted on the screen with animation, allowing the user to understand the operation more intuitively.

[0105] If users have questions during their learning process, they can submit inquiries to the server in real time via their device. The server uses a generative AI model to generate appropriate answers and presents them to the user via voice or other means. This process allows users to resolve their questions on the spot and continue their learning.

[0106] Furthermore, the device records the user's actions and learning progress, which is used to optimize the next learning plan. In this way, users can continuously learn at the pace and in the way that is best suited to them.

[0107] For example, when a user learns how to make a video call with their grandchild, the application provides voice guidance saying, "Open the calling app and tap the call button," and displays an arrow on the screen indicating the button. This allows the user to intuitively understand how to operate it. By using a prompt such as, "How can I instruct elderly people on the flow of starting a video call using voice and visuals?", the generative AI model can provide appropriate instruction.

[0108] Hardware examples include tablets and smartphones, while the software includes Python, the Flask framework, and the Google® Text-to-Speech API. This enables integrated voice and visual guidance for user assistance.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user activates the device and enters characteristic data. This data includes age, experience using digital devices, and what they want to learn. The device then sends the entered data to the server.

[0112] Step 2:

[0113] The server generates a learning plan based on the received characteristic data. This includes data processing to select content and teaching methods appropriate for the user from the database. The generated learning plan is then sent to the terminal.

[0114] Step 3:

[0115] The device prepares audio and visual guides. It uses the Google Text-to-Speech API to generate audio guides based on the learning plan. Visually, it provides visual effects to highlight the relevant on-screen actions.

[0116] Step 4:

[0117] The user operates the digital device following the terminal's instructions. Based on the terminal's guidance, the user attempts specific operations. The results of the operations are recorded in real time and used to optimize future learning plans.

[0118] Step 5:

[0119] When a user asks a question about something they don't understand, the device communicates with the server to generate an appropriate answer in real time using a generation AI model. The prompt is input into the AI ​​model, and a response that analyzes its meaning is provided to the user.

[0120] Step 6:

[0121] The device records user actions and learning progress, and uses this information to generate future learning plans. This ensures continuous improvement of the individual learning experience. It analyzes user interaction logs and proposes optimized learning plans.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention relates to an information processing system incorporating an emotion engine that recognizes the user's emotions. This system optimizes the next learning plan by personalizing the learning plan based on the user's attribute information, providing real-time instructions on how to operate a smart device, and recording operation history and learning progress. It also evaluates the user's emotions and adjusts the learning content and method of progress more effectively.

[0124] First, the user activates their smart device and enters their attribute information into the application. The server then receives the user's information and generates a customized plan tailored to their learning needs.

[0125] Next, the server sends the generated learning plan to the user's device. The device uses an emotion engine to analyze the user's facial expressions and tone of voice to assess the user's emotional state towards learning. This emotion data is used to personalize the user's learning experience and provide optimal content tailored to their emotional state.

[0126] Once a learning session begins, the device provides audio and visual guidance, explaining how to operate the smart device. If the user shows signs of anxiety or confusion during operation, the emotion engine detects this and adjusts the guidance content and pace via the server. For example, if the user shows anxiety about a complex operation, the device will provide more detailed and easy-to-understand instructions while also displaying encouraging messages.

[0127] Furthermore, user questions are received in real time via the device and immediately resolved in conjunction with the server. Additionally, user emotional feedback is recorded during the learning process, which is used to improve user engagement in subsequent sessions.

[0128] Thus, this system achieves a high level of personalization that meets the individual learning needs of users through both emotion recognition and the provision of learning content. For example, when learning photo editing functions, if the emotion engine detects the user's joy, it may suggest additional editing techniques to enhance this positive experience.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user activates their smart device and enters attribute information using a dedicated application. This information includes age, past device usage experience, and desired learning content.

[0132] Step 2:

[0133] The server receives attribute information sent by the user and generates an optimized learning plan based on that data. The server then sends this learning plan to the user's device.

[0134] Step 3:

[0135] The device prepares for the learning session based on the received learning plan. Simultaneously, it activates the emotion engine to analyze the user's facial expressions and tone of voice in real time and evaluate the user's emotional state.

[0136] Step 4:

[0137] The user begins a learning session. The device uses audio and visual guidance to guide the user step-by-step through the operations they need to learn. Depending on the user's emotional state, the device may, for example, elaborate on specific steps or adjust the pace of the learning process.

[0138] Step 5:

[0139] When a user asks a question or encounters difficulties during learning, the device communicates with the server in real time, providing the user with detailed instructions and answers immediately.

[0140] Step 6:

[0141] The device records the user's operation history and changes in emotional state during the learning process. This allows for detailed analysis of which steps the user struggled with and which parts elicited positive responses.

[0142] Step 7:

[0143] The server analyzes the operation history and sentiment feedback sent from the terminal to optimize the plan for the next learning session. The server saves the adjusted plan for the next session.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Providing an effective learning experience tailored to each user's individual attributes and emotional state requires the creation of appropriate learning plans, continuous monitoring of emotions during the process, and real-time feedback and adjustments. However, few systems can achieve these efficiently and accurately, and there is a particular need for systems that can flexibly respond to changes in emotions and appropriately adjust the learning pace and content.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information; means for an information processing device to present instructions on how to operate electronic devices in real time using voice and visual guidance; means for an information processing device to record the user's behavior history and learning progress and optimize the next learning plan; and means for an information processing device to evaluate the user's emotional state and use an emotion analysis engine to adjust the learning content and progress. This makes it possible to provide a flexible and advanced learning experience tailored to the individual needs and emotional state of the user.

[0149] An "information processing device" refers to any hardware or software that receives information from users and performs data analysis and processing based on that information.

[0150] "Attribute information" refers to data that indicates the individual characteristics and needs of users, such as their age, learning objectives, and skill level.

[0151] A "learning plan" refers to a plan that is individually designed based on the user's attribute information, including learning materials, pace, and methods.

[0152] "Personalization" means optimizing learning content and services for each user based on their attributes and needs.

[0153] "Audio and visual guidance" refers to methods of providing information to assist users through audio instructions and visual presentations.

[0154] "Electronic devices" refer to all devices that process digital data, such as computers, smartphones, and tablets.

[0155] "Real-time" refers to processing or responses that occur instantly without delay.

[0156] "Behavioral history" refers to a record of a user's past operations and actions, and is data used to optimize future learning and service delivery.

[0157] "Learning progress" refers to the stage or progress a user has reached in their current learning process.

[0158] An "emotion analysis engine" refers to a program that uses technology to analyze a user's facial expressions, tone of voice, etc., to evaluate their current psychological state and emotions.

[0159] "Generative AI technology" refers to technology that uses artificial intelligence to respond to user questions and requests in natural language and to make creative suggestions.

[0160] A system implementing this invention includes a server and a terminal as information processing equipment.

[0161] The user activates their smart device and enters their attribute information into a dedicated application. This application retrieves data such as age, learning objectives, and skill level, and sends it to a server. The server uses this attribute information to perform appropriate data processing in order to generate an individualized learning plan. In this process, machine learning algorithms can be used to suggest the optimal learning path.

[0162] The learning plan generated on the server is sent to the user's device. The device uses its built-in emotion analysis engine to analyze the user's facial expressions and tone of voice, and evaluates their emotions during learning. Based on this evaluation, the device presents learning content tailored to the user in real time through audio and visual guidance. If the user shows signs of confusion or anxiety, the device sends this emotion information to the server, which then adjusts the support and pace accordingly.

[0163] Furthermore, if a user has a question during the learning process, the device sends that question to the server. The server uses generative AI technology to utilize prompts and generate appropriate answers in real time. For example, by entering a prompt such as, "Analyze the user's emotions and suggest ways to optimize the learning content," an immediate response can be obtained.

[0164] For example, if a user is learning photo editing functions, and the emotion analysis engine detects the user's feelings of joy, the device can suggest additional editing techniques to further enhance that positive experience. In this way, the system can provide a highly personalized learning experience based on the user's emotional state and attribute information.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user activates their smart device and enters their attribute information into a dedicated application. This data includes age, learning objectives, and skill level. This attribute information is then transmitted to the server via the smart device's terminal.

[0168] Step 2:

[0169] The server performs data analysis based on the received attribute information and generates the optimal learning plan. Specifically, it selects the most suitable learning path for the user by analyzing data from similar users using a machine learning algorithm. As an output of this process, a personalized learning plan is generated and sent to the terminal.

[0170] Step 3:

[0171] The device uses the learning plan received from the server to activate its built-in emotion analysis engine. This engine analyzes the user's facial expressions and tone of voice in real time, acquiring the results as data. This allows the system to evaluate the user's emotional state and provide foundational data for adjusting the learning content and pace.

[0172] Step 4:

[0173] The device explains how to operate the smart device by providing audio and visual guidance based on the evaluation results. The input is the user's emotional state, which was evaluated earlier, and the output is guide information tailored to the user's needs.

[0174] Step 5:

[0175] During the learning process, the user can input questions through their device. The device sends this as input data to the server. The server uses a generative AI model to generate prompt sentences and construct the optimal answer based on these prompt sentences. The server's answer is then presented to the user as output data.

[0176] Step 6:

[0177] The device records the user's activity history and learning progress. This recorded data is sent to the server to optimize future learning plans. This allows for more precise delivery of learning content tailored to individual needs.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] In caregiving settings, accurately understanding the emotional state of each individual user and providing appropriate care and instructions accordingly is challenging. Furthermore, there is a need for techniques that improve the quality of care services while reducing the burden on caregivers.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information, means for an information processing device to present instructions on how to operate a computing device in real time using voice and visual instructions, and means for an information processing device to analyze the user's facial expressions and tone of voice and evaluate their emotional state. This makes it possible to grasp the user's emotions in real time in a care setting and provide appropriate care instructions.

[0183] An "information processing device" refers to the entire system that analyzes user attribute information and emotional state to provide appropriate learning plans and care instructions.

[0184] "Attribute information" refers to specific profile data and information that indicates individual characteristics about a user.

[0185] A "learning plan" refers to guidelines or programs for education and training that are optimized based on the user's attributes and emotional state.

[0186] "Emotional state" refers to a psychological state identified based on the user's facial expressions, voice, and other physiological indicators.

[0187] "Computing device" refers to hardware that functions as an interface with the user, such as smart glasses or other digital devices.

[0188] The server receives user attribute information and creates an individualized learning plan based on that information. This plan is generated considering the user's profile data and past behavior. The server then provides voice and visual instructions to computing devices such as smart glasses. This allows caregivers to understand the user's emotional state in real time.

[0189] The smart glasses, which serve as the device, analyze the user's facial expressions and evaluate their emotions using TENSORFLOW® and OpenCV. They also use the Google Cloud Speech-to-Text API for voice processing, analyzing the tone of the user's voice. This analyzed emotion data is processed by a server, and appropriate countermeasures are presented to the caregiver.

[0190] For example, if a caregiver is interacting with an elderly client, the device will recognize that the elderly person is feeling anxious. The server will then immediately display instructions on the glasses, such as, "To reassure them, it would be good to slow down your speaking speed and speak in a soft voice."

[0191] An example of a prompt using a generative AI model is: "Consider how to generate a guide that analyzes the user's emotions in the context of elderly care and provides guidance on how to make them feel at ease." Based on this prompt, the AI ​​model can generate instructions to support the optimal action.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user activates the device and enters their personal attribute information. The device sends this information to the server. Based on the input, the server compares it with the user's past data and personalizes the learning plan. As output, a personalized learning plan is generated.

[0195] Step 2:

[0196] The server sends the generated learning plan to the terminal. The terminal prepares audio and visual instructions to notify the user. These instructions include specific guidelines to support the user's learning. The input is the generated learning plan, and the output is the instructions displayed on the terminal.

[0197] Step 3:

[0198] The device captures the user's facial expressions with a camera and generates image data. This image data is then analyzed using TensorFlow or OpenCV to evaluate the user's emotional state. The input is the facial image data, and the output is the analyzed emotional state.

[0199] Step 4:

[0200] The device also collects voice data and analyzes it using the Google Cloud Speech-to-Text API. The input is voice data, and it outputs emotion labels based on an emotion survey. The results are sent to a server to understand the overall emotional state.

[0201] Step 5:

[0202] The server generates user-appropriate responses and instructions based on evaluated emotion data and individualized learning plans. These are then sent to the terminal. The input is the emotion state and learning plan, while the output is the specific content of the countermeasures and instructions.

[0203] Step 6:

[0204] The terminal receives instructions from the server and presents them to the caregiver as visual and audio guides. Specifically, it is recommended that the caregiver take actions based on the instructions to reassure the user.

[0205] Step 7:

[0206] After a care session ends, the terminal sends the user's operation history and emotional feedback to the server. This data is used to optimize the next learning plan. The input is the operation history and emotional feedback at the end of the session, and the output is the preparation of data for the next optimization.

[0207] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 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.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention relates to an information processing system for elderly people and beginners with digital devices to learn how to operate smart devices more efficiently. This system provides individually personalized learning plans based on the user's attribute information and can demonstrate how to operate the device in real time. The operation of the system is described in detail below.

[0224] First, the user activates the device and enters attribute information using a dedicated application. This information includes the user's age, experience with digital devices, and specific learning goals. The server receives the entered information and generates an optimal learning plan for each user.

[0225] Next, the server sends the generated learning plan to the user's device. Based on this learning plan, the device prepares to provide the user with appropriate audio and visual guidance. For example, if the user wants to learn how to use the camera app, the device will automatically start the camera app and provide the user with audio instructions and visual instructions on the screen for taking photos.

[0226] During a learning session, users can ask any question they like. The device that receives the question will communicate with the server as needed to obtain an answer and provide it to the user in real time. For example, if a user asks, "How do I set aperture priority mode?", the device will explain the method in detail and display the operating procedure on the screen.

[0227] Furthermore, the device records the user's operation history and monitors their learning progress. This information is sent to a server and used to optimize the next learning session. In this way, an environment is provided that supports users in efficiently progressing through their learning and gaining confidence in operating digital devices. This entire process makes digital technology more accessible to beginners, such as the elderly, and enables smoother social interaction. As a concrete example, when a user learns how to share photos using a messaging app, the device automatically provides guidance and shows the steps of operation.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user activates their smart device and enters their personal information using a dedicated application. This includes registering their name, age, smart device usage experience, and learning objectives.

[0231] Step 2:

[0232] The server receives attribute information sent by the user and stores it in a database. Next, it analyzes this information to generate a learning plan optimized for the user.

[0233] Step 3:

[0234] The server sends the generated learning plan to the user's device. The device then prepares for the learning session based on this plan.

[0235] Step 4:

[0236] The user starts a learning session on their device. The device's AI assistant provides voice and visual guidance according to the learning plan, explaining how to operate the smart device. For example, if learning how to use the camera app, it will automatically launch the camera and show specific functions and settings.

[0237] Step 5:

[0238] If a user has questions or concerns, they can submit inquiries via voice or text to their device. The device then communicates with the server to provide solutions and additional explanations in real time.

[0239] Step 6:

[0240] During a learning session, the device records the user's activity history and learning progress. This data is periodically sent to the server and used to optimize the next learning plan to improve the user's learning efficiency.

[0241] Step 7:

[0242] The server analyzes the collected data and provides feedback to further improve the approach tailored to each user. This leads to a continuous improvement in the learning experience.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In recent years, learning how to operate electronic devices efficiently and effectively has become a difficult challenge for the elderly and those new to digital devices. Traditional learning methods rely on general procedures and information, making it difficult to provide a personalized learning experience tailored to the individual user's attributes, interests, and skill levels. Furthermore, real-time instruction and question support are insufficient, sometimes leaving users unable to operate devices with confidence. There is a growing need for information processing systems that can solve these problems and provide a more appropriate learning experience.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for acquiring user attribute information and personalizing a learning plan from prompt sentences using a generative AI model; means for presenting instructions on how to operate electronic devices in real time using voice and visual hints; and means for recording the user's operation history and learning progress and optimizing the next learning plan. This enables elderly people and beginners with digital devices to efficiently learn how to operate devices under an optimal learning plan tailored to their individual attributes and to acquire skills with confidence through real-time question support.

[0248] An "information processing device" is an electronic device that analyzes data collected from users and processes that data according to a specific purpose.

[0249] "User attribute information" refers to data that represents the individual characteristics of a user, such as age, experience using digital devices, interests, and what they want to learn.

[0250] A "generative AI model" is a type of algorithm that uses machine learning techniques to analyze data and make judgments and predictions.

[0251] A "prompt sentence" is initial input data or instruction sentence used to derive specific information or answers from a generative AI model.

[0252] A "learning plan" is a plan that includes procedures and materials to help users efficiently learn how to use digital technologies and devices.

[0253] "Audio and visual cues" refer to auditory and visual support information used to explain how to operate a device to the user.

[0254] "Operation history" refers to data that records past operations and actions performed by a user on a digital device.

[0255] "Learning progress" refers to information indicating the extent to which the user has achieved their set learning goals at this point in time.

[0256] "Presenting in real time" means that information and instructions are provided to users immediately without any time delay.

[0257] This invention relates to an information processing system for elderly people and those new to digital devices to learn how to operate electronic devices efficiently.

[0258] The user first activates their electronic device and enters their attribute information using a dedicated application. This attribute information includes age, experience using digital devices, and what they want to learn. Once this data entry is complete, the server receives the information and uses a generative AI model to create a learning plan tailored to the user. In this process, the generative AI model provides a customized learning plan based on prompts and according to the user's needs. For example, a possible prompt might be, "Please create a learning plan for taking pictures using a camera app."

[0259] Next, the server sends the generated personalized learning plan to the user's device. The device uses this plan to prepare to provide the user with audio and visual guidance. Audio guidance includes voice output through a microphone and speaker, while visual guidance includes step-by-step instructions on the display. This combination allows the user to learn the operation procedures in real time. For example, if the user wants to learn how to use the camera app, the device will automatically launch the camera app and guide them through the shooting process.

[0260] Furthermore, if a user has a question during their learning process, they can send it to the server via their device. The server uses a generative AI model to generate an appropriate response to the question and provides it to the user in real time. In this way, users can progress with their learning while resolving any questions they have about how to use the system.

[0261] As learning progresses, the device records the user's operation history and learning progress. This information is sent to a server and used to optimize the next learning session. As a result, users can effectively acquire digital skills at their own pace and gain greater confidence in operating the device.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user activates their electronic device and opens a dedicated application. The user then enters attribute information such as age, experience with digital devices, and what they wish to learn. This entered information is then sent to the server.

[0265] Step 2:

[0266] The server analyzes the attribute information received from the user. Using a generative AI model, the server creates a personalized learning plan based on the prompt "Create the optimal learning plan for the user based on this information." This prompt prompt allows the generative AI model to determine the learning steps and content that best suit the user's needs. As a result, a specific learning plan is generated.

[0267] Step 3:

[0268] The server sends the generated learning plan to the user's device. The device receives this learning plan and prepares to provide the user with audio instruction and visual guidance. The device configures its speaker and display to present the learning content clearly.

[0269] Step 4:

[0270] The user begins learning through their device. The device automatically launches the appropriate app based on what the user wants to learn, such as how to use the camera app. If necessary, it provides real-time audio and visual guidance to help users resolve any questions they may have while learning how to use the device.

[0271] Step 5:

[0272] When a user has a question during the learning process, they send the question from their device to the server. The server uses a generative AI model to analyze the question and generate the best possible answer. The generated answer is sent to the device and presented to the user in real time. This allows the user to operate with confidence.

[0273] Step 6:

[0274] The device records the user's operation history and learning progress as the learning process progresses. This recorded data is sent to a server and used for analysis to make the next learning session more effective. Based on this historical information, the server optimizes the content of the next learning session to better meet the individual needs of the user.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In modern society, the difficulties elderly and those new to digital technology face in accessing technology pose a problem that hinders their social interactions and daily lives. In particular, learning the digital technologies necessary for elderly people to communicate with their families is often complex and stressful. Therefore, there is a need for technology that provides personalized, real-time learning support.

[0278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes means for an information processing device to collect user characteristic data and personalize a learning plan based on said characteristic data; means for the information processing device to immediately show how to operate an electronic device using voice and visual instructions; means for the information processing device to save the user's operation behavior and learning progress and optimize the next learning plan; and means for the information processing device to guide the user's interface operation on the digital screen using visual effects. This makes it possible for elderly people and digital device beginners to receive guidance tailored to their individual learning needs and to operate digital devices with confidence.

[0280] The "information processing device" is a device that collects the characteristic data of users, creates individualized learning plans based on it, and provides real-time guidance in cooperation with servers and client devices.

[0281] The "characteristic data" refers to the attribute information of individual users, such as the age of the user, experience with digital devices, interests, learning motivation, etc., and is the data used to formulate individualized learning plans.

[0282] The "learning plan" or "learning program" is personalized guidance content for learning how to operate digital devices, created based on the characteristic data of individual users.

[0283] The "voice and visual instructions" is a technology that provides voice guidance and visual guides displayed on the screen to make it easier for users to understand the operation of digital devices.

[0284] The "operation behavior" refers to a series of actions and interactions that users perform when using digital devices, and is used for evaluating the progress of learning and optimizing the next learning plan.

[0285] The "progress" is a measure for evaluating the progress and understanding level of users in the process of learning how to operate digital devices.

[0286] The "visual effect" refers to visual techniques such as animations and highlighted displays used on the user's screen to assist in the operation of the interface.

[0287] To realize this invention, an information processing system is essential. This system provides an individualized learning experience tailored to the attributes of users, targeting the elderly and beginners who use digital devices.

[0288] The server receives characteristic data from the user and generates a learning plan based on it. This characteristic data includes age and experience using digital devices. The generated learning plan is sent to the user's device, and instructions on how to operate the digital device are provided through voice and visual guidance.

[0289] The device uses visual effects to assist the user with interface operations on the screen. For example, when a user makes a video call, in addition to voice guidance such as "Please tap the call button," the button is highlighted on the screen with animation, allowing the user to understand the operation more intuitively.

[0290] If users have questions during their learning process, they can submit inquiries to the server in real time via their device. The server uses a generative AI model to generate appropriate answers and presents them to the user via voice or other means. This process allows users to resolve their questions on the spot and continue their learning.

[0291] Furthermore, the device records the user's actions and learning progress, which is used to optimize the next learning plan. In this way, users can continuously learn at the pace and in the way that is best suited to them.

[0292] For example, when a user learns how to make a video call with their grandchild, the application provides voice guidance saying, "Open the calling app and tap the call button," and displays an arrow on the screen indicating the button. This allows the user to intuitively understand how to operate it. By using a prompt such as, "How can I instruct elderly people on the flow of starting a video call using voice and visuals?", the generative AI model can provide appropriate instruction.

[0293] Hardware examples include tablets and smartphones, while software includes Python, the Flask framework, and the Google Text-to-Speech API. This enables integrated voice and visual guidance for user assistance.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user activates the device and enters characteristic data. This data includes age, experience using digital devices, and what they want to learn. The device then sends the entered data to the server.

[0297] Step 2:

[0298] The server generates a learning plan based on the received characteristic data. This includes data processing to select content and teaching methods appropriate for the user from the database. The generated learning plan is then sent to the terminal.

[0299] Step 3:

[0300] The device prepares audio and visual guides. It uses the Google Text-to-Speech API to generate audio guides based on the learning plan. Visually, it provides visual effects to highlight the relevant on-screen actions.

[0301] Step 4:

[0302] The user operates the digital device following the terminal's instructions. Based on the terminal's guidance, the user attempts specific operations. The results of the operations are recorded in real time and used to optimize future learning plans.

[0303] Step 5:

[0304] When a user asks a question about something they don't understand, the device communicates with the server to generate an appropriate answer in real time using a generation AI model. The prompt is input into the AI ​​model, and a response that analyzes its meaning is provided to the user.

[0305] Step 6:

[0306] The terminal records the user's operation behaviors and learning progress, and utilizes them for the next plan generation. As a result, individual learning experiences are continuously improved. Analyze the user's interaction log and propose an optimized learning plan.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0308] The present invention relates to an information processing system incorporating an emotion engine for recognizing the emotions of users. This system optimizes the next learning plan through personalization of the learning plan based on the user's attribute information, real-time presentation of the operation method of the smart device, and recording of the operation history and learning progress. Also, evaluate the user's emotions and adjust the learning content and progress method more effectively.

[0309] First, the user activates the smart device and inputs the attribute information into the application. Thereby, the server receives the user's information and generates a customized plan according to the learning needs.

[0310] Next, the server transmits the generated learning plan to the user's terminal. The terminal analyzes the user's facial expressions and voice tones by the emotion engine and evaluates what kind of emotional state the user is in regarding learning. This emotion data is used to individualize the user's learning experience and provide optimal content according to the emotional state.

[0311] When the learning session starts, the terminal provides audio and visual guides and explains the operation method of the smart device. If the user shows uneasiness or confusion during the operation, the emotion engine detects this and adjusts the guide content and progress pace via the server. For example, if the user shows uneasiness about a complex operation, the terminal provides the operation procedure in more detail and clearly, and at the same time displays an encouraging message.

[0312] Furthermore, user questions are received in real time via the device and immediately resolved in conjunction with the server. Additionally, user emotional feedback is recorded during the learning process, which is used to improve user engagement in subsequent sessions.

[0313] Thus, this system achieves a high level of personalization that meets the individual learning needs of users through both emotion recognition and the provision of learning content. For example, when learning photo editing functions, if the emotion engine detects the user's joy, it may suggest additional editing techniques to enhance this positive experience.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The user activates their smart device and enters attribute information using a dedicated application. This information includes age, past device usage experience, and desired learning content.

[0317] Step 2:

[0318] The server receives attribute information sent by the user and generates an optimized learning plan based on that data. The server then sends this learning plan to the user's device.

[0319] Step 3:

[0320] The device prepares for the learning session based on the received learning plan. Simultaneously, it activates the emotion engine to analyze the user's facial expressions and tone of voice in real time and evaluate the user's emotional state.

[0321] Step 4:

[0322] The user begins a learning session. The device uses audio and visual guidance to guide the user step-by-step through the operations they need to learn. Depending on the user's emotional state, the device may, for example, elaborate on specific steps or adjust the pace of the learning process.

[0323] Step 5:

[0324] When a user asks a question or encounters difficulties during learning, the device communicates with the server in real time, providing the user with detailed instructions and answers immediately.

[0325] Step 6:

[0326] The device records the user's operation history and changes in emotional state during the learning process. This allows for detailed analysis of which steps the user struggled with and which parts elicited positive responses.

[0327] Step 7:

[0328] The server analyzes the operation history and sentiment feedback sent from the terminal to optimize the plan for the next learning session. The server saves the adjusted plan for the next session.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] Providing an effective learning experience tailored to each user's individual attributes and emotional state requires the creation of appropriate learning plans, continuous monitoring of emotions during the process, and real-time feedback and adjustments. However, few systems can achieve these efficiently and accurately, and there is a particular need for systems that can flexibly respond to changes in emotions and appropriately adjust the learning pace and content.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information; means for an information processing device to present instructions on how to operate electronic devices in real time using voice and visual guidance; means for an information processing device to record the user's behavior history and learning progress and optimize the next learning plan; and means for an information processing device to evaluate the user's emotional state and use an emotion analysis engine to adjust the learning content and progress. This makes it possible to provide a flexible and advanced learning experience tailored to the individual needs and emotional state of the user.

[0334] An "information processing device" refers to any hardware or software that receives information from users and performs data analysis and processing based on that information.

[0335] "Attribute information" refers to data that indicates the individual characteristics and needs of users, such as their age, learning objectives, and skill level.

[0336] A "learning plan" refers to a plan that is individually designed based on the user's attribute information, including learning materials, pace, and methods.

[0337] "Personalization" means optimizing learning content and services for each user based on their attributes and needs.

[0338] "Audio and visual guidance" refers to methods of providing information to assist users through audio instructions and visual presentations.

[0339] "Electronic devices" refer to all devices that process digital data, such as computers, smartphones, and tablets.

[0340] "Real-time" refers to processing or responses that occur instantly without delay.

[0341] "Behavioral history" refers to a record of a user's past operations and actions, and is data used to optimize future learning and service delivery.

[0342] "Learning progress" refers to the stage or progress a user has reached in their current learning process.

[0343] An "emotion analysis engine" refers to a program that uses technology to analyze a user's facial expressions, tone of voice, etc., to evaluate their current psychological state and emotions.

[0344] "Generative AI technology" refers to technology that uses artificial intelligence to respond to user questions and requests in natural language and to make creative suggestions.

[0345] A system implementing this invention includes a server and a terminal as information processing equipment.

[0346] The user activates their smart device and enters their attribute information into a dedicated application. This application retrieves data such as age, learning objectives, and skill level, and sends it to a server. The server uses this attribute information to perform appropriate data processing in order to generate an individualized learning plan. In this process, machine learning algorithms can be used to suggest the optimal learning path.

[0347] The learning plan generated on the server is sent to the user's device. The device uses its built-in emotion analysis engine to analyze the user's facial expressions and tone of voice, and evaluates their emotions during learning. Based on this evaluation, the device presents learning content tailored to the user in real time through audio and visual guidance. If the user shows signs of confusion or anxiety, the device sends this emotion information to the server, which then adjusts the support and pace accordingly.

[0348] Furthermore, if a user has a question during the learning process, the device sends that question to the server. The server uses generative AI technology to utilize prompts and generate appropriate answers in real time. For example, by entering a prompt such as, "Analyze the user's emotions and suggest ways to optimize the learning content," an immediate response can be obtained.

[0349] For example, if a user is learning photo editing functions, and the emotion analysis engine detects the user's feelings of joy, the device can suggest additional editing techniques to further enhance that positive experience. In this way, the system can provide a highly personalized learning experience based on the user's emotional state and attribute information.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user activates their smart device and enters their attribute information into a dedicated application. This data includes age, learning objectives, and skill level. This attribute information is then transmitted to the server via the smart device's terminal.

[0353] Step 2:

[0354] The server performs data analysis based on the received attribute information and generates the optimal learning plan. Specifically, it selects the most suitable learning path for the user by analyzing data from similar users using a machine learning algorithm. As an output of this process, a personalized learning plan is generated and sent to the terminal.

[0355] Step 3:

[0356] The device uses the learning plan received from the server to activate its built-in emotion analysis engine. This engine analyzes the user's facial expressions and tone of voice in real time, acquiring the results as data. This allows the system to evaluate the user's emotional state and provide foundational data for adjusting the learning content and pace.

[0357] Step 4:

[0358] The device explains how to operate the smart device by providing audio and visual guidance based on the evaluation results. The input is the user's emotional state, which was evaluated earlier, and the output is guide information tailored to the user's needs.

[0359] Step 5:

[0360] During the learning process, the user can input questions through their device. The device sends this as input data to the server. The server uses a generative AI model to generate prompt sentences and construct the optimal answer based on these prompt sentences. The server's answer is then presented to the user as output data.

[0361] Step 6:

[0362] The device records the user's activity history and learning progress. This recorded data is sent to the server to optimize future learning plans. This allows for more precise delivery of learning content tailored to individual needs.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0365] In caregiving settings, accurately understanding the emotional state of each individual user and providing appropriate care and instructions accordingly is challenging. Furthermore, there is a need for techniques that improve the quality of care services while reducing the burden on caregivers.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information, means for an information processing device to present instructions on how to operate a computing device in real time using voice and visual instructions, and means for an information processing device to analyze the user's facial expressions and tone of voice and evaluate their emotional state. This makes it possible to grasp the user's emotions in real time in a care setting and provide appropriate care instructions.

[0368] An "information processing device" refers to the entire system that analyzes user attribute information and emotional state to provide appropriate learning plans and care instructions.

[0369] "Attribute information" refers to specific profile data and information that indicates individual characteristics about a user.

[0370] A "learning plan" refers to guidelines or programs for education and training that are optimized based on the user's attributes and emotional state.

[0371] "Emotional state" refers to a psychological state identified based on the user's facial expressions, voice, and other physiological indicators.

[0372] "Computing device" refers to hardware that functions as an interface with the user, such as smart glasses or other digital devices.

[0373] The server receives user attribute information and creates an individualized learning plan based on that information. This plan is generated considering the user's profile data and past behavior. The server then provides voice and visual instructions to computing devices such as smart glasses. This allows caregivers to understand the user's emotional state in real time.

[0374] The smart glasses, which serve as the device, analyze the user's facial expressions and evaluate their emotions using TensorFlow and OpenCV. They also use the Google Cloud Speech-to-Text API for voice processing, analyzing the tone of the user's voice. This analyzed emotion data is processed by a server, and appropriate countermeasures are presented to the caregiver.

[0375] For example, if a caregiver is interacting with an elderly client, the device will recognize that the elderly person is feeling anxious. The server will then immediately display instructions on the glasses, such as, "To reassure them, it would be good to slow down your speaking speed and speak in a soft voice."

[0376] An example of a prompt using a generative AI model is: "Consider how to generate a guide that analyzes the user's emotions in the context of elderly care and provides guidance on how to make them feel at ease." Based on this prompt, the AI ​​model can generate instructions to support the optimal action.

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The user activates the device and enters their personal attribute information. The device sends this information to the server. Based on the input, the server compares it with the user's past data and personalizes the learning plan. As output, a personalized learning plan is generated.

[0380] Step 2:

[0381] The server sends the generated learning plan to the terminal. The terminal prepares audio and visual instructions to notify the user. These instructions include specific guidelines to support the user's learning. The input is the generated learning plan, and the output is the instructions displayed on the terminal.

[0382] Step 3:

[0383] The device captures the user's facial expressions with a camera and generates image data. This image data is then analyzed using TensorFlow or OpenCV to evaluate the user's emotional state. The input is the facial image data, and the output is the analyzed emotional state.

[0384] Step 4:

[0385] The device also collects voice data and analyzes it using the Google Cloud Speech-to-Text API. The input is voice data, and it outputs emotion labels based on an emotion survey. The results are sent to a server to understand the overall emotional state.

[0386] Step 5:

[0387] The server generates user-appropriate responses and instructions based on evaluated emotion data and individualized learning plans. These are then sent to the terminal. The input is the emotion state and learning plan, while the output is the specific content of the countermeasures and instructions.

[0388] Step 6:

[0389] The terminal receives instructions from the server and presents them to the caregiver as visual and audio guides. Specifically, it is recommended that the caregiver take actions based on the instructions to reassure the user.

[0390] Step 7:

[0391] After a care session ends, the terminal sends the user's operation history and emotional feedback to the server. This data is used to optimize the next learning plan. The input is the operation history and emotional feedback at the end of the session, and the output is the preparation of data for the next optimization.

[0392] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0393] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0394] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0398] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0399] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0400] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0401] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0402] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0403] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0404] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0405] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0406] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0407] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0408] This invention relates to an information processing system for elderly people and beginners with digital devices to learn how to operate smart devices more efficiently. This system provides individually personalized learning plans based on the user's attribute information and can demonstrate how to operate the device in real time. The operation of the system is described in detail below.

[0409] First, the user activates the device and enters attribute information using a dedicated application. This information includes the user's age, experience with digital devices, and specific learning goals. The server receives the entered information and generates an optimal learning plan for each user.

[0410] Next, the server sends the generated learning plan to the user's device. Based on this learning plan, the device prepares to provide the user with appropriate audio and visual guidance. For example, if the user wants to learn how to use the camera app, the device will automatically start the camera app and provide the user with audio instructions and visual instructions on the screen for taking photos.

[0411] During a learning session, users can ask any question they like. The device that receives the question will communicate with the server as needed to obtain an answer and provide it to the user in real time. For example, if a user asks, "How do I set aperture priority mode?", the device will explain the method in detail and display the operating procedure on the screen.

[0412] Furthermore, the device records the user's operation history and monitors their learning progress. This information is sent to a server and used to optimize the next learning session. In this way, an environment is provided that supports users in efficiently progressing through their learning and gaining confidence in operating digital devices. This entire process makes digital technology more accessible to beginners, such as the elderly, and enables smoother social interaction. As a concrete example, when a user learns how to share photos using a messaging app, the device automatically provides guidance and shows the steps of operation.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user activates their smart device and enters their personal information using a dedicated application. This includes registering their name, age, smart device usage experience, and learning objectives.

[0416] Step 2:

[0417] The server receives attribute information sent by the user and stores it in a database. Next, it analyzes this information to generate a learning plan optimized for the user.

[0418] Step 3:

[0419] The server sends the generated learning plan to the user's device. The device then prepares for the learning session based on this plan.

[0420] Step 4:

[0421] The user starts a learning session on their device. The device's AI assistant provides voice and visual guidance according to the learning plan, explaining how to operate the smart device. For example, if learning how to use the camera app, it will automatically launch the camera and show specific functions and settings.

[0422] Step 5:

[0423] If a user has questions or concerns, they can submit inquiries via voice or text to their device. The device then communicates with the server to provide solutions and additional explanations in real time.

[0424] Step 6:

[0425] During a learning session, the device records the user's activity history and learning progress. This data is periodically sent to the server and used to optimize the next learning plan to improve the user's learning efficiency.

[0426] Step 7:

[0427] The server analyzes the collected data and provides feedback to further improve the approach tailored to each user. This leads to a continuous improvement in the learning experience.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] In recent years, learning how to operate electronic devices efficiently and effectively has become a difficult challenge for the elderly and those new to digital devices. Traditional learning methods rely on general procedures and information, making it difficult to provide a personalized learning experience tailored to the individual user's attributes, interests, and skill levels. Furthermore, real-time instruction and question support are insufficient, sometimes leaving users unable to operate devices with confidence. There is a growing need for information processing systems that can solve these problems and provide a more appropriate learning experience.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes means for acquiring user attribute information and personalizing a learning plan from prompt sentences using a generative AI model; means for presenting instructions on how to operate electronic devices in real time using voice and visual hints; and means for recording the user's operation history and learning progress and optimizing the next learning plan. This enables elderly people and beginners with digital devices to efficiently learn how to operate devices under an optimal learning plan tailored to their individual attributes and to acquire skills with confidence through real-time question support.

[0433] An "information processing device" is an electronic device that analyzes data collected from users and processes that data according to a specific purpose.

[0434] "User attribute information" refers to data that represents the individual characteristics of a user, such as age, experience using digital devices, interests, and what they want to learn.

[0435] A "generative AI model" is a type of algorithm that uses machine learning techniques to analyze data and make judgments and predictions.

[0436] A "prompt sentence" is initial input data or instruction sentence used to derive specific information or answers from a generative AI model.

[0437] A "learning plan" is a plan that includes procedures and materials to help users efficiently learn how to use digital technologies and devices.

[0438] "Audio and visual cues" refer to auditory and visual support information used to explain how to operate a device to the user.

[0439] "Operation history" refers to data that records past operations and actions performed by a user on a digital device.

[0440] "Learning progress" refers to information indicating the extent to which the user has achieved their set learning goals at this point in time.

[0441] "Presenting in real time" means that information and instructions are provided to users immediately without any time delay.

[0442] This invention relates to an information processing system for elderly people and those new to digital devices to learn how to operate electronic devices efficiently.

[0443] The user first activates their electronic device and enters their attribute information using a dedicated application. This attribute information includes age, experience using digital devices, and what they want to learn. Once this data entry is complete, the server receives the information and uses a generative AI model to create a learning plan tailored to the user. In this process, the generative AI model provides a customized learning plan based on prompts and according to the user's needs. For example, a possible prompt might be, "Please create a learning plan for taking pictures using a camera app."

[0444] Next, the server sends the generated personalized learning plan to the user's device. The device uses this plan to prepare to provide the user with audio and visual guidance. Audio guidance includes voice output through a microphone and speaker, while visual guidance includes step-by-step instructions on the display. This combination allows the user to learn the operation procedures in real time. For example, if the user wants to learn how to use the camera app, the device will automatically launch the camera app and guide them through the shooting process.

[0445] Furthermore, if a user has a question during their learning process, they can send it to the server via their device. The server uses a generative AI model to generate an appropriate response to the question and provides it to the user in real time. In this way, users can progress with their learning while resolving any questions they have about how to use the system.

[0446] As learning progresses, the device records the user's operation history and learning progress. This information is sent to a server and used to optimize the next learning session. As a result, users can effectively acquire digital skills at their own pace and gain greater confidence in operating the device.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The user activates their electronic device and opens a dedicated application. The user then enters attribute information such as age, experience with digital devices, and what they wish to learn. This entered information is then sent to the server.

[0450] Step 2:

[0451] The server analyzes the attribute information received from the user. Using a generative AI model, the server creates a personalized learning plan based on the prompt "Create the optimal learning plan for the user based on this information." This prompt prompt allows the generative AI model to determine the learning steps and content that best suit the user's needs. As a result, a specific learning plan is generated.

[0452] Step 3:

[0453] The server sends the generated learning plan to the user's device. The device receives this learning plan and prepares to provide the user with audio instruction and visual guidance. The device configures its speaker and display to present the learning content clearly.

[0454] Step 4:

[0455] The user begins learning through their device. The device automatically launches the appropriate app based on what the user wants to learn, such as how to use the camera app. If necessary, it provides real-time audio and visual guidance to help users resolve any questions they may have while learning how to use the device.

[0456] Step 5:

[0457] When a user has a question during the learning process, they send the question from their device to the server. The server uses a generative AI model to analyze the question and generate the best possible answer. The generated answer is sent to the device and presented to the user in real time. This allows the user to operate with confidence.

[0458] Step 6:

[0459] The device records the user's operation history and learning progress as the learning process progresses. This recorded data is sent to a server and used for analysis to make the next learning session more effective. Based on this historical information, the server optimizes the content of the next learning session to better meet the individual needs of the user.

[0460] (Application Example 1)

[0461] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] In modern society, the difficulties elderly and those new to digital technology face in accessing technology pose a problem that hinders their social interactions and daily lives. In particular, learning the digital technologies necessary for elderly people to communicate with their families is often complex and stressful. Therefore, there is a need for technology that provides personalized, real-time learning support.

[0463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0464] In this invention, the server includes means for an information processing device to collect user characteristic data and personalize a learning plan based on said characteristic data; means for the information processing device to immediately show how to operate an electronic device using voice and visual instructions; means for the information processing device to save the user's operation behavior and learning progress and optimize the next learning plan; and means for the information processing device to guide the user's interface operation on the digital screen using visual effects. This makes it possible for elderly people and digital device beginners to receive guidance tailored to their individual learning needs and to operate digital devices with confidence.

[0465] An "information processing device" is a device that collects user characteristic data, creates individualized learning plans based on that data, and provides real-time instruction in cooperation with servers and client devices.

[0466] "Characteristic data" refers to attribute information of individual users, such as their age, experience with digital devices, interests, and motivation to learn, and is used to develop personalized learning plans.

[0467] A "learning plan" or "study schedule" is a personalized instructional program created based on the individual user's characteristics data to teach them how to use digital devices.

[0468] "Audio and visual instructions" refers to technologies that provide audio guidance and on-screen visual instructions to help users understand how to operate digital devices.

[0469] "Operational behavior" refers to a series of actions and interactions that users perform when using digital devices, and is used to evaluate learning progress and optimize future learning plans.

[0470] "Progress" is a measure used to evaluate the user's progress and understanding in the process of learning how to operate digital devices.

[0471] "Visual effects" refer to visual techniques such as animations and highlighting used on the user's screen to assist in navigating the interface.

[0472] An information processing system is essential to realize this invention. This system provides a personalized learning experience tailored to the user's attributes, targeting elderly people and beginners who use digital devices.

[0473] The server receives characteristic data from the user and generates a learning plan based on it. This characteristic data includes age and experience using digital devices. The generated learning plan is sent to the user's device, and instructions on how to operate the digital device are provided through voice and visual guidance.

[0474] The device uses visual effects to assist the user with interface operations on the screen. For example, when a user makes a video call, in addition to voice guidance such as "Please tap the call button," the button is highlighted on the screen with animation, allowing the user to understand the operation more intuitively.

[0475] If users have questions during their learning process, they can submit inquiries to the server in real time via their device. The server uses a generative AI model to generate appropriate answers and presents them to the user via voice or other means. This process allows users to resolve their questions on the spot and continue their learning.

[0476] Furthermore, the device records the user's actions and learning progress, which is used to optimize the next learning plan. In this way, users can continuously learn at the pace and in the way that is best suited to them.

[0477] For example, when a user learns how to make a video call with their grandchild, the application provides voice guidance saying, "Open the calling app and tap the call button," and displays an arrow on the screen indicating the button. This allows the user to intuitively understand how to operate it. By using a prompt such as, "How can I instruct elderly people on the flow of starting a video call using voice and visuals?", the generative AI model can provide appropriate instruction.

[0478] Hardware examples include tablets and smartphones, while software includes Python, the Flask framework, and the Google Text-to-Speech API. This enables integrated voice and visual guidance for user assistance.

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The user activates the device and enters characteristic data. This data includes age, experience using digital devices, and what they want to learn. The device then sends the entered data to the server.

[0482] Step 2:

[0483] The server generates a learning plan based on the received characteristic data. This includes data processing to select content and teaching methods appropriate for the user from the database. The generated learning plan is then sent to the terminal.

[0484] Step 3:

[0485] The device prepares audio and visual guides. It uses the Google Text-to-Speech API to generate audio guides based on the learning plan. Visually, it provides visual effects to highlight the relevant on-screen actions.

[0486] Step 4:

[0487] The user operates the digital device following the terminal's instructions. Based on the terminal's guidance, the user attempts specific operations. The results of the operations are recorded in real time and used to optimize future learning plans.

[0488] Step 5:

[0489] When a user asks a question about something they don't understand, the device communicates with the server to generate an appropriate answer in real time using a generation AI model. The prompt is input into the AI ​​model, and a response that analyzes its meaning is provided to the user.

[0490] Step 6:

[0491] The device records user actions and learning progress, and uses this information to generate future learning plans. This ensures continuous improvement of the individual learning experience. It analyzes user interaction logs and proposes optimized learning plans.

[0492] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0493] This invention relates to an information processing system incorporating an emotion engine that recognizes the user's emotions. This system optimizes the next learning plan by personalizing the learning plan based on the user's attribute information, providing real-time instructions on how to operate a smart device, and recording operation history and learning progress. It also evaluates the user's emotions and adjusts the learning content and method of progress more effectively.

[0494] First, the user activates their smart device and enters their attribute information into the application. The server then receives the user's information and generates a customized plan tailored to their learning needs.

[0495] Next, the server sends the generated learning plan to the user's device. The device uses an emotion engine to analyze the user's facial expressions and tone of voice to assess the user's emotional state towards learning. This emotion data is used to personalize the user's learning experience and provide optimal content tailored to their emotional state.

[0496] Once a learning session begins, the device provides audio and visual guidance, explaining how to operate the smart device. If the user shows signs of anxiety or confusion during operation, the emotion engine detects this and adjusts the guidance content and pace via the server. For example, if the user shows anxiety about a complex operation, the device will provide more detailed and easy-to-understand instructions while also displaying encouraging messages.

[0497] Furthermore, user questions are received in real time via the device and immediately resolved in conjunction with the server. Additionally, user emotional feedback is recorded during the learning process, which is used to improve user engagement in subsequent sessions.

[0498] Thus, this system achieves a high level of personalization that meets the individual learning needs of users through both emotion recognition and the provision of learning content. For example, when learning photo editing functions, if the emotion engine detects the user's joy, it may suggest additional editing techniques to enhance this positive experience.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The user activates their smart device and enters attribute information using a dedicated application. This information includes age, past device usage experience, and desired learning content.

[0502] Step 2:

[0503] The server receives attribute information sent by the user and generates an optimized learning plan based on that data. The server then sends this learning plan to the user's device.

[0504] Step 3:

[0505] The device prepares for the learning session based on the received learning plan. Simultaneously, it activates the emotion engine to analyze the user's facial expressions and tone of voice in real time and evaluate the user's emotional state.

[0506] Step 4:

[0507] The user begins a learning session. The device uses audio and visual guidance to guide the user step-by-step through the operations they need to learn. Depending on the user's emotional state, the device may, for example, elaborate on specific steps or adjust the pace of the learning process.

[0508] Step 5:

[0509] When a user asks a question or encounters difficulties during learning, the device communicates with the server in real time, providing the user with detailed instructions and answers immediately.

[0510] Step 6:

[0511] The device records the user's operation history and changes in emotional state during the learning process. This allows for detailed analysis of which steps the user struggled with and which parts elicited positive responses.

[0512] Step 7:

[0513] The server analyzes the operation history and sentiment feedback sent from the terminal to optimize the plan for the next learning session. The server saves the adjusted plan for the next session.

[0514] (Example 2)

[0515] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] Providing an effective learning experience tailored to each user's individual attributes and emotional state requires the creation of appropriate learning plans, continuous monitoring of emotions during the process, and real-time feedback and adjustments. However, few systems can achieve these efficiently and accurately, and there is a particular need for systems that can flexibly respond to changes in emotions and appropriately adjust the learning pace and content.

[0517] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0518] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information; means for an information processing device to present instructions on how to operate electronic devices in real time using voice and visual guidance; means for an information processing device to record the user's behavior history and learning progress and optimize the next learning plan; and means for an information processing device to evaluate the user's emotional state and use an emotion analysis engine to adjust the learning content and progress. This makes it possible to provide a flexible and advanced learning experience tailored to the individual needs and emotional state of the user.

[0519] An "information processing device" refers to any hardware or software that receives information from users and performs data analysis and processing based on that information.

[0520] "Attribute information" refers to data that indicates the individual characteristics and needs of users, such as their age, learning objectives, and skill level.

[0521] A "learning plan" refers to a plan that is individually designed based on the user's attribute information, including learning materials, pace, and methods.

[0522] "Personalization" means optimizing learning content and services for each user based on their attributes and needs.

[0523] "Audio and visual guidance" refers to methods of providing information to assist users through audio instructions and visual presentations.

[0524] "Electronic devices" refer to all devices that process digital data, such as computers, smartphones, and tablets.

[0525] "Real-time" refers to processing or responses that occur instantly without delay.

[0526] "Behavioral history" refers to a record of a user's past operations and actions, and is data used to optimize future learning and service delivery.

[0527] "Learning progress" refers to the stage or progress a user has reached in their current learning process.

[0528] An "emotion analysis engine" refers to a program that uses technology to analyze a user's facial expressions, tone of voice, etc., to evaluate their current psychological state and emotions.

[0529] "Generative AI technology" refers to technology that uses artificial intelligence to respond to user questions and requests in natural language and to make creative suggestions.

[0530] A system implementing this invention includes a server and a terminal as information processing equipment.

[0531] The user activates their smart device and enters their attribute information into a dedicated application. This application retrieves data such as age, learning objectives, and skill level, and sends it to a server. The server uses this attribute information to perform appropriate data processing in order to generate an individualized learning plan. In this process, machine learning algorithms can be used to suggest the optimal learning path.

[0532] The learning plan generated on the server is sent to the user's device. The device uses its built-in emotion analysis engine to analyze the user's facial expressions and tone of voice, and evaluates their emotions during learning. Based on this evaluation, the device presents learning content tailored to the user in real time through audio and visual guidance. If the user shows signs of confusion or anxiety, the device sends this emotion information to the server, which then adjusts the support and pace accordingly.

[0533] Furthermore, if a user has a question during the learning process, the device sends that question to the server. The server uses generative AI technology to utilize prompts and generate appropriate answers in real time. For example, by entering a prompt such as, "Analyze the user's emotions and suggest ways to optimize the learning content," an immediate response can be obtained.

[0534] For example, if a user is learning photo editing functions, and the emotion analysis engine detects the user's feelings of joy, the device can suggest additional editing techniques to further enhance that positive experience. In this way, the system can provide a highly personalized learning experience based on the user's emotional state and attribute information.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] The user activates their smart device and enters their attribute information into a dedicated application. This data includes age, learning objectives, and skill level. This attribute information is then transmitted to the server via the smart device's terminal.

[0538] Step 2:

[0539] The server performs data analysis based on the received attribute information and generates the optimal learning plan. Specifically, it selects the most suitable learning path for the user by analyzing data from similar users using a machine learning algorithm. As an output of this process, a personalized learning plan is generated and sent to the terminal.

[0540] Step 3:

[0541] The device uses the learning plan received from the server to activate its built-in emotion analysis engine. This engine analyzes the user's facial expressions and tone of voice in real time, acquiring the results as data. This allows the system to evaluate the user's emotional state and provide foundational data for adjusting the learning content and pace.

[0542] Step 4:

[0543] The device explains how to operate the smart device by providing audio and visual guidance based on the evaluation results. The input is the user's emotional state, which was evaluated earlier, and the output is guide information tailored to the user's needs.

[0544] Step 5:

[0545] During the learning process, the user can input questions through their device. The device sends this as input data to the server. The server uses a generative AI model to generate prompt sentences and construct the optimal answer based on these prompt sentences. The server's answer is then presented to the user as output data.

[0546] Step 6:

[0547] The device records the user's activity history and learning progress. This recorded data is sent to the server to optimize future learning plans. This allows for more precise delivery of learning content tailored to individual needs.

[0548] (Application Example 2)

[0549] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] In caregiving settings, accurately understanding the emotional state of each individual user and providing appropriate care and instructions accordingly is challenging. Furthermore, there is a need for techniques that improve the quality of care services while reducing the burden on caregivers.

[0551] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0552] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information, means for an information processing device to present instructions on how to operate a computing device in real time using voice and visual instructions, and means for an information processing device to analyze the user's facial expressions and tone of voice and evaluate their emotional state. This makes it possible to grasp the user's emotions in real time in a care setting and provide appropriate care instructions.

[0553] An "information processing device" refers to the entire system that analyzes user attribute information and emotional state to provide appropriate learning plans and care instructions.

[0554] "Attribute information" refers to specific profile data and information that indicates individual characteristics about a user.

[0555] A "learning plan" refers to guidelines or programs for education and training that are optimized based on the user's attributes and emotional state.

[0556] "Emotional state" refers to a psychological state identified based on the user's facial expressions, voice, and other physiological indicators.

[0557] "Computing device" refers to hardware that functions as an interface with the user, such as smart glasses or other digital devices.

[0558] The server receives user attribute information and creates an individualized learning plan based on that information. This plan is generated considering the user's profile data and past behavior. The server then provides voice and visual instructions to computing devices such as smart glasses. This allows caregivers to understand the user's emotional state in real time.

[0559] The smart glasses, which serve as the device, analyze the user's facial expressions and evaluate their emotions using TensorFlow and OpenCV. They also use the Google Cloud Speech-to-Text API for voice processing, analyzing the tone of the user's voice. This analyzed emotion data is processed by a server, and appropriate countermeasures are presented to the caregiver.

[0560] For example, if a caregiver is interacting with an elderly client, the device will recognize that the elderly person is feeling anxious. The server will then immediately display instructions on the glasses, such as, "To reassure them, it would be good to slow down your speaking speed and speak in a soft voice."

[0561] An example of a prompt using a generative AI model is: "Consider how to generate a guide that analyzes the user's emotions in the context of elderly care and provides guidance on how to make them feel at ease." Based on this prompt, the AI ​​model can generate instructions to support the optimal action.

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] The user activates the device and enters their personal attribute information. The device sends this information to the server. Based on the input, the server compares it with the user's past data and personalizes the learning plan. As output, a personalized learning plan is generated.

[0565] Step 2:

[0566] The server sends the generated learning plan to the terminal. The terminal prepares audio and visual instructions to notify the user. These instructions include specific guidelines to support the user's learning. The input is the generated learning plan, and the output is the instructions displayed on the terminal.

[0567] Step 3:

[0568] The device captures the user's facial expressions with a camera and generates image data. This image data is then analyzed using TensorFlow or OpenCV to evaluate the user's emotional state. The input is the facial image data, and the output is the analyzed emotional state.

[0569] Step 4:

[0570] The device also collects voice data and analyzes it using the Google Cloud Speech-to-Text API. The input is voice data, and it outputs emotion labels based on an emotion survey. The results are sent to a server to understand the overall emotional state.

[0571] Step 5:

[0572] The server generates user-appropriate responses and instructions based on evaluated emotion data and individualized learning plans. These are then sent to the terminal. The input is the emotion state and learning plan, while the output is the specific content of the countermeasures and instructions.

[0573] Step 6:

[0574] The terminal receives instructions from the server and presents them to the caregiver as visual and audio guides. Specifically, it is recommended that the caregiver take actions based on the instructions to reassure the user.

[0575] Step 7:

[0576] After a care session ends, the terminal sends the user's operation history and emotional feedback to the server. This data is used to optimize the next learning plan. The input is the operation history and emotional feedback at the end of the session, and the output is the preparation of data for the next optimization.

[0577] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0584] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0586] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0588] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] This invention relates to an information processing system for elderly people and beginners with digital devices to learn how to operate smart devices more efficiently. This system provides individually personalized learning plans based on the user's attribute information and can demonstrate how to operate the device in real time. The operation of the system is described in detail below.

[0595] First, the user activates the device and enters attribute information using a dedicated application. This information includes the user's age, experience with digital devices, and specific learning goals. The server receives the entered information and generates an optimal learning plan for each user.

[0596] Next, the server sends the generated learning plan to the user's device. Based on this learning plan, the device prepares to provide the user with appropriate audio and visual guidance. For example, if the user wants to learn how to use the camera app, the device will automatically start the camera app and provide the user with audio instructions and visual instructions on the screen for taking photos.

[0597] During a learning session, users can ask any question they like. The device that receives the question will communicate with the server as needed to obtain an answer and provide it to the user in real time. For example, if a user asks, "How do I set aperture priority mode?", the device will explain the method in detail and display the operating procedure on the screen.

[0598] Furthermore, the device records the user's operation history and monitors their learning progress. This information is sent to a server and used to optimize the next learning session. In this way, an environment is provided that supports users in efficiently progressing through their learning and gaining confidence in operating digital devices. This entire process makes digital technology more accessible to beginners, such as the elderly, and enables smoother social interaction. As a concrete example, when a user learns how to share photos using a messaging app, the device automatically provides guidance and shows the steps of operation.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The user activates their smart device and enters their personal information using a dedicated application. This includes registering their name, age, smart device usage experience, and learning objectives.

[0602] Step 2:

[0603] The server receives attribute information sent by the user and stores it in a database. Next, it analyzes this information to generate a learning plan optimized for the user.

[0604] Step 3:

[0605] The server sends the generated learning plan to the user's device. The device then prepares for the learning session based on this plan.

[0606] Step 4:

[0607] The user starts a learning session on their device. The device's AI assistant provides voice and visual guidance according to the learning plan, explaining how to operate the smart device. For example, if learning how to use the camera app, it will automatically launch the camera and show specific functions and settings.

[0608] Step 5:

[0609] If a user has questions or concerns, they can submit inquiries via voice or text to their device. The device then communicates with the server to provide solutions and additional explanations in real time.

[0610] Step 6:

[0611] During a learning session, the device records the user's activity history and learning progress. This data is periodically sent to the server and used to optimize the next learning plan to improve the user's learning efficiency.

[0612] Step 7:

[0613] The server analyzes the collected data and provides feedback to further improve the approach tailored to each user. This leads to a continuous improvement in the learning experience.

[0614] (Example 1)

[0615] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] In recent years, learning how to operate electronic devices efficiently and effectively has become a difficult challenge for the elderly and those new to digital devices. Traditional learning methods rely on general procedures and information, making it difficult to provide a personalized learning experience tailored to the individual user's attributes, interests, and skill levels. Furthermore, real-time instruction and question support are insufficient, sometimes leaving users unable to operate devices with confidence. There is a growing need for information processing systems that can solve these problems and provide a more appropriate learning experience.

[0617] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0618] In this invention, the server includes means for acquiring user attribute information and personalizing a learning plan from prompt sentences using a generative AI model; means for presenting instructions on how to operate electronic devices in real time using voice and visual hints; and means for recording the user's operation history and learning progress and optimizing the next learning plan. This enables elderly people and beginners with digital devices to efficiently learn how to operate devices under an optimal learning plan tailored to their individual attributes and to acquire skills with confidence through real-time question support.

[0619] An "information processing device" is an electronic device that analyzes data collected from users and processes that data according to a specific purpose.

[0620] "User attribute information" refers to data that represents the individual characteristics of a user, such as age, experience using digital devices, interests, and what they want to learn.

[0621] A "generative AI model" is a type of algorithm that uses machine learning techniques to analyze data and make judgments and predictions.

[0622] A "prompt sentence" is initial input data or instruction sentence used to derive specific information or answers from a generative AI model.

[0623] A "learning plan" is a plan that includes procedures and materials to help users efficiently learn how to use digital technologies and devices.

[0624] "Audio and visual cues" refer to auditory and visual support information used to explain how to operate a device to the user.

[0625] "Operation history" refers to data that records past operations and actions performed by a user on a digital device.

[0626] "Learning progress" refers to information indicating the extent to which the user has achieved their set learning goals at this point in time.

[0627] "Presenting in real time" means that information and instructions are provided to users immediately without any time delay.

[0628] This invention relates to an information processing system for elderly people and those new to digital devices to learn how to operate electronic devices efficiently.

[0629] The user first activates their electronic device and enters their attribute information using a dedicated application. This attribute information includes age, experience using digital devices, and what they want to learn. Once this data entry is complete, the server receives the information and uses a generative AI model to create a learning plan tailored to the user. In this process, the generative AI model provides a customized learning plan based on prompts and according to the user's needs. For example, a possible prompt might be, "Please create a learning plan for taking pictures using a camera app."

[0630] Next, the server sends the generated personalized learning plan to the user's device. The device uses this plan to prepare to provide the user with audio and visual guidance. Audio guidance includes voice output through a microphone and speaker, while visual guidance includes step-by-step instructions on the display. This combination allows the user to learn the operation procedures in real time. For example, if the user wants to learn how to use the camera app, the device will automatically launch the camera app and guide them through the shooting process.

[0631] Furthermore, if a user has a question during their learning process, they can send it to the server via their device. The server uses a generative AI model to generate an appropriate response to the question and provides it to the user in real time. In this way, users can progress with their learning while resolving any questions they have about how to use the system.

[0632] As learning progresses, the device records the user's operation history and learning progress. This information is sent to a server and used to optimize the next learning session. As a result, users can effectively acquire digital skills at their own pace and gain greater confidence in operating the device.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The user activates their electronic device and opens a dedicated application. The user then enters attribute information such as age, experience with digital devices, and what they wish to learn. This entered information is then sent to the server.

[0636] Step 2:

[0637] The server analyzes the attribute information received from the user. Using a generative AI model, the server creates a personalized learning plan based on the prompt "Create the optimal learning plan for the user based on this information." This prompt prompt allows the generative AI model to determine the learning steps and content that best suit the user's needs. As a result, a specific learning plan is generated.

[0638] Step 3:

[0639] The server sends the generated learning plan to the user's device. The device receives this learning plan and prepares to provide the user with audio instruction and visual guidance. The device configures its speaker and display to present the learning content clearly.

[0640] Step 4:

[0641] The user begins learning through their device. The device automatically launches the appropriate app based on what the user wants to learn, such as how to use the camera app. If necessary, it provides real-time audio and visual guidance to help users resolve any questions they may have while learning how to use the device.

[0642] Step 5:

[0643] When a user has a question during the learning process, they send the question from their device to the server. The server uses a generative AI model to analyze the question and generate the best possible answer. The generated answer is sent to the device and presented to the user in real time. This allows the user to operate with confidence.

[0644] Step 6:

[0645] The device records the user's operation history and learning progress as the learning process progresses. This recorded data is sent to a server and used for analysis to make the next learning session more effective. Based on this historical information, the server optimizes the content of the next learning session to better meet the individual needs of the user.

[0646] (Application Example 1)

[0647] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] In modern society, the difficulties elderly and those new to digital technology face in accessing technology pose a problem that hinders their social interactions and daily lives. In particular, learning the digital technologies necessary for elderly people to communicate with their families is often complex and stressful. Therefore, there is a need for technology that provides personalized, real-time learning support.

[0649] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0650] In this invention, the server includes means for an information processing device to collect user characteristic data and personalize a learning plan based on said characteristic data; means for the information processing device to immediately show how to operate an electronic device using voice and visual instructions; means for the information processing device to save the user's operation behavior and learning progress and optimize the next learning plan; and means for the information processing device to guide the user's interface operation on the digital screen using visual effects. This makes it possible for elderly people and digital device beginners to receive guidance tailored to their individual learning needs and to operate digital devices with confidence.

[0651] An "information processing device" is a device that collects user characteristic data, creates individualized learning plans based on that data, and provides real-time instruction in cooperation with servers and client devices.

[0652] "Characteristic data" refers to attribute information of individual users, such as their age, experience with digital devices, interests, and motivation to learn, and is used to develop personalized learning plans.

[0653] A "learning plan" or "study schedule" is a personalized instructional program created based on the individual user's characteristics data to teach them how to use digital devices.

[0654] "Audio and visual instructions" refers to technologies that provide audio guidance and on-screen visual instructions to help users understand how to operate digital devices.

[0655] "Operational behavior" refers to a series of actions and interactions that users perform when using digital devices, and is used to evaluate learning progress and optimize future learning plans.

[0656] "Progress" is a measure used to evaluate the user's progress and understanding in the process of learning how to operate digital devices.

[0657] "Visual effects" refer to visual techniques such as animations and highlighting used on the user's screen to assist in navigating the interface.

[0658] An information processing system is essential to realize this invention. This system provides a personalized learning experience tailored to the user's attributes, targeting elderly people and beginners who use digital devices.

[0659] The server receives characteristic data from the user and generates a learning plan based on it. This characteristic data includes age and experience using digital devices. The generated learning plan is sent to the user's device, and instructions on how to operate the digital device are provided through voice and visual guidance.

[0660] The device uses visual effects to assist the user with interface operations on the screen. For example, when a user makes a video call, in addition to voice guidance such as "Please tap the call button," the button is highlighted on the screen with animation, allowing the user to understand the operation more intuitively.

[0661] If users have questions during their learning process, they can submit inquiries to the server in real time via their device. The server uses a generative AI model to generate appropriate answers and presents them to the user via voice or other means. This process allows users to resolve their questions on the spot and continue their learning.

[0662] Furthermore, the device records the user's actions and learning progress, which is used to optimize the next learning plan. In this way, users can continuously learn at the pace and in the way that is best suited to them.

[0663] For example, when a user learns how to make a video call with their grandchild, the application provides voice guidance saying, "Open the calling app and tap the call button," and displays an arrow on the screen indicating the button. This allows the user to intuitively understand how to operate it. By using a prompt such as, "How can I instruct elderly people on the flow of starting a video call using voice and visuals?", the generative AI model can provide appropriate instruction.

[0664] Hardware examples include tablets and smartphones, while software includes Python, the Flask framework, and the Google Text-to-Speech API. This enables integrated voice and visual guidance for user assistance.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The user activates the device and enters characteristic data. This data includes age, experience using digital devices, and what they want to learn. The device then sends the entered data to the server.

[0668] Step 2:

[0669] The server generates a learning plan based on the received characteristic data. This includes data processing to select content and teaching methods appropriate for the user from the database. The generated learning plan is then sent to the terminal.

[0670] Step 3:

[0671] The device prepares audio and visual guides. It uses the Google Text-to-Speech API to generate audio guides based on the learning plan. Visually, it provides visual effects to highlight the relevant on-screen actions.

[0672] Step 4:

[0673] The user operates the digital device following the terminal's instructions. Based on the terminal's guidance, the user attempts specific operations. The results of the operations are recorded in real time and used to optimize future learning plans.

[0674] Step 5:

[0675] When a user asks a question about something they don't understand, the device communicates with the server to generate an appropriate answer in real time using a generation AI model. The prompt is input into the AI ​​model, and a response that analyzes its meaning is provided to the user.

[0676] Step 6:

[0677] The device records user actions and learning progress, and uses this information to generate future learning plans. This ensures continuous improvement of the individual learning experience. It analyzes user interaction logs and proposes optimized learning plans.

[0678] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0679] This invention relates to an information processing system incorporating an emotion engine that recognizes the user's emotions. This system optimizes the next learning plan by personalizing the learning plan based on the user's attribute information, providing real-time instructions on how to operate a smart device, and recording operation history and learning progress. It also evaluates the user's emotions and adjusts the learning content and method of progress more effectively.

[0680] First, the user activates their smart device and enters their attribute information into the application. The server then receives the user's information and generates a customized plan tailored to their learning needs.

[0681] Next, the server sends the generated learning plan to the user's device. The device uses an emotion engine to analyze the user's facial expressions and tone of voice to assess the user's emotional state towards learning. This emotion data is used to personalize the user's learning experience and provide optimal content tailored to their emotional state.

[0682] Once a learning session begins, the device provides audio and visual guidance, explaining how to operate the smart device. If the user shows signs of anxiety or confusion during operation, the emotion engine detects this and adjusts the guidance content and pace via the server. For example, if the user shows anxiety about a complex operation, the device will provide more detailed and easy-to-understand instructions while also displaying encouraging messages.

[0683] Furthermore, user questions are received in real time via the device and immediately resolved in conjunction with the server. Additionally, user emotional feedback is recorded during the learning process, which is used to improve user engagement in subsequent sessions.

[0684] Thus, this system achieves a high level of personalization that meets the individual learning needs of users through both emotion recognition and the provision of learning content. For example, when learning photo editing functions, if the emotion engine detects the user's joy, it may suggest additional editing techniques to enhance this positive experience.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user activates their smart device and enters attribute information using a dedicated application. This information includes age, past device usage experience, and desired learning content.

[0688] Step 2:

[0689] The server receives attribute information sent by the user and generates an optimized learning plan based on that data. The server then sends this learning plan to the user's device.

[0690] Step 3:

[0691] The device prepares for the learning session based on the received learning plan. Simultaneously, it activates the emotion engine to analyze the user's facial expressions and tone of voice in real time and evaluate the user's emotional state.

[0692] Step 4:

[0693] The user begins a learning session. The device uses audio and visual guidance to guide the user step-by-step through the operations they need to learn. Depending on the user's emotional state, the device may, for example, elaborate on specific steps or adjust the pace of the learning process.

[0694] Step 5:

[0695] When a user asks a question or encounters difficulties during learning, the device communicates with the server in real time, providing the user with detailed instructions and answers immediately.

[0696] Step 6:

[0697] The device records the user's operation history and changes in emotional state during the learning process. This allows for detailed analysis of which steps the user struggled with and which parts elicited positive responses.

[0698] Step 7:

[0699] The server analyzes the operation history and sentiment feedback sent from the terminal to optimize the plan for the next learning session. The server saves the adjusted plan for the next session.

[0700] (Example 2)

[0701] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0702] Providing an effective learning experience tailored to each user's individual attributes and emotional state requires the creation of appropriate learning plans, continuous monitoring of emotions during the process, and real-time feedback and adjustments. However, few systems can achieve these efficiently and accurately, and there is a particular need for systems that can flexibly respond to changes in emotions and appropriately adjust the learning pace and content.

[0703] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0704] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information; means for an information processing device to present instructions on how to operate electronic devices in real time using voice and visual guidance; means for an information processing device to record the user's behavior history and learning progress and optimize the next learning plan; and means for an information processing device to evaluate the user's emotional state and use an emotion analysis engine to adjust the learning content and progress. This makes it possible to provide a flexible and advanced learning experience tailored to the individual needs and emotional state of the user.

[0705] An "information processing device" refers to any hardware or software that receives information from users and performs data analysis and processing based on that information.

[0706] "Attribute information" refers to data that indicates the individual characteristics and needs of users, such as their age, learning objectives, and skill level.

[0707] A "learning plan" refers to a plan that is individually designed based on the user's attribute information, including learning materials, pace, and methods.

[0708] "Personalization" means optimizing learning content and services for each user based on their attributes and needs.

[0709] "Audio and visual guidance" refers to methods of providing information to assist users through audio instructions and visual presentations.

[0710] "Electronic devices" refer to all devices that process digital data, such as computers, smartphones, and tablets.

[0711] "Real-time" refers to processing or responses that occur instantly without delay.

[0712] "Behavioral history" refers to a record of a user's past operations and actions, and is data used to optimize future learning and service delivery.

[0713] "Learning progress" refers to the stage or progress a user has reached in their current learning process.

[0714] An "emotion analysis engine" refers to a program that uses technology to analyze a user's facial expressions, tone of voice, etc., to evaluate their current psychological state and emotions.

[0715] "Generative AI technology" refers to technology that uses artificial intelligence to respond to user questions and requests in natural language and to make creative suggestions.

[0716] A system implementing this invention includes a server and a terminal as information processing equipment.

[0717] The user activates their smart device and enters their attribute information into a dedicated application. This application retrieves data such as age, learning objectives, and skill level, and sends it to a server. The server uses this attribute information to perform appropriate data processing in order to generate an individualized learning plan. In this process, machine learning algorithms can be used to suggest the optimal learning path.

[0718] The learning plan generated on the server is sent to the user's device. The device uses its built-in emotion analysis engine to analyze the user's facial expressions and tone of voice, and evaluates their emotions during learning. Based on this evaluation, the device presents learning content tailored to the user in real time through audio and visual guidance. If the user shows signs of confusion or anxiety, the device sends this emotion information to the server, which then adjusts the support and pace accordingly.

[0719] Furthermore, if a user has a question during the learning process, the device sends that question to the server. The server uses generative AI technology to utilize prompts and generate appropriate answers in real time. For example, by entering a prompt such as, "Analyze the user's emotions and suggest ways to optimize the learning content," an immediate response can be obtained.

[0720] For example, if a user is learning photo editing functions, and the emotion analysis engine detects the user's feelings of joy, the device can suggest additional editing techniques to further enhance that positive experience. In this way, the system can provide a highly personalized learning experience based on the user's emotional state and attribute information.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The user activates their smart device and enters their attribute information into a dedicated application. This data includes age, learning objectives, and skill level. This attribute information is then transmitted to the server via the smart device's terminal.

[0724] Step 2:

[0725] The server performs data analysis based on the received attribute information and generates the optimal learning plan. Specifically, it selects the most suitable learning path for the user by analyzing data from similar users using a machine learning algorithm. As an output of this process, a personalized learning plan is generated and sent to the terminal.

[0726] Step 3:

[0727] The device uses the learning plan received from the server to activate its built-in emotion analysis engine. This engine analyzes the user's facial expressions and tone of voice in real time, acquiring the results as data. This allows the system to evaluate the user's emotional state and provide foundational data for adjusting the learning content and pace.

[0728] Step 4:

[0729] The device explains how to operate the smart device by providing audio and visual guidance based on the evaluation results. The input is the user's emotional state, which was evaluated earlier, and the output is guide information tailored to the user's needs.

[0730] Step 5:

[0731] During the learning process, the user can input questions through their device. The device sends this as input data to the server. The server uses a generative AI model to generate prompt sentences and construct the optimal answer based on these prompt sentences. The server's answer is then presented to the user as output data.

[0732] Step 6:

[0733] The device records the user's activity history and learning progress. This recorded data is sent to the server to optimize future learning plans. This allows for more precise delivery of learning content tailored to individual needs.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In caregiving settings, accurately understanding the emotional state of each individual user and providing appropriate care and instructions accordingly is challenging. Furthermore, there is a need for techniques that improve the quality of care services while reducing the burden on caregivers.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0738] In this invention, the server includes means for an information processing device to acquire user attribute information and personalize a learning plan based on said attribute information, means for an information processing device to present instructions on how to operate a computing device in real time using voice and visual instructions, and means for an information processing device to analyze the user's facial expressions and tone of voice and evaluate their emotional state. This makes it possible to grasp the user's emotions in real time in a care setting and provide appropriate care instructions.

[0739] An "information processing device" refers to the entire system that analyzes user attribute information and emotional state to provide appropriate learning plans and care instructions.

[0740] "Attribute information" refers to specific profile data and information that indicates individual characteristics about a user.

[0741] A "learning plan" refers to guidelines or programs for education and training that are optimized based on the user's attributes and emotional state.

[0742] "Emotional state" refers to a psychological state identified based on the user's facial expressions, voice, and other physiological indicators.

[0743] "Computing device" refers to hardware that functions as an interface with the user, such as smart glasses or other digital devices.

[0744] The server receives user attribute information and creates an individualized learning plan based on that information. This plan is generated considering the user's profile data and past behavior. The server then provides voice and visual instructions to computing devices such as smart glasses. This allows caregivers to understand the user's emotional state in real time.

[0745] The smart glasses, which serve as the device, analyze the user's facial expressions and evaluate their emotions using TensorFlow and OpenCV. They also use the Google Cloud Speech-to-Text API for voice processing, analyzing the tone of the user's voice. This analyzed emotion data is processed by a server, and appropriate countermeasures are presented to the caregiver.

[0746] For example, if a caregiver is interacting with an elderly client, the device will recognize that the elderly person is feeling anxious. The server will then immediately display instructions on the glasses, such as, "To reassure them, it would be good to slow down your speaking speed and speak in a soft voice."

[0747] An example of a prompt using a generative AI model is: "Consider how to generate a guide that analyzes the user's emotions in the context of elderly care and provides guidance on how to make them feel at ease." Based on this prompt, the AI ​​model can generate instructions to support the optimal action.

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The user activates the device and enters their personal attribute information. The device sends this information to the server. Based on the input, the server compares it with the user's past data and personalizes the learning plan. As output, a personalized learning plan is generated.

[0751] Step 2:

[0752] The server sends the generated learning plan to the terminal. The terminal prepares audio and visual instructions to notify the user. These instructions include specific guidelines to support the user's learning. The input is the generated learning plan, and the output is the instructions displayed on the terminal.

[0753] Step 3:

[0754] The device captures the user's facial expressions with a camera and generates image data. This image data is then analyzed using TensorFlow or OpenCV to evaluate the user's emotional state. The input is the facial image data, and the output is the analyzed emotional state.

[0755] Step 4:

[0756] The device also collects voice data and analyzes it using the Google Cloud Speech-to-Text API. The input is voice data, and it outputs emotion labels based on an emotion survey. The results are sent to a server to understand the overall emotional state.

[0757] Step 5:

[0758] The server generates user-appropriate responses and instructions based on evaluated emotion data and individualized learning plans. These are then sent to the terminal. The input is the emotion state and learning plan, while the output is the specific content of the countermeasures and instructions.

[0759] Step 6:

[0760] The terminal receives instructions from the server and presents them to the caregiver as visual and audio guides. Specifically, it is recommended that the caregiver take actions based on the instructions to reassure the user.

[0761] Step 7:

[0762] After a care session ends, the terminal sends the user's operation history and emotional feedback to the server. This data is used to optimize the next learning plan. The input is the operation history and emotional feedback at the end of the session, and the output is the preparation of data for the next optimization.

[0763] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0764] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0765] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0766] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0767] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0768] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0769] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0770] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0771] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0772] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0773] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0774] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0775] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0776] 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.

[0777] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0778] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0779] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0780] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0781] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0782] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0783] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] An information processing device acquires user attribute information and provides means for personalizing a learning plan based on said attribute information.

[0787] An information processing device provides means for presenting instructions on how to operate a smart device in real time using voice and visual hints,

[0788] The information processing device records the user's operation history and learning progress, and provides means for optimizing the next learning plan.

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the information processing device provides a real-time response to a question from a user.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the information processing device selects learning content according to the user's interests and skills.

[0794] "Example 1"

[0795] (Claim 1)

[0796] The information processing device acquires user attribute information, uses a generated AI model based on said attribute information, and provides means for personalizing a learning plan from prompt sentences.

[0797] An information processing device provides means for presenting instructions on how to operate an electronic device in real time using audio and visual hints,

[0798] The information processing device records the user's operation history and learning progress, and provides means for optimizing the next learning plan.

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the information processing device provides a real-time response to a user's question and complements that response with information from a generating AI model.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the information processing device selects learning content according to the user's interests and skills, and uses a generative AI model for the selection.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] Information processing equipment includes means for collecting user characteristic data and personalizing learning plans based on said characteristic data,

[0807] Information processing equipment provides means for immediately indicating how to operate an electronic device using voice and visual instructions,

[0808] Information processing equipment provides means for saving user operation behavior and learning progress, and optimizing the next learning plan,

[0809] Information processing equipment provides means for guiding users to operate the interface on their digital screens using visual effects,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the information processing device provides an immediate response to a user's inquiry.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the information processing device selects learning content that matches the user's interests and skills.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] An information processing device includes means for acquiring user attribute information and personalizing a learning plan based on said attribute information,

[0818] An information processing device provides means for presenting instructions on how to operate electronic devices in real time using audio and visual guidance,

[0819] The information processing device records the user's behavior history and learning progress, and provides means for optimizing the next learning plan.

[0820] An information processing device includes means for using an emotion analysis engine to evaluate the user's emotional state and adjust the learning content and progression method,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the information processing device provides a real-time response to a user's question using generation AI technology.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the information processing device selects the optimal learning content and progress speed according to the user's emotional state.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] An information processing device includes means for acquiring user attribute information and personalizing a learning plan based on said attribute information,

[0829] An information processing device provides means for presenting instructions on how to operate a computing device in real time using voice and visual instructions,

[0830] The information processing device records the user's operation history and learning progress, and provides means for optimizing the next learning plan.

[0831] The information processing device analyzes the user's facial expressions and tone of voice, and provides means for evaluating their emotional state.

[0832] An information processing device provides means for providing appropriate content and instructions based on the user's emotional state,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the information processing device provides a real-time response to an inquiry from a user.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the information processing device uses emotion recognition technology incorporated into the computing device to provide assistance content according to the user's emotional state. [Explanation of symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information processing device acquires user attribute information and provides means for personalizing a learning plan based on said attribute information. An information processing device provides means for presenting instructions on how to operate a smart device in real time using voice and visual hints, The information processing device records the user's operation history and learning progress, and provides means for optimizing the next learning plan. A system that includes this.

2. The system according to claim 1, wherein the information processing device provides a real-time response to a question from a user.

3. The system according to claim 1, wherein the information processing device selects learning content according to the user's interests and skills.

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