system

Abstract

We provide the system. [Solution] A means of receiving inquiries from users, An analysis means for analyzing the content of the aforementioned inquiry and classifying the problem, A generation means for generating solutions based on the classified problems, A means for presenting the aforementioned solution to the user, An execution mechanism that receives the solution selected by the user and remotely performs the corresponding operation, A notification means for notifying the user of the results of the aforementioned operation, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , , ,

[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, the method 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 in 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 there is a lack of support means that can quickly and easily handle troubles such as the difficulty of operation and accidental deletion of data faced by the elderly during the use of smartphones. In the conventional method, reservations and waiting times occur by relying on support desks or mobile shops, and complicated procedures are required, which is a great burden for the elderly. Solving such problems and providing an environment in which the elderly can more comfortably use digital devices is an issue.

Means for Solving the Problems

[0005] This invention provides a system that analyzes inquiries received from users, immediately classifies the problem based on its content, and generates and presents solutions. This system enables rapid problem resolution by performing remote operations based on the solution selected by the user. Remote operation is performed with permission from the user's terminal, and efficient analysis is achieved by classifying the inquiry content using an artificial intelligence model. This provides a support environment that allows elderly people to easily and quickly resolve smartphone problems.

[0006] "Receiving means" refers to the function of a device or system that acquires the content of inquiries from users.

[0007] "Analysis means" refers to the system's function of analyzing acquired inquiry content and classifying it into appropriate categories.

[0008] "Generative means" refers to the function of a system that creates solutions based on an analyzed problem.

[0009] "Presentation method" refers to the system's function of displaying the generated solution to the user.

[0010] "Execution method" refers to a system function that allows for remote operation based on the solution selected by the user.

[0011] A "notification mechanism" is a system function that informs the user of the results of an operation that has been performed.

[0012] "Remote access" is a technology that allows you to access and operate terminals and systems located in a remote location.

[0013] An "artificial intelligence model" is a computational model that uses machine learning to perform data analysis and decision-making.

[0014] "Inquiry details" refer to the detailed information about the problem that a user provides when seeking support.

[0015] A "terminal" is a device that can be directly operated by a user and communicate with a server.

Brief Description of Drawings

[0016] [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 Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a 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, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The present invention provides remote support to users by allowing them to input problems requiring assistance using their smartphones, quickly analyzing the input, and generating solutions. This system is realized by linking the user's terminal, an analysis server, and various analysis means. The embodiments of this system will be described in detail below.

[0038] Users utilize a dedicated support application on their smartphones. This application receives inquiries from users and records the information on the device. The entered information is then sent from the device to the server. Users typically enter details of the problem in text format.

[0039] The server has the ability to analyze information received from the terminal. It uses an artificial intelligence model to automatically classify problems. For example, inquiries such as "The app isn't working" or "I accidentally deleted a photo" are examples of issues that can be classified. Based on the analysis results, the server generates potential solutions. These solutions include steps and methods for performing the corresponding operations.

[0040] The server then sends candidate solutions to the terminal and displays them to the user. The user can select the best solution from the presented options. This selection information is sent back to the server, which then prepares to perform the specific actions based on that selection.

[0041] When the server performs remote access to a user's terminal, it first requests permission from the terminal. After receiving approval from the user, the terminal grants permission to the server. If the user grants permission, the server remotely performs operations on the terminal, such as retrieving necessary data or changing settings.

[0042] Ultimately, the device notifies the user of the results of the operation performed by the server. This allows the user to verify whether the problem has been resolved and to rate their satisfaction. In this way, it becomes possible to quickly and accurately resolve smartphone problems faced by the elderly. As a specific example, consider a scenario where accidentally deleted photos are restored from a cloud backup. In this case, the server retrieves the backup data of the photos and performs the restore operation remotely. The device also notifies the user whether the restored photos are displayed correctly.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches a dedicated app on their smartphone and enters details of the problem requiring support. The input is primarily in text format.

[0046] Step 2:

[0047] The terminal sends the user's input to the server. This information is used as data for analysis.

[0048] Step 3:

[0049] The server activates an artificial intelligence model to analyze the inquiry received from the terminal. The model analyzes the inquiry and classifies the problem into the appropriate category.

[0050] Step 4:

[0051] The server generates multiple solutions based on the analysis results, using specific problem-solving techniques depending on the type of problem.

[0052] Step 5:

[0053] The server sends the generated solution to the terminal and instructs it to present it to the user.

[0054] Step 6:

[0055] The user reviews the solutions presented on the device and selects the solution they prefer. This selection influences the next processing step.

[0056] Step 7:

[0057] The terminal notifies the server of the solution selected by the user. The server then develops a plan of action based on the selected solution.

[0058] Step 8:

[0059] The server requests permission from the terminal for remote access in order to execute the selected solution.

[0060] Step 9:

[0061] The user grants permission for remote access by reviewing and agreeing to the remote access permission request displayed on the device.

[0062] Step 10:

[0063] After obtaining permission for remote access from the user, the server performs the necessary operations on the terminal. Specifically, it performs operations such as data recovery and configuration changes.

[0064] Step 11:

[0065] The terminal receives the operation results sent from the server and notifies the user of the details.

[0066] Step 12:

[0067] Users can verify that the problem has been resolved and enter feedback on their device. This feedback is sent to the server to improve the service.

[0068] (Example 1)

[0069] 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."

[0070] Modern information processing devices, especially portable devices, are prone to various technical problems during use. Users want to address these issues quickly and accurately, but often lack the necessary expertise, making self-resolution difficult. Therefore, rapid remote support is essential.

[0071] 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.

[0072] In this invention, the server includes means for receiving inquiry information from a user, means for analyzing the inquiry information and classifying its contents, and means for generating solutions based on the classified contents. This makes it possible to efficiently analyze the technical problems faced by the user and quickly provide the optimal solution.

[0073] "Receiving means" refers to a function or device for receiving inquiry information from users via data communication.

[0074] "Analysis means" refers to a function or device for processing received inquiry information and classifying its contents.

[0075] "Generation means" refers to a function or device that automatically creates an appropriate solution based on the analyzed content.

[0076] "Presentation means" refers to a function or device that displays the generated solution on the user's terminal.

[0077] "Execution means" refers to a function or device for performing remote operation based on a solution selected by the user.

[0078] "Notification means" refers to a function or device for communicating the results of an performed operation to the user.

[0079] "Permission" refers to data or actions that indicate authorization information or consent from the user to authorize remote control.

[0080] A "learning model" refers to a machine learning algorithm used to analyze received information and classify its contents.

[0081] This invention provides remote, rapid, and effective support when users encounter technical problems while using an information processing device. An embodiment of this system is described below.

[0082] Users utilize a dedicated support application using a portable information processing device, such as a smartphone. This application provides an interface for inputting inquiry information from the user and has the function to temporarily store the entered information in local memory. The device then establishes a means of communication to send this information to the server and transmits it via a REST API.

[0083] The server uses a generative AI model to analyze the received information. This model has the ability to automatically classify the query content into different categories. Once the analysis is complete, the server generates a problem-based solution. The solution generation utilizes historical database queries and AI-learned models.

[0084] The generated solutions are sent to the user's information processing device and displayed on the terminal. The user can review the presented solutions and select the one they deem best. Once the selection is confirmed, the information is sent back to the server.

[0085] The server will only perform remote access to the information processing device if it has obtained consent from the user. This makes it possible to perform specified operations remotely.

[0086] A concrete example would be a user wanting to recover images they accidentally deleted. In this case, the server retrieves the images from the cloud backup and performs the restoration remotely. As a result, the device notifies the user that the restoration is complete, allowing them to view the restored images. An example of a prompt message would be, "How do I recover photos I accidentally deleted from my smartphone from the backup?"

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user launches the device's dedicated support application and enters details of the problem into the inquiry form. The entered information is temporarily stored in local memory in text format. The input here is a description of the specific problem.

[0090] Step 2:

[0091] The terminal sends the stored query information to the server. This is done by the terminal establishing communication with the server and sending the information via the API. The output is the data of the query information sent to the server.

[0092] Step 3:

[0093] The server retrieves the received query information and begins analysis using a generative AI model. This analysis classifies the information to determine which category the problem belongs to. The input data is the text information of the query, and the output is the classification result.

[0094] Step 4:

[0095] The server generates solutions based on the analysis results. This generation process involves referencing AI models and historical databases, and combining similar problem-solving solutions. The input is the classification result, and the output is a list of potential solutions.

[0096] Step 5:

[0097] The server sends the generated solutions to the terminal. The terminal displays the received solutions on the user interface. The user reviews the solutions and selects the most suitable one. The input is the proposed solutions, and the output is the user's selection information.

[0098] Step 6:

[0099] The user selects a solution and, if necessary, approves the remote work. The terminal sends the approval information to the server and prepares to initiate the corresponding operation. Here, the input is the user's selection information and approval, and the output is the approval information sent to the server.

[0100] Step 7:

[0101] The server, upon receiving user approval, then performs further remote operations. These operations include retrieving necessary data and modifying the settings of information processing devices. The output is the result of the operations.

[0102] Step 8:

[0103] The server notifies the terminal of the result of the operation. The terminal checks the result and notifies the user of that result. The user then checks whether the problem has been resolved based on that result. The input is the result of the operation, and the output is the notification to the user.

[0104] (Application Example 1)

[0105] 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."

[0106] In modern homes and personal lives, the challenges of operating devices are increasing, particularly for those unfamiliar with technology or the elderly. The increasing complexity of many devices and equipment is raising user burdens and stress levels. There is a need to alleviate these challenges and provide an environment where anyone can easily utilize technology.

[0107] 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.

[0108] In this invention, the server includes a device for receiving inquiries from users, a device for analyzing the inquiries and classifying the issues, and a device for receiving commands via voice input and controlling autonomously operating mechanical devices to perform physical tasks. This makes it possible to easily control home appliances and household devices via voice commands, reducing the technical burden on users.

[0109] A "user" is someone who uses this system to seek solutions to problems or support.

[0110] "Inquiry details" refer to information that describes the specific problems or requests that the user wishes to have resolved.

[0111] A "device" is a piece of equipment or unit that has a specific function and operates electronically or mechanically.

[0112] A "challenge" refers to a difficulty or obstacle that users face, and it is a situation for which a solution is desired.

[0113] A "solution" is a set of steps or methods provided to resolve or mitigate a problem.

[0114] "Remote" refers to performing operations or controls over a physical distance.

[0115] "Voice input" refers to a technology or method that electronically recognizes and analyzes the content of a user's speech.

[0116] An "autonomous operating machine" is a robot or mechanical device that operates automatically according to programmed instructions.

[0117] A "generative intelligence model" is a program that uses artificial intelligence technology to create new information and make decisions.

[0118] This invention relates to a system that controls autonomously operating machinery in the home by allowing users to input operating commands via voice using a device such as a smartphone, and by having a server analyze that voice. The server utilizes voice recognition technology and analyzes the input commands using a generative intelligence model. Through voice input, users can make the machinery perform specific actions.

[0119] When the server receives voice input, it first converts the voice data into text. Then, it uses a generative intelligence model to analyze the commands based on this text data and determine the appropriate action. In this analysis process, for example, the Python speech_recognition library is used for speech recognition, and the virtual robot_sdk library is used for controlling the mechanical device. Using these technologies, commands requiring physical action are appropriately classified and executed.

[0120] When a user gives a command via a smartphone app, such as "Pick up the paper that's fallen on the living room table," the server transcribes the voice into text and analyzes the content of the command. For example, it uses a generative intelligence model in the cloud to understand the command and relay it to the robot. An example of a prompt to the generative AI model in this process would be, "Receive the user's voice command and create instructions for the robot to pick up the paper."

[0121] This system allows users to easily control home robots via voice commands without complex operations, providing an accessible environment even for those unfamiliar with technology.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user launches the app on their smartphone and issues instructions using the voice input function. At this stage, the user's voice commands serve as input, and that voice data is captured by the app.

[0125] Step 2:

[0126] The terminal sends audio data to the server. The server uses a speech recognition engine to convert this audio data into text data. Specifically, it processes the audio data (input) to generate text data (output). In this process, the speech recognition engine is used to analyze the audio and convert it into understandable text.

[0127] Step 3:

[0128] The server receives the text data and analyzes the content of the instructions using a generative AI model. This analysis identifies the user's intended instruction. Here, the generative AI model processes the text data (input) and obtains the interpreted instructions (output). This process includes the specific action of inputting prompt sentences into the generative AI model as needed to perform semantic understanding.

[0129] Step 4:

[0130] Based on the analyzed commands, the server instructs the robot to perform specific actions. At this stage, the interpreted commands (inputs) are converted into robot control commands and sent to the robot (outputs). Specifically, the server gives the robot appropriate instructions so that mechanical actions are performed according to the command content.

[0131] Step 5:

[0132] Robots perform physical actions based on the instructions they receive. This is how user commands are materialized in the real world. Robots realize control commands (inputs) as executable mechanical actions (outputs). These actions include specific actions such as picking up or moving objects.

[0133] 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.

[0134] This invention is a system designed for elderly smartphone users that supports them with issues such as difficulty in operation and accidental data deletion, using an emotion engine. This system recognizes the user's emotions and provides appropriate support, thereby realizing a more effective and comfortable user experience.

[0135] In this system, users first input the problem they need support for using a dedicated app on their smartphone. The device is equipped with a camera and microphone, allowing the user's facial expressions and tone of voice to be analyzed by an emotion engine. This identifies the emotional state of the user when they make an inquiry and determines the severity of the problem and the urgency of the user's situation.

[0136] The server activates an artificial intelligence model based on the inquiry content and sentiment data received from the terminal. The AI ​​model analyzes the problem and generates a solution, while simultaneously adjusting the solution based on the user's emotional state. This adjustment ensures that the most suitable solution is presented to the user.

[0137] From the presented solutions, the user selects the one that best suits their situation. The selected solution is sent to the server, which then prepares for remote access. Remote access allows the necessary actions to be performed quickly. The results of these actions and any changes in emotional state are notified to the user from the terminal.

[0138] For example, if a user requests support regarding an app crash, the emotion engine will detect the user's anxious expression or frustrated tone, and the server will prioritize providing a quicker solution. Furthermore, after the support is complete, the system will confirm the user's reassurance, send a thoughtful feedback message, and make adjustments to ensure a better support experience in the future. In this way, by providing support that takes the user's emotions into consideration, the system helps make the use of digital devices easier and more comfortable for the elderly.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user launches a dedicated app on their smartphone and enters a text description of the problem they need support for. The device's camera and microphone activate, capturing the user's facial expressions and voice for the emotion engine.

[0142] Step 2:

[0143] The terminal sends the user's inquiry content and sentiment data to the server. This data is necessary for analysis.

[0144] Step 3:

[0145] The server analyzes the received inquiry using an artificial intelligence model. The AI ​​model identifies the problem and generates potential appropriate solutions.

[0146] Step 4:

[0147] The server analyzes the received emotional data and estimates the user's current emotional state. Based on this analysis, the solution is adjusted to match the user's emotions.

[0148] Step 5:

[0149] The server sends a list of adjusted solution options to the terminal. The user is presented with solutions that take their emotional state into consideration.

[0150] Step 6:

[0151] The user reviews the presented solutions and selects the one they deem most appropriate. This selection information is then sent to the server via the terminal.

[0152] Step 7:

[0153] The server requests permission from the terminal for remote access to execute the selected solution. The user decides whether to grant permission.

[0154] Step 8:

[0155] When a user grants remote access, the terminal communicates the permission to the server, and the server remotely performs operations on the terminal.

[0156] Step 9:

[0157] The terminal receives the operation results sent from the server and notifies the user of the results. At the same time, it acquires the user's new emotional state and confirms their sense of security and satisfaction.

[0158] Step 10:

[0159] Users will verify that the problem has been resolved and provide feedback to the server. This feedback will be used to improve future services.

[0160] (Example 2)

[0161] 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".

[0162] Elderly people often experience anxiety and stress when using smartphones and other electronic devices due to the difficulty of operation and the risk of unexpected data loss. Furthermore, they may have difficulty finding appropriate solutions when technical problems arise, and their emotional state can also influence their support experience. Therefore, a system is needed that provides smooth and reassuring support while considering the user's emotional state.

[0163] 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.

[0164] In this invention, the server includes means for receiving inquiries from users, means for acquiring the user's facial expressions and voice data based on the received content, and means for analyzing the acquired data to recognize the user's emotional state. This makes it possible to propose appropriate solutions that take into account not only the user's inquiries but also their emotional state.

[0165] "User" refers to a person who uses this system, and in the context of this invention, this includes elderly people who feel uneasy about operating electronic devices.

[0166] "Inquiry details" refers to the detailed information of problems or questions that users report to the system seeking support.

[0167] "Electronic devices" refers to hardware devices that users use to access this system, such as smartphones and tablets.

[0168] "Facial expression and voice data" refers to information about the user's facial expressions and speech that is acquired in order to identify the user's emotional state.

[0169] "Emotional state" refers to the psychological and emotional state of a user when they make an inquiry, and this is analyzed by the system to adjust its support actions.

[0170] A "generative AI model" refers to artificial intelligence technology that makes suggestions for problem-solving based on the user's inquiry and emotional state.

[0171] "Remote access" refers to a technology that allows a server to remotely connect to a user's electronic device and perform necessary operations.

[0172] "The result of the operation" refers to the outcome obtained from the countermeasures taken remotely, and the user is notified of this result to confirm that the problem has been resolved.

[0173] This invention is a system designed to alleviate the anxiety that arises from difficulties in operation and unexpected data loss when users use smartphones and other electronic devices, and to provide smooth support. This system is particularly aimed at the elderly and provides appropriate support that takes into account the user's emotional state.

[0174] First, users install a dedicated app on their smartphone and enter the problem they need support for within the app. During this process, the device's camera and microphone are used to collect the user's facial expressions and voice tone. This data is then used to analyze the user's emotional state using an emotion engine. This information is processed through dedicated software or an application, utilizing existing hardware such as camera sensors and microphones.

[0175] The analyzed sentiment data and inquiry content are sent to the server. The server receives this data and activates a generative AI model. This AI model generates the optimal solution from the inquiry content and sentiment state, makes adjustments, and then presents it to the user. This process ensures that solutions that take the user's emotions into consideration are smoothly provided.

[0176] For example, when a user reports an application crash, if the emotion engine detects the user's anxiety or frustration, the server will immediately prioritize and propose a rapid solution. Simultaneously, the server will create feedback that includes approaches to alleviate these emotions. Depending on the nature of the problem and the scope of the emergency response, the server may also prepare remote access to the terminal and perform necessary operations remotely. The results, along with any changes in the user's mood, will be communicated to the terminal, providing further reassurance.

[0177] An example of a prompt to input into a generative AI model is: "The user is requesting support regarding an app crash, and the sentiment engine has detected that the user is anxious. What quick solution would you suggest?" This prompt uses natural language processing techniques to instruct the AI ​​model and guide it to suggest solutions.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The user launches a dedicated app on their smartphone and enters the problem they need support for. This input includes details about the specific problem, such as the application's unstable operation. As output, the user's problem details are temporarily saved on the device.

[0181] Step 2:

[0182] The device captures the user's facial expressions and voice data in real time using its built-in camera and microphone, simultaneously with the input question. This input data is then prepared to be sent to the emotion engine. The output is emotional data, such as the user's facial expressions and voice tone.

[0183] Step 3:

[0184] The device transmits the acquired facial expression and voice data to an emotion engine, which then analyzes the user's emotional state. During this process, image processing and voice analysis technologies are used to output numerical data representing emotions such as anxiety and frustration. The analysis results are then ready for transmission to the server.

[0185] Step 4:

[0186] The terminal sends the analyzed sentiment data and user inquiry to the server. This input information triggers further processing on the server side. The server receives this data as output.

[0187] Step 5:

[0188] The server activates a generative AI model based on the received inquiry content and sentiment data. The AI ​​model understands the problem and generates solutions that also take into account the user's emotional state. Using data analysis and natural language processing techniques, proposed solutions are generated as output.

[0189] Step 6:

[0190] The server sends solutions generated by the AI ​​model to the terminal. The terminal receives these solutions and presents them to the user. The user selects the solution they believe to be the best from the multiple solutions on the screen. The user's selection is recorded on the terminal as output.

[0191] Step 7:

[0192] Based on the user's selection, the server prepares for the necessary remote operations and executes them on the terminal. Remote operations include restarting applications and changing settings. The success or failure of the operation is recorded as output.

[0193] Step 8:

[0194] The terminal notifies the user based on the results of operations from the server and the newly acquired sentiment data. This includes information that the issue has been resolved and feedback to promote the user's sense of security. As output, the user is notified that the problem has been resolved and receives feedback.

[0195] (Application Example 2)

[0196] 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".

[0197] Elderly people often experience stress due to the difficulty of operation and technical problems when using smart devices. These issues can be a significant burden for users unfamiliar with digital devices. Furthermore, the process of resolving these problems can reduce convenience and satisfaction. As a result, the use of digital devices remains low, leading to problems such as a decline in communication and quality of life.

[0198] 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.

[0199] In this invention, the server includes communication means for receiving inquiries from users, data analysis means for analyzing the inquiries and classifying problems, and generation means for generating solutions based on the classified problems and the user's emotional state. This makes it possible to provide appropriate and prompt support that takes the user's emotions into consideration, and to make it easier and more comfortable for elderly people to use smart devices.

[0200] "Communication means" refers to the function for receiving inquiries from users.

[0201] "Data analysis means" refers to a function for analyzing the content of received inquiries and classifying the problems.

[0202] A "generation mechanism" is a function for generating solutions based on classified problems and the emotional state of the users.

[0203] A "solution adjustment and presentation method" is a function that adjusts and presents solutions according to the user's emotions.

[0204] "Action execution means" refers to a function that allows for remote operation to be performed according to the solution selected by the user.

[0205] A "notification mechanism" is a function that notifies the user of the results of the actions they have performed and any changes in their emotional state.

[0206] An "artificial intelligence model" is a technology used to classify inquiries and analyze the emotional state of users.

[0207] This invention aims to improve the usability of digital devices for the elderly and is a system in which a server and a user's terminal work in cooperation. The server receives inquiries from the user using communication means and classifies the problems using data analysis means. Artificial intelligence models are used in the classification process, and along with the content of the user's inquiry, the emotional state is also analyzed from facial expressions and tone of voice.

[0208] Based on these analysis results, the generation means creates a solution to the user's problem. Simultaneously, the adjustment and presentation means adjusts the generated solution according to the user's emotions and presents it in the most optimal way. Once the user selects a solution, the server remotely performs the corresponding operation via the operation execution means. The results of the executed operation and any changes in the user's emotional state are fed back to the user's terminal via the notification means.

[0209] This system utilizes user terminal hardware such as cameras and microphones, and leverages software libraries like OpenCV and speech_recognition to accurately recognize user emotions and provide appropriate support. For example, if a user inquires with an anxious voice saying, "The application has stopped working," the server, after confirming the user's emotional state, will prioritize providing prompt operational guidance.

[0210] An example of a prompt used as input to a generative AI model is: "Please consider how to respond when an elderly person becomes emotional. The emotional data includes 'anxiety' and 'confusion.' Design quick and gentle support assuming the user has a problem with their medication." In this way, appropriate services that take the user's emotions into consideration can be implemented.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] Users submit inquiries through an application on their device. During this process, text and audio related to the user's problem are sent as input. The device also uses its camera and microphone to capture the user's facial expressions and voice. This data is transferred to the server. The server then prepares the user's inquiry content and sentiment data as output.

[0214] Step 2:

[0215] The server inputs the received inquiry content into data analysis using communication methods. The input data also includes emotional data based on the user's facial expression analysis. The server utilizes an artificial intelligence model to classify the problem and perform the necessary data calculations. The output consists of the classified problem and the user's emotional state.

[0216] Step 3:

[0217] The server's generation mechanism takes classified problems and emotional states as input and sends prompt messages to a generation AI model to generate solutions. Based on these prompt messages, the generation AI model creates a solution. The role of these prompt messages is to guide the user to an appropriate solution for the situation. As output, the user receives the optimal solution.

[0218] Step 4:

[0219] The server uses an adjustment presentation mechanism to fine-tune solutions based on the user's emotional state. This allows for suggestions that take the user's mental state into consideration. The output is the adjusted solution.

[0220] Step 5:

[0221] The user reviews and selects a solution from the presented options. The selected solution is then sent back to the server. The terminal notifies the server that the selection is complete. The selected solution is confirmed as output.

[0222] Step 6:

[0223] The server uses its execution mechanism to initiate remote operation based on the selected solution. It performs the necessary data calculations and sends execution instructions to the user terminal. The output is the result of the remote operation.

[0224] Step 7:

[0225] The server sends the completion status of the operation and changes in emotional state to the user's terminal using a notification mechanism. The terminal displays this status and provides feedback to the user. The output is a notification of the operation result and emotional feedback.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] The present invention provides remote support to users by allowing them to input problems requiring assistance using their smartphones, quickly analyzing the input, and generating solutions. This system is realized by linking the user's terminal, an analysis server, and various analysis means. The embodiments of this system will be described in detail below.

[0243] Users utilize a dedicated support application on their smartphones. This application receives inquiries from users and records the information on the device. The entered information is then sent from the device to the server. Users typically enter details of the problem in text format.

[0244] The server has the ability to analyze information received from the terminal. It uses an artificial intelligence model to automatically classify problems. For example, inquiries such as "The app isn't working" or "I accidentally deleted a photo" are examples of issues that can be classified. Based on the analysis results, the server generates potential solutions. These solutions include steps and methods for performing the corresponding operations.

[0245] The server then sends candidate solutions to the terminal and displays them to the user. The user can select the best solution from the presented options. This selection information is sent back to the server, which then prepares to perform the specific actions based on that selection.

[0246] When the server performs remote access to a user's terminal, it first requests permission from the terminal. After receiving approval from the user, the terminal grants permission to the server. If the user grants permission, the server remotely performs operations on the terminal, such as retrieving necessary data or changing settings.

[0247] Ultimately, the device notifies the user of the results of the operation performed by the server. This allows the user to verify whether the problem has been resolved and to rate their satisfaction. In this way, it becomes possible to quickly and accurately resolve smartphone problems faced by the elderly. As a specific example, consider a scenario where accidentally deleted photos are restored from a cloud backup. In this case, the server retrieves the backup data of the photos and performs the restore operation remotely. The device also notifies the user whether the restored photos are displayed correctly.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user launches a dedicated app on their smartphone and enters details of the problem requiring support. The input is primarily in text format.

[0251] Step 2:

[0252] The terminal sends the user's input to the server. This information is used as data for analysis.

[0253] Step 3:

[0254] The server activates an artificial intelligence model to analyze the inquiry received from the terminal. The model analyzes the inquiry and classifies the problem into the appropriate category.

[0255] Step 4:

[0256] The server generates multiple solutions based on the analysis results, using specific problem-solving techniques depending on the type of problem.

[0257] Step 5:

[0258] The server sends the generated solution to the terminal and instructs it to present it to the user.

[0259] Step 6:

[0260] The user reviews the solutions presented on the device and selects the solution they prefer. This selection influences the next processing step.

[0261] Step 7:

[0262] The terminal notifies the server of the solution selected by the user. The server then develops a plan of action based on the selected solution.

[0263] Step 8:

[0264] The server requests permission from the terminal for remote access in order to execute the selected solution.

[0265] Step 9:

[0266] The user grants permission for remote access by reviewing and agreeing to the remote access permission request displayed on the device.

[0267] Step 10:

[0268] After obtaining permission for remote access from the user, the server performs the necessary operations on the terminal. Specifically, it performs operations such as data recovery and configuration changes.

[0269] Step 11:

[0270] The terminal receives the operation results sent from the server and notifies the user of the details.

[0271] Step 12:

[0272] Users can verify that the problem has been resolved and enter feedback on their device. This feedback is sent to the server to improve the service.

[0273] (Example 1)

[0274] 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."

[0275] Modern information processing devices, especially portable devices, are prone to various technical problems during use. Users want to address these issues quickly and accurately, but often lack the necessary expertise, making self-resolution difficult. Therefore, rapid remote support is essential.

[0276] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following means.

[0277] In this invention, the server includes means for receiving inquiry information from a user, means for analyzing the inquiry information and classifying the content, and means for generating a solution based on the classified content. Thereby, it becomes possible to efficiently analyze the technical problems faced by the user and quickly provide an optimal solution.

[0278] The "receiving means" is a function or device for receiving inquiry information from a user through data communication.

[0279] The "analyzing means" is a function or device for processing the received inquiry information and classifying its content.

[0280] The "generating means" is a function or device for automatically creating an appropriate solution based on the analyzed content.

[0281] The "presenting means" is a function or device for displaying the generated solution on the user's terminal.

[0282] The "executing means" is a function or device for executing a remote operation based on the solution selected by the user.

[0283] The "notifying means" is a function or device for transmitting the result of the executed operation to the user.

[0284] "Permission" is approval information or data or an operation indicating consent for the user to approve a remote operation.

[0285] The "learning model" refers to a machine learning algorithm used for analyzing the received information and classifying its content.

[0286] This invention provides remote, rapid, and effective support when users encounter technical problems using an information processing device. The following shows embodiments of this system.

[0287] The user uses a portable information processing device, such as a smartphone, to utilize a dedicated support application. This application provides an interface for inputting inquiry information from the user and has a function to temporarily record the input information in local memory. Next, the terminal establishes communication means for transmitting this information to the server and transmits it through the REST API.

[0288] The server uses a generated AI model to analyze the received information. This model has a function to automatically classify the inquiry content into different categories. When the analysis is completed, the server generates a solution based on the problem. For generating the solution, queries of the past database and the learning model by AI are utilized.

[0289] The generated solution is transmitted to the user's information processing device and displayed on the terminal. The user can confirm the presented solution and select the one that seems optimal. When the selection is confirmed, the information is transmitted to the server again.

[0290] The server executes remote access to the information processing device only when approval is obtained from the user. This makes it possible to remotely execute the specified operation.

[0291] As a specific example, consider the case where the user wants to restore an accidentally deleted image. In this case, the server retrieves the image from the cloud backup and remotely executes the restoration. As a result, the terminal presents a notification of the restoration completion to the user and enables the restored image to be confirmed. An example of the prompt text is "Please teach me how to restore a photo on my smartphone that was accidentally deleted from the backup."

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user launches the device's dedicated support application and enters details of the problem into the inquiry form. The entered information is temporarily stored in local memory in text format. The input here is a description of the specific problem.

[0295] Step 2:

[0296] The terminal sends the stored query information to the server. This is done by the terminal establishing communication with the server and sending the information via the API. The output is the data of the query information sent to the server.

[0297] Step 3:

[0298] The server retrieves the received query information and begins analysis using a generative AI model. This analysis classifies the information to determine which category the problem belongs to. The input data is the text information of the query, and the output is the classification result.

[0299] Step 4:

[0300] The server generates solutions based on the analysis results. This generation process involves referencing AI models and historical databases, and combining similar problem-solving solutions. The input is the classification result, and the output is a list of potential solutions.

[0301] Step 5:

[0302] The server sends the generated solutions to the terminal. The terminal displays the received solutions on the user interface. The user reviews the solutions and selects the most suitable one. The input is the proposed solutions, and the output is the user's selection information.

[0303] Step 6:

[0304] The user selects a solution and approves the remote work if necessary. The terminal sends the approval information to the server and prepares to start the corresponding operation. Here, the input is the user's selection information and approval, and the output is the approval information to the server.

[0305] Step 7:

[0306] The server receives the approval from the user and further executes the remote operation. This operation includes obtaining necessary data and changing the settings of the information processing device. The output is the execution result of the operation.

[0307] Step 8:

[0308] The server notifies the terminal of the result of the operation. The terminal checks the result and notifies the user of the result. The user checks whether the problem is solved based on the result. The input is the execution result of the operation, and the output is the notification to the user.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In modern households and personal lives, the problems of operating devices that inexperienced or elderly people face daily are increasing. In particular, since many facilities and devices have become more complex, the burden and stress on users have been increasing. There is a need to reduce such problems and provide an environment where anyone can easily utilize technology.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes a device for receiving inquiries from users, a device for analyzing the inquiries and classifying the issues, and a device for receiving commands via voice input and controlling autonomously operating mechanical devices to perform physical tasks. This makes it possible to easily control home appliances and household devices via voice commands, reducing the technical burden on users.

[0314] A "user" is someone who uses this system to seek solutions to problems or support.

[0315] "Inquiry details" refer to information that describes the specific problems or requests that the user wishes to have resolved.

[0316] A "device" is a piece of equipment or unit that has a specific function and operates electronically or mechanically.

[0317] A "challenge" refers to a difficulty or obstacle that users face, and it is a situation for which a solution is desired.

[0318] A "solution" is a set of steps or methods provided to resolve or mitigate a problem.

[0319] "Remote" refers to performing operations or controls over a physical distance.

[0320] "Voice input" refers to a technology or method that electronically recognizes and analyzes the content of a user's speech.

[0321] An "autonomous operating machine" is a robot or mechanical device that operates automatically according to programmed instructions.

[0322] A "generative intelligence model" is a program that uses artificial intelligence technology to create new information and make decisions.

[0323] This invention relates to a system that controls autonomously operating machinery in the home by allowing users to input operating commands via voice using a device such as a smartphone, and by having a server analyze that voice. The server utilizes voice recognition technology and analyzes the input commands using a generative intelligence model. Through voice input, users can make the machinery perform specific actions.

[0324] When the server receives voice input, it first converts the voice data into text. Then, it uses a generative intelligence model to analyze the commands based on this text data and determine the appropriate action. In this analysis process, for example, the Python speech_recognition library is used for speech recognition, and the virtual robot_sdk library is used for controlling the mechanical device. Using these technologies, commands requiring physical action are appropriately classified and executed.

[0325] When a user gives a command via a smartphone app, such as "Pick up the paper that's fallen on the living room table," the server transcribes the voice into text and analyzes the content of the command. For example, it uses a generative intelligence model in the cloud to understand the command and relay it to the robot. An example of a prompt to the generative AI model in this process would be, "Receive the user's voice command and create instructions for the robot to pick up the paper."

[0326] This system allows users to easily control home robots via voice commands without complex operations, providing an accessible environment even for those unfamiliar with technology.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user launches the app on their smartphone and issues instructions using the voice input function. At this stage, the user's voice commands serve as input, and that voice data is captured by the app.

[0330] Step 2:

[0331] The terminal sends audio data to the server. The server uses a speech recognition engine to convert this audio data into text data. Specifically, it processes the audio data (input) to generate text data (output). In this process, the speech recognition engine is used to analyze the audio and convert it into understandable text.

[0332] Step 3:

[0333] The server receives the text data and analyzes the content of the instructions using a generative AI model. This analysis identifies the user's intended instruction. Here, the generative AI model processes the text data (input) and obtains the interpreted instructions (output). This process includes the specific action of inputting prompt sentences into the generative AI model as needed to perform semantic understanding.

[0334] Step 4:

[0335] Based on the analyzed commands, the server instructs the robot to perform specific actions. At this stage, the interpreted commands (inputs) are converted into robot control commands and sent to the robot (outputs). Specifically, the server gives the robot appropriate instructions so that mechanical actions are performed according to the command content.

[0336] Step 5:

[0337] Robots perform physical actions based on the instructions they receive. This is how user commands are materialized in the real world. Robots realize control commands (inputs) as executable mechanical actions (outputs). These actions include specific actions such as picking up or moving objects.

[0338] 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.

[0339] This invention is a system designed for elderly smartphone users that supports them with issues such as difficulty in operation and accidental data deletion, using an emotion engine. This system recognizes the user's emotions and provides appropriate support, thereby realizing a more effective and comfortable user experience.

[0340] In this system, users first input the problem they need support for using a dedicated app on their smartphone. The device is equipped with a camera and microphone, allowing the user's facial expressions and tone of voice to be analyzed by an emotion engine. This identifies the emotional state of the user when they make an inquiry and determines the severity of the problem and the urgency of the user's situation.

[0341] The server activates an artificial intelligence model based on the inquiry content and sentiment data received from the terminal. The AI ​​model analyzes the problem and generates a solution, while simultaneously adjusting the solution based on the user's emotional state. This adjustment ensures that the most suitable solution is presented to the user.

[0342] From the presented solutions, the user selects the one that best suits their situation. The selected solution is sent to the server, which then prepares for remote access. Remote access allows the necessary actions to be performed quickly. The results of these actions and any changes in emotional state are notified to the user from the terminal.

[0343] For example, if a user requests support regarding an app crash, the emotion engine will detect the user's anxious expression or frustrated tone, and the server will prioritize providing a quicker solution. Furthermore, after the support is complete, the system will confirm the user's reassurance, send a thoughtful feedback message, and make adjustments to ensure a better support experience in the future. In this way, by providing support that takes the user's emotions into consideration, the system helps make the use of digital devices easier and more comfortable for the elderly.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] The user launches a dedicated app on their smartphone and enters a text description of the problem they need support for. The device's camera and microphone activate, capturing the user's facial expressions and voice for the emotion engine.

[0347] Step 2:

[0348] The terminal sends the user's inquiry content and sentiment data to the server. This data is necessary for analysis.

[0349] Step 3:

[0350] The server analyzes the received inquiry using an artificial intelligence model. The AI ​​model identifies the problem and generates potential appropriate solutions.

[0351] Step 4:

[0352] The server analyzes the received emotional data and estimates the user's current emotional state. Based on this analysis, the solution is adjusted to match the user's emotions.

[0353] Step 5:

[0354] The server sends a list of adjusted solution options to the terminal. The user is presented with solutions that take their emotional state into consideration.

[0355] Step 6:

[0356] The user reviews the presented solutions and selects the one they deem most appropriate. This selection information is then sent to the server via the terminal.

[0357] Step 7:

[0358] The server requests permission from the terminal for remote access to execute the selected solution. The user decides whether to grant permission.

[0359] Step 8:

[0360] When a user grants remote access, the terminal communicates the permission to the server, and the server remotely performs operations on the terminal.

[0361] Step 9:

[0362] The terminal receives the operation results sent from the server and notifies the user of the results. At the same time, it acquires the user's new emotional state and confirms their sense of security and satisfaction.

[0363] Step 10:

[0364] Users will verify that the problem has been resolved and provide feedback to the server. This feedback will be used to improve future services.

[0365] (Example 2)

[0366] 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".

[0367] Elderly people often experience anxiety and stress when using smartphones and other electronic devices due to the difficulty of operation and the risk of unexpected data loss. Furthermore, they may have difficulty finding appropriate solutions when technical problems arise, and their emotional state can also influence their support experience. Therefore, a system is needed that provides smooth and reassuring support while considering the user's emotional state.

[0368] 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.

[0369] In this invention, the server includes means for receiving inquiries from users, means for acquiring the user's facial expressions and voice data based on the received content, and means for analyzing the acquired data to recognize the user's emotional state. This makes it possible to propose appropriate solutions that take into account not only the user's inquiries but also their emotional state.

[0370] "User" refers to a person who uses this system, and in the context of this invention, this includes elderly people who feel uneasy about operating electronic devices.

[0371] "Inquiry details" refers to the detailed information of problems or questions that users report to the system seeking support.

[0372] "Electronic devices" refers to hardware devices that users use to access this system, such as smartphones and tablets.

[0373] "Facial expression and voice data" refers to information about the user's facial expressions and speech that is acquired in order to identify the user's emotional state.

[0374] "Emotional state" refers to the psychological and emotional state of a user when they make an inquiry, and this is analyzed by the system to adjust its support actions.

[0375] A "generative AI model" refers to artificial intelligence technology that makes suggestions for problem-solving based on the user's inquiry and emotional state.

[0376] "Remote access" refers to a technology that allows a server to remotely connect to a user's electronic device and perform necessary operations.

[0377] "The result of the operation" refers to the outcome obtained from the countermeasures taken remotely, and the user is notified of this result to confirm that the problem has been resolved.

[0378] This invention is a system designed to alleviate the anxiety that arises from difficulties in operation and unexpected data loss when users use smartphones and other electronic devices, and to provide smooth support. This system is particularly aimed at the elderly and provides appropriate support that takes into account the user's emotional state.

[0379] First, users install a dedicated app on their smartphone and enter the problem they need support for within the app. During this process, the device's camera and microphone are used to collect the user's facial expressions and voice tone. This data is then used to analyze the user's emotional state using an emotion engine. This information is processed through dedicated software or an application, utilizing existing hardware such as camera sensors and microphones.

[0380] The analyzed sentiment data and inquiry content are sent to the server. The server receives this data and activates a generative AI model. This AI model generates the optimal solution from the inquiry content and sentiment state, makes adjustments, and then presents it to the user. This process ensures that solutions that take the user's emotions into consideration are smoothly provided.

[0381] For example, when a user reports an application crash, if the emotion engine detects the user's anxiety or frustration, the server will immediately prioritize and propose a rapid solution. Simultaneously, the server will create feedback that includes approaches to alleviate these emotions. Depending on the nature of the problem and the scope of the emergency response, the server may also prepare remote access to the terminal and perform necessary operations remotely. The results, along with any changes in the user's mood, will be communicated to the terminal, providing further reassurance.

[0382] An example of a prompt to input into a generative AI model is: "The user is requesting support regarding an app crash, and the sentiment engine has detected that the user is anxious. What quick solution would you suggest?" This prompt uses natural language processing techniques to instruct the AI ​​model and guide it to suggest solutions.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The user launches a dedicated app on their smartphone and enters the problem they need support for. This input includes details about the specific problem, such as the application's unstable operation. As output, the user's problem details are temporarily saved on the device.

[0386] Step 2:

[0387] The device captures the user's facial expressions and voice data in real time using its built-in camera and microphone, simultaneously with the input question. This input data is then prepared to be sent to the emotion engine. The output is emotional data, such as the user's facial expressions and voice tone.

[0388] Step 3:

[0389] The device transmits the acquired facial expression and voice data to an emotion engine, which then analyzes the user's emotional state. During this process, image processing and voice analysis technologies are used to output numerical data representing emotions such as anxiety and frustration. The analysis results are then ready for transmission to the server.

[0390] Step 4:

[0391] The terminal sends the analyzed sentiment data and user inquiry to the server. This input information triggers further processing on the server side. The server receives this data as output.

[0392] Step 5:

[0393] The server activates a generative AI model based on the received inquiry content and sentiment data. The AI ​​model understands the problem and generates solutions that also take into account the user's emotional state. Using data analysis and natural language processing techniques, proposed solutions are generated as output.

[0394] Step 6:

[0395] The server sends solutions generated by the AI ​​model to the terminal. The terminal receives these solutions and presents them to the user. The user selects the solution they believe to be the best from the multiple solutions on the screen. The user's selection is recorded on the terminal as output.

[0396] Step 7:

[0397] Based on the user's selection, the server prepares for the necessary remote operations and executes them on the terminal. Remote operations include restarting applications and changing settings. The success or failure of the operation is recorded as output.

[0398] Step 8:

[0399] The terminal notifies the user based on the results of operations from the server and the newly acquired sentiment data. This includes information that the issue has been resolved and feedback to promote the user's sense of security. As output, the user is notified that the problem has been resolved and receives feedback.

[0400] (Application Example 2)

[0401] 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".

[0402] Elderly people often experience stress due to the difficulty of operation and technical problems when using smart devices. These issues can be a significant burden for users unfamiliar with digital devices. Furthermore, the process of resolving these problems can reduce convenience and satisfaction. As a result, the use of digital devices remains low, leading to problems such as a decline in communication and quality of life.

[0403] 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.

[0404] In this invention, the server includes communication means for receiving inquiries from users, data analysis means for analyzing the inquiries and classifying problems, and generation means for generating solutions based on the classified problems and the user's emotional state. This makes it possible to provide appropriate and prompt support that takes the user's emotions into consideration, and to make it easier and more comfortable for elderly people to use smart devices.

[0405] "Communication means" refers to the function for receiving inquiries from users.

[0406] "Data analysis means" refers to a function for analyzing the content of received inquiries and classifying the problems.

[0407] A "generation mechanism" is a function for generating solutions based on classified problems and the emotional state of the users.

[0408] A "solution adjustment and presentation method" is a function that adjusts and presents solutions according to the user's emotions.

[0409] "Action execution means" refers to a function that allows for remote operation to be performed according to the solution selected by the user.

[0410] A "notification mechanism" is a function that notifies the user of the results of the actions they have performed and any changes in their emotional state.

[0411] An "artificial intelligence model" is a technology used to classify inquiries and analyze the emotional state of users.

[0412] This invention aims to improve the usability of digital devices for the elderly and is a system in which a server and a user's terminal work in cooperation. The server receives inquiries from the user using communication means and classifies the problems using data analysis means. Artificial intelligence models are used in the classification process, and along with the content of the user's inquiry, the emotional state is also analyzed from facial expressions and tone of voice.

[0413] Based on these analysis results, the generation means creates a solution to the user's problem. Simultaneously, the adjustment and presentation means adjusts the generated solution according to the user's emotions and presents it in the most optimal way. Once the user selects a solution, the server remotely performs the corresponding operation via the operation execution means. The results of the executed operation and any changes in the user's emotional state are fed back to the user's terminal via the notification means.

[0414] This system utilizes user terminal hardware such as cameras and microphones, and leverages software libraries like OpenCV and speech_recognition to accurately recognize user emotions and provide appropriate support. For example, if a user inquires with an anxious voice saying, "The application has stopped working," the server, after confirming the user's emotional state, will prioritize providing prompt operational guidance.

[0415] An example of a prompt used as input to a generative AI model is: "Please consider how to respond when an elderly person becomes emotional. The emotional data includes 'anxiety' and 'confusion.' Design quick and gentle support assuming the user has a problem with their medication." In this way, appropriate services that take the user's emotions into consideration can be implemented.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] Users submit inquiries through an application on their device. During this process, text and audio related to the user's problem are sent as input. The device also uses its camera and microphone to capture the user's facial expressions and voice. This data is transferred to the server. The server then prepares the user's inquiry content and sentiment data as output.

[0419] Step 2:

[0420] The server inputs the received inquiry content into data analysis using communication methods. The input data also includes emotional data based on the user's facial expression analysis. The server utilizes an artificial intelligence model to classify the problem and perform the necessary data calculations. The output consists of the classified problem and the user's emotional state.

[0421] Step 3:

[0422] The server's generation mechanism takes classified problems and emotional states as input and sends prompt messages to a generation AI model to generate solutions. Based on these prompt messages, the generation AI model creates a solution. The role of these prompt messages is to guide the user to an appropriate solution for the situation. As output, the user receives the optimal solution.

[0423] Step 4:

[0424] The server uses an adjustment presentation mechanism to fine-tune solutions based on the user's emotional state. This allows for suggestions that take the user's mental state into consideration. The output is the adjusted solution.

[0425] Step 5:

[0426] The user reviews and selects a solution from the presented options. The selected solution is then sent back to the server. The terminal notifies the server that the selection is complete. The selected solution is confirmed as output.

[0427] Step 6:

[0428] The server uses its execution mechanism to initiate remote operation based on the selected solution. It performs the necessary data calculations and sends execution instructions to the user terminal. The output is the result of the remote operation.

[0429] Step 7:

[0430] The server sends the completion status of the operation and changes in emotional state to the user's terminal using a notification mechanism. The terminal displays this status and provides feedback to the user. The output is a notification of the operation result and emotional feedback.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] The present invention provides remote support to users by allowing them to input problems requiring assistance using their smartphones, quickly analyzing the input, and generating solutions. This system is realized by linking the user's terminal, an analysis server, and various analysis means. The embodiments of this system will be described in detail below.

[0448] Users utilize a dedicated support application on their smartphones. This application receives inquiries from users and records the information on the device. The entered information is then sent from the device to the server. Users typically enter details of the problem in text format.

[0449] The server has the ability to analyze information received from the terminal. It uses an artificial intelligence model to automatically classify problems. For example, inquiries such as "The app isn't working" or "I accidentally deleted a photo" are examples of issues that can be classified. Based on the analysis results, the server generates potential solutions. These solutions include steps and methods for performing the corresponding operations.

[0450] The server then sends candidate solutions to the terminal and displays them to the user. The user can select the best solution from the presented options. This selection information is sent back to the server, which then prepares to perform the specific actions based on that selection.

[0451] When the server performs remote access to a user's terminal, it first requests permission from the terminal. After receiving approval from the user, the terminal grants permission to the server. If the user grants permission, the server remotely performs operations on the terminal, such as retrieving necessary data or changing settings.

[0452] Ultimately, the device notifies the user of the results of the operation performed by the server. This allows the user to verify whether the problem has been resolved and to rate their satisfaction. In this way, it becomes possible to quickly and accurately resolve smartphone problems faced by the elderly. As a specific example, consider a scenario where accidentally deleted photos are restored from a cloud backup. In this case, the server retrieves the backup data of the photos and performs the restore operation remotely. The device also notifies the user whether the restored photos are displayed correctly.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The user launches a dedicated app on their smartphone and enters details of the problem requiring support. The input is primarily in text format.

[0456] Step 2:

[0457] The terminal sends the user's input to the server. This information is used as data for analysis.

[0458] Step 3:

[0459] The server activates an artificial intelligence model to analyze the inquiry received from the terminal. The model analyzes the inquiry and classifies the problem into the appropriate category.

[0460] Step 4:

[0461] The server generates multiple solutions based on the analysis results, using specific problem-solving techniques depending on the type of problem.

[0462] Step 5:

[0463] The server sends the generated solution to the terminal and instructs it to present it to the user.

[0464] Step 6:

[0465] The user reviews the solutions presented on the device and selects the solution they prefer. This selection influences the next processing step.

[0466] Step 7:

[0467] The terminal notifies the server of the solution selected by the user. The server then develops a plan of action based on the selected solution.

[0468] Step 8:

[0469] The server requests permission from the terminal for remote access in order to execute the selected solution.

[0470] Step 9:

[0471] The user grants permission for remote access by reviewing and agreeing to the remote access permission request displayed on the device.

[0472] Step 10:

[0473] After obtaining permission for remote access from the user, the server performs the necessary operations on the terminal. Specifically, it performs operations such as data recovery and configuration changes.

[0474] Step 11:

[0475] The terminal receives the operation results sent from the server and notifies the user of the details.

[0476] Step 12:

[0477] Users can verify that the problem has been resolved and enter feedback on their device. This feedback is sent to the server to improve the service.

[0478] (Example 1)

[0479] 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."

[0480] Modern information processing devices, especially portable devices, are prone to various technical problems during use. Users want to address these issues quickly and accurately, but often lack the necessary expertise, making self-resolution difficult. Therefore, rapid remote support is essential.

[0481] 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.

[0482] In this invention, the server includes means for receiving inquiry information from a user, means for analyzing the inquiry information and classifying its contents, and means for generating solutions based on the classified contents. This makes it possible to efficiently analyze the technical problems faced by the user and quickly provide the optimal solution.

[0483] "Receiving means" refers to a function or device for receiving inquiry information from users via data communication.

[0484] "Analysis means" refers to a function or device for processing received inquiry information and classifying its contents.

[0485] "Generation means" refers to a function or device that automatically creates an appropriate solution based on the analyzed content.

[0486] "Presentation means" refers to a function or device that displays the generated solution on the user's terminal.

[0487] "Execution means" refers to a function or device for performing remote operation based on a solution selected by the user.

[0488] "Notification means" refers to a function or device for communicating the results of an performed operation to the user.

[0489] "Permission" refers to data or actions that indicate authorization information or consent from the user to authorize remote control.

[0490] A "learning model" refers to a machine learning algorithm used to analyze received information and classify its contents.

[0491] This invention provides remote, rapid, and effective support when users encounter technical problems while using an information processing device. An embodiment of this system is described below.

[0492] Users utilize a dedicated support application using a portable information processing device, such as a smartphone. This application provides an interface for inputting inquiry information from the user and has the function to temporarily store the entered information in local memory. The device then establishes a means of communication to send this information to the server and transmits it via a REST API.

[0493] The server uses a generative AI model to analyze the received information. This model has the ability to automatically classify the query content into different categories. Once the analysis is complete, the server generates a problem-based solution. The solution generation utilizes historical database queries and AI-learned models.

[0494] The generated solutions are sent to the user's information processing device and displayed on the terminal. The user can review the presented solutions and select the one they deem best. Once the selection is confirmed, the information is sent back to the server.

[0495] The server will only perform remote access to the information processing device if it has obtained consent from the user. This makes it possible to perform specified operations remotely.

[0496] A concrete example would be a user wanting to recover images they accidentally deleted. In this case, the server retrieves the images from the cloud backup and performs the restoration remotely. As a result, the device notifies the user that the restoration is complete, allowing them to view the restored images. An example of a prompt message would be, "How do I recover photos I accidentally deleted from my smartphone from the backup?"

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The user launches the device's dedicated support application and enters details of the problem into the inquiry form. The entered information is temporarily stored in local memory in text format. The input here is a description of the specific problem.

[0500] Step 2:

[0501] The terminal sends the stored query information to the server. This is done by the terminal establishing communication with the server and sending the information via the API. The output is the data of the query information sent to the server.

[0502] Step 3:

[0503] The server retrieves the received query information and begins analysis using a generative AI model. This analysis classifies the information to determine which category the problem belongs to. The input data is the text information of the query, and the output is the classification result.

[0504] Step 4:

[0505] The server generates solutions based on the analysis results. This generation process involves referencing AI models and historical databases, and combining similar problem-solving solutions. The input is the classification result, and the output is a list of potential solutions.

[0506] Step 5:

[0507] The server sends the generated solutions to the terminal. The terminal displays the received solutions on the user interface. The user reviews the solutions and selects the most suitable one. The input is the proposed solutions, and the output is the user's selection information.

[0508] Step 6:

[0509] The user selects a solution and, if necessary, approves the remote work. The terminal sends the approval information to the server and prepares to initiate the corresponding operation. Here, the input is the user's selection information and approval, and the output is the approval information sent to the server.

[0510] Step 7:

[0511] The server, upon receiving user approval, then performs further remote operations. These operations include retrieving necessary data and modifying the settings of information processing devices. The output is the result of the operations.

[0512] Step 8:

[0513] The server notifies the terminal of the result of the operation. The terminal checks the result and notifies the user of that result. The user then checks whether the problem has been resolved based on that result. The input is the result of the operation, and the output is the notification to the user.

[0514] (Application Example 1)

[0515] 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."

[0516] In modern homes and personal lives, the challenges of operating devices are increasing, particularly for those unfamiliar with technology or the elderly. The increasing complexity of many devices and equipment is raising user burdens and stress levels. There is a need to alleviate these challenges and provide an environment where anyone can easily utilize technology.

[0517] 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.

[0518] In this invention, the server includes a device for receiving inquiries from users, a device for analyzing the inquiries and classifying the issues, and a device for receiving commands via voice input and controlling autonomously operating mechanical devices to perform physical tasks. This makes it possible to easily control home appliances and household devices via voice commands, reducing the technical burden on users.

[0519] A "user" is someone who uses this system to seek solutions to problems or support.

[0520] "Inquiry details" refer to information that describes the specific problems or requests that the user wishes to have resolved.

[0521] A "device" is a piece of equipment or unit that has a specific function and operates electronically or mechanically.

[0522] A "challenge" refers to a difficulty or obstacle that users face, and it is a situation for which a solution is desired.

[0523] A "solution" is a set of steps or methods provided to resolve or mitigate a problem.

[0524] "Remote" refers to performing operations or controls over a physical distance.

[0525] "Voice input" refers to a technology or method that electronically recognizes and analyzes the content of a user's speech.

[0526] An "autonomous operating machine" is a robot or mechanical device that operates automatically according to programmed instructions.

[0527] A "generative intelligence model" is a program that uses artificial intelligence technology to create new information and make decisions.

[0528] This invention relates to a system that controls autonomously operating machinery in the home by allowing users to input operating commands via voice using a device such as a smartphone, and by having a server analyze that voice. The server utilizes voice recognition technology and analyzes the input commands using a generative intelligence model. Through voice input, users can make the machinery perform specific actions.

[0529] When the server receives voice input, it first converts the voice data into text. Then, it uses a generative intelligence model to analyze the commands based on this text data and determine the appropriate action. In this analysis process, for example, the Python speech_recognition library is used for speech recognition, and the virtual robot_sdk library is used for controlling the mechanical device. Using these technologies, commands requiring physical action are appropriately classified and executed.

[0530] When a user gives a command via a smartphone app, such as "Pick up the paper that's fallen on the living room table," the server transcribes the voice into text and analyzes the content of the command. For example, it uses a generative intelligence model in the cloud to understand the command and relay it to the robot. An example of a prompt to the generative AI model in this process would be, "Receive the user's voice command and create instructions for the robot to pick up the paper."

[0531] This system allows users to easily control home robots via voice commands without complex operations, providing an accessible environment even for those unfamiliar with technology.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The user launches the app on their smartphone and issues instructions using the voice input function. At this stage, the user's voice commands serve as input, and that voice data is captured by the app.

[0535] Step 2:

[0536] The terminal sends audio data to the server. The server uses a speech recognition engine to convert this audio data into text data. Specifically, it processes the audio data (input) to generate text data (output). In this process, the speech recognition engine is used to analyze the audio and convert it into understandable text.

[0537] Step 3:

[0538] The server receives the text data and analyzes the content of the instructions using a generative AI model. This analysis identifies the user's intended instruction. Here, the generative AI model processes the text data (input) and obtains the interpreted instructions (output). This process includes the specific action of inputting prompt sentences into the generative AI model as needed to perform semantic understanding.

[0539] Step 4:

[0540] Based on the analyzed commands, the server instructs the robot to perform specific actions. At this stage, the interpreted commands (inputs) are converted into robot control commands and sent to the robot (outputs). Specifically, the server gives the robot appropriate instructions so that mechanical actions are performed according to the command content.

[0541] Step 5:

[0542] Robots perform physical actions based on the instructions they receive. This is how user commands are materialized in the real world. Robots realize control commands (inputs) as executable mechanical actions (outputs). These actions include specific actions such as picking up or moving objects.

[0543] 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.

[0544] This invention is a system designed for elderly smartphone users that supports them with issues such as difficulty in operation and accidental data deletion, using an emotion engine. This system recognizes the user's emotions and provides appropriate support, thereby realizing a more effective and comfortable user experience.

[0545] In this system, users first input the problem they need support for using a dedicated app on their smartphone. The device is equipped with a camera and microphone, allowing the user's facial expressions and tone of voice to be analyzed by an emotion engine. This identifies the emotional state of the user when they make an inquiry and determines the severity of the problem and the urgency of the user's situation.

[0546] The server activates an artificial intelligence model based on the inquiry content and sentiment data received from the terminal. The AI ​​model analyzes the problem and generates a solution, while simultaneously adjusting the solution based on the user's emotional state. This adjustment ensures that the most suitable solution is presented to the user.

[0547] From the presented solutions, the user selects the one that best suits their situation. The selected solution is sent to the server, which then prepares for remote access. Remote access allows the necessary actions to be performed quickly. The results of these actions and any changes in emotional state are notified to the user from the terminal.

[0548] For example, if a user requests support regarding an app crash, the emotion engine will detect the user's anxious expression or frustrated tone, and the server will prioritize providing a quicker solution. Furthermore, after the support is complete, the system will confirm the user's reassurance, send a thoughtful feedback message, and make adjustments to ensure a better support experience in the future. In this way, by providing support that takes the user's emotions into consideration, the system helps make the use of digital devices easier and more comfortable for the elderly.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The user launches a dedicated app on their smartphone and enters a text description of the problem they need support for. The device's camera and microphone activate, capturing the user's facial expressions and voice for the emotion engine.

[0552] Step 2:

[0553] The terminal sends the user's inquiry content and sentiment data to the server. This data is necessary for analysis.

[0554] Step 3:

[0555] The server analyzes the received inquiry using an artificial intelligence model. The AI ​​model identifies the problem and generates potential appropriate solutions.

[0556] Step 4:

[0557] The server analyzes the received emotional data and estimates the user's current emotional state. Based on this analysis, the solution is adjusted to match the user's emotions.

[0558] Step 5:

[0559] The server sends a list of adjusted solution options to the terminal. The user is presented with solutions that take their emotional state into consideration.

[0560] Step 6:

[0561] The user reviews the presented solutions and selects the one they deem most appropriate. This selection information is then sent to the server via the terminal.

[0562] Step 7:

[0563] The server requests permission from the terminal for remote access to execute the selected solution. The user decides whether to grant permission.

[0564] Step 8:

[0565] When a user grants remote access, the terminal communicates the permission to the server, and the server remotely performs operations on the terminal.

[0566] Step 9:

[0567] The terminal receives the operation results sent from the server and notifies the user of the results. At the same time, it acquires the user's new emotional state and confirms their sense of security and satisfaction.

[0568] Step 10:

[0569] Users will verify that the problem has been resolved and provide feedback to the server. This feedback will be used to improve future services.

[0570] (Example 2)

[0571] 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."

[0572] Elderly people often experience anxiety and stress when using smartphones and other electronic devices due to the difficulty of operation and the risk of unexpected data loss. Furthermore, they may have difficulty finding appropriate solutions when technical problems arise, and their emotional state can also influence their support experience. Therefore, a system is needed that provides smooth and reassuring support while considering the user's emotional state.

[0573] 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.

[0574] In this invention, the server includes means for receiving inquiries from users, means for acquiring the user's facial expressions and voice data based on the received content, and means for analyzing the acquired data to recognize the user's emotional state. This makes it possible to propose appropriate solutions that take into account not only the user's inquiries but also their emotional state.

[0575] "User" refers to a person who uses this system, and in the context of this invention, this includes elderly people who feel uneasy about operating electronic devices.

[0576] "Inquiry details" refers to the detailed information of problems or questions that users report to the system seeking support.

[0577] "Electronic devices" refers to hardware devices that users use to access this system, such as smartphones and tablets.

[0578] "Facial expression and voice data" refers to information about the user's facial expressions and speech that is acquired in order to identify the user's emotional state.

[0579] "Emotional state" refers to the psychological and emotional state of a user when they make an inquiry, and this is analyzed by the system to adjust its support actions.

[0580] A "generative AI model" refers to artificial intelligence technology that makes suggestions for problem-solving based on the user's inquiry and emotional state.

[0581] "Remote access" refers to a technology that allows a server to remotely connect to a user's electronic device and perform necessary operations.

[0582] "The result of the operation" refers to the outcome obtained from the countermeasures taken remotely, and the user is notified of this result to confirm that the problem has been resolved.

[0583] This invention is a system designed to alleviate the anxiety that arises from difficulties in operation and unexpected data loss when users use smartphones and other electronic devices, and to provide smooth support. This system is particularly aimed at the elderly and provides appropriate support that takes into account the user's emotional state.

[0584] First, users install a dedicated app on their smartphone and enter the problem they need support for within the app. During this process, the device's camera and microphone are used to collect the user's facial expressions and voice tone. This data is then used to analyze the user's emotional state using an emotion engine. This information is processed through dedicated software or an application, utilizing existing hardware such as camera sensors and microphones.

[0585] The analyzed sentiment data and inquiry content are sent to the server. The server receives this data and activates a generative AI model. This AI model generates the optimal solution from the inquiry content and sentiment state, makes adjustments, and then presents it to the user. This process ensures that solutions that take the user's emotions into consideration are smoothly provided.

[0586] For example, when a user reports an application crash, if the emotion engine detects the user's anxiety or frustration, the server will immediately prioritize and propose a rapid solution. Simultaneously, the server will create feedback that includes approaches to alleviate these emotions. Depending on the nature of the problem and the scope of the emergency response, the server may also prepare remote access to the terminal and perform necessary operations remotely. The results, along with any changes in the user's mood, will be communicated to the terminal, providing further reassurance.

[0587] An example of a prompt to input into a generative AI model is: "The user is requesting support regarding an app crash, and the sentiment engine has detected that the user is anxious. What quick solution would you suggest?" This prompt uses natural language processing techniques to instruct the AI ​​model and guide it to suggest solutions.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The user launches a dedicated app on their smartphone and enters the problem they need support for. This input includes details about the specific problem, such as the application's unstable operation. As output, the user's problem details are temporarily saved on the device.

[0591] Step 2:

[0592] The device captures the user's facial expressions and voice data in real time using its built-in camera and microphone, simultaneously with the input question. This input data is then prepared to be sent to the emotion engine. The output is emotional data, such as the user's facial expressions and voice tone.

[0593] Step 3:

[0594] The device transmits the acquired facial expression and voice data to an emotion engine, which then analyzes the user's emotional state. During this process, image processing and voice analysis technologies are used to output numerical data representing emotions such as anxiety and frustration. The analysis results are then ready for transmission to the server.

[0595] Step 4:

[0596] The terminal sends the analyzed sentiment data and user inquiry to the server. This input information triggers further processing on the server side. The server receives this data as output.

[0597] Step 5:

[0598] The server activates a generative AI model based on the received inquiry content and sentiment data. The AI ​​model understands the problem and generates solutions that also take into account the user's emotional state. Using data analysis and natural language processing techniques, proposed solutions are generated as output.

[0599] Step 6:

[0600] The server sends solutions generated by the AI ​​model to the terminal. The terminal receives these solutions and presents them to the user. The user selects the solution they believe to be the best from the multiple solutions on the screen. The user's selection is recorded on the terminal as output.

[0601] Step 7:

[0602] Based on the user's selection, the server prepares for the necessary remote operations and executes them on the terminal. Remote operations include restarting applications and changing settings. The success or failure of the operation is recorded as output.

[0603] Step 8:

[0604] The terminal notifies the user based on the results of operations from the server and the newly acquired sentiment data. This includes information that the issue has been resolved and feedback to promote the user's sense of security. As output, the user is notified that the problem has been resolved and receives feedback.

[0605] (Application Example 2)

[0606] 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."

[0607] Elderly people often experience stress due to the difficulty of operation and technical problems when using smart devices. These issues can be a significant burden for users unfamiliar with digital devices. Furthermore, the process of resolving these problems can reduce convenience and satisfaction. As a result, the use of digital devices remains low, leading to problems such as a decline in communication and quality of life.

[0608] 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.

[0609] In this invention, the server includes communication means for receiving inquiries from users, data analysis means for analyzing the inquiries and classifying problems, and generation means for generating solutions based on the classified problems and the user's emotional state. This makes it possible to provide appropriate and prompt support that takes the user's emotions into consideration, and to make it easier and more comfortable for elderly people to use smart devices.

[0610] "Communication means" refers to the function for receiving inquiries from users.

[0611] "Data analysis means" refers to a function for analyzing the content of received inquiries and classifying the problems.

[0612] A "generation mechanism" is a function for generating solutions based on classified problems and the emotional state of the users.

[0613] A "solution adjustment and presentation method" is a function that adjusts and presents solutions according to the user's emotions.

[0614] "Action execution means" refers to a function that allows for remote operation to be performed according to the solution selected by the user.

[0615] A "notification mechanism" is a function that notifies the user of the results of the actions they have performed and any changes in their emotional state.

[0616] An "artificial intelligence model" is a technology used to classify inquiries and analyze the emotional state of users.

[0617] This invention aims to improve the usability of digital devices for the elderly and is a system in which a server and a user's terminal work in cooperation. The server receives inquiries from the user using communication means and classifies the problems using data analysis means. Artificial intelligence models are used in the classification process, and along with the content of the user's inquiry, the emotional state is also analyzed from facial expressions and tone of voice.

[0618] Based on these analysis results, the generation means creates a solution to the user's problem. Simultaneously, the adjustment and presentation means adjusts the generated solution according to the user's emotions and presents it in the most optimal way. Once the user selects a solution, the server remotely performs the corresponding operation via the operation execution means. The results of the executed operation and any changes in the user's emotional state are fed back to the user's terminal via the notification means.

[0619] This system utilizes user terminal hardware such as cameras and microphones, and leverages software libraries like OpenCV and speech_recognition to accurately recognize user emotions and provide appropriate support. For example, if a user inquires with an anxious voice saying, "The application has stopped working," the server, after confirming the user's emotional state, will prioritize providing prompt operational guidance.

[0620] An example of a prompt used as input to a generative AI model is: "Please consider how to respond when an elderly person becomes emotional. The emotional data includes 'anxiety' and 'confusion.' Design quick and gentle support assuming the user has a problem with their medication." In this way, appropriate services that take the user's emotions into consideration can be implemented.

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] Users submit inquiries through an application on their device. During this process, text and audio related to the user's problem are sent as input. The device also uses its camera and microphone to capture the user's facial expressions and voice. This data is transferred to the server. The server then prepares the user's inquiry content and sentiment data as output.

[0624] Step 2:

[0625] The server inputs the received inquiry content into data analysis using communication methods. The input data also includes emotional data based on the user's facial expression analysis. The server utilizes an artificial intelligence model to classify the problem and perform the necessary data calculations. The output consists of the classified problem and the user's emotional state.

[0626] Step 3:

[0627] The server's generation mechanism takes classified problems and emotional states as input and sends prompt messages to a generation AI model to generate solutions. Based on these prompt messages, the generation AI model creates a solution. The role of these prompt messages is to guide the user to an appropriate solution for the situation. As output, the user receives the optimal solution.

[0628] Step 4:

[0629] The server uses an adjustment presentation mechanism to fine-tune solutions based on the user's emotional state. This allows for suggestions that take the user's mental state into consideration. The output is the adjusted solution.

[0630] Step 5:

[0631] The user reviews and selects a solution from the presented options. The selected solution is then sent back to the server. The terminal notifies the server that the selection is complete. The selected solution is confirmed as output.

[0632] Step 6:

[0633] The server uses its execution mechanism to initiate remote operation based on the selected solution. It performs the necessary data calculations and sends execution instructions to the user terminal. The output is the result of the remote operation.

[0634] Step 7:

[0635] The server sends the completion status of the operation and changes in emotional state to the user's terminal using a notification mechanism. The terminal displays this status and provides feedback to the user. The output is a notification of the operation result and emotional feedback.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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".

[0653] The present invention provides remote support to users by allowing them to input problems requiring assistance using their smartphones, quickly analyzing the input, and generating solutions. This system is realized by linking the user's terminal, an analysis server, and various analysis means. The embodiments of this system will be described in detail below.

[0654] Users utilize a dedicated support application on their smartphones. This application receives inquiries from users and records the information on the device. The entered information is then sent from the device to the server. Users typically enter details of the problem in text format.

[0655] The server has the ability to analyze information received from the terminal. It uses an artificial intelligence model to automatically classify problems. For example, inquiries such as "The app isn't working" or "I accidentally deleted a photo" are examples of issues that can be classified. Based on the analysis results, the server generates potential solutions. These solutions include steps and methods for performing the corresponding operations.

[0656] The server then sends candidate solutions to the terminal and displays them to the user. The user can select the best solution from the presented options. This selection information is sent back to the server, which then prepares to perform the specific actions based on that selection.

[0657] When the server performs remote access to a user's terminal, it first requests permission from the terminal. After receiving approval from the user, the terminal grants permission to the server. If the user grants permission, the server remotely performs operations on the terminal, such as retrieving necessary data or changing settings.

[0658] Ultimately, the device notifies the user of the results of the operation performed by the server. This allows the user to verify whether the problem has been resolved and to rate their satisfaction. In this way, it becomes possible to quickly and accurately resolve smartphone problems faced by the elderly. As a specific example, consider a scenario where accidentally deleted photos are restored from a cloud backup. In this case, the server retrieves the backup data of the photos and performs the restore operation remotely. The device also notifies the user whether the restored photos are displayed correctly.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user launches a dedicated app on their smartphone and enters details of the problem requiring support. The input is primarily in text format.

[0662] Step 2:

[0663] The terminal sends the user's input to the server. This information is used as data for analysis.

[0664] Step 3:

[0665] The server activates an artificial intelligence model to analyze the inquiry received from the terminal. The model analyzes the inquiry and classifies the problem into the appropriate category.

[0666] Step 4:

[0667] The server generates multiple solutions based on the analysis results, using specific problem-solving techniques depending on the type of problem.

[0668] Step 5:

[0669] The server sends the generated solution to the terminal and instructs it to present it to the user.

[0670] Step 6:

[0671] The user reviews the solutions presented on the device and selects the solution they prefer. This selection influences the next processing step.

[0672] Step 7:

[0673] The terminal notifies the server of the solution selected by the user. The server then develops a plan of action based on the selected solution.

[0674] Step 8:

[0675] The server requests permission from the terminal for remote access in order to execute the selected solution.

[0676] Step 9:

[0677] The user grants permission for remote access by reviewing and agreeing to the remote access permission request displayed on the device.

[0678] Step 10:

[0679] After obtaining permission for remote access from the user, the server performs the necessary operations on the terminal. Specifically, it performs operations such as data recovery and configuration changes.

[0680] Step 11:

[0681] The terminal receives the operation results sent from the server and notifies the user of the details.

[0682] Step 12:

[0683] Users can verify that the problem has been resolved and enter feedback on their device. This feedback is sent to the server to improve the service.

[0684] (Example 1)

[0685] 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".

[0686] Modern information processing devices, especially portable devices, are prone to various technical problems during use. Users want to address these issues quickly and accurately, but often lack the necessary expertise, making self-resolution difficult. Therefore, rapid remote support is essential.

[0687] 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.

[0688] In this invention, the server includes means for receiving inquiry information from a user, means for analyzing the inquiry information and classifying its contents, and means for generating solutions based on the classified contents. This makes it possible to efficiently analyze the technical problems faced by the user and quickly provide the optimal solution.

[0689] "Receiving means" refers to a function or device for receiving inquiry information from users via data communication.

[0690] "Analysis means" refers to a function or device for processing received inquiry information and classifying its contents.

[0691] "Generation means" refers to a function or device that automatically creates an appropriate solution based on the analyzed content.

[0692] "Presentation means" refers to a function or device that displays the generated solution on the user's terminal.

[0693] "Execution means" refers to a function or device for performing remote operation based on a solution selected by the user.

[0694] "Notification means" refers to a function or device for communicating the results of an performed operation to the user.

[0695] "Permission" refers to data or actions that indicate authorization information or consent from the user to authorize remote control.

[0696] A "learning model" refers to a machine learning algorithm used to analyze received information and classify its contents.

[0697] This invention provides remote, rapid, and effective support when users encounter technical problems while using an information processing device. An embodiment of this system is described below.

[0698] Users utilize a dedicated support application using a portable information processing device, such as a smartphone. This application provides an interface for inputting inquiry information from the user and has the function to temporarily store the entered information in local memory. The device then establishes a means of communication to send this information to the server and transmits it via a REST API.

[0699] The server uses a generative AI model to analyze the received information. This model has the ability to automatically classify the query content into different categories. Once the analysis is complete, the server generates a problem-based solution. The solution generation utilizes historical database queries and AI-learned models.

[0700] The generated solutions are sent to the user's information processing device and displayed on the terminal. The user can review the presented solutions and select the one they deem best. Once the selection is confirmed, the information is sent back to the server.

[0701] The server will only perform remote access to the information processing device if it has obtained consent from the user. This makes it possible to perform specified operations remotely.

[0702] A concrete example would be a user wanting to recover images they accidentally deleted. In this case, the server retrieves the images from the cloud backup and performs the restoration remotely. As a result, the device notifies the user that the restoration is complete, allowing them to view the restored images. An example of a prompt message would be, "How do I recover photos I accidentally deleted from my smartphone from the backup?"

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The user launches the device's dedicated support application and enters details of the problem into the inquiry form. The entered information is temporarily stored in local memory in text format. The input here is a description of the specific problem.

[0706] Step 2:

[0707] The terminal sends the stored query information to the server. This is done by the terminal establishing communication with the server and sending the information via the API. The output is the data of the query information sent to the server.

[0708] Step 3:

[0709] The server retrieves the received query information and begins analysis using a generative AI model. This analysis classifies the information to determine which category the problem belongs to. The input data is the text information of the query, and the output is the classification result.

[0710] Step 4:

[0711] The server generates solutions based on the analysis results. This generation process involves referencing AI models and historical databases, and combining similar problem-solving solutions. The input is the classification result, and the output is a list of potential solutions.

[0712] Step 5:

[0713] The server sends the generated solutions to the terminal. The terminal displays the received solutions on the user interface. The user reviews the solutions and selects the most suitable one. The input is the proposed solutions, and the output is the user's selection information.

[0714] Step 6:

[0715] The user selects a solution and, if necessary, approves the remote work. The terminal sends the approval information to the server and prepares to initiate the corresponding operation. Here, the input is the user's selection information and approval, and the output is the approval information sent to the server.

[0716] Step 7:

[0717] The server, upon receiving user approval, then performs further remote operations. These operations include retrieving necessary data and modifying the settings of information processing devices. The output is the result of the operations.

[0718] Step 8:

[0719] The server notifies the terminal of the result of the operation. The terminal checks the result and notifies the user of that result. The user then checks whether the problem has been resolved based on that result. The input is the result of the operation, and the output is the notification to the user.

[0720] (Application Example 1)

[0721] 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".

[0722] In modern homes and personal lives, the challenges of operating devices are increasing, particularly for those unfamiliar with technology or the elderly. The increasing complexity of many devices and equipment is raising user burdens and stress levels. There is a need to alleviate these challenges and provide an environment where anyone can easily utilize technology.

[0723] 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.

[0724] In this invention, the server includes a device for receiving inquiries from users, a device for analyzing the inquiries and classifying the issues, and a device for receiving commands via voice input and controlling autonomously operating mechanical devices to perform physical tasks. This makes it possible to easily control home appliances and household devices via voice commands, reducing the technical burden on users.

[0725] A "user" is someone who uses this system to seek solutions to problems or support.

[0726] "Inquiry details" refer to information that describes the specific problems or requests that the user wishes to have resolved.

[0727] A "device" is a piece of equipment or unit that has a specific function and operates electronically or mechanically.

[0728] A "challenge" refers to a difficulty or obstacle that users face, and it is a situation for which a solution is desired.

[0729] A "solution" is a set of steps or methods provided to resolve or mitigate a problem.

[0730] "Remote" refers to performing operations or controls over a physical distance.

[0731] "Voice input" refers to a technology or method that electronically recognizes and analyzes the content of a user's speech.

[0732] An "autonomous operating machine" is a robot or mechanical device that operates automatically according to programmed instructions.

[0733] A "generative intelligence model" is a program that uses artificial intelligence technology to create new information and make decisions.

[0734] This invention relates to a system that controls autonomously operating machinery in the home by allowing users to input operating commands via voice using a device such as a smartphone, and by having a server analyze that voice. The server utilizes voice recognition technology and analyzes the input commands using a generative intelligence model. Through voice input, users can make the machinery perform specific actions.

[0735] When the server receives voice input, it first converts the voice data into text. Then, it uses a generative intelligence model to analyze the commands based on this text data and determine the appropriate action. In this analysis process, for example, the Python speech_recognition library is used for speech recognition, and the virtual robot_sdk library is used for controlling the mechanical device. Using these technologies, commands requiring physical action are appropriately classified and executed.

[0736] When a user gives a command via a smartphone app, such as "Pick up the paper that's fallen on the living room table," the server transcribes the voice into text and analyzes the content of the command. For example, it uses a generative intelligence model in the cloud to understand the command and relay it to the robot. An example of a prompt to the generative AI model in this process would be, "Receive the user's voice command and create instructions for the robot to pick up the paper."

[0737] This system allows users to easily control home robots via voice commands without complex operations, providing an accessible environment even for those unfamiliar with technology.

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The user launches the app on their smartphone and issues instructions using the voice input function. At this stage, the user's voice commands serve as input, and that voice data is captured by the app.

[0741] Step 2:

[0742] The terminal sends audio data to the server. The server uses a speech recognition engine to convert this audio data into text data. Specifically, it processes the audio data (input) to generate text data (output). In this process, the speech recognition engine is used to analyze the audio and convert it into understandable text.

[0743] Step 3:

[0744] The server receives the text data and analyzes the content of the instructions using a generative AI model. This analysis identifies the user's intended instruction. Here, the generative AI model processes the text data (input) and obtains the interpreted instructions (output). This process includes the specific action of inputting prompt sentences into the generative AI model as needed to perform semantic understanding.

[0745] Step 4:

[0746] Based on the analyzed commands, the server instructs the robot to perform specific actions. At this stage, the interpreted commands (inputs) are converted into robot control commands and sent to the robot (outputs). Specifically, the server gives the robot appropriate instructions so that mechanical actions are performed according to the command content.

[0747] Step 5:

[0748] Robots perform physical actions based on the instructions they receive. This is how user commands are materialized in the real world. Robots realize control commands (inputs) as executable mechanical actions (outputs). These actions include specific actions such as picking up or moving objects.

[0749] 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.

[0750] This invention is a system designed for elderly smartphone users that supports them with issues such as difficulty in operation and accidental data deletion, using an emotion engine. This system recognizes the user's emotions and provides appropriate support, thereby realizing a more effective and comfortable user experience.

[0751] In this system, users first input the problem they need support for using a dedicated app on their smartphone. The device is equipped with a camera and microphone, allowing the user's facial expressions and tone of voice to be analyzed by an emotion engine. This identifies the emotional state of the user when they make an inquiry and determines the severity of the problem and the urgency of the user's situation.

[0752] The server activates an artificial intelligence model based on the inquiry content and sentiment data received from the terminal. The AI ​​model analyzes the problem and generates a solution, while simultaneously adjusting the solution based on the user's emotional state. This adjustment ensures that the most suitable solution is presented to the user.

[0753] From the presented solutions, the user selects the one that best suits their situation. The selected solution is sent to the server, which then prepares for remote access. Remote access allows the necessary actions to be performed quickly. The results of these actions and any changes in emotional state are notified to the user from the terminal.

[0754] For example, if a user requests support regarding an app crash, the emotion engine will detect the user's anxious expression or frustrated tone, and the server will prioritize providing a quicker solution. Furthermore, after the support is complete, the system will confirm the user's reassurance, send a thoughtful feedback message, and make adjustments to ensure a better support experience in the future. In this way, by providing support that takes the user's emotions into consideration, the system helps make the use of digital devices easier and more comfortable for the elderly.

[0755] The following describes the processing flow.

[0756] Step 1:

[0757] The user launches a dedicated app on their smartphone and enters a text description of the problem they need support for. The device's camera and microphone activate, capturing the user's facial expressions and voice for the emotion engine.

[0758] Step 2:

[0759] The terminal sends the user's inquiry content and sentiment data to the server. This data is necessary for analysis.

[0760] Step 3:

[0761] The server analyzes the received inquiry using an artificial intelligence model. The AI ​​model identifies the problem and generates potential appropriate solutions.

[0762] Step 4:

[0763] The server analyzes the received emotional data and estimates the user's current emotional state. Based on this analysis, the solution is adjusted to match the user's emotions.

[0764] Step 5:

[0765] The server sends a list of adjusted solution options to the terminal. The user is presented with solutions that take their emotional state into consideration.

[0766] Step 6:

[0767] The user reviews the presented solutions and selects the one they deem most appropriate. This selection information is then sent to the server via the terminal.

[0768] Step 7:

[0769] The server requests permission from the terminal for remote access to execute the selected solution. The user decides whether to grant permission.

[0770] Step 8:

[0771] When a user grants remote access, the terminal communicates the permission to the server, and the server remotely performs operations on the terminal.

[0772] Step 9:

[0773] The terminal receives the operation results sent from the server and notifies the user of the results. At the same time, it acquires the user's new emotional state and confirms their sense of security and satisfaction.

[0774] Step 10:

[0775] Users will verify that the problem has been resolved and provide feedback to the server. This feedback will be used to improve future services.

[0776] (Example 2)

[0777] 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".

[0778] Elderly people often experience anxiety and stress when using smartphones and other electronic devices due to the difficulty of operation and the risk of unexpected data loss. Furthermore, they may have difficulty finding appropriate solutions when technical problems arise, and their emotional state can also influence their support experience. Therefore, a system is needed that provides smooth and reassuring support while considering the user's emotional state.

[0779] 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.

[0780] In this invention, the server includes means for receiving inquiries from users, means for acquiring the user's facial expressions and voice data based on the received content, and means for analyzing the acquired data to recognize the user's emotional state. This makes it possible to propose appropriate solutions that take into account not only the user's inquiries but also their emotional state.

[0781] "User" refers to a person who uses this system, and in the context of this invention, this includes elderly people who feel uneasy about operating electronic devices.

[0782] "Inquiry details" refers to the detailed information of problems or questions that users report to the system seeking support.

[0783] "Electronic devices" refers to hardware devices that users use to access this system, such as smartphones and tablets.

[0784] "Facial expression and voice data" refers to information about the user's facial expressions and speech that is acquired in order to identify the user's emotional state.

[0785] "Emotional state" refers to the psychological and emotional state of a user when they make an inquiry, and this is analyzed by the system to adjust its support actions.

[0786] A "generative AI model" refers to artificial intelligence technology that makes suggestions for problem-solving based on the user's inquiry and emotional state.

[0787] "Remote access" refers to a technology that allows a server to remotely connect to a user's electronic device and perform necessary operations.

[0788] "The result of the operation" refers to the outcome obtained from the countermeasures taken remotely, and the user is notified of this result to confirm that the problem has been resolved.

[0789] This invention is a system designed to alleviate the anxiety that arises from difficulties in operation and unexpected data loss when users use smartphones and other electronic devices, and to provide smooth support. This system is particularly aimed at the elderly and provides appropriate support that takes into account the user's emotional state.

[0790] First, users install a dedicated app on their smartphone and enter the problem they need support for within the app. During this process, the device's camera and microphone are used to collect the user's facial expressions and voice tone. This data is then used to analyze the user's emotional state using an emotion engine. This information is processed through dedicated software or an application, utilizing existing hardware such as camera sensors and microphones.

[0791] The analyzed sentiment data and inquiry content are sent to the server. The server receives this data and activates a generative AI model. This AI model generates the optimal solution from the inquiry content and sentiment state, makes adjustments, and then presents it to the user. This process ensures that solutions that take the user's emotions into consideration are smoothly provided.

[0792] For example, when a user reports an application crash, if the emotion engine detects the user's anxiety or frustration, the server will immediately prioritize and propose a rapid solution. Simultaneously, the server will create feedback that includes approaches to alleviate these emotions. Depending on the nature of the problem and the scope of the emergency response, the server may also prepare remote access to the terminal and perform necessary operations remotely. The results, along with any changes in the user's mood, will be communicated to the terminal, providing further reassurance.

[0793] An example of a prompt to input into a generative AI model is: "The user is requesting support regarding an app crash, and the sentiment engine has detected that the user is anxious. What quick solution would you suggest?" This prompt uses natural language processing techniques to instruct the AI ​​model and guide it to suggest solutions.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The user launches a dedicated app on their smartphone and enters the problem they need support for. This input includes details about the specific problem, such as the application's unstable operation. As output, the user's problem details are temporarily saved on the device.

[0797] Step 2:

[0798] The device captures the user's facial expressions and voice data in real time using its built-in camera and microphone, simultaneously with the input question. This input data is then prepared to be sent to the emotion engine. The output is emotional data, such as the user's facial expressions and voice tone.

[0799] Step 3:

[0800] The device transmits the acquired facial expression and voice data to an emotion engine, which then analyzes the user's emotional state. During this process, image processing and voice analysis technologies are used to output numerical data representing emotions such as anxiety and frustration. The analysis results are then ready for transmission to the server.

[0801] Step 4:

[0802] The terminal sends the analyzed sentiment data and user inquiry to the server. This input information triggers further processing on the server side. The server receives this data as output.

[0803] Step 5:

[0804] The server activates a generative AI model based on the received inquiry content and sentiment data. The AI ​​model understands the problem and generates solutions that also take into account the user's emotional state. Using data analysis and natural language processing techniques, proposed solutions are generated as output.

[0805] Step 6:

[0806] The server sends solutions generated by the AI ​​model to the terminal. The terminal receives these solutions and presents them to the user. The user selects the solution they believe to be the best from the multiple solutions on the screen. The user's selection is recorded on the terminal as output.

[0807] Step 7:

[0808] Based on the user's selection, the server prepares for the necessary remote operations and executes them on the terminal. Remote operations include restarting applications and changing settings. The success or failure of the operation is recorded as output.

[0809] Step 8:

[0810] The terminal notifies the user based on the results of operations from the server and the newly acquired sentiment data. This includes information that the issue has been resolved and feedback to promote the user's sense of security. As output, the user is notified that the problem has been resolved and receives feedback.

[0811] (Application Example 2)

[0812] 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".

[0813] Elderly people often experience stress due to the difficulty of operation and technical problems when using smart devices. These issues can be a significant burden for users unfamiliar with digital devices. Furthermore, the process of resolving these problems can reduce convenience and satisfaction. As a result, the use of digital devices remains low, leading to problems such as a decline in communication and quality of life.

[0814] 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.

[0815] In this invention, the server includes communication means for receiving inquiries from users, data analysis means for analyzing the inquiries and classifying problems, and generation means for generating solutions based on the classified problems and the user's emotional state. This makes it possible to provide appropriate and prompt support that takes the user's emotions into consideration, and to make it easier and more comfortable for elderly people to use smart devices.

[0816] "Communication means" refers to the function for receiving inquiries from users.

[0817] "Data analysis means" refers to a function for analyzing the content of received inquiries and classifying the problems.

[0818] A "generation mechanism" is a function for generating solutions based on classified problems and the emotional state of the users.

[0819] A "solution adjustment and presentation method" is a function that adjusts and presents solutions according to the user's emotions.

[0820] "Action execution means" refers to a function that allows for remote operation to be performed according to the solution selected by the user.

[0821] A "notification mechanism" is a function that notifies the user of the results of the actions they have performed and any changes in their emotional state.

[0822] An "artificial intelligence model" is a technology used to classify inquiries and analyze the emotional state of users.

[0823] This invention aims to improve the usability of digital devices for the elderly and is a system in which a server and a user's terminal work in cooperation. The server receives inquiries from the user using communication means and classifies the problems using data analysis means. Artificial intelligence models are used in the classification process, and along with the content of the user's inquiry, the emotional state is also analyzed from facial expressions and tone of voice.

[0824] Based on these analysis results, the generation means creates a solution to the user's problem. Simultaneously, the adjustment and presentation means adjusts the generated solution according to the user's emotions and presents it in the most optimal way. Once the user selects a solution, the server remotely performs the corresponding operation via the operation execution means. The results of the executed operation and any changes in the user's emotional state are fed back to the user's terminal via the notification means.

[0825] This system utilizes user terminal hardware such as cameras and microphones, and leverages software libraries like OpenCV and speech_recognition to accurately recognize user emotions and provide appropriate support. For example, if a user inquires with an anxious voice saying, "The application has stopped working," the server, after confirming the user's emotional state, will prioritize providing prompt operational guidance.

[0826] An example of a prompt used as input to a generative AI model is: "Please consider how to respond when an elderly person becomes emotional. The emotional data includes 'anxiety' and 'confusion.' Design quick and gentle support assuming the user has a problem with their medication." In this way, appropriate services that take the user's emotions into consideration can be implemented.

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0828] Step 1:

[0829] Users submit inquiries through an application on their device. During this process, text and audio related to the user's problem are sent as input. The device also uses its camera and microphone to capture the user's facial expressions and voice. This data is transferred to the server. The server then prepares the user's inquiry content and sentiment data as output.

[0830] Step 2:

[0831] The server inputs the received inquiry content into data analysis using communication methods. The input data also includes emotional data based on the user's facial expression analysis. The server utilizes an artificial intelligence model to classify the problem and perform the necessary data calculations. The output consists of the classified problem and the user's emotional state.

[0832] Step 3:

[0833] The server's generation mechanism takes classified problems and emotional states as input and sends prompt messages to a generation AI model to generate solutions. Based on these prompt messages, the generation AI model creates a solution. The role of these prompt messages is to guide the user to an appropriate solution for the situation. As output, the user receives the optimal solution.

[0834] Step 4:

[0835] The server uses an adjustment presentation mechanism to fine-tune solutions based on the user's emotional state. This allows for suggestions that take the user's mental state into consideration. The output is the adjusted solution.

[0836] Step 5:

[0837] The user reviews and selects a solution from the presented options. The selected solution is then sent back to the server. The terminal notifies the server that the selection is complete. The selected solution is confirmed as output.

[0838] Step 6:

[0839] The server uses its execution mechanism to initiate remote operation based on the selected solution. It performs the necessary data calculations and sends execution instructions to the user terminal. The output is the result of the remote operation.

[0840] Step 7:

[0841] The server sends the completion status of the operation and changes in emotional state to the user's terminal using a notification mechanism. The terminal displays this status and provides feedback to the user. The output is a notification of the operation result and emotional feedback.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] A means of receiving inquiries from users,

[0866] An analysis means for analyzing the content of the aforementioned inquiry and classifying the problem,

[0867] A generation means for generating solutions based on the classified problems,

[0868] A means for presenting the aforementioned solution to the user,

[0869] An execution mechanism that receives the solution selected by the user and remotely performs the corresponding operation,

[0870] A notification means for notifying the user of the results of the aforementioned operation,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the execution means receives permission to perform remote operations from a user's terminal and performs remote access to the terminal based on the permission.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the analysis means uses an artificial intelligence model to classify the content of the inquiry.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means of receiving inquiry information from users,

[0879] An analysis means for analyzing the aforementioned inquiry information and classifying its contents,

[0880] A generation means for generating solutions based on the classified content,

[0881] A presentation means for presenting the aforementioned solution to the user's terminal,

[0882] An execution means that receives a solution selected by the user and remotely performs an operation corresponding to that selection,

[0883] A notification means for communicating the results of the aforementioned operation to the user,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the execution means receives permission to perform remote operation from the user's information processing device and performs a remote connection to the device based on the permission.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the analysis means classifies the query information using a learning model.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] A device that receives inquiries from users,

[0892] A device that analyzes the content of the aforementioned inquiry and classifies the issues,

[0893] A device for generating solutions based on the aforementioned classified problems,

[0894] A device for presenting the aforementioned solution to the user,

[0895] A device that receives the solution selected by the user and remotely performs the corresponding operation,

[0896] A device for notifying the user of the results of the aforementioned operation,

[0897] A device that receives commands via voice input and controls autonomously operating machinery to perform physical tasks,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the execution device receives permission to remotely operate from the user's information processing device, and remotely accesses the information processing device based on the permission.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the analysis device uses a generative intelligence model to classify the content of the inquiry.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A means of receiving inquiries from users,

[0906] A means of acquiring the user's facial expressions and voice data based on the received content,

[0907] A means for analyzing the acquired data to recognize the emotional state,

[0908] A means for activating a generation AI model based on the aforementioned inquiry content and recognized emotional state to generate a solution,

[0909] A means of presenting the generated solution to the user,

[0910] A means of receiving the solution selected by the user and remotely executing the corresponding operation,

[0911] Means for notifying the user of the results of the operation and changes in emotional state,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the execution means receives permission to perform remote operation from the user's electronic device and performs remote access to the electronic device based on the permission.

[0915] (Claim 3)

[0916] The system according to claim 1, wherein the analysis means uses an artificial intelligence model to analyze the content of the inquiry and the emotional state.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A means of receiving inquiries from users,

[0920] A data analysis means for analyzing the content of the aforementioned inquiry and classifying the problem,

[0921] A generation means for generating solutions based on the classified problems and the emotional state of the users,

[0922] An adjustment and presentation means for adjusting and presenting the aforementioned solution according to the user's emotions,

[0923] An action execution means that receives the solution selected by the user and remotely performs the corresponding operation,

[0924] A notification means for notifying the user of the results of the operation and changes in emotional state,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, wherein the operation execution means receives permission to perform remote operations from a user's terminal and performs remote access to the terminal based on the permission.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the data analysis means uses an artificial intelligence model to classify the content of the inquiry and analyze the emotional state. [Explanation of symbols]

[0930] 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. A means of receiving inquiries from users, An analysis means for analyzing the content of the aforementioned inquiry and classifying the problem, A generation means for generating solutions based on the classified problems, A means for presenting the aforementioned solution to the user, An execution mechanism that receives the solution selected by the user and remotely performs the corresponding operation, A notification means for notifying the user of the results of the aforementioned operation, A system that includes this.

2. The system according to claim 1, wherein the execution means receives permission to perform remote operations from the user's terminal and performs remote access to the terminal based on the permission.

3. The system according to claim 1, wherein the analysis means uses an artificial intelligence model to classify the content of the inquiry.

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