system

Abstract

The system according to this embodiment aims to enable senior citizens to easily operate smartphones and use digital services for daily life. [Solution] The system according to the embodiment comprises an operation support unit, a reservation support unit, an order support unit, and a notification provision unit. The operation support unit supports basic smartphone operations. The reservation support unit supports hospital reservations. The order support unit supports shopping and restaurant orders. The notification provision unit provides notifications from any app in a consistent interface.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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] In the prior art, the elderly often have difficulties when using smartphone operations and digital services in daily life, and there is room for improvement.

[0005] The system according to the embodiment aims to enable the elderly to easily use smartphone operations and digital services in daily life.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an operation support unit, a reservation support unit, an order support unit, and a notification provision unit. The operation support unit supports basic smartphone operations. The reservation support unit supports hospital reservations. The order support unit supports shopping and restaurant orders. The notification provision unit provides notifications from any app in a consistent interface. [Effects of the Invention]

[0007] The system according to this embodiment can enable senior citizens to easily operate smartphones and use digital services in their daily lives. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an innovative AI service that gently supports the digital lives of senior citizens. This system supports the basic operation of smartphones for senior citizens. For example, if typing is difficult, messages can be easily sent using voice input. Also, if the user does not know how to send a photo, the system will guide them through the process on the screen. Next, as support for hospital reservations, the system reads a two-dimensional code (e.g., QR code®) and assists in accessing the reservation form. Furthermore, it analyzes the input content of the reservation form and automatically enters the necessary information. For example, basic information such as name and date of birth is memorized by the system, saving the user the trouble of entering it. It also supports shopping and restaurant ordering. The system will handle online shopping and restaurant orders on behalf of the user simply by having them voice input the desired products or menu items. For example, if the user says, "I want to buy milk," the system will search for milk in an online store and complete the purchase procedure. Furthermore, the system provides notifications from all apps in a consistent interface. For example, notifications from different apps such as messaging apps, email, and social media can be viewed on a single screen, reducing the complexity of operation. In addition, all operations can be performed using only voice control, making it easy for senior citizens to use with confidence. In this way, the system aims to comprehensively support the digital lives of senior citizens and realize a society where everyone can benefit from technology.

[0029] The system according to this embodiment comprises an operation support unit, a reservation support unit, an order support unit, and a notification provision unit. The operation support unit supports the basic operation of a smartphone. For example, if text input is difficult, the operation support unit can easily send messages using voice input. The operation support unit can also provide support by showing the procedure on the screen if the user does not know how to send photos. For example, the operation support unit can display a step-by-step guide on the screen to make it easier for the user to understand the procedure for sending photos. The reservation support unit supports hospital reservations. The reservation support unit can, for example, read a 2D code (e.g., a QR code) to assist in accessing the reservation form. It can also analyze the input content of the reservation form and automatically input necessary information. For example, it can store basic information such as name and date of birth, saving the user the trouble of entering it. The order support unit assists with shopping and restaurant orders. For example, it can handle online shopping and restaurant orders simply by having the user voice-input the desired products or menu items. For example, if the user says, "I want to buy milk," the order support unit will search for milk in an online store and proceed with the purchase. The notification unit provides notifications from all apps through a consistent interface. For example, it can display notifications from different apps such as messaging apps, email, and social media on a single screen, reducing the complexity of operation. Furthermore, all operations of the notification unit can be performed solely by voice. Thus, the system according to this embodiment can comprehensively support the digital lives of senior citizens and aim to realize a society where everyone can benefit from technology.

[0030] The operation support unit assists with basic smartphone operations. Specifically, if text input is difficult, users can easily send messages using voice input. Voice input converts what the user says into text in real time and inputs it into the messaging app. For example, if the user says, "Hello, how are you?", the operation support unit converts the voice into text and inputs it into the messaging app. The operation support unit can also assist users who don't know how to send photos by showing them the steps on the screen. For example, the operation support unit displays a step-by-step guide on the screen to make it easier for the user to understand how to send photos. The guide explains in detail how to select photos, select recipients, and press the send button. Furthermore, the operation support unit can learn the user's operation history and improve operational efficiency by prioritizing the display of frequently used functions and operations. For example, it can place frequently used messaging and camera apps on the home screen for one-tap access. The operation support unit can also launch specific apps or change settings in response to the user's voice commands. This allows the operation support unit to assist users in operating their smartphones more intuitively and efficiently, making the use of digital devices easier.

[0031] The reservation support unit assists with hospital reservations. Specifically, it can read QR codes and assist in accessing reservation forms. When a user scans a hospital's QR code with their camera, the reservation support unit automatically opens the reservation form, allowing the user to complete the reservation process smoothly. The reservation support unit can also analyze the information entered in the reservation form and automatically fill in necessary information. For example, the reservation support unit remembers basic information such as name and date of birth, saving the user the trouble of entering it. Furthermore, based on the user's past reservation history, the reservation support unit can suggest appropriate dates, times, and departments for future reservations. For example, if a certain period has passed since the last consultation, it will remind the user to make a reservation for their next regular check-up and suggest an appropriate date and time. The reservation support unit can also use voice input to confirm and change reservation details. For example, if a user says, "I want to change my reservation to next Tuesday," the reservation support unit will change the reservation details according to that instruction. In this way, the reservation support unit helps users make hospital reservations easily and quickly, making the use of medical facilities smoother.

[0032] The Order Support Unit assists with shopping and restaurant orders. Specifically, it can handle online shopping and restaurant orders on behalf of users simply by having them voice-input the products or menu items they want. For example, if a user says, "I want to buy milk," the Order Support Unit will search for milk in an online store and complete the purchase process. The Order Support Unit can also suggest frequently purchased items and preferred menu items based on the user's past purchase history. For example, it can list items the user has previously purchased and encourage them to repurchase them. In addition, the Order Support Unit can display menus from specific stores or restaurants in response to the user's voice commands and confirm the order details. For example, if a user says, "I want to order a pizza from a nearby restaurant," the Order Support Unit will display pizza menus from nearby restaurants and order the menu item the user selected. Furthermore, the Order Support Unit also supports payment procedures, making it easy for users to complete payments. For example, it can store credit card information and complete payments with a single tap. The Order Support Unit can also track the order status in real time and notify the user. In this way, the Order Support Unit helps users shop and order restaurants efficiently, improving the convenience of daily life.

[0033] The notification provider delivers notifications from all apps through a consistent interface. Specifically, it allows users to view notifications from different apps such as messaging apps, email, and social media on a single screen, reducing the complexity of operation. The notification provider integrates notifications from each app, allowing users to grasp important information at a glance. For example, it displays new messages from messaging apps, incoming emails, and comments and likes from social media on a single screen, enabling users to respond quickly. Furthermore, the notification provider can be operated entirely by voice. For example, if a user says, "Read my new message," the notification provider will read the message aloud, and the user can reply by voice. In addition, the notification provider allows users to set notification priorities and display important notifications first. For example, urgent messages and important emails will be displayed more prominently than other notifications to prevent users from missing them. The notification provider can also learn user behavior patterns and deliver notifications at the appropriate time. For example, if a user has a habit of checking notifications at a specific time, it will display notifications according to that time. In this way, the notification provider helps users efficiently manage notifications and avoid missing important information.

[0034] The operation support unit can send messages using voice input. For example, the operation support unit can use speech recognition technology to convert the user's voice into text and send that text as a message. The operation support unit can also specify the message recipient using voice commands. For example, if the user says "Send a message," the operation support unit will start voice input, convert what the user says into text, and send it. This allows even users who have difficulty typing to easily send messages by using voice input. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can send a message using an AI model that uses speech recognition technology to convert the user's voice into text and sends that text as a message.

[0035] The operation support unit can support the sending of photos by showing the steps on the screen. For example, the operation support unit can display a step-by-step guide on the screen to make it easier for the user to understand the procedure for sending photos. The operation support unit can also use visual aids to visually show how to send photos. For example, the operation support unit can display arrows or icons on the screen to indicate which button the user should press. This makes it easier for the user to understand how to send photos by showing the steps on the screen. Some or all of the above processing in the operation support unit may be performed using AI, for example, or not using AI. For example, the operation support unit can support the sending of photos using an AI model that suggests the optimal procedure based on the user's operation history.

[0036] The reservation support unit can read 2D codes and assist in accessing the reservation form. For example, the reservation support unit can read 2D codes using a 2D code reader (e.g., a QR code reader) and obtain the URL of the reservation form. The reservation support unit can also analyze the content of the read 2D code and assist in accessing the reservation form. For example, when the reservation support unit reads a 2D code, it can automatically open the reservation form, making it easy for the user to access. This makes accessing the reservation form easy by reading a 2D code. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or without AI. For example, the reservation support unit can assist in accessing the reservation form by using an AI model that analyzes the content of the 2D code and assists in accessing the reservation form.

[0037] The reservation support unit can analyze the input content of the reservation form and automatically input the necessary information. For example, the reservation support unit can use natural language processing technology to analyze the input content of the reservation form and extract the necessary information. The reservation support unit can also use data analysis methods to memorize the user's basic information and automatically input it into the reservation form. For example, the reservation support unit memorizes basic information such as name and date of birth, saving the user the trouble of entering it. This reduces the user's effort by automatically inputting the content of the reservation form. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or without AI. For example, the reservation support unit can use an AI model that analyzes the input content of the reservation form using natural language processing technology and extracts the necessary information to automatically input the content of the reservation form.

[0038] The order support unit can handle online shopping and restaurant orders on behalf of users simply by having them input their desired products or menu items by voice. For example, the order support unit can use speech recognition technology to convert the user's voice into text and then search for products or menu items based on that text. The order support unit can also handle ordering procedures using voice commands. For example, if the user says, "I want to buy milk," the order support unit will search for milk in the online store and proceed with the purchase. This reduces the burden on users by allowing them to place orders via voice input. Some or all of the above processes in the order support unit may be performed using AI, or not. For example, the order support unit can handle orders using an AI model that uses speech recognition technology to convert the user's voice into text and then searches for products or menu items based on that text.

[0039] The notification delivery unit allows users to view notifications from different apps, such as messaging apps, email apps, and social networking apps, on a single screen. For example, the notification delivery unit can integrate notifications from different apps and display them on a single screen. Furthermore, the notification delivery unit can adjust the display method according to the type and importance of the notification. For example, it can prioritize important notifications to ensure users don't miss them. This reduces the complexity of operation by allowing users to view notifications from different apps on a single screen. Some or all of the above processing in the notification delivery unit may be performed using AI, for example, or not. For example, the notification delivery unit can provide notifications using an AI model that integrates notifications from different apps and displays them on a single screen.

[0040] The notification unit allows all operations to be performed solely by voice commands. For example, the notification unit analyzes the user's voice commands using speech recognition technology and executes operations based on those commands. The notification unit can also adjust its operation method depending on the type and content of the voice command. For example, if the user says "Show notifications," the notification unit will display the latest notifications on the screen. This makes it easy for senior citizens to use, as all operations can be performed solely by voice commands. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can perform operations using an AI model that analyzes the user's voice commands using speech recognition technology and executes operations based on those commands.

[0041] The operation support unit can analyze the user's past operation history and select the optimal support method. For example, the operation support unit can save operation logs and analyze past operation history. The operation support unit can also use data analysis techniques to identify the user's operation patterns and select the optimal support method. For example, the operation support unit can prioritize support for operations that the user has frequently performed in the past, enabling efficient operation. The operation support unit can also focus on supporting operations that the user has found difficult in the past, improving the smoothness of operation. Furthermore, the operation support unit can predict operations to be performed at specific times based on the user's operation history and provide appropriate support. This enables efficient operation support by analyzing past operation history. Some or all of the above processes in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can save operation logs and use an AI model that analyzes past operation history to select the optimal support method.

[0042] The operation support unit can filter support based on the user's current app usage. For example, the operation support unit can acquire information about the app currently being used and filter the support content based on that information. The operation support unit can also eliminate unnecessary support using a filtering algorithm. For example, the operation support unit can support only operations related to the app currently being used by the user, eliminating unnecessary support. Furthermore, if the user is using multiple apps simultaneously, the operation support unit can prioritize supporting the operations of the most important app. In addition, if the user is using a particular app for an extended period, the operation support unit can focus on supporting operations related to that app. This allows for the elimination of unnecessary support by filtering based on the current app usage. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can acquire information about the app currently being used and perform filtering using an AI model that filters the support content based on that information.

[0043] The operation support unit can prioritize support for operations that are highly relevant to the user, taking into account the user's geographical location information. For example, the operation support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. The operation support unit can also use location information services to prioritize support for operations related to the user's current location. For example, if the user is in a specific location, the operation support unit will prioritize support for operations related to that location. Furthermore, if the user is traveling, the operation support unit can prioritize support for operations related to travel. In addition, if the user is at home, the operation support unit can prioritize support for operations related to life at home. In this way, by considering geographical location information, highly relevant operations can be prioritized. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can acquire the user's geographical location information using GPS technology and use an AI model to prioritize support for highly relevant operations by adjusting the support content based on that information.

[0044] The operation support unit can analyze the user's social media activity and provide support for relevant operations during operation support. For example, the operation support unit can analyze the content of social media posts and identify the user's behavior patterns. The operation support unit can also prioritize support for operations that the user frequently performs based on social media activity data. For example, the operation support unit prioritizes support for operations that the user frequently performs on social media. The operation support unit can also focus on supporting operations that the user finds difficult on social media. Furthermore, the operation support unit can predict operations performed at specific times based on the user's social media activity and provide appropriate support. This allows for focused support for relevant operations by analyzing social media activity. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can use an AI model that analyzes the content of social media posts and identifies the user's behavior patterns to support relevant operations.

[0045] The reservation support department can adjust the level of detail of support based on the importance of the reservation. For example, the reservation support department can adjust the level of detail of support based on criteria such as the urgency of the reservation or the importance of the event. The reservation support department can also adjust the frequency and content of support according to the importance of the reservation. For example, for important reservations, the reservation support department can provide detailed support to ensure that the user completes the reservation. For less important reservations, the reservation support department can provide simplified support to reduce the effort required from the user. Furthermore, the reservation support department can adjust the frequency and content of support according to the importance of the reservation. This allows for user effort to be reduced by adjusting the level of detail of support based on the importance of the reservation. Some or all of the above processes in the reservation support department may be performed using AI, for example, or not. For example, the reservation support department can adjust the level of detail of support using an AI model that adjusts the level of detail of support based on criteria such as the urgency of the reservation or the importance of the event.

[0046] The reservation support unit can apply different support algorithms depending on the reservation category when providing reservation support. For example, the reservation support unit can apply different support algorithms depending on the category, such as medical reservations, restaurant reservations, and event reservations. Furthermore, the reservation support unit can provide support content specific to each category. For example, in the case of medical reservations, the reservation support unit can provide detailed information to ensure that users can reliably enter the necessary information. In the case of restaurant reservations, the reservation support unit can also provide menu and seating options to make it easier for users to complete the reservation. In addition, in the case of event reservations, the reservation support unit can provide detailed event information to make it easier for users to find events that interest them. By applying different support algorithms depending on the reservation category, users can be assured that they can complete their reservations. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or not. For example, the reservation support unit can apply support algorithms using an AI model that applies different support algorithms depending on the category, such as medical reservations, restaurant reservations, and event reservations.

[0047] The reservation support department can prioritize support based on the timing of reservation submissions. For example, the department prioritizes support based on criteria such as the reservation deadline and submission timing. The department can also adjust the frequency and content of support depending on the submission timing. For example, in the case of urgent reservations, the department provides top-priority support to ensure quick completion. For reservations with early submission dates, the department can provide detailed support to ensure the user completes the reservation. Furthermore, for reservations with late submission dates, the department can provide simplified support to reduce user effort. This allows for quick reservation completion by prioritizing support based on the reservation submission timing. Some or all of the above processes in the reservation support department may be performed using AI, or not. For example, the reservation support department can prioritize support using an AI model that determines support priorities based on criteria such as the reservation deadline and submission timing.

[0048] The reservation support unit can adjust the order of support based on the relevance of reservations. For example, the reservation support unit adjusts the order of support based on criteria such as related events or consecutive reservations. The reservation support unit can also prioritize support for highly relevant reservations. For example, for important reservations, the reservation support unit can provide top-priority support to ensure quick completion. Furthermore, for highly relevant reservations, the reservation support unit can provide detailed support to ensure the user completes the reservation. In addition, for less relevant reservations, the reservation support unit can provide simplified support to reduce user effort. This reduces user effort by adjusting the order of support based on the relevance of reservations. Some or all of the above processes in the reservation support unit may be performed using AI, or not. For example, the reservation support unit can adjust the order of support using an AI model that adjusts the order of support based on criteria such as related events or consecutive reservations.

[0049] The order support department can analyze a user's past order history to select the optimal support method when providing order support. For example, the order support department analyzes past order history using order data storage methods and data analysis techniques. Furthermore, the order support department can identify user order patterns and select the optimal support method. For example, it can prioritize supporting products that users have frequently ordered in the past, enabling efficient ordering. It can also focus on supporting orders that users have previously found difficult, improving the smoothness of the ordering process. In addition, the order support department can predict orders to be placed during specific time periods based on the user's order history and provide appropriate support. This enables efficient order support through the analysis of past order history. Some or all of the above processes in the order support department may be performed using AI, or not. For example, the order support department can select the optimal support method using an AI model that analyzes past order history using order data storage methods and data analysis techniques.

[0050] The order support unit can customize the order support methods based on the user's current living situation. For example, the order support unit can acquire information such as lifestyle patterns and health status and adjust the support content based on that information. The order support unit can also customize the support methods according to the user's current living situation. For example, if the user is busy, the order support unit can customize the support methods to allow the user to complete the order quickly. If the user is relaxed, the order support unit can also provide detailed information to ensure the user is satisfied with the order. Furthermore, if the user is in a specific living situation, the order support unit can provide support tailored to that situation. In this way, by customizing the order support methods based on the current living situation, more appropriate support can be provided. Some or all of the above processing in the order support unit may be performed using AI, for example, or not. For example, the order support unit can customize the order support methods using an AI model that acquires information such as lifestyle patterns and health status and adjusts the support content based on that information.

[0051] The order support unit can select the optimal order support method by considering the user's geographical location information when providing order support. For example, the order support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. The order support unit can also use location information services to prioritize support for products related to the user's current location. For example, if the user is in a specific location, the order support unit will prioritize support for products related to that location. Furthermore, if the user is traveling, the order support unit can prioritize support for travel-related products. In addition, if the user is at home, the order support unit can prioritize support for products related to home life. In this way, the optimal order support method can be selected by considering geographical location information. Some or all of the above processing in the order support unit may be performed using AI, for example, or without AI. For example, the order support unit can select the optimal order support method using an AI model that acquires the user's geographical location information using GPS technology and adjusts the support content based on that information.

[0052] The order support department can analyze a user's social media activity and propose order support methods during order support. For example, the order support department can analyze the content of social media posts and identify the user's behavior patterns. The order support department can also prioritize supporting products that the user frequently mentions based on social media activity data. For example, the order support department will prioritize supporting products that the user frequently mentions on social media. The order support department can also focus on supporting orders that the user is experiencing difficulties with on social media. Furthermore, the order support department can predict orders to be placed at specific times based on the user's social media activity and provide appropriate support. This allows for focused support on relevant orders by analyzing social media activity. Some or all of the above processes in the order support department may be performed using AI, for example, or not. For example, the order support department can propose order support methods using an AI model that analyzes the content of social media posts and identifies the user's behavior patterns.

[0053] The notification delivery unit can select the optimal notification method by referring to the user's past notification history when delivering notifications. The notification delivery unit can analyze past notification history using, for example, notification data storage methods and data analysis techniques. The notification delivery unit can also identify the user's notification patterns and select the optimal notification method. For example, the notification delivery unit can prioritize displaying notifications that the user has frequently checked in the past. The notification delivery unit can also filter out notifications that the user has ignored in the past and display only important notifications. Furthermore, the notification delivery unit can prioritize displaying important notifications during specific time periods based on the user's notification history. This enables efficient notification delivery by referring to past notification history. Some or all of the above processing in the notification delivery unit may be performed using, for example, AI, or not using AI. For example, the notification delivery unit can select the optimal notification method using an AI model that analyzes past notification history using notification data storage methods and data analysis techniques.

[0054] The notification provider can filter notifications based on the user's current app usage when delivering them. For example, the notification provider can obtain information about the app currently being used and filter the notification content based on that information. The notification provider can also eliminate unnecessary notifications using a filtering algorithm. For example, the notification provider can display only notifications related to the app the user is currently using and eliminate unnecessary notifications. Furthermore, if the user is using multiple apps simultaneously, the notification provider can prioritize displaying notifications from the most important app. In addition, if the user has been using a particular app for a long time, the notification provider can focus on displaying notifications related to that app. In this way, unnecessary notifications can be eliminated by filtering notifications based on the current app usage. Some or all of the above processing in the notification provider may be performed using AI, for example, or not using AI. For example, the notification provider can filter notifications using an AI model that obtains information about the app currently being used and filters the notification content based on that information.

[0055] The notification provider can prioritize providing highly relevant notifications by considering the user's geographical location information when providing notifications. For example, the notification provider can acquire the user's geographical location information using GPS technology and adjust the notification content based on that information. The notification provider can also use location information services to prioritize providing notifications related to the user's current location. For example, if the user is in a specific location, the notification provider can prioritize providing notifications related to that location. The notification provider can also prioritize providing travel-related notifications if the user is traveling. Furthermore, if the notification provider is at home, the notification provider can prioritize providing notifications related to life at home. In this way, highly relevant notifications can be prioritized by considering geographical location information. Some or all of the above processing in the notification provider may be performed using AI, for example, or without AI. For example, the notification provider can acquire the user's geographical location information using GPS technology and use an AI model to prioritize providing highly relevant notifications by adjusting the notification content based on that information.

[0056] The notification provider can analyze a user's social media activity and provide relevant notifications when providing notifications. For example, the notification provider can analyze the content of social media posts and identify the user's behavioral patterns. The notification provider can also prioritize notifications related to topics that the user frequently mentions, based on social media activity data. For example, the notification provider can prioritize notifications related to topics that the user frequently mentions on social media. The notification provider can also focus on providing notifications related to topics that the user is experiencing difficulties with on social media. Furthermore, the notification provider can prioritize important notifications at specific times based on the user's social media activity. This allows for the provision of relevant notifications by analyzing social media activity. Some or all of the above processing in the notification provider may be performed using AI, for example, or not. For example, the notification provider can provide relevant notifications using an AI model that analyzes the content of social media posts and identifies the user's behavioral patterns.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The operation support unit can suggest the optimal operating procedure based on the user's operation history. For example, the operation support unit can analyze past operation history and prioritize support for operations that the user frequently performs. It can also focus on supporting operations that the user previously found difficult, improving the smoothness of operation. Furthermore, the operation support unit can predict operations performed during specific time periods based on the user's operation history and provide appropriate support. This enables efficient operation support by analyzing past operation history.

[0059] The reservation support department can analyze a user's past reservation history and propose the optimal reservation procedure. For example, by analyzing past reservation history, the department can prioritize supporting reservations that the user makes frequently. Furthermore, the department can focus on supporting reservations that the user has previously found difficult, improving the smoothness of the reservation process. In addition, the department can predict reservations to be made during specific time slots based on the user's reservation history and provide appropriate support. This enables efficient reservation support through the analysis of past reservation history.

[0060] The order support department can analyze a user's past order history and propose the optimal order procedure. For example, by analyzing past order history, the order support department can prioritize support for orders that the user frequently places. Furthermore, the order support department can focus on supporting orders that the user has previously found difficult, improving the smoothness of the ordering process. In addition, the order support department can predict orders placed during specific time periods based on the user's order history and provide appropriate support. This enables efficient order support through the analysis of past order history.

[0061] The notification system can analyze a user's past notification history and suggest the optimal notification procedure. For example, it can analyze past notification history and prioritize displaying notifications that the user frequently checks. It can also filter out notifications that the user has ignored in the past, displaying only important notifications. Furthermore, it can prioritize displaying important notifications during specific time periods based on the user's notification history. This enables efficient notification delivery by analyzing past notification history.

[0062] The operation support unit can propose the optimal operating procedure by taking into account the user's geographical location information. For example, the operation support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. Furthermore, the operation support unit can use location information services to prioritize support for operations related to the user's current location. If the user is in a specific location, it will prioritize support for operations related to that location. Similarly, if the user is traveling, it can prioritize support for operations related to travel. In this way, by considering geographical location information, the optimal operating procedure can be proposed.

[0063] The reservation support department can propose the optimal reservation procedure by taking into account the user's geographical location. For example, the reservation support department can obtain the user's geographical location using GPS technology and adjust the support content based on that information. Furthermore, the reservation support department can use location services to prioritize reservations related to the user's current location. If the user is in a specific location, reservations related to that location will be prioritized. Similarly, if the user is traveling, reservations related to their trip will be prioritized. This allows the department to propose the optimal reservation procedure by considering geographical location information.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The operation support section provides support for basic smartphone operations. For example, if text input is difficult, you can easily send messages using voice input. Also, if you don't know how to send photos, it can provide support by showing the steps on the screen. A step-by-step guide is displayed on the screen to make it easy for the user to understand how to send photos. Step 2: The reservation support unit assists with hospital reservations. For example, it can read 2D codes (e.g., QR codes) and assist in accessing the reservation form. It can also analyze the input content of the reservation form and automatically fill in the necessary information. It remembers basic information such as name and date of birth, saving the user the trouble of entering it. Step 3: The Order Support Department assists with shopping and restaurant orders. For example, it can handle online shopping and restaurant orders on behalf of users simply by having them voice-input the products or menu items they want. If a user says, "I want to buy milk," the department will search for milk in the online store and proceed with the purchase. Step 4: The notification system provides notifications from all apps through a consistent interface. For example, notifications from different apps such as messaging apps, email, and social media can be viewed on a single screen, reducing the complexity of operation. Furthermore, all operations can be performed using only voice commands.

[0066] (Example of form 2) The system according to an embodiment of the present invention is an innovative AI service that gently supports the digital lives of senior citizens. This system supports the basic operation of smartphones for senior citizens. For example, if typing is difficult, messages can be easily sent using voice input. Also, if the user does not know how to send a photo, the system will guide them through the process on the screen. Next, to support hospital reservations, the system reads a 2D code (e.g., a QR code) and assists in accessing the reservation form. Furthermore, it analyzes the input content of the reservation form and automatically enters the necessary information. For example, basic information such as name and date of birth is memorized by the system, saving the user the trouble of entering it. It also supports shopping and restaurant ordering. The system will handle online shopping and restaurant orders on behalf of the user simply by having them voice input the desired products or menu items. For example, if the user says, "I want to buy milk," the system will search for milk in an online store and complete the purchase procedure. Furthermore, the system provides notifications from all apps in a consistent interface. For example, notifications from different apps such as messaging apps, email, and social media can be viewed on a single screen, reducing the complexity of operation. In addition, all operations can be performed using only voice control, making it easy and safe for senior citizens to use. In this way, the system aims to comprehensively support the digital lives of senior citizens and realize a society where everyone can benefit from technology.

[0067] The system according to this embodiment comprises an operation support unit, a reservation support unit, an order support unit, and a notification provision unit. The operation support unit supports the basic operation of a smartphone. For example, if text input is difficult, the operation support unit can easily send messages using voice input. The operation support unit can also provide support by showing the procedure on the screen if the user does not know how to send photos. For example, the operation support unit can display a step-by-step guide on the screen to make it easier for the user to understand the procedure for sending photos. The reservation support unit supports hospital reservations. For example, the reservation support unit can read a two-dimensional code (for example, a QR code) and assist in accessing the reservation form. The reservation support unit can also analyze the input content of the reservation form and automatically input the necessary information. For example, the reservation support unit stores basic information such as name and date of birth, saving the user the trouble of inputting it. The order support unit supports shopping and restaurant ordering. For example, the order support unit can perform online shopping and restaurant ordering on behalf of the user simply by having the user input the desired products or menu by voice. For example, the order support unit will search for milk in the online store and proceed with the purchase if the user says, "I want to buy milk." The notification unit provides notifications from all apps through a consistent interface. The notification unit allows users to view notifications from different apps, such as messaging apps, email, and social media, on a single screen, reducing the complexity of operation. Furthermore, the notification unit can be operated entirely by voice commands. As a result, the system according to this embodiment can comprehensively support the digital lives of senior citizens and aim to realize a society where everyone can benefit from technology.

[0068] The operation support unit assists with basic smartphone operations. Specifically, if text input is difficult, users can easily send messages using voice input. Voice input converts what the user says into text in real time and inputs it into the messaging app. For example, if the user says, "Hello, how are you?", the operation support unit converts the voice into text and inputs it into the messaging app. The operation support unit can also assist users who don't know how to send photos by showing them the steps on the screen. For example, the operation support unit displays a step-by-step guide on the screen to make it easier for the user to understand how to send photos. The guide explains in detail how to select photos, select recipients, and press the send button. Furthermore, the operation support unit can learn the user's operation history and improve operational efficiency by prioritizing the display of frequently used functions and operations. For example, it can place frequently used messaging and camera apps on the home screen for one-tap access. The operation support unit can also launch specific apps or change settings in response to the user's voice commands. This allows the operation support unit to assist users in operating their smartphones more intuitively and efficiently, making the use of digital devices easier.

[0069] The reservation support unit assists with hospital reservations. Specifically, it can read QR codes and assist in accessing reservation forms. When a user scans a hospital's QR code with their camera, the reservation support unit automatically opens the reservation form, allowing the user to complete the reservation process smoothly. The reservation support unit can also analyze the information entered in the reservation form and automatically fill in necessary information. For example, the reservation support unit remembers basic information such as name and date of birth, saving the user the trouble of entering it. Furthermore, based on the user's past reservation history, the reservation support unit can suggest appropriate dates, times, and departments for future reservations. For example, if a certain period has passed since the last consultation, it will remind the user to make a reservation for their next regular check-up and suggest an appropriate date and time. The reservation support unit can also use voice input to confirm and change reservation details. For example, if a user says, "I want to change my reservation to next Tuesday," the reservation support unit will change the reservation details according to that instruction. In this way, the reservation support unit helps users make hospital reservations easily and quickly, making the use of medical facilities smoother.

[0070] The Order Support Unit assists with shopping and restaurant orders. Specifically, it can handle online shopping and restaurant orders on behalf of users simply by having them voice-input the products or menu items they want. For example, if a user says, "I want to buy milk," the Order Support Unit will search for milk in an online store and complete the purchase process. The Order Support Unit can also suggest frequently purchased items and preferred menu items based on the user's past purchase history. For example, it can list items the user has previously purchased and encourage them to repurchase them. In addition, the Order Support Unit can display menus from specific stores or restaurants in response to the user's voice commands and confirm the order details. For example, if a user says, "I want to order a pizza from a nearby restaurant," the Order Support Unit will display pizza menus from nearby restaurants and order the menu item the user selected. Furthermore, the Order Support Unit also supports payment procedures, making it easy for users to complete payments. For example, it can store credit card information and complete payments with a single tap. The Order Support Unit can also track the order status in real time and notify the user. In this way, the Order Support Unit helps users shop and order restaurants efficiently, improving the convenience of daily life.

[0071] The notification provider delivers notifications from all apps through a consistent interface. Specifically, it allows users to view notifications from different apps such as messaging apps, email, and social media on a single screen, reducing the complexity of operation. The notification provider integrates notifications from each app, allowing users to grasp important information at a glance. For example, it displays new messages from messaging apps, incoming emails, and comments and likes from social media on a single screen, enabling users to respond quickly. Furthermore, the notification provider can be operated entirely by voice. For example, if a user says, "Read my new message," the notification provider will read the message aloud, and the user can reply by voice. In addition, the notification provider allows users to set notification priorities and display important notifications first. For example, urgent messages and important emails will be displayed more prominently than other notifications to prevent users from missing them. The notification provider can also learn user behavior patterns and deliver notifications at the appropriate time. For example, if a user has a habit of checking notifications at a specific time, it will display notifications according to that time. In this way, the notification provider helps users efficiently manage notifications and avoid missing important information.

[0072] The operation support unit can send messages using voice input. For example, the operation support unit can use speech recognition technology to convert the user's voice into text and send that text as a message. The operation support unit can also specify the message recipient using voice commands. For example, if the user says "Send a message," the operation support unit will start voice input, convert what the user says into text, and send it. This allows even users who have difficulty typing to easily send messages by using voice input. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can send a message using an AI model that uses speech recognition technology to convert the user's voice into text and sends that text as a message.

[0073] The operation support unit can support the sending of photos by showing the steps on the screen. For example, the operation support unit can display a step-by-step guide on the screen to make it easier for the user to understand the procedure for sending photos. The operation support unit can also use visual aids to visually show how to send photos. For example, the operation support unit can display arrows or icons on the screen to indicate which button the user should press. This makes it easier for the user to understand how to send photos by showing the steps on the screen. Some or all of the above processing in the operation support unit may be performed using AI, for example, or not using AI. For example, the operation support unit can support the sending of photos using an AI model that suggests the optimal procedure based on the user's operation history.

[0074] The reservation support unit can read 2D codes and assist in accessing the reservation form. For example, the reservation support unit can read 2D codes using a 2D code reader (e.g., a QR code reader) and obtain the URL of the reservation form. The reservation support unit can also analyze the content of the read 2D code and assist in accessing the reservation form. For example, when the reservation support unit reads a 2D code, it can automatically open the reservation form, making it easy for the user to access. This makes accessing the reservation form easy by reading a 2D code. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or not using AI. For example, the reservation support unit can assist in accessing the reservation form by using an AI model that analyzes the content of the 2D code and assists in accessing the reservation form.

[0075] The reservation support unit can analyze the input content of the reservation form and automatically input the necessary information. For example, the reservation support unit can use natural language processing technology to analyze the input content of the reservation form and extract the necessary information. The reservation support unit can also use data analysis methods to memorize the user's basic information and automatically input it into the reservation form. For example, the reservation support unit memorizes basic information such as name and date of birth, saving the user the trouble of entering it. This reduces the user's effort by automatically inputting the content of the reservation form. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or without AI. For example, the reservation support unit can use an AI model that analyzes the input content of the reservation form using natural language processing technology and extracts the necessary information to automatically input the content of the reservation form.

[0076] The order support unit can handle online shopping and restaurant orders on behalf of users simply by having them input their desired products or menu items by voice. For example, the order support unit can use speech recognition technology to convert the user's voice into text and then search for products or menu items based on that text. The order support unit can also handle ordering procedures using voice commands. For example, if the user says, "I want to buy milk," the order support unit will search for milk in the online store and proceed with the purchase. This reduces the burden on users by allowing them to place orders via voice input. Some or all of the above processes in the order support unit may be performed using AI, or not. For example, the order support unit can handle orders using an AI model that uses speech recognition technology to convert the user's voice into text and then searches for products or menu items based on that text.

[0077] The notification delivery unit allows users to view notifications from different apps, such as messaging apps, email apps, and social networking apps, on a single screen. For example, the notification delivery unit can integrate notifications from different apps and display them on a single screen. Furthermore, the notification delivery unit can adjust the display method according to the type and importance of the notification. For example, it can prioritize important notifications to ensure users don't miss them. This reduces the complexity of operation by allowing users to view notifications from different apps on a single screen. Some or all of the above processing in the notification delivery unit may be performed using AI, for example, or not. For example, the notification delivery unit can provide notifications using an AI model that integrates notifications from different apps and displays them on a single screen.

[0078] The notification unit allows all operations to be performed solely by voice commands. For example, the notification unit analyzes the user's voice commands using speech recognition technology and executes operations based on those commands. The notification unit can also adjust its operation method depending on the type and content of the voice command. For example, if the user says "Show notifications," the notification unit will display the latest notifications on the screen. This makes it easy for senior citizens to use, as all operations can be performed solely by voice commands. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can perform operations using an AI model that analyzes the user's voice commands using speech recognition technology and executes operations based on those commands.

[0079] The operation support unit can estimate the user's emotions and adjust the timing of operation support based on the estimated emotions. For example, the operation support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the operation support unit can reduce the frequency of operation support and provide only the minimum necessary support. If the user is relaxed, the operation support unit can provide detailed operation instructions, allowing the user to learn how to operate the device on their own. Furthermore, if the user is in a hurry, the operation support unit can prioritize support for only the most important operations, enabling them to complete the operation quickly. This allows for more appropriate support by adjusting the timing of operation support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can analyze the user's facial expressions using facial recognition technology and adjust the timing of operation support using an AI model that estimates emotions.

[0080] The operation support unit can analyze the user's past operation history and select the optimal support method. For example, the operation support unit can save operation logs and analyze past operation history. The operation support unit can also use data analysis techniques to identify the user's operation patterns and select the optimal support method. For example, the operation support unit can prioritize support for operations that the user has frequently performed in the past, enabling efficient operation. The operation support unit can also focus on supporting operations that the user has found difficult in the past, improving the smoothness of operation. Furthermore, the operation support unit can predict operations to be performed at specific times based on the user's operation history and provide appropriate support. This enables efficient operation support by analyzing past operation history. Some or all of the above processes in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can save operation logs and use an AI model that analyzes past operation history to select the optimal support method.

[0081] The operation support unit can filter support based on the user's current app usage. For example, the operation support unit can acquire information about the app currently being used and filter the support content based on that information. The operation support unit can also eliminate unnecessary support using a filtering algorithm. For example, the operation support unit can support only operations related to the app currently being used by the user, eliminating unnecessary support. Furthermore, if the user is using multiple apps simultaneously, the operation support unit can prioritize supporting the operations of the most important app. In addition, if the user is using a particular app for an extended period, the operation support unit can focus on supporting operations related to that app. This allows for the elimination of unnecessary support by filtering based on the current app usage. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can acquire information about the app currently being used and perform filtering using an AI model that filters the support content based on that information.

[0082] The operation support unit can estimate the user's emotions and determine the priority of operations to support based on the estimated emotions. For example, the operation support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For instance, if the user is stressed, the operation support unit can prioritize supporting the simplest operations to reduce the user's burden. If the user is relaxed, it can prioritize supporting complex operations to allow the user to learn new skills. Furthermore, if the user is in a hurry, it can prioritize supporting the most important operations to complete them quickly. This allows for more appropriate support by prioritizing operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can analyze the user's facial expressions using facial recognition technology and determine the priority of operations to support using an AI model that estimates emotions.

[0083] The operation support unit can prioritize support for operations that are highly relevant to the user, taking into account the user's geographical location information. For example, the operation support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. The operation support unit can also use location information services to prioritize support for operations related to the user's current location. For example, if the user is in a specific location, the operation support unit will prioritize support for operations related to that location. Furthermore, if the user is traveling, the operation support unit can prioritize support for operations related to travel. In addition, if the user is at home, the operation support unit can prioritize support for operations related to life at home. In this way, by considering geographical location information, highly relevant operations can be prioritized. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can acquire the user's geographical location information using GPS technology and use an AI model to prioritize support for highly relevant operations by adjusting the support content based on that information.

[0084] The operation support unit can analyze the user's social media activity and provide support for relevant operations during operation support. For example, the operation support unit can analyze the content of social media posts and identify the user's behavior patterns. The operation support unit can also prioritize support for operations that the user frequently performs based on social media activity data. For example, the operation support unit prioritizes support for operations that the user frequently performs on social media. The operation support unit can also focus on supporting operations that the user finds difficult on social media. Furthermore, the operation support unit can predict operations performed at specific times based on the user's social media activity and provide appropriate support. This allows for focused support for relevant operations by analyzing social media activity. Some or all of the above processing in the operation support unit may be performed using AI, for example, or without AI. For example, the operation support unit can use an AI model that analyzes the content of social media posts and identifies the user's behavior patterns to support relevant operations.

[0085] The reservation support unit can estimate the user's emotions and adjust the way it expresses reservation support based on the estimated emotions. For example, the reservation support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the reservation support unit can provide a simple and highly visible expression. If the user is relaxed, it can also provide an expression that includes detailed information. Furthermore, if the user is in a hurry, it can provide an expression that gets straight to the point. In this way, by adjusting the way it expresses reservation support according to the user's emotions, it can provide more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or without AI. For example, the reservation support department can use facial recognition technology to analyze the user's facial expressions and use an AI model to estimate their emotions, thereby adjusting the way reservation support is presented.

[0086] The reservation support department can adjust the level of detail of support based on the importance of the reservation. For example, the reservation support department can adjust the level of detail of support based on criteria such as the urgency of the reservation or the importance of the event. The reservation support department can also adjust the frequency and content of support according to the importance of the reservation. For example, for important reservations, the reservation support department can provide detailed support to ensure that the user completes the reservation. For less important reservations, the reservation support department can provide simplified support to reduce the effort required from the user. Furthermore, the reservation support department can adjust the frequency and content of support according to the importance of the reservation. This allows for user effort to be reduced by adjusting the level of detail of support based on the importance of the reservation. Some or all of the above processes in the reservation support department may be performed using AI, for example, or not. For example, the reservation support department can adjust the level of detail of support using an AI model that adjusts the level of detail of support based on criteria such as the urgency of the reservation or the importance of the event.

[0087] The reservation support unit can apply different support algorithms depending on the reservation category when providing reservation support. For example, the reservation support unit can apply different support algorithms depending on the category, such as medical reservations, restaurant reservations, and event reservations. Furthermore, the reservation support unit can provide support content specific to each category. For example, in the case of medical reservations, the reservation support unit can provide detailed information to ensure that users can reliably enter the necessary information. In the case of restaurant reservations, the reservation support unit can also provide menu and seating options to make it easier for users to complete the reservation. In addition, in the case of event reservations, the reservation support unit can provide detailed event information to make it easier for users to find events that interest them. By applying different support algorithms depending on the reservation category, users can be assured that they can complete their reservations. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or not. For example, the reservation support unit can apply support algorithms using an AI model that applies different support algorithms depending on the category, such as medical reservations, restaurant reservations, and event reservations.

[0088] The reservation support unit can estimate the user's emotions and adjust the length of the reservation support based on the estimated emotions. For example, the reservation support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is feeling stressed, the reservation support unit can provide short, to-the-point support. If the user is relaxed, it can provide longer support including detailed explanations. Furthermore, if the user is in a hurry, the reservation support unit can adjust the length of the support to allow for quick completion of the reservation. In this way, by adjusting the length of the reservation support according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation support unit may be performed using AI, for example, or without AI. For example, the reservation support unit can use facial recognition technology to analyze the user's facial expressions and use an AI model to estimate their emotions, thereby adjusting the length of the reservation support.

[0089] The reservation support department can prioritize support based on the timing of reservation submissions. For example, the department prioritizes support based on criteria such as the reservation deadline and submission timing. The department can also adjust the frequency and content of support depending on the submission timing. For example, in the case of urgent reservations, the department provides top-priority support to ensure quick completion. For reservations with early submission dates, the department can provide detailed support to ensure the user completes the reservation. Furthermore, for reservations with late submission dates, the department can provide simplified support to reduce user effort. This allows for quick reservation completion by prioritizing support based on the reservation submission timing. Some or all of the above processes in the reservation support department may be performed using AI, or not. For example, the reservation support department can prioritize support using an AI model that determines support priorities based on criteria such as the reservation deadline and submission timing.

[0090] The reservation support unit can adjust the order of support based on the relevance of reservations. For example, the reservation support unit adjusts the order of support based on criteria such as related events or consecutive reservations. The reservation support unit can also prioritize support for highly relevant reservations. For example, for important reservations, the reservation support unit can provide top-priority support to ensure quick completion. Furthermore, for highly relevant reservations, the reservation support unit can provide detailed support to ensure the user completes the reservation. In addition, for less relevant reservations, the reservation support unit can provide simplified support to reduce user effort. This reduces user effort by adjusting the order of support based on the relevance of reservations. Some or all of the above processes in the reservation support unit may be performed using AI, or not. For example, the reservation support unit can adjust the order of support using an AI model that adjusts the order of support based on criteria such as related events or consecutive reservations.

[0091] The order support unit can estimate the user's emotions and adjust the order support method based on the estimated emotions. For example, the order support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the order support unit can provide a simple and easy-to-understand ordering method. If the user is relaxed, the order support unit can also provide an ordering method that includes detailed information. Furthermore, if the user is in a hurry, the order support unit can provide a concise ordering method. In this way, by adjusting the order support method according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order support unit may be performed using AI, for example, or without AI. For example, the order support department can use facial recognition technology to analyze the user's facial expressions and use an AI model to estimate their emotions, thereby adjusting the order support method.

[0092] The order support department can analyze a user's past order history to select the optimal support method when providing order support. For example, the order support department analyzes past order history using order data storage methods and data analysis techniques. Furthermore, the order support department can identify user order patterns and select the optimal support method. For example, it can prioritize supporting products that users have frequently ordered in the past, enabling efficient ordering. It can also focus on supporting orders that users have previously found difficult, improving the smoothness of the ordering process. In addition, the order support department can predict orders to be placed during specific time periods based on the user's order history and provide appropriate support. This enables efficient order support through the analysis of past order history. Some or all of the above processes in the order support department may be performed using AI, or not. For example, the order support department can select the optimal support method using an AI model that analyzes past order history using order data storage methods and data analysis techniques.

[0093] The order support unit can customize the order support methods based on the user's current living situation. For example, the order support unit can acquire information such as lifestyle patterns and health status and adjust the support content based on that information. The order support unit can also customize the support methods according to the user's current living situation. For example, if the user is busy, the order support unit can customize the support methods to allow the user to complete the order quickly. If the user is relaxed, the order support unit can also provide detailed information to ensure the user is satisfied with the order. Furthermore, if the user is in a specific living situation, the order support unit can provide support tailored to that situation. In this way, by customizing the order support methods based on the current living situation, more appropriate support can be provided. Some or all of the above processing in the order support unit may be performed using AI, for example, or not. For example, the order support unit can customize the order support methods using an AI model that acquires information such as lifestyle patterns and health status and adjusts the support content based on that information.

[0094] The order support unit can estimate the user's emotions and prioritize order support based on those emotions. For example, the order support unit can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For instance, if the user is stressed, the order support unit can prioritize the simplest orders to reduce the user's burden. If the user is relaxed, it can prioritize complex orders, allowing the user to try new products. Furthermore, if the user is in a hurry, it can prioritize the most important orders to complete them quickly. This allows for more appropriate support by prioritizing order support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the order support department may be performed using AI, for example, or without AI. For example, the order support department can use facial recognition technology to analyze the user's facial expressions and use an AI model that estimates emotions to determine the priority of order support.

[0095] The order support unit can select the optimal order support method by considering the user's geographical location information when providing order support. For example, the order support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. The order support unit can also use location information services to prioritize support for products related to the user's current location. For example, if the user is in a specific location, the order support unit will prioritize support for products related to that location. Furthermore, if the user is traveling, the order support unit can prioritize support for travel-related products. In addition, if the user is at home, the order support unit can prioritize support for products related to home life. In this way, the optimal order support method can be selected by considering geographical location information. Some or all of the above processing in the order support unit may be performed using AI, for example, or without AI. For example, the order support unit can select the optimal order support method using an AI model that acquires the user's geographical location information using GPS technology and adjusts the support content based on that information.

[0096] The order support department can analyze a user's social media activity and propose order support methods during order support. For example, the order support department can analyze the content of social media posts and identify the user's behavior patterns. The order support department can also prioritize supporting products that the user frequently mentions based on social media activity data. For example, the order support department will prioritize supporting products that the user frequently mentions on social media. The order support department can also focus on supporting orders that the user is experiencing difficulties with on social media. Furthermore, the order support department can predict orders to be placed at specific times based on the user's social media activity and provide appropriate support. This allows for focused support on relevant orders by analyzing social media activity. Some or all of the above processes in the order support department may be performed using AI, for example, or not. For example, the order support department can propose order support methods using an AI model that analyzes the content of social media posts and identifies the user's behavior patterns.

[0097] The notification provider can estimate the user's emotions and adjust the notification display method based on the estimated emotions. For example, the notification provider can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the notification provider can provide a simple and highly visible notification display method. If the user is relaxed, it can also provide a notification display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a notification display method that gets straight to the point. By adjusting the notification display method according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification provider may be performed using AI, for example, or without AI. For example, the notification delivery unit can analyze the user's facial expressions using facial recognition technology and adjust the way notifications are displayed using an AI model that estimates emotions.

[0098] The notification delivery unit can select the optimal notification method by referring to the user's past notification history when delivering notifications. The notification delivery unit can analyze past notification history using, for example, notification data storage methods and data analysis techniques. The notification delivery unit can also identify the user's notification patterns and select the optimal notification method. For example, the notification delivery unit can prioritize displaying notifications that the user has frequently checked in the past. The notification delivery unit can also filter out notifications that the user has ignored in the past and display only important notifications. Furthermore, the notification delivery unit can prioritize displaying important notifications during specific time periods based on the user's notification history. This enables efficient notification delivery by referring to past notification history. Some or all of the above processing in the notification delivery unit may be performed using, for example, AI, or not using AI. For example, the notification delivery unit can select the optimal notification method using an AI model that analyzes past notification history using notification data storage methods and data analysis techniques.

[0099] The notification provider can filter notifications based on the user's current app usage when delivering them. For example, the notification provider can obtain information about the app currently being used and filter the notification content based on that information. The notification provider can also eliminate unnecessary notifications using a filtering algorithm. For example, the notification provider can display only notifications related to the app the user is currently using and eliminate unnecessary notifications. Furthermore, if the user is using multiple apps simultaneously, the notification provider can prioritize displaying notifications from the most important app. In addition, if the user has been using a particular app for a long time, the notification provider can focus on displaying notifications related to that app. In this way, unnecessary notifications can be eliminated by filtering notifications based on the current app usage. Some or all of the above processing in the notification provider may be performed using AI, for example, or not using AI. For example, the notification provider can filter notifications using an AI model that obtains information about the app currently being used and filters the notification content based on that information.

[0100] The notification provider can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, the notification provider can analyze the user's facial expressions using facial recognition technology to estimate emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the notification provider can prioritize displaying only the most important notifications to reduce the user's burden. If the user is relaxed, the notification provider can also prioritize displaying notifications containing detailed information. Furthermore, if the user is in a hurry, the notification provider can prioritize displaying notifications that get straight to the point. In this way, by determining the priority of notifications according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification provider may be performed using AI, for example, or without AI. For example, the notification delivery unit can analyze the user's facial expressions using facial recognition technology and determine the priority of notifications using an AI model that estimates emotions.

[0101] The notification provider can prioritize providing highly relevant notifications by considering the user's geographical location information when providing notifications. For example, the notification provider can acquire the user's geographical location information using GPS technology and adjust the notification content based on that information. The notification provider can also use location information services to prioritize providing notifications related to the user's current location. For example, if the user is in a specific location, the notification provider can prioritize providing notifications related to that location. The notification provider can also prioritize providing travel-related notifications if the user is traveling. Furthermore, if the notification provider is at home, the notification provider can prioritize providing notifications related to life at home. In this way, highly relevant notifications can be prioritized by considering geographical location information. Some or all of the above processing in the notification provider may be performed using AI, for example, or without AI. For example, the notification provider can acquire the user's geographical location information using GPS technology and use an AI model to prioritize providing highly relevant notifications by adjusting the notification content based on that information.

[0102] The notification provider can analyze a user's social media activity and provide relevant notifications when providing notifications. For example, the notification provider can analyze the content of social media posts and identify the user's behavioral patterns. The notification provider can also prioritize notifications related to topics that the user frequently mentions, based on social media activity data. For example, the notification provider can prioritize notifications related to topics that the user frequently mentions on social media. The notification provider can also focus on providing notifications related to topics that the user is experiencing difficulties with on social media. Furthermore, the notification provider can prioritize important notifications at specific times based on the user's social media activity. This allows for the provision of relevant notifications by analyzing social media activity. Some or all of the above processing in the notification provider may be performed using AI, for example, or not. For example, the notification provider can provide relevant notifications using an AI model that analyzes the content of social media posts and identifies the user's behavioral patterns.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The operation support unit can suggest the optimal operating procedure based on the user's operation history. For example, the operation support unit can analyze past operation history and prioritize support for operations that the user frequently performs. It can also focus on supporting operations that the user previously found difficult, improving the smoothness of operation. Furthermore, the operation support unit can predict operations performed during specific time periods based on the user's operation history and provide appropriate support. This enables efficient operation support by analyzing past operation history.

[0105] The operation support unit can estimate the user's emotions and adjust the operating procedures based on those emotions. For example, the operation support unit can analyze the user's facial expressions using facial recognition technology to estimate their emotions. It can also analyze the tone and speed of the user's voice using voice analysis technology to estimate their emotions. If the user is stressed, the operation support unit can provide simple operating procedures to reduce the user's burden. If the user is relaxed, it can provide detailed operating procedures to help the user learn new skills. In this way, by adjusting the operating procedures according to the user's emotions, more appropriate support can be provided.

[0106] The reservation support department can analyze a user's past reservation history and propose the optimal reservation procedure. For example, by analyzing past reservation history, the department can prioritize supporting reservations that the user makes frequently. Furthermore, the department can focus on supporting reservations that the user has previously found difficult, improving the smoothness of the reservation process. In addition, the department can predict reservations to be made during specific time slots based on the user's reservation history and provide appropriate support. This enables efficient reservation support through the analysis of past reservation history.

[0107] The reservation support department can estimate the user's emotions and adjust the reservation procedure based on those emotions. For example, the reservation support department can use facial recognition technology to analyze the user's facial expressions and estimate their emotions. It can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. If the user is feeling stressed, the reservation support department can provide a simple reservation procedure to reduce the user's burden. Conversely, if the user is relaxed, it can provide a detailed reservation procedure to allow the user to learn new skills. In this way, by adjusting the reservation procedure according to the user's emotions, more appropriate support can be provided.

[0108] The order support department can analyze a user's past order history and propose the optimal order procedure. For example, by analyzing past order history, the order support department can prioritize support for orders that the user frequently places. Furthermore, the order support department can focus on supporting orders that the user has previously found difficult, improving the smoothness of the ordering process. In addition, the order support department can predict orders placed during specific time periods based on the user's order history and provide appropriate support. This enables efficient order support through the analysis of past order history.

[0109] The order support department can estimate the user's emotions and adjust the ordering process based on those emotions. For example, it can use facial recognition technology to analyze the user's facial expressions and estimate their emotions. It can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. If the user is stressed, the order support department can provide a simple ordering process to reduce the user's burden. If the user is relaxed, it can provide a detailed ordering process to help the user learn new skills. In this way, by adjusting the ordering process according to the user's emotions, more appropriate support can be provided.

[0110] The notification system can analyze a user's past notification history and suggest the optimal notification procedure. For example, it can analyze past notification history and prioritize displaying notifications that the user frequently checks. It can also filter out notifications that the user has ignored in the past, displaying only important notifications. Furthermore, it can prioritize displaying important notifications during specific time periods based on the user's notification history. This enables efficient notification delivery by analyzing past notification history.

[0111] The notification system can estimate the user's emotions and adjust notification procedures based on those estimates. For example, it can use facial recognition technology to analyze the user's facial expressions and estimate their emotions. It can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. If the user is stressed, the notification system can provide simple notification procedures to reduce the user's burden. If the user is relaxed, it can provide detailed notification procedures to help the user learn new skills. By adjusting notification procedures according to the user's emotions, it can provide more appropriate support.

[0112] The operation support unit can propose the optimal operating procedure by taking into account the user's geographical location information. For example, the operation support unit can acquire the user's geographical location information using GPS technology and adjust the support content based on that information. Furthermore, the operation support unit can use location information services to prioritize support for operations related to the user's current location. If the user is in a specific location, it will prioritize support for operations related to that location. Similarly, if the user is traveling, it can prioritize support for operations related to travel. In this way, by considering geographical location information, the optimal operating procedure can be proposed.

[0113] The reservation support department can propose the optimal reservation procedure by taking into account the user's geographical location. For example, the reservation support department can obtain the user's geographical location using GPS technology and adjust the support content based on that information. Furthermore, the reservation support department can use location services to prioritize reservations related to the user's current location. If the user is in a specific location, reservations related to that location will be prioritized. Similarly, if the user is traveling, reservations related to their trip will be prioritized. This allows the department to propose the optimal reservation procedure by considering geographical location information.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The operation support section provides support for basic smartphone operations. For example, if text input is difficult, you can easily send messages using voice input. Also, if you don't know how to send photos, it can provide support by showing the steps on the screen. A step-by-step guide is displayed on the screen to make it easy for the user to understand how to send photos. Step 2: The reservation support unit assists with hospital reservations. For example, it can read 2D codes (e.g., QR codes) and assist in accessing the reservation form. It can also analyze the input content of the reservation form and automatically fill in the necessary information. It remembers basic information such as name and date of birth, saving the user the trouble of entering it. Step 3: The Order Support Department assists with shopping and restaurant orders. For example, it can handle online shopping and restaurant orders on behalf of users simply by having them voice-input the products or menu items they want. If a user says, "I want to buy milk," the department will search for milk in the online store and proceed with the purchase. Step 4: The notification system provides notifications from all apps through a consistent interface. For example, notifications from different apps such as messaging apps, email, and social media can be viewed on a single screen, reducing the complexity of operation. Furthermore, all operations can be performed using only voice commands.

[0116] 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.

[0117] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the operation support unit, reservation support unit, order support unit, and notification provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the operation support unit is implemented by the control unit 46A of the smart device 14 and sends messages using voice input. The reservation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and reads a two-dimensional code (e.g., a QR code) to assist in accessing the reservation form. The order support unit is implemented by the control unit 46A of the smart device 14 and orders products and menus entered by voice online. The notification provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides notifications from different applications on a single screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0123] 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.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0125] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] 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.

[0127] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the operation support unit, reservation support unit, order support unit, and notification provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the operation support unit is implemented by the control unit 46A of the smart glasses 214 and sends messages using voice input. The reservation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and reads a two-dimensional code (e.g., a QR code) to assist in accessing the reservation form. The order support unit is implemented by the control unit 46A of the smart glasses 214 and orders products and menus entered by voice online. The notification provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides notifications from different applications on a single screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0139] 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.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0141] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] 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.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the operation support unit, reservation support unit, order support unit, and notification provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the operation support unit is implemented by the control unit 46A of the headset terminal 314 and sends messages using voice input. The reservation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and reads a two-dimensional code (e.g., a QR code) to assist in accessing the reservation form. The order support unit is implemented by the control unit 46A of the headset terminal 314 and orders products and menus entered by voice online. The notification provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides notifications from different applications on a single screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0155] 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.

[0156] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0157] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] 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.

[0159] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] 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.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the operation support unit, reservation support unit, order support unit, and notification provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the operation support unit is implemented by the control unit 46A of the robot 414 and sends messages using voice input. The reservation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and reads a two-dimensional code (e.g., a QR code) to assist in accessing the reservation form. The order support unit is implemented by the control unit 46A of the robot 414 and orders products and menus entered by voice online. The notification provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides notifications from different applications on a single screen. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] 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.

[0170] Figure 9 shows the 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.

[0171] 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.

[0172] 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.

[0173] 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, and motorcycles, 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 based, for example, 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.

[0174] 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."

[0175] 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.

[0176] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] 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 other things 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.

[0186] 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.

[0187] (Note 1) An operation support unit that supports the basic operation of the smartphone, The reservation support department assists with hospital reservations, The Order Support Department assists with shopping and restaurant orders, It comprises a notification provider that provides notifications from all apps in a consistent interface. A system characterized by the following features. (Note 2) The aforementioned operation support unit is Send a message using voice input The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned operation support unit is Supports the process of sending photos by showing the steps on the screen. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reservation support department, Reads 2D codes to assist with accessing reservation forms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reservation support department, Analyze the information entered in the reservation form and automatically fill in the necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned order support unit, The service allows users to simply input their desired products and menu items by voice, and it will handle online shopping and restaurant orders on their behalf. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification provision unit, You can view notifications from different apps, such as email and social media, on a single screen. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned notification provision unit, All operations can be performed using only voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned operation support unit is It estimates the user's emotions and adjusts the timing of operational support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned operation support unit is Analyze the user's past operation history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned operation support unit is When providing operational support, filtering is performed based on the user's current app usage. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operation support unit is It estimates the user's emotions and determines the priority of actions to support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned operation support unit is When providing operational support, the system prioritizes supporting operations that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned operation support unit is During operation support, we analyze the user's social media activity and provide support for relevant operations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reservation support department, The system estimates the user's emotions and adjusts the way reservation support is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reservation support department, When providing booking support, we adjust the level of detail of the support based on the importance of the booking. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation support department, When providing reservation support, different support algorithms are applied depending on the reservation category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reservation support department, It estimates the user's emotions and adjusts the length of the booking support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation support department, When providing support for a reservation, we prioritize support based on when the reservation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation support department, When providing support for reservations, we adjust the order of support based on the relevance of the reservations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned order support unit, We estimate the user's emotions and adjust the order support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned order support unit, When providing order support, the system analyzes the user's past order history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned order support unit, When providing order support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned order support unit, The system estimates the user's emotions and prioritizes order support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned order support unit, When providing order support, the system selects the most suitable order support method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned order support unit, When providing order support, we analyze the user's social media activity and suggest ways to support their order. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification provision unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification provision unit, When providing notifications, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification provision unit, When providing notifications, filter them based on the user's current app usage. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification provision unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification provision unit, When providing notifications, we prioritize delivering the most relevant notifications by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification provision unit, When providing notifications, we analyze the user's social media activity and provide relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An operation support unit that supports the basic operation of the smartphone, The reservation support department assists with hospital reservations, The Order Support Department assists with shopping and restaurant orders, It comprises a notification provider that provides notifications from all apps in a consistent interface. A system characterized by the following features.

2. The aforementioned operation support unit is Send a message using voice input The system according to feature 1.

3. The aforementioned operation support unit is Supports the process of sending photos by showing the steps on the screen. The system according to feature 1.

4. The aforementioned reservation support department, Reads QR codes to assist with accessing reservation forms. The system according to feature 1.

5. The aforementioned reservation support department, Analyze the information entered in the reservation form and automatically fill in the necessary information. The system according to feature 1.

6. The aforementioned order support unit, The service allows users to simply input their desired products and menu items by voice, and it will handle online shopping and restaurant orders on their behalf. The system according to feature 1.

7. The aforementioned notification provision unit, You can view notifications from different apps, such as email and social media, on a single screen. The system according to feature 1.

8. The aforementioned notification provision unit, All operations can be performed using only voice commands. The system according to feature 1.

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