system

Abstract

The system according to this embodiment aims to flexibly expand its functions in response to user requests and propose appropriate actions. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an expansion unit, and a support unit. The reception unit receives user requests. The generation unit understands the requests received by the reception unit and proposes appropriate actions. The expansion unit performs functional expansion through a plugin marketplace. The support unit provides real-time interactive support.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0007] The system according to this embodiment can flexibly expand its functions in response to user requests and propose appropriate actions. [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 controls 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 smartphone system according to an embodiment of the present invention is equipped with a generative AI as its operating system, allowing users to perform actions such as email, calendar changes, and setting changes through a chat UI or voice conversation. This smartphone system is operated by the user through a chat UI or voice conversation. The generative AI understands the user's requests and proposes appropriate actions. For example, if a user says, "Tell me my schedule for tomorrow," the generative AI checks the calendar and displays the schedule. Furthermore, functionality can be expanded through a plugin market, enabling customization to meet individual needs. For example, if a user wants to add a specific application, they can download and install it from the plugin market. This improves user productivity, enhances personalization through customization, and improves the user experience. In addition, the AI ​​agent provides real-time conversational support, responding quickly to user needs. For example, if a user says, "I want to change the settings," the AI ​​agent displays the appropriate settings screen and supports the user in easily performing the changes. In this way, the complexity of using a smartphone is eliminated, saving time and improving efficiency. This enables the smartphone system to efficiently receive and understand user requests, propose appropriate actions, and provide functionality expansion and conversational support.

[0029] The smartphone system according to this embodiment comprises a reception unit, a generation unit, an extension unit, and a support unit. The reception unit receives user requests. User requests include, but are not limited to, service requests and information inquiries. The reception unit receives user requests, for example, through a chat UI. The reception unit can also receive user requests through voice conversation. The generation unit understands the requests received by the reception unit and proposes appropriate actions. The generation unit analyzes user requests using, for example, a generation AI and proposes appropriate actions. For example, if a user says, "Tell me my schedule for tomorrow," the generation unit checks the calendar and displays the schedule. Also, if a user says, "I want to send an email," the generation unit launches an email application and supports sending an email. The extension unit extends functionality through a plugin marketplace. For example, if a user wants to add a specific application, the extension unit allows them to download and install it from the plugin marketplace. For example, if a user wants to add a new function, the extension unit allows them to select and install an appropriate plugin from the plugin marketplace. The support unit provides real-time interactive support. The support unit, for example, displays the appropriate settings screen when a user says, "I want to change the settings," and provides support to enable the user to easily operate the settings. The support unit also provides appropriate help information when a user says, "I need help." As a result, the smartphone system according to the embodiment can efficiently receive and understand user requests, propose appropriate actions, and provide functional enhancements and interactive support.

[0030] The reception desk receives user requests. User requests include, but are not limited to, service requests and information inquiries. The reception desk can receive user requests, for example, through a chat UI. The chat UI is a text-based interface that analyzes the text entered by the user to understand the request. For example, if a user types "Tell me about nearby restaurants," the chat UI analyzes the request and provides appropriate information. The reception desk can also receive user requests through voice conversation. In the case of voice conversation, when a user speaks into their smartphone, speech recognition technology is used to convert the speech into text, and that text is analyzed to understand the request. For example, if a user says "Tell me the weather forecast," speech recognition technology converts the speech into text, and the chat UI analyzes that text to provide the weather forecast. Through these interfaces, the reception desk can respond to a wide range of user requests. Furthermore, the reception desk can centrally manage user requests and save a history of requests. This makes it possible to understand user preferences and trends based on past requests and provide more personalized services. For example, if a user frequently searches for a particular restaurant, the reception desk can record that information and prioritize its display in future searches. This allows the reception desk to efficiently receive, understand, and provide appropriate information to users.

[0031] The generation unit understands the requests received by the reception unit and proposes appropriate actions. For example, the generation unit uses generation AI to analyze user requests and propose appropriate actions. The generation AI utilizes natural language processing technology to accurately understand user requests. For example, if a user says, "Tell me tomorrow's schedule," the generation AI analyzes the request, accesses a calendar application to retrieve tomorrow's schedule, and displays it to the user. Also, if a user says, "I want to send an email," the generation AI analyzes the request, launches an email application, and supports email sending. Specifically, the generation AI provides an interface for the user to input the content of the email they want to send, confirms the entered content, and allows the email to be sent simply by pressing the send button. Furthermore, the generation unit can propose multiple actions in response to user requests. For example, if a user says, "Tell me nearby restaurants," the generation AI analyzes the request, searches for nearby restaurants based on the current location, and presents multiple options to the user. The user can then select from these options to obtain detailed information. In this way, the generation unit can accurately understand user requests and propose appropriate actions. Furthermore, the generation unit can suggest more personalized actions based on the user's past request history. For example, if a user has frequently searched for a particular restaurant in the past, the generation unit can take that information into account and prioritize displaying that restaurant in future searches. This allows the generation unit to respond quickly and accurately to user requests, improving the user experience.

[0032] The extension unit expands functionality through a plugin marketplace. For example, if a user wants to add a specific application, they can download and install it from the plugin marketplace. The plugin marketplace offers a variety of applications and functions, allowing users to choose according to their needs. For instance, if a user wants to add a new camera application, they can search for, download, and install the appropriate application from the plugin marketplace. Once installed, the user can immediately use the application. Furthermore, if a user wants to add new functionality, they can select and install the appropriate plugin from the plugin marketplace. For example, if a user wants to add voice recognition functionality, they can download and install the voice recognition plugin from the plugin marketplace. Once installed, the user can immediately use the functionality. Additionally, the extension unit manages installed plugins. Users can view a list of installed plugins and uninstall unnecessary ones. When plugin updates are needed, the extension unit automatically notifies users, making it easy for them to update. This allows the extension unit to flexibly expand functionality according to user needs and improve system usability. Finally, to ensure the security of plugins, the extension unit performs regular security checks and responds quickly to any problems found. This allows the expansion unit to provide users with safe and reliable functional enhancements.

[0033] The support department provides real-time, interactive support. For example, if a user says, "I want to change the settings," the support department will display the appropriate settings screen and support the user in easily performing the operation. Specifically, the support department analyzes the user's request and automatically displays the relevant settings screen. The user can then make the necessary changes on the displayed settings screen. Furthermore, if a user says, "I need help," the support department will provide appropriate help information. For example, if a user asks how to use a particular function, the support department will display detailed help information about that function and support the user in an easy-to-understand manner. In addition, the support department can provide appropriate support based on the user's operation history. For example, if a user has a history of changing a particular setting in the past, the support department can refer to that history and provide appropriate support when making similar setting changes. This allows the support department to respond quickly and accurately to user requests and improve the user experience. Furthermore, the support department can collect feedback from users and use it to improve the support content. For example, by providing feedback after receiving support, the support department can use that feedback to improve the support content and provide better service. In addition, the support department can reliably transmit information to users using multiple communication methods. For example, by using not only a chat UI but also voice calls and email in conjunction, important information can be reliably delivered. This allows the support department to provide users with quick and reliable support, improving the user experience.

[0034] The calendar unit can check the calendar and display appointments. For example, if a user says, "Tell me what I have to do tomorrow," the calendar unit will check the calendar and display the appointments. For example, if a user says, "Show me what I have to do this week," the calendar unit can also display the appointments for the current week. For example, if a user says, "Tell me what I have to do next month," the calendar unit can also display the appointments for the following month. This makes it easier for users to keep track of their appointments by checking the calendar and displaying their appointments. The calendar may include, but is not limited to, dates, appointments, and events. Some or all of the above-described processes in the calendar unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the calendar unit can input the user's request into a generative AI, which can then check the calendar and display the appointments.

[0035] The download unit can perform downloads to add specific applications. For example, if a user wants to add a specific application, the download unit can download and install it from the plugin market. The download unit can also select and install an appropriate plugin from the plugin market if a user wants to add new functionality. The download unit can also perform downloads to add specific applications. This allows for functionality expansion tailored to the user's needs by adding specific applications. Specific applications include, but are not limited to, business applications and entertainment applications. Some or all of the above-described processes in the download unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the download unit can input a user request into a generative AI, which can then download and install a specific application.

[0036] The settings unit can support setting changes. For example, if a user says, "I want to change the settings," the settings unit can display the appropriate settings screen and support the user in easily operating it. For example, if a user says, "Turn on Wi-Fi," the settings unit can also support the setting to turn on Wi-Fi. For example, if a user says, "Adjust the screen brightness," the settings unit can also support the setting to adjust the screen brightness. In this way, by supporting setting changes, users can easily change settings. Setting changes include, but are not limited to, changing the user interface or enabling / disabling functions. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the settings unit can input the user's request into a generative AI, which can then display the appropriate settings screen and support the user in easily operating it.

[0037] The generation unit can suggest appropriate actions based on user requests. For example, if a user says, "Tell me what's on my schedule tomorrow," the generation unit will check the calendar and display the schedule. If a user says, "I want to send an email," the generation unit can also launch an email application and support email sending. If a user says, "Tell me the weather forecast," the generation unit can also display the weather forecast. This improves user convenience by suggesting appropriate actions based on user requests. Appropriate actions include, but are not limited to, providing information or performing services. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input user requests into a generation AI, which can then suggest appropriate actions.

[0038] The extension unit can be extended through a plugin marketplace. For example, if a user wants to add a specific application, the extension unit can download and install it from the plugin marketplace. The extension unit can also select and install an appropriate plugin from the plugin marketplace if a user wants to add a new function. The extension unit can also download a specific application for the user to add. This allows for customization to meet user needs by extending functionality through the plugin marketplace. Functional extensions include, but are not limited to, the addition of new functions or improvements to existing functions. Some or all of the above processes in the extension unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the extension unit can input a user request into a generative AI, which can then select and install an appropriate plugin from the plugin marketplace.

[0039] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display as candidates the operations the user has frequently performed in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest operations to be performed during a specific time period based on the user's past request history. This allows the reception desk to select the optimal reception method by analyzing the user's past request history. The optimal reception method includes, but is not limited to, prompt response and appropriate information provision. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past request history data into a generative AI, which can then select the optimal reception method.

[0040] The reception unit can filter requests based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying relevant information based on the user's current situation. The reception unit can also make relevant suggestions based on the user's areas of interest. The reception unit can also filter out unnecessary information based on the user's current situation and areas of interest. This allows for the provision of highly relevant information by filtering based on the user's current situation and areas of interest. Current situation includes, but is not limited to, the user's location information and current activities. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current situation data and areas of interest data into a generative AI, which can then perform the filtering.

[0041] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize processing requests related to that location. The reception unit can also suggest the most suitable service based on the user's current location. The reception unit can also filter out less relevant requests, taking into account the user's geographical location information. This allows for the priority of receiving highly relevant requests by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's geographical location data into a generative AI, which can then prioritize receiving highly relevant requests.

[0042] The reception unit can analyze the user's social media activity upon receiving a request and accept relevant requests. For example, the reception unit can identify the user's current interests from their social media activity and prioritize processing relevant requests. The reception unit can also analyze the user's social media activity and make optimal suggestions. The reception unit can also filter out unnecessary information based on the user's social media activity. This allows for the priority acceptance of relevant requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input the user's social media activity data into a generative AI, which can then accept relevant requests.

[0043] The generation unit can adjust the level of detail of its proposals based on the importance of the request when proposing actions. For example, the generation unit will provide detailed proposals for important requests. For example, the generation unit may provide standard proposals for normal requests. For example, the generation unit may provide expedited proposals for urgent requests. By adjusting the level of detail of proposals based on the importance of the request, appropriate proposals can be made. The importance of a request includes, but is not limited to, urgency and impact. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request importance data into the generation AI, and the generation AI can adjust the level of detail of the proposals.

[0044] The generation unit can apply different suggestion algorithms depending on the category of the request when proposing actions. For example, the generation unit can apply an email-specific suggestion algorithm to requests related to email. For example, the generation unit can also apply a calendar-specific suggestion algorithm to requests related to calendars. For example, the generation unit can also apply a configuration change-specific suggestion algorithm to requests related to configuration changes. By applying different suggestion algorithms depending on the category of the request, more appropriate suggestions can be made. Request categories include, but are not limited to, technical support and customer service. Some or all of the processing described above in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request category data into a generation AI, and the generation AI can apply different suggestion algorithms.

[0045] The generation unit can determine the priority of proposals based on when the requests were submitted when proposing actions. For example, the generation unit may prioritize recently submitted requests. The generation unit may also postpone older requests. The generation unit may also adjust the order of proposals based on the submission date. This allows for appropriate proposals by prioritizing proposals based on when the requests were submitted. The submission date of a request includes, but is not limited to, the submission date and time, and the frequency of submission. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request submission date data into a generation AI, which can then determine the priority of proposals.

[0046] The generation unit can adjust the order of suggestions based on the relevance of the requests when proposing actions. For example, the generation unit may prioritize suggesting highly relevant requests. For example, the generation unit may postpone suggesting less relevant requests. The generation unit can also adjust the order of suggestions based on relevance. This allows for appropriate suggestions by adjusting the order of suggestions based on the relevance of the requests. The relevance of requests includes, but is not limited to, related topics and related users. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request relevance data into a generation AI, and the generation AI can adjust the order of suggestions.

[0047] The extension unit can analyze the user's past extension history to select the optimal extension method when extending functionality. For example, the extension unit can propose the optimal extension method based on the functions the user has added in the past. For example, the extension unit can also propose relevant functions from the user's past extension history. For example, the extension unit can analyze the user's past extension history to select the optimal extension method. This allows the extension unit to select the optimal extension method by analyzing the user's past extension history. The optimal extension method includes, but is not limited to, adding functions based on user needs or improving existing functions. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's past extension history data into a generative AI, which can then select the optimal extension method.

[0048] The extension unit can customize the means of extension based on the user's current needs when extending functionality. For example, the extension unit can suggest the optimal extension method according to the user's current needs. The extension unit can also customize the means of extension based on the user's current needs. The extension unit can also filter out unnecessary extension options according to the user's current needs. This allows for more appropriate extensions by customizing the means of extension based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's current needs data into a generative AI, which can then customize the means of extension.

[0049] The extension unit can select the optimal extension method when extending functionality, taking into account the user's geographical location information. For example, if the user is in a specific location, the extension unit will prioritize extending functions related to that location. The extension unit can also suggest the optimal extension method based on the user's current location. The extension unit can also filter out unnecessary extension options by taking into account the user's geographical location information. This allows the extension unit to select the optimal extension method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's geographical location data into a generative AI, which can then select the optimal extension method.

[0050] The extension unit can analyze the user's social media activity and propose means of extension when extending functionality. For example, the extension unit can understand the user's current interests from their social media activity and propose relevant functions. The extension unit can also analyze the user's social media activity and propose the optimal extension method. The extension unit can also filter out unnecessary extension options based on the user's social media activity. This allows the extension unit to propose the optimal means of extension by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the extension unit can input the user's social media activity data into a generative AI, which can then propose the optimal means of extension.

[0051] The support department can analyze the user's past support history to select the optimal support method during support. For example, the support department can propose the optimal support method based on the support the user has received in the past. The support department can also propose relevant support methods based on the user's past support history. The support department can also select the optimal support method by analyzing the user's past support history. This allows the optimal support method to be selected by analyzing the user's past support history. The optimal support method includes, but is not limited to, prompt response and provision of appropriate information. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the user's past support history data into a generative AI, which can then select the optimal support method.

[0052] The support unit can customize the means of support based on the user's current situation during support. For example, the support unit can suggest the optimal support method according to the user's current situation. The support unit can also customize the means of support based on the user's current situation. For example, the support unit can filter out unnecessary support options according to the user's current situation. This allows for more appropriate support by customizing the means of support based on the user's current situation. Current situation includes, but is not limited to, the user's location information and current activities. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's current situation data into a generative AI, which can then customize the means of support.

[0053] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user is in a specific location, the support unit will prioritize support related to that location. The support unit can also suggest the optimal support method based on the user's current location. The support unit can also filter out unnecessary support options by taking into account the user's geographical location. This allows the support unit to select the optimal support method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's geographical location data into a generative AI, which can then select the optimal support method.

[0054] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can understand the user's current interests from their social media activity and propose relevant support. The support unit can also analyze a user's social media activity and propose the most suitable support method. The support unit can also filter out unnecessary support options based on a user's social media activity. This allows the support unit to propose the most suitable support measures by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not. For example, the support unit can input user social media activity data into a generative AI, which can then propose the most suitable support measures.

[0055] The calendar unit can select the optimal display method by referring to the user's past schedule history when displaying the calendar. For example, the calendar unit can suggest the optimal display method based on the calendar display methods the user has frequently used in the past. For example, the calendar unit can also suggest relevant display methods from the user's past schedule history. For example, the calendar unit can select the optimal display method by analyzing the user's past schedule history. This allows the optimal calendar display method to be selected by referring to the user's past schedule history. The optimal display method includes, but is not limited to, display formats and information priorities based on the user's needs. Some or all of the above processing in the calendar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the calendar unit can input the user's past schedule history data into a generation AI, which can then select the optimal display method.

[0056] The calendar unit can customize the displayed content based on the user's current status when displaying the calendar. For example, the calendar unit can suggest the optimal calendar display method according to the user's current status. The calendar unit can also customize the displayed content of the calendar based on the user's current status. For example, the calendar unit can filter out unnecessary calendar display options according to the user's current status. This allows for a more appropriate calendar display by customizing the displayed content based on the user's current status. Current status includes, but is not limited to, the user's location information and current activities. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calendar unit can input the user's current status data into a generative AI, which can then customize the displayed content.

[0057] The calendar unit can select the optimal display method when displaying the calendar, taking into account the user's geographical location information. For example, if the user is in a specific location, the calendar unit will prioritize displaying appointments related to that location. The calendar unit can also suggest the optimal calendar display method based on the user's current location. The calendar unit can also filter out unnecessary calendar display options by taking into account the user's geographical location information. This allows the system to select the optimal calendar display method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calendar unit can input the user's geographical location data into a generative AI, which can then select the optimal display method.

[0058] The calendar unit can analyze the user's social media activity and suggest display content when displaying the calendar. For example, the calendar unit can understand the user's current interests from their social media activity and suggest relevant appointments. The calendar unit can also analyze the user's social media activity and suggest the optimal calendar display method. The calendar unit can also filter out unnecessary calendar display options based on the user's social media activity. This allows the calendar unit to suggest the optimal display content by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or not. For example, the calendar unit can input the user's social media activity data into a generative AI, which can then suggest the optimal display content.

[0059] The download unit can analyze the user's past download history to select the optimal download method during the download process. For example, the download unit can suggest the optimal download method based on the content the user has downloaded in the past. The download unit can also suggest relevant download methods based on the user's past download history. The download unit can also select the optimal download method by analyzing the user's past download history. This allows the optimal download method to be selected by analyzing the user's past download history. The optimal download method includes, but is not limited to, download formats and download priorities based on the user's needs. Some or all of the above processing in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's past download history data into a generative AI, which can then select the optimal download method.

[0060] The download unit can customize the download method based on the user's current needs during the download process. For example, the download unit can suggest the optimal download method according to the user's current needs. The download unit can also customize the download method based on the user's current needs. For example, the download unit can filter out unnecessary download options according to the user's current needs. This allows for more appropriate downloads by customizing the download method based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the processing described above in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's current needs data into a generative AI, which can then customize the download method.

[0061] The download unit can select the optimal download method by considering the user's geographical location information during the download process. For example, if the user is in a specific location, the download unit will prioritize downloads related to that location. The download unit can also suggest the optimal download method based on the user's current location. The download unit can also filter out unnecessary download options by considering the user's geographical location information. This allows the system to select the optimal download method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's geographical location data into a generative AI, which can then select the optimal download method.

[0062] The download unit can analyze the user's social media activity during the download process and suggest download methods. For example, the download unit can identify the user's current interests from their social media activity and suggest relevant downloads. The download unit can also analyze the user's social media activity and suggest the optimal download method. The download unit can also filter out unnecessary download options based on the user's social media activity. This allows the system to suggest the optimal download method by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's social media activity data into a generative AI, which can then suggest the optimal download method.

[0063] The configuration unit can analyze the user's past configuration history to select the optimal configuration method when a configuration change is made. For example, the configuration unit can suggest the optimal configuration method based on the user's past configuration changes. For example, the configuration unit can suggest relevant configuration methods from the user's past configuration history. For example, the configuration unit can analyze the user's past configuration history to select the optimal configuration method. This allows the optimal configuration method to be selected by analyzing the user's past configuration history. The optimal configuration method includes, but is not limited to, configuration formats and configuration priorities based on the user's needs. Some or all of the above-described processes in the configuration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the configuration unit can input the user's past configuration history data into a generative AI, which can then select the optimal configuration method.

[0064] The settings unit can customize the settings based on the user's current needs when the settings are changed. For example, the settings unit can suggest the optimal settings method according to the user's current needs. The settings unit can also customize the settings based on the user's current needs. For example, the settings unit can filter out unnecessary setting options according to the user's current needs. This allows for more appropriate settings by customizing the settings based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings unit can input the user's current needs data into a generative AI, which can then customize the settings.

[0065] The settings unit can select the optimal settings method when the settings are changed, taking into account the user's geographical location information. For example, if the user is in a specific location, the settings unit will prioritize settings related to that location. The settings unit can also suggest the optimal settings method based on the user's current location, for example. The settings unit can also filter out unnecessary settings options by taking into account the user's geographical location information. This allows the optimal settings method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the settings unit may be performed using, for example, a generating AI, or without a generating AI. For example, the settings unit can input the user's geographical location data into a generating AI, which can then select the optimal settings method.

[0066] The settings unit can analyze the user's social media activity and suggest settings when settings are changed. For example, the settings unit can understand the user's current interests from their social media activity and suggest relevant settings. The settings unit can also analyze the user's social media activity and suggest the optimal settings method. The settings unit can also filter out unnecessary setting options based on the user's social media activity. This allows the settings unit to suggest the optimal settings by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the settings unit can input the user's social media activity data into a generative AI, which can then suggest the optimal settings.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The generation unit can analyze the user's past behavior patterns and propose the optimal action. For example, the generation unit can propose the optimal action based on operations the user has frequently performed in the past. For example, the generation unit can predict and propose operations to be performed during a specific time period based on the user's past behavior patterns. For example, the generation unit can analyze the user's past behavior patterns and propose relevant actions. In this way, the optimal action can be proposed by analyzing the user's past behavior patterns. Optimal actions include, but are not limited to, prompt responses and appropriate information provision. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past behavior pattern data into a generation AI, and the generation AI can propose the optimal action.

[0069] The extension unit can collect user feedback and adjust the method of feature enhancement based on the collected feedback. For example, if a user provides positive feedback on a particular feature, the extension unit will prioritize enhancing that feature. For example, if a user provides negative feedback on a particular feature, the extension unit may also prioritize improving that feature. The extension unit can also analyze user feedback and suggest the optimal enhancement method. This allows for more appropriate enhancements by adjusting the method of feature enhancement based on user feedback. Feedback is collected, for example, through surveys or reviews. Some or all of the above processing in the extension unit may be performed using generative AI, or without generative AI. For example, the extension unit can input user feedback data into generative AI, which can analyze the feedback and adjust the enhancement method.

[0070] The support unit can analyze the user's learning history and select the optimal support method. For example, the support unit can propose the optimal support method based on what the user has learned in the past. For example, the support unit can also propose relevant support methods based on the user's learning history. For example, the support unit can select the optimal support method by analyzing the user's learning history. This allows the optimal support method to be selected by analyzing the user's learning history. Optimal support methods include, but are not limited to, prompt responses and appropriate information provision. Some or all of the above processing in the support unit may be performed using a generative AI, or not. For example, the support unit can input the user's learning history data into a generative AI, which can then select the optimal support method.

[0071] The download unit can monitor the storage status of the user's device and suggest the optimal download method. For example, if the user's device has limited storage, the download unit can suggest a lightweight download option. For example, if the user's device has sufficient storage, the download unit can also suggest a standard download option. The download unit can also monitor the user's device's storage status and filter out unnecessary download options. This allows for more appropriate downloads by suggesting the optimal download method based on the user's device's storage status. Monitoring of storage status is performed, for example, by obtaining the device's system information. Some or all of the above processing in the download unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the download unit can input the user's device's storage status data into a generative AI, which can then suggest the optimal download method.

[0072] The reception unit can monitor the user's current activity status and select the most appropriate reception method. For example, if the user is exercising, the reception unit may prioritize voice input. For example, if the user is stationary, the reception unit may also prioritize text input. The reception unit can also monitor the user's current activity status and filter out unnecessary reception options. This allows for more appropriate reception by selecting the most appropriate reception method based on the user's current activity status. Activity status monitoring is performed using devices such as smartwatches or fitness trackers. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can input user activity data into a generative AI, which can then select the most appropriate reception method.

[0073] The extension unit can monitor the performance status of the user's device and propose the optimal extension method. For example, if the performance of the user's device is degraded, the extension unit may propose a lightweight extension option. For example, if the performance of the user's device is good, the extension unit may also propose a standard extension option. The extension unit can also monitor the performance status of the user's device and filter out unnecessary extension options. This enables more appropriate extensions by proposing the optimal extension method based on the performance status of the user's device. Performance status monitoring is performed, for example, by acquiring the device's system information. Some or all of the above processing in the extension unit may be performed using a generative AI, or not using a generative AI. For example, the extension unit can input the performance status data of the user's device into a generative AI, which can then propose the optimal extension method.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The reception desk receives user requests. User requests include service requests and information inquiries. The reception desk can receive user requests through a chat UI or voice conversation. Step 2: The generation unit understands the request received by the reception unit and proposes an appropriate action. The generation unit uses generation AI to analyze the user's request and, for example, displays calendar events or launches an email application to support sending emails. Step 3: The extension section extends functionality through the plugin marketplace. If a user wants to add a specific application or new features, they can select the appropriate plugin from the plugin marketplace, download it, and install it. Step 4: The support team provides real-time interactive support. When a user says, "I want to change the settings," they display the appropriate settings screen and provide support to help the user easily perform the operation. Also, when a user says, "I need help," they provide appropriate help information.

[0076] (Example of form 2) The smartphone system according to an embodiment of the present invention is equipped with a generative AI as its operating system, allowing users to perform actions such as email, calendar changes, and setting changes through a chat UI or voice conversation. This smartphone system is operated by the user through a chat UI or voice conversation. The generative AI understands the user's requests and proposes appropriate actions. For example, if a user says, "Tell me my schedule for tomorrow," the generative AI checks the calendar and displays the schedule. Furthermore, functionality can be expanded through a plugin market, enabling customization to meet individual needs. For example, if a user wants to add a specific application, they can download and install it from the plugin market. This improves user productivity, enhances personalization through customization, and improves the user experience. In addition, the AI ​​agent provides real-time conversational support, responding quickly to user needs. For example, if a user says, "I want to change the settings," the AI ​​agent displays the appropriate settings screen and supports the user in easily performing the changes. In this way, the complexity of using a smartphone is eliminated, saving time and improving efficiency. This enables the smartphone system to efficiently receive and understand user requests, propose appropriate actions, and provide functionality expansion and conversational support.

[0077] The smartphone system according to this embodiment comprises a reception unit, a generation unit, an extension unit, and a support unit. The reception unit receives user requests. User requests include, but are not limited to, service requests and information inquiries. The reception unit receives user requests, for example, through a chat UI. The reception unit can also receive user requests through voice conversation. The generation unit understands the requests received by the reception unit and proposes appropriate actions. The generation unit analyzes user requests using, for example, a generation AI and proposes appropriate actions. For example, if a user says, "Tell me my schedule for tomorrow," the generation unit checks the calendar and displays the schedule. Also, if a user says, "I want to send an email," the generation unit launches an email application and supports sending an email. The extension unit extends functionality through a plugin marketplace. For example, if a user wants to add a specific application, the extension unit allows them to download and install it from the plugin marketplace. For example, if a user wants to add a new function, the extension unit allows them to select and install an appropriate plugin from the plugin marketplace. The support unit provides real-time interactive support. The support unit, for example, displays the appropriate settings screen when a user says, "I want to change the settings," and provides support to enable the user to easily operate the settings. The support unit also provides appropriate help information when a user says, "I need help." As a result, the smartphone system according to the embodiment can efficiently receive and understand user requests, propose appropriate actions, and provide functional enhancements and interactive support.

[0078] The reception desk receives user requests. User requests include, but are not limited to, service requests and information inquiries. The reception desk can receive user requests, for example, through a chat UI. The chat UI is a text-based interface that analyzes the text entered by the user to understand the request. For example, if a user types "Tell me about nearby restaurants," the chat UI analyzes the request and provides appropriate information. The reception desk can also receive user requests through voice conversation. In the case of voice conversation, when a user speaks into their smartphone, speech recognition technology is used to convert the speech into text, and that text is analyzed to understand the request. For example, if a user says "Tell me the weather forecast," speech recognition technology converts the speech into text, and the chat UI analyzes that text to provide the weather forecast. Through these interfaces, the reception desk can respond to a wide range of user requests. Furthermore, the reception desk can centrally manage user requests and save a history of requests. This makes it possible to understand user preferences and trends based on past requests and provide more personalized services. For example, if a user frequently searches for a particular restaurant, the reception desk can record that information and prioritize its display in future searches. This allows the reception desk to efficiently receive, understand, and provide appropriate information to users.

[0079] The generation unit understands the requests received by the reception unit and proposes appropriate actions. For example, the generation unit uses generation AI to analyze user requests and propose appropriate actions. The generation AI utilizes natural language processing technology to accurately understand user requests. For example, if a user says, "Tell me tomorrow's schedule," the generation AI analyzes the request, accesses a calendar application to retrieve tomorrow's schedule, and displays it to the user. Also, if a user says, "I want to send an email," the generation AI analyzes the request, launches an email application, and supports email sending. Specifically, the generation AI provides an interface for the user to input the content of the email they want to send, confirms the entered content, and allows the email to be sent simply by pressing the send button. Furthermore, the generation unit can propose multiple actions in response to user requests. For example, if a user says, "Tell me nearby restaurants," the generation AI analyzes the request, searches for nearby restaurants based on the current location, and presents multiple options to the user. The user can then select from these options to obtain detailed information. In this way, the generation unit can accurately understand user requests and propose appropriate actions. Furthermore, the generation unit can suggest more personalized actions based on the user's past request history. For example, if a user has frequently searched for a particular restaurant in the past, the generation unit can take that information into account and prioritize displaying that restaurant in future searches. This allows the generation unit to respond quickly and accurately to user requests, improving the user experience.

[0080] The extension unit expands functionality through a plugin marketplace. For example, if a user wants to add a specific application, they can download and install it from the plugin marketplace. The plugin marketplace offers a variety of applications and functions, allowing users to choose according to their needs. For instance, if a user wants to add a new camera application, they can search for, download, and install the appropriate application from the plugin marketplace. Once installed, the user can immediately use the application. Furthermore, if a user wants to add new functionality, they can select and install the appropriate plugin from the plugin marketplace. For example, if a user wants to add voice recognition functionality, they can download and install the voice recognition plugin from the plugin marketplace. Once installed, the user can immediately use the functionality. Additionally, the extension unit manages installed plugins. Users can view a list of installed plugins and uninstall unnecessary ones. When plugin updates are needed, the extension unit automatically notifies users, making it easy for them to update. This allows the extension unit to flexibly expand functionality according to user needs and improve system usability. Finally, to ensure the security of plugins, the extension unit performs regular security checks and responds quickly to any problems found. This allows the expansion unit to provide users with safe and reliable functional enhancements.

[0081] The support department provides real-time, interactive support. For example, if a user says, "I want to change the settings," the support department will display the appropriate settings screen and support the user in easily performing the operation. Specifically, the support department analyzes the user's request and automatically displays the relevant settings screen. The user can then make the necessary changes on the displayed settings screen. Furthermore, if a user says, "I need help," the support department will provide appropriate help information. For example, if a user asks how to use a particular function, the support department will display detailed help information about that function and support the user in an easy-to-understand manner. In addition, the support department can provide appropriate support based on the user's operation history. For example, if a user has a history of changing a particular setting in the past, the support department can refer to that history and provide appropriate support when making similar setting changes. This allows the support department to respond quickly and accurately to user requests and improve the user experience. Furthermore, the support department can collect feedback from users and use it to improve the support content. For example, by providing feedback after receiving support, the support department can use that feedback to improve the support content and provide better service. In addition, the support department can reliably transmit information to users using multiple communication methods. For example, by using not only a chat UI but also voice calls and email in conjunction, important information can be reliably delivered. This allows the support department to provide users with quick and reliable support, improving the user experience.

[0082] The calendar unit can check the calendar and display appointments. For example, if a user says, "Tell me what I have to do tomorrow," the calendar unit will check the calendar and display the appointments. For example, if a user says, "Show me what I have to do this week," the calendar unit can also display the appointments for the current week. For example, if a user says, "Tell me what I have to do next month," the calendar unit can also display the appointments for the following month. This makes it easier for users to keep track of their appointments by checking the calendar and displaying their appointments. The calendar may include, but is not limited to, dates, appointments, and events. Some or all of the above-described processes in the calendar unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the calendar unit can input the user's request into a generative AI, which can then check the calendar and display the appointments.

[0083] The download unit can perform downloads to add specific applications. For example, if a user wants to add a specific application, the download unit can download and install it from the plugin market. The download unit can also select and install an appropriate plugin from the plugin market if a user wants to add new functionality. The download unit can also perform downloads to add specific applications. This allows for functionality expansion tailored to the user's needs by adding specific applications. Specific applications include, but are not limited to, business applications and entertainment applications. Some or all of the above-described processes in the download unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the download unit can input a user request into a generative AI, which can then download and install a specific application.

[0084] The settings unit can support setting changes. For example, if a user says, "I want to change the settings," the settings unit can display the appropriate settings screen and support the user in easily operating it. For example, if a user says, "Turn on Wi-Fi," the settings unit can also support the setting to turn on Wi-Fi. For example, if a user says, "Adjust the screen brightness," the settings unit can also support the setting to adjust the screen brightness. In this way, by supporting setting changes, users can easily change settings. Setting changes include, but are not limited to, changing the user interface or enabling / disabling functions. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the settings unit can input the user's request into a generative AI, which can then display the appropriate settings screen and support the user in easily operating it.

[0085] The generation unit can suggest appropriate actions based on user requests. For example, if a user says, "Tell me what's on my schedule tomorrow," the generation unit will check the calendar and display the schedule. If a user says, "I want to send an email," the generation unit can also launch an email application and support email sending. If a user says, "Tell me the weather forecast," the generation unit can also display the weather forecast. This improves user convenience by suggesting appropriate actions based on user requests. Appropriate actions include, but are not limited to, providing information or performing services. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input user requests into a generation AI, which can then suggest appropriate actions.

[0086] The extension unit can be extended through a plugin marketplace. For example, if a user wants to add a specific application, the extension unit can download and install it from the plugin marketplace. The extension unit can also select and install an appropriate plugin from the plugin marketplace if a user wants to add a new function. The extension unit can also download a specific application for the user to add. This allows for customization to meet user needs by extending functionality through the plugin marketplace. Functional extensions include, but are not limited to, the addition of new functions or improvements to existing functions. Some or all of the above processes in the extension unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the extension unit can input a user request into a generative AI, which can then select and install an appropriate plugin from the plugin marketplace.

[0087] The reception unit can estimate the user's emotions and adjust how requests are processed based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input to process the request quickly. This allows for more appropriate request processing by adjusting the request processing method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust how requests are processed.

[0088] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display as candidates the operations the user has frequently performed in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest operations to be performed during a specific time period based on the user's past request history. This allows the reception desk to select the optimal reception method by analyzing the user's past request history. The optimal reception method includes, but is not limited to, prompt response and appropriate information provision. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past request history data into a generative AI, which can then select the optimal reception method.

[0089] The reception unit can filter requests based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying relevant information based on the user's current situation. The reception unit can also make relevant suggestions based on the user's areas of interest. The reception unit can also filter out unnecessary information based on the user's current situation and areas of interest. This allows for the provision of highly relevant information by filtering based on the user's current situation and areas of interest. Current situation includes, but is not limited to, the user's location information and current activities. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current situation data and areas of interest data into a generative AI, which can then perform the filtering.

[0090] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize important requests. If the user is relaxed, the reception desk may process requests with normal priority. If the user is in a hurry, the reception desk may prioritize urgent requests. This allows for prioritizing important requests by determining the priority of requests 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of requests.

[0091] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize processing requests related to that location. The reception unit can also suggest the most suitable service based on the user's current location. The reception unit can also filter out less relevant requests, taking into account the user's geographical location information. This allows for the priority of receiving highly relevant requests by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's geographical location data into a generative AI, which can then prioritize receiving highly relevant requests.

[0092] The reception unit can analyze the user's social media activity upon receiving a request and accept relevant requests. For example, the reception unit can identify the user's current interests from their social media activity and prioritize processing relevant requests. The reception unit can also analyze the user's social media activity and make optimal suggestions. The reception unit can also filter out unnecessary information based on the user's social media activity. This allows for the priority acceptance of relevant requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input the user's social media activity data into a generative AI, which can then accept relevant requests.

[0093] The generation unit can estimate the user's emotions and adjust the method of suggesting actions based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed suggestions. If the user is in a hurry, the generation unit can provide concise suggestions. If the user is stressed, the generation unit can provide simple suggestions. By adjusting the method of suggesting actions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of suggesting actions.

[0094] The generation unit can adjust the level of detail of its proposals based on the importance of the request when proposing actions. For example, the generation unit will provide detailed proposals for important requests. For example, the generation unit may provide standard proposals for normal requests. For example, the generation unit may provide expedited proposals for urgent requests. By adjusting the level of detail of proposals based on the importance of the request, appropriate proposals can be made. The importance of a request includes, but is not limited to, urgency and impact. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request importance data into the generation AI, and the generation AI can adjust the level of detail of the proposals.

[0095] The generation unit can apply different suggestion algorithms depending on the category of the request when proposing actions. For example, the generation unit can apply an email-specific suggestion algorithm to requests related to email. For example, the generation unit can also apply a calendar-specific suggestion algorithm to requests related to calendars. For example, the generation unit can also apply a configuration change-specific suggestion algorithm to requests related to configuration changes. By applying different suggestion algorithms depending on the category of the request, more appropriate suggestions can be made. Request categories include, but are not limited to, technical support and customer service. Some or all of the processing described above in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request category data into a generation AI, and the generation AI can apply different suggestion algorithms.

[0096] The generation unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed suggestions. If the user is in a hurry, the generation unit can provide concise suggestions. If the user is stressed, the generation unit can provide simple suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of suggestions.

[0097] The generation unit can determine the priority of proposals based on when the requests were submitted when proposing actions. For example, the generation unit may prioritize recently submitted requests. The generation unit may also postpone older requests. The generation unit may also adjust the order of proposals based on the submission date. This allows for appropriate proposals by prioritizing proposals based on when the requests were submitted. The submission date of a request includes, but is not limited to, the submission date and time, and the frequency of submission. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request submission date data into a generation AI, which can then determine the priority of proposals.

[0098] The generation unit can adjust the order of suggestions based on the relevance of the requests when proposing actions. For example, the generation unit may prioritize suggesting highly relevant requests. For example, the generation unit may postpone suggesting less relevant requests. The generation unit can also adjust the order of suggestions based on relevance. This allows for appropriate suggestions by adjusting the order of suggestions based on the relevance of the requests. The relevance of requests includes, but is not limited to, related topics and related users. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input request relevance data into a generation AI, and the generation AI can adjust the order of suggestions.

[0099] The extension unit can estimate the user's emotions and adjust the method of feature enhancement based on the estimated user emotions. For example, if the user is relaxed, the extension unit can provide detailed enhancement options. For example, if the user is in a hurry, the extension unit can also provide concise enhancement options. For example, if the user is stressed, the extension unit can also provide simple enhancement options. This allows for more appropriate enhancements by adjusting the method of feature enhancement according to the user's emotions. 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 extension unit may be performed using a generative AI, or not using a generative AI. For example, the extension unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of feature enhancement.

[0100] The extension unit can analyze the user's past extension history to select the optimal extension method when extending functionality. For example, the extension unit can propose the optimal extension method based on the functions the user has added in the past. For example, the extension unit can also propose relevant functions from the user's past extension history. For example, the extension unit can analyze the user's past extension history to select the optimal extension method. This allows the extension unit to select the optimal extension method by analyzing the user's past extension history. The optimal extension method includes, but is not limited to, adding functions based on user needs or improving existing functions. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's past extension history data into a generative AI, which can then select the optimal extension method.

[0101] The extension unit can customize the means of extension based on the user's current needs when extending functionality. For example, the extension unit can suggest the optimal extension method according to the user's current needs. The extension unit can also customize the means of extension based on the user's current needs. The extension unit can also filter out unnecessary extension options according to the user's current needs. This allows for more appropriate extensions by customizing the means of extension based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's current needs data into a generative AI, which can then customize the means of extension.

[0102] The extension unit can estimate the user's emotions and determine the priority of the features to be extended based on the estimated emotions. For example, if the user is relaxed, the extension unit will perform extensions with normal priority. For example, if the user is in a hurry, the extension unit may prioritize extending important features. For example, if the user is stressed, the extension unit may prioritize extending simple features. This allows for prioritizing the extension of important features by determining the priority of features to be extended 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 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 extension unit may be performed using a generative AI, or not using a generative AI. For example, the extension unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the features to be extended.

[0103] The extension unit can select the optimal extension method when extending functionality, taking into account the user's geographical location information. For example, if the user is in a specific location, the extension unit will prioritize extending functions related to that location. The extension unit can also suggest the optimal extension method based on the user's current location. The extension unit can also filter out unnecessary extension options by taking into account the user's geographical location information. This allows the extension unit to select the optimal extension method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the extension unit can input the user's geographical location data into a generative AI, which can then select the optimal extension method.

[0104] The extension unit can analyze the user's social media activity and propose means of extension when extending functionality. For example, the extension unit can understand the user's current interests from their social media activity and propose relevant functions. The extension unit can also analyze the user's social media activity and propose the optimal extension method. The extension unit can also filter out unnecessary extension options based on the user's social media activity. This allows the extension unit to propose the optimal means of extension by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the extension unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the extension unit can input the user's social media activity data into a generative AI, which can then propose the optimal means of extension.

[0105] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is relaxed, the support unit can provide detailed support. If the user is in a hurry, the support unit can provide concise support. If the user is stressed, the support unit can provide simple support. By adjusting the support methods according to the user's emotions, more appropriate support becomes possible. 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 support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the support methods.

[0106] The support department can analyze the user's past support history to select the optimal support method during support. For example, the support department can propose the optimal support method based on the support the user has received in the past. The support department can also propose relevant support methods based on the user's past support history. The support department can also select the optimal support method by analyzing the user's past support history. This allows the optimal support method to be selected by analyzing the user's past support history. The optimal support method includes, but is not limited to, prompt response and provision of appropriate information. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the user's past support history data into a generative AI, which can then select the optimal support method.

[0107] The support unit can customize the means of support based on the user's current situation during support. For example, the support unit can suggest the optimal support method according to the user's current situation. The support unit can also customize the means of support based on the user's current situation. For example, the support unit can filter out unnecessary support options according to the user's current situation. This allows for more appropriate support by customizing the means of support based on the user's current situation. Current situation includes, but is not limited to, the user's location information and current activities. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's current situation data into a generative AI, which can then customize the means of support.

[0108] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit will provide support with normal priority. For example, if the user is in a hurry, the support unit may prioritize important support. For example, if the user is stressed, the support unit may prioritize simple support. This allows for prioritizing important support by determining the priority of support according to the user's emotions. 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 support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of support.

[0109] The support unit can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user is in a specific location, the support unit will prioritize support related to that location. The support unit can also suggest the optimal support method based on the user's current location. The support unit can also filter out unnecessary support options by taking into account the user's geographical location. This allows the support unit to select the optimal support method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's geographical location data into a generative AI, which can then select the optimal support method.

[0110] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can understand the user's current interests from their social media activity and propose relevant support. The support unit can also analyze a user's social media activity and propose the most suitable support method. The support unit can also filter out unnecessary support options based on a user's social media activity. This allows the support unit to propose the most suitable support measures by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not. For example, the support unit can input user social media activity data into a generative AI, which can then propose the most suitable support measures.

[0111] The calendar unit can estimate the user's emotions and adjust the calendar display method based on the estimated emotions. For example, if the user is relaxed, the calendar unit can provide a detailed calendar display. If the user is in a hurry, the calendar unit can also provide a concise calendar display. If the user is stressed, the calendar unit can also provide a simple calendar display. By adjusting the calendar display method according to the user's emotions, a more appropriate calendar display becomes possible. 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 calendar unit may be performed using a generative AI, or not using a generative AI. For example, the calendar unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the calendar display method.

[0112] The calendar unit can select the optimal display method by referring to the user's past schedule history when displaying the calendar. For example, the calendar unit can suggest the optimal display method based on the calendar display methods the user has frequently used in the past. For example, the calendar unit can also suggest relevant display methods from the user's past schedule history. For example, the calendar unit can select the optimal display method by analyzing the user's past schedule history. This allows the optimal calendar display method to be selected by referring to the user's past schedule history. The optimal display method includes, but is not limited to, display formats and information priorities based on the user's needs. Some or all of the above processing in the calendar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the calendar unit can input the user's past schedule history data into a generation AI, which can then select the optimal display method.

[0113] The calendar unit can customize the displayed content based on the user's current status when displaying the calendar. For example, the calendar unit can suggest the optimal calendar display method according to the user's current status. The calendar unit can also customize the displayed content of the calendar based on the user's current status. For example, the calendar unit can filter out unnecessary calendar display options according to the user's current status. This allows for a more appropriate calendar display by customizing the displayed content based on the user's current status. Current status includes, but is not limited to, the user's location information and current activities. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calendar unit can input the user's current status data into a generative AI, which can then customize the displayed content.

[0114] The calendar unit can estimate the user's emotions and determine calendar priorities based on the estimated emotions. For example, if the user is relaxed, the calendar unit will display the calendar with normal priority. If the user is in a hurry, the calendar unit can also prioritize displaying important appointments. If the user is stressed, the calendar unit can also prioritize displaying a simple calendar. This allows for the prioritization of important appointments by determining calendar priorities 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calendar unit may be performed using a generative AI, or not. For example, the calendar unit can input user emotion data into a generative AI, which can estimate the emotions and determine calendar priorities.

[0115] The calendar unit can select the optimal display method when displaying the calendar, taking into account the user's geographical location information. For example, if the user is in a specific location, the calendar unit will prioritize displaying appointments related to that location. The calendar unit can also suggest the optimal calendar display method based on the user's current location. The calendar unit can also filter out unnecessary calendar display options by taking into account the user's geographical location information. This allows the system to select the optimal calendar display method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calendar unit can input the user's geographical location data into a generative AI, which can then select the optimal display method.

[0116] The calendar unit can analyze the user's social media activity and suggest display content when displaying the calendar. For example, the calendar unit can understand the user's current interests from their social media activity and suggest relevant appointments. The calendar unit can also analyze the user's social media activity and suggest the optimal calendar display method. The calendar unit can also filter out unnecessary calendar display options based on the user's social media activity. This allows the calendar unit to suggest the optimal display content by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the calendar unit may be performed using, for example, a generative AI, or not. For example, the calendar unit can input the user's social media activity data into a generative AI, which can then suggest the optimal display content.

[0117] The download unit can estimate the user's emotions and adjust the download method based on the estimated emotions. For example, if the user is relaxed, the download unit can provide detailed download options. If the user is in a hurry, the download unit can also provide concise download options. If the user is stressed, the download unit can also provide simple download options. This allows for more appropriate downloads by adjusting the download method 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 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 download unit may be performed using a generative AI, or not. For example, the download unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the download method.

[0118] The download unit can analyze the user's past download history to select the optimal download method during the download process. For example, the download unit can suggest the optimal download method based on the content the user has downloaded in the past. The download unit can also suggest relevant download methods based on the user's past download history. The download unit can also select the optimal download method by analyzing the user's past download history. This allows the optimal download method to be selected by analyzing the user's past download history. The optimal download method includes, but is not limited to, download formats and download priorities based on the user's needs. Some or all of the above processing in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's past download history data into a generative AI, which can then select the optimal download method.

[0119] The download unit can customize the download method based on the user's current needs during the download process. For example, the download unit can suggest the optimal download method according to the user's current needs. The download unit can also customize the download method based on the user's current needs. For example, the download unit can filter out unnecessary download options according to the user's current needs. This allows for more appropriate downloads by customizing the download method based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the processing described above in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's current needs data into a generative AI, which can then customize the download method.

[0120] The download unit can estimate the user's emotions and determine download priorities based on those emotions. For example, if the user is relaxed, the download unit will perform downloads with normal priority. If the user is in a hurry, the download unit can prioritize important downloads. If the user is stressed, the download unit can prioritize simple downloads. This allows for prioritizing important downloads by determining download priorities 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 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 download unit may be performed using a generative AI, or not. For example, the download unit can input user emotion data into a generative AI, which can estimate the emotions and determine download priorities.

[0121] The download unit can select the optimal download method by considering the user's geographical location information during the download process. For example, if the user is in a specific location, the download unit will prioritize downloads related to that location. The download unit can also suggest the optimal download method based on the user's current location. The download unit can also filter out unnecessary download options by considering the user's geographical location information. This allows the system to select the optimal download method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's geographical location data into a generative AI, which can then select the optimal download method.

[0122] The download unit can analyze the user's social media activity during the download process and suggest download methods. For example, the download unit can identify the user's current interests from their social media activity and suggest relevant downloads. The download unit can also analyze the user's social media activity and suggest the optimal download method. The download unit can also filter out unnecessary download options based on the user's social media activity. This allows the system to suggest the optimal download method by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the download unit may be performed using, for example, a generative AI, or without a generative AI. For example, the download unit can input the user's social media activity data into a generative AI, which can then suggest the optimal download method.

[0123] The settings unit can estimate the user's emotions and adjust the settings based on the estimated emotions. For example, if the user is relaxed, the settings unit can provide detailed settings options. For example, if the user is in a hurry, the settings unit can provide concise settings options. For example, if the user is stressed, the settings unit can provide simple settings options. This allows for more appropriate settings by adjusting the settings according to the user's emotions. 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 settings unit may be performed using a generative AI, or not using a generative AI. For example, the settings unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the settings.

[0124] The configuration unit can analyze the user's past configuration history to select the optimal configuration method when a configuration change is made. For example, the configuration unit can suggest the optimal configuration method based on the user's past configuration changes. For example, the configuration unit can suggest relevant configuration methods from the user's past configuration history. For example, the configuration unit can analyze the user's past configuration history to select the optimal configuration method. This allows the optimal configuration method to be selected by analyzing the user's past configuration history. The optimal configuration method includes, but is not limited to, configuration formats and configuration priorities based on the user's needs. Some or all of the above-described processes in the configuration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the configuration unit can input the user's past configuration history data into a generative AI, which can then select the optimal configuration method.

[0125] The settings unit can customize the settings based on the user's current needs when the settings are changed. For example, the settings unit can suggest the optimal settings method according to the user's current needs. The settings unit can also customize the settings based on the user's current needs. For example, the settings unit can filter out unnecessary setting options according to the user's current needs. This allows for more appropriate settings by customizing the settings based on the user's current needs. Current needs include, but are not limited to, the user's current requests and current usage. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or without a generative AI. For example, the settings unit can input the user's current needs data into a generative AI, which can then customize the settings.

[0126] The settings unit can estimate the user's emotions and determine the priority of settings based on the estimated emotions. For example, if the user is relaxed, the settings unit will set settings with normal priority. For example, if the user is in a hurry, the settings unit can also prioritize important settings. For example, if the user is stressed, the settings unit can also prioritize simple settings. In this way, by determining the priority of settings according to the user's emotions, important settings can be prioritized. 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 settings unit may be performed using a generative AI, or not using a generative AI. For example, the settings unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of settings.

[0127] The settings unit can select the optimal settings method when the settings are changed, taking into account the user's geographical location information. For example, if the user is in a specific location, the settings unit will prioritize settings related to that location. The settings unit can also suggest the optimal settings method based on the user's current location, for example. The settings unit can also filter out unnecessary settings options by taking into account the user's geographical location information. This allows the optimal settings method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the settings unit may be performed using, for example, a generating AI, or without a generating AI. For example, the settings unit can input the user's geographical location data into a generating AI, which can then select the optimal settings method.

[0128] The settings unit can analyze the user's social media activity and suggest settings when settings are changed. For example, the settings unit can understand the user's current interests from their social media activity and suggest relevant settings. The settings unit can also analyze the user's social media activity and suggest the optimal settings method. The settings unit can also filter out unnecessary setting options based on the user's social media activity. This allows the settings unit to suggest the optimal settings by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the settings unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the settings unit can input the user's social media activity data into a generative AI, which can then suggest the optimal settings.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The reception unit can acquire the user's biometric information and adjust the request acceptance method based on the acquired biometric information. For example, if the user's heart rate is high, the reception unit can provide a simple interface and minimize the input steps. For example, if the user's body temperature is high, the reception unit can prioritize voice input to quickly accept the request. For example, if the user's blood pressure is high, the reception unit can provide a relaxing interface to reduce stress. This allows for more appropriate request acceptance by adjusting the request acceptance method according to the user's biometric information. Biometric information is acquired using devices such as smartwatches or fitness trackers. Some or all of the above processing in the reception unit may be performed using generative AI, or it may be performed without generative AI. For example, the reception unit can input the user's biometric data into a generative AI, which can analyze the biometric information and adjust the request acceptance method.

[0131] The generation unit can analyze the user's past behavior patterns and propose the optimal action. For example, the generation unit can propose the optimal action based on operations the user has frequently performed in the past. For example, the generation unit can predict and propose operations to be performed during a specific time period based on the user's past behavior patterns. For example, the generation unit can analyze the user's past behavior patterns and propose relevant actions. In this way, the optimal action can be proposed by analyzing the user's past behavior patterns. Optimal actions include, but are not limited to, prompt responses and appropriate information provision. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past behavior pattern data into a generation AI, and the generation AI can propose the optimal action.

[0132] The extension unit can collect user feedback and adjust the method of feature enhancement based on the collected feedback. For example, if a user provides positive feedback on a particular feature, the extension unit will prioritize enhancing that feature. For example, if a user provides negative feedback on a particular feature, the extension unit may also prioritize improving that feature. The extension unit can also analyze user feedback and suggest the optimal enhancement method. This allows for more appropriate enhancements by adjusting the method of feature enhancement based on user feedback. Feedback is collected, for example, through surveys or reviews. Some or all of the above processing in the extension unit may be performed using generative AI, or without generative AI. For example, the extension unit can input user feedback data into generative AI, which can analyze the feedback and adjust the enhancement method.

[0133] The support unit can analyze the user's learning history and select the optimal support method. For example, the support unit can propose the optimal support method based on what the user has learned in the past. For example, the support unit can also propose relevant support methods based on the user's learning history. For example, the support unit can select the optimal support method by analyzing the user's learning history. This allows the optimal support method to be selected by analyzing the user's learning history. Optimal support methods include, but are not limited to, prompt responses and appropriate information provision. Some or all of the above processing in the support unit may be performed using a generative AI, or not. For example, the support unit can input the user's learning history data into a generative AI, which can then select the optimal support method.

[0134] The calendar unit can estimate the user's emotions and adjust the calendar notification method based on the estimated emotions. For example, if the user is relaxed, the calendar unit can provide detailed notifications. If the user is in a hurry, the calendar unit can provide concise notifications. If the user is stressed, the calendar unit can provide simple notifications. By adjusting the calendar notification method according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 calendar unit may be performed using generative AI or not. For example, the calendar unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the notification method.

[0135] The download unit can monitor the storage status of the user's device and suggest the optimal download method. For example, if the user's device has limited storage, the download unit can suggest a lightweight download option. For example, if the user's device has sufficient storage, the download unit can also suggest a standard download option. The download unit can also monitor the user's device's storage status and filter out unnecessary download options. This allows for more appropriate downloads by suggesting the optimal download method based on the user's device's storage status. Monitoring of storage status is performed, for example, by obtaining the device's system information. Some or all of the above processing in the download unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the download unit can input the user's device's storage status data into a generative AI, which can then suggest the optimal download method.

[0136] The settings unit can estimate the user's emotions and adjust the settings interface based on the estimated emotions. For example, if the user is relaxed, the settings unit can provide a detailed settings interface. For example, if the user is in a hurry, the settings unit can provide a concise settings interface. For example, if the user is stressed, the settings unit can provide a simple settings interface. This allows for more appropriate settings by adjusting the settings interface according to the user's emotions. 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 settings unit may be performed using or without a generative AI. For example, the settings unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the settings interface.

[0137] The reception unit can monitor the user's current activity status and select the most appropriate reception method. For example, if the user is exercising, the reception unit may prioritize voice input. For example, if the user is stationary, the reception unit may also prioritize text input. The reception unit can also monitor the user's current activity status and filter out unnecessary reception options. This allows for more appropriate reception by selecting the most appropriate reception method based on the user's current activity status. Activity status monitoring is performed using devices such as smartwatches or fitness trackers. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can input user activity data into a generative AI, which can then select the most appropriate reception method.

[0138] The generation unit can estimate the user's emotions and determine the priority of actions based on the estimated emotions. For example, if the user is relaxed, the generation unit will suggest actions with normal priority. If the user is in a hurry, the generation unit can also prioritize suggesting urgent actions. If the user is stressed, the generation unit can also prioritize suggesting simple actions. This allows for the prioritization of important actions by determining the priority of actions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of actions.

[0139] The extension unit can monitor the performance status of the user's device and propose the optimal extension method. For example, if the performance of the user's device is degraded, the extension unit may propose a lightweight extension option. For example, if the performance of the user's device is good, the extension unit may also propose a standard extension option. The extension unit can also monitor the performance status of the user's device and filter out unnecessary extension options. This enables more appropriate extensions by proposing the optimal extension method based on the performance status of the user's device. Performance status monitoring is performed, for example, by acquiring the device's system information. Some or all of the above processing in the extension unit may be performed using a generative AI, or not using a generative AI. For example, the extension unit can input the performance status data of the user's device into a generative AI, which can then propose the optimal extension method.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The reception desk receives user requests. User requests include service requests and information inquiries. The reception desk can receive user requests through a chat UI or voice conversation. Step 2: The generation unit understands the request received by the reception unit and proposes an appropriate action. The generation unit uses generation AI to analyze the user's request and, for example, displays calendar events or launches an email application to support sending emails. Step 3: The extension section extends functionality through the plugin marketplace. If a user wants to add a specific application or new features, they can select the appropriate plugin from the plugin marketplace, download it, and install it. Step 4: The support team provides real-time interactive support. When a user says, "I want to change the settings," they display the appropriate settings screen and provide support to help the user easily perform the operation. Also, when a user says, "I need help," they provide appropriate help information.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, expansion unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user requests using the chat UI or voice conversation function of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes user requests using generation AI and proposes appropriate actions. The expansion unit is implemented in the control unit 46A of the smart device 14, for example, and performs functional expansion through a plug-in marketplace. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides real-time interactive support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, generation unit, expansion unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user requests using the chat UI or voice conversation function of the smart glasses 214. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes user requests using generation AI and proposes appropriate actions. The expansion unit is implemented in the control unit 46A of the smart glasses 214, for example, and performs functional expansion through a plug-in marketplace. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides real-time interactive support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, generation unit, expansion unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user requests using the chat UI or voice conversation function of the headset terminal 314. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes user requests using generation AI and proposes appropriate actions. The expansion unit is implemented in the control unit 46A of the headset terminal 314, for example, and performs functional expansion through a plug-in marketplace. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides real-time interactive support. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the reception unit, generation unit, expansion unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user requests using the chat UI or voice conversation function of the robot 414. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes user requests using generation AI and proposes appropriate actions. The expansion unit is implemented in the control unit 46A of the robot 414, for example, and performs functional expansion through a plug-in marketplace. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides real-time interactive support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) A reception desk that receives user requests, A generation unit that understands the request received by the reception unit and proposes an appropriate action, An expansion unit that extends functionality through a plugin marketplace, It includes a support unit that provides real-time interactive support. A system characterized by the following features. (Note 2) It features a calendar section that allows you to check the calendar and display your schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a download section for adding specific applications. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a settings section that supports configuration changes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Suggest appropriate actions based on user requests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned extension is Extend functionality through the Plugin Marketplace. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a request is received, it is filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how it suggests actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When proposing actions, adjust the level of detail in the proposal based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When proposing actions, different proposal algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When proposing actions, prioritize the proposals based on when the requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When proposing actions, adjust the order of proposals based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned extension is We estimate the user's emotions and adjust the way we enhance features based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned extension is When adding functionality, the system analyzes the user's past enhancement history to select the optimal enhancement method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned extension is When extending functionality, customize the means of extension based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned extension is It estimates the user's emotions and determines the priority of features to enhance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned extension is When expanding functionality, the optimal expansion method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned extension is When expanding functionality, we analyze users' social media activity and propose ways to enhance it. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, we analyze the user's past support history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, customize the support methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned calendar section is It estimates the user's emotions and adjusts how the calendar is displayed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned calendar section is When displaying the calendar, the system selects the optimal display method by referring to the user's past schedule history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned calendar section is When displaying the calendar, customize the displayed content based on the user's current status. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned calendar section is It estimates the user's emotions and determines calendar priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned calendar section is When displaying the calendar, the system selects the optimal display method considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned calendar section is When displaying the calendar, the system analyzes the user's social media activity and suggests content to display. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned download section is It estimates the user's emotions and adjusts the download method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned download section is During the download process, the system analyzes the user's past download history to select the optimal download method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned download section is During the download process, customize the download method based on the user's current needs. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned download section is It estimates user sentiment and determines download priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned download section is During the download process, the system will select the optimal download method by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned download section is During the download process, the system analyzes the user's social media activity and suggests download methods. The system described in Appendix 3, characterized by the features described herein. (Note 43) The setting unit is, It estimates the user's emotions and adjusts the settings based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The setting unit is, When settings are changed, the system analyzes the user's past settings history to select the optimal setting method. The system described in Appendix 4, characterized by the features described herein. (Note 45) The setting unit is, When changing settings, customize the settings based on the user's current needs. The system described in Appendix 4, characterized by the features described herein. (Note 46) The setting unit is, It estimates the user's emotions and determines the priority of settings based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The setting unit is, When changing settings, the system selects the optimal setting method by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 48) The setting unit is, When changing settings, the system analyzes the user's social media activity and suggests appropriate settings. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0214] 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. A reception desk that receives user requests, A generation unit that understands the request received by the reception unit and proposes an appropriate action, An expansion unit that extends functionality through a plugin marketplace, It includes a support unit that provides real-time interactive support. A system characterized by the following features.

2. It features a calendar section that allows you to check the calendar and display your schedule. The system according to feature 1.

3. It includes a download section for adding specific applications. The system according to feature 1.

4. It features a settings section that supports configuration changes. The system according to feature 1.

5. The generating unit is Suggest appropriate actions based on user requests. The system according to feature 1.

6. The aforementioned extension is Extend functionality through the Plugin Marketplace. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system according to feature 1.

9. The aforementioned reception unit is When a request is received, it is filtered based on the user's current situation and areas of interest. The system according to feature 1.

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