System

Abstract

An object of a system according to an embodiment is to make a telephone response or a reservation on behalf of a user to reduce the time and effort of the user.SOLUTION: A system according to an embodiment includes a telephone answering unit, a reservation unit, a schedule confirmation unit, and a chat unit. The telephone answering unit answers the telephone on behalf of the user. The reservation section makes a phone call to a plurality of shops to confirm an idle state and makes a reservation. The schedule confirmation unit confirms a schedule of a user. The chatting unit converts the call content into text and displays the text in a chat format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to make a reservation by calling the service themselves, which is time-consuming.

[0005] The system according to the embodiment aims to reduce the burden on the user by answering calls and making reservations on behalf of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a telephone answering unit, a reservation unit, a schedule confirmation unit, and a chat unit. The telephone answering unit answers telephone calls on behalf of the user. The reservation unit calls multiple stores to check availability and make a reservation. The schedule confirmation unit checks the user's schedule. The chat unit converts the contents of the call into text and displays it in chat format. [Effects of the Invention]

[0007] The system according to the embodiment can answer calls and make reservations on behalf of the user, thereby reducing the burden on the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The telephone answering system according to the embodiment of the present invention is a system that answers telephone calls on behalf of the user, and the generation AI handles reservations, schedule confirmation, and converts the contents of the call into chat. This reduces the burden on the user and enables the system to process tasks efficiently.

[0029] A telephone answering system according to an embodiment includes a telephone answering unit, a reservation unit, a schedule confirmation unit, and a chat conversion unit. The telephone answering unit answers telephone calls on behalf of a user. For example, if a user is out or in a meeting and cannot answer the phone, the telephone answering unit automatically receives the call and asks the caller for their business. The generation AI uses speech recognition technology to understand what the caller is saying and generates an appropriate response. For example, the generation AI responds with a response such as, "Hello, this is an automated response system. How can I help you?" The reservation unit calls multiple restaurants to check availability and makes a reservation. For example, if a user requests a restaurant reservation, the reservation unit calls the specified restaurant, checks availability, and makes the reservation. The generation AI makes a call based on information such as the user's desired restaurant and date and time and makes the reservation. The schedule confirmation unit checks the user's schedule. For example, if a user is in a meeting and cannot answer the phone, the schedule confirmation unit checks the user's schedule, determines when the meeting ends, and responds at that time. The generation AI generates a response based on the user's schedule information. The chat conversion unit converts the call content into text and displays it in chat format. For example, if a user wants to check the contents of a telephone conversation later, the chat conversion unit converts the contents of the call into text and displays it in chat format. The generation AI converts the contents of the call into text based on an instruction to convert the contents of the call into text. This allows the telephone answering system according to the embodiment to reduce the burden on the user and process tasks efficiently. For example, the user can respond to important calls without missing them even while in a meeting. It also eliminates the need to call multiple stores. Furthermore, by checking the contents of the call in chat format, the user can review the contents later.

[0030] The telephone answering unit learns the user's past call history and generates the optimal response for each caller. For example, the telephone answering unit analyzes the user's past call history and learns the optimal response pattern for each caller. For example, polite language is used for certain callers and casual language for others. The telephone answering unit also understands the preferences and tendencies of the caller based on the past call history and generates the optimal response. For example, a response is generated based on the caller's frequently used phrases and topics. Furthermore, the telephone answering unit predicts the caller's emotions and reactions based on the past call history and generates the optimal response. For example, the content and tone of the response are adjusted based on how the caller has responded in the past. In this way, optimal responses are generated based on the past call history, improving the response to each caller.

[0031] The telephone answering unit translates the contents of a call in real time, making it possible to handle calls from parties that speak different languages. The telephone answering unit, for example, translates the contents of a call in real time and makes it possible to handle calls from parties that speak different languages. For example, real-time translation is performed between a user speaking Japanese and a party that speaks English, achieving smooth communication. The telephone answering unit also translates the contents of a call in real time and makes it possible to handle multiple languages. For example, it makes it possible to handle calls from parties that speak multiple languages, such as French and Spanish. Furthermore, the telephone answering unit translates the contents of a call in real time and makes it possible to handle technical terms and industry jargon. For example, it translates the contents of a call that include technical terms from specific fields, such as medicine or law. This makes it possible to handle calls from parties that speak different languages, making international communication smoother.

[0032] The telephone answering unit not only answers telephone calls but also automatically responds to emails and messaging apps. The telephone answering unit not only answers telephone calls but also automatically responds to emails and messaging apps. For example, the telephone answering unit automatically generates and sends a reply to an email received by a user. The telephone answering unit also automatically responds to messaging apps. For example, the telephone answering unit automatically generates and sends a reply to a message received by a user. Furthermore, the telephone answering unit supports multiple email accounts and messaging apps. For example, if a user uses multiple email accounts or messaging apps, an automatic response is provided for each account. This reduces the communication burden on the user by automatically responding to emails and messaging apps as well as phone calls.

[0033] The telephone answering unit makes suggestions and reminders taking into consideration the user's preferences and past behavioral patterns. The telephone answering unit makes suggestions and reminders taking into consideration the user's preferences and past behavioral patterns, for example. For example, it suggests making a reservation at a restaurant that the user frequently visits. The telephone answering unit also makes reminders based on the user's past behavioral patterns. For example, it reminds the user of tasks and events that they regularly perform. Furthermore, the telephone answering unit makes customized suggestions based on the user's preferences and behavioral patterns. For example, it suggests events and services that the user might be interested in. In this way, by making suggestions and reminders that take into consideration the user's preferences and behavioral patterns, convenience for the user is improved.

[0034] The reservation unit learns the user's past reservation history and suggests the most suitable restaurant based on their preferences and tendencies. For example, the reservation unit analyzes the user's past reservation history, learns their preferences and tendencies, and suggests the most suitable restaurant. For example, it prioritizes suggestions of restaurants that the user frequently visits. The reservation unit also understands the user's preferences and tendencies based on the past reservation history and makes customized suggestions. For example, it suggests restaurants that offer the user's favorite dishes and services. Furthermore, the reservation unit predicts the user's preferences and tendencies based on the past reservation history and suggests new restaurants. For example, it suggests new restaurants that the user might be interested in. In this way, user satisfaction is improved by suggesting the most suitable restaurant based on the user's past reservation history.

[0035] The reservation unit analyzes store congestion status and reviews in real time to select the optimal reservation destination. The reservation unit, for example, analyzes store congestion status in real time to select the optimal reservation destination. For example, reservations are made during less crowded times. The reservation unit also analyzes store reviews in real time to select stores with high ratings. For example, stores with high user ratings are selected preferentially. Furthermore, the reservation unit suggests the optimal reservation destination based on the congestion status and reviews. For example, it suggests stores where the user can spend time comfortably. In this way, the optimal reservation destination can be selected by analyzing store congestion status and reviews in real time.

[0036] The reservation unit not only makes reservations but also automatically handles cancellation and change procedures. For example, the reservation unit not only makes reservations but also automatically handles cancellation and change procedures. For example, if a user changes their plans, the reservation unit automatically cancels the reservation and makes a new reservation. Furthermore, because the reservation unit automatically handles cancellation and change procedures, it saves the user the trouble of having to do it manually. For example, the user simply issues a cancellation or change instruction, and the reservation unit automatically handles the procedure. Furthermore, because the reservation unit quickly handles cancellation and change procedures, the user can smoothly change their plans. For example, the reservation unit handles cancellation and change procedures in real time and notifies the user immediately. In this way, not only reservations but also cancellation and change procedures are handled automatically, saving the user the trouble of having to do it manually.

[0037] The reservation unit manages multiple reservations collectively and proposes an optimal schedule. The reservation unit, for example, manages multiple reservations collectively and proposes an optimal schedule. For example, if a user has multiple reservations, the reservation unit manages them collectively and proposes an optimal schedule. The reservation unit also adjusts multiple reservations to match the user's schedule. For example, it adjusts reservations to match the user's free time. Furthermore, because the reservation unit manages multiple reservations collectively, the user can efficiently manage their schedule. For example, the reservation unit automatically adjusts the schedule and notifies the user. In this way, by managing multiple reservations collectively and proposing an optimal schedule, the user's schedule management becomes more efficient.

[0038] The schedule confirmation unit updates the user's schedule in real time and responds immediately if any changes occur. The schedule confirmation unit, for example, updates the user's schedule in real time and responds immediately if any changes occur. For example, if the time of a meeting is changed, the schedule confirmation unit automatically adjusts the response. The schedule confirmation unit also monitors the user's schedule in real time and immediately notifies the user if any changes occur. For example, if the user changes their plans, the schedule confirmation unit immediately notifies the user. Furthermore, since the schedule confirmation unit updates the user's schedule in real time, the user can smoothly manage their schedule. For example, the schedule confirmation unit automatically updates the schedule and notifies the user. In this way, by updating the user's schedule in real time and responding immediately if any changes occur, schedule management becomes more efficient.

[0039] The schedule confirmation unit suggests optimal break times and reminders based on the user's schedule. The schedule confirmation unit suggests optimal break times based on the user's schedule, for example. For example, suggesting a break after a long meeting. The schedule confirmation unit also suggests reminders based on the user's schedule. For example, setting a reminder before an important task or event. Furthermore, the schedule confirmation unit makes customized suggestions based on the user's schedule. For example, suggesting optimal break times and reminders based on the user's work patterns and health data. In this way, by suggesting optimal break times and reminders based on the user's schedule, the user's efficiency is improved.

[0040] The schedule confirmation unit not only manages schedules, but also manages tasks and projects. The schedule confirmation unit, for example, not only manages schedules but also manages tasks. For example, it automatically organizes and prioritizes the user's tasks. The schedule confirmation unit also manages projects. For example, it manages the progress of the user's projects and suggests necessary tasks. Furthermore, the schedule confirmation unit integrates schedule management, task management, and project management. For example, it manages the progress of tasks and projects based on the user's schedule. This improves the user's work efficiency by managing not only schedules but also tasks and projects.

[0041] The schedule confirmation unit suggests the optimal means of transportation and route based on the user's schedule. The schedule confirmation unit suggests the optimal means of transportation based on the user's schedule, for example. For example, it suggests a train or taxi depending on the location of a meeting. The schedule confirmation unit also suggests the optimal route based on the user's schedule. For example, it suggests the shortest route based on traffic information. Furthermore, the schedule confirmation unit customizes and suggests means of transportation and routes based on the user's schedule. For example, it suggests the optimal means of transportation and route based on the user's preferences and past travel history. In this way, by suggesting the optimal means of transportation and route based on the user's schedule, travel efficiency is improved.

[0042] The chat conversion unit summarizes the contents of the call and extracts and displays only the important points. For example, the chat conversion unit summarizes the contents of the call and extracts and displays only the important points. For example, it displays a concise summary of the main points of the conversation. The chat conversion unit also summarizes the contents of the call in real time and extracts the important points. For example, it picks out and displays particularly important information from the conversation. Furthermore, the chat conversion unit efficiently summarizes the contents of the call using an algorithm for summarizing the contents of the call. For example, it automatically summarizes the contents of the call using natural language processing technology. In this way, the contents of the call can be summarized and only the important points can be extracted and displayed, allowing for efficient understanding of the information.

[0043] The chat conversion unit automatically classifies the content of the call and provides related information as a link. The chat conversion unit, for example, automatically classifies the content of the call and provides related information as a link. For example, websites and materials related to the content of the conversation are displayed as links. The chat conversion unit also classifies the content of the call in real time and provides related information. For example, information related to topics mentioned in the conversation is displayed as a link. Furthermore, the chat conversion unit efficiently classifies the content of the call using an algorithm for classifying the content of the call. For example, the chat conversion unit automatically classifies the content of the call using machine learning technology. This automatically classifies the content of the call and provides related information as a link, making it easier to search for and refer to information.

[0044] The chat conversion unit not only converts the contents of phone calls into chats, but also converts the contents of voice memos and video calls into text. The chat conversion unit, for example, not only converts the contents of phone calls into chats, but also converts the contents of voice memos into text. For example, it automatically converts voice memos recorded by a user into text. The chat conversion unit also converts the contents of video calls into text. For example, it converts the contents of conversations during video calls into text in real time. Furthermore, the chat conversion unit efficiently converts the contents of voice memos and video calls into text using an algorithm for converting the contents of voice memos and video calls into text. For example, it automatically converts the contents of voice memos and video calls into text using voice recognition technology or video analysis technology. This converts not only the contents of phone calls but also the contents of voice memos and video calls into text, making it convenient for checking later.

[0045] The chat conversion unit links the chat content with other applications and utilizes it for task management and project management. For example, the chat conversion unit links the chat content with a task management application and automatically generates tasks. For example, tasks mentioned in the conversation are automatically added to the task management application. The chat conversion unit also links with a project management application to manage the progress of a project. For example, the project progress mentioned in the conversation is automatically reflected in the project management application. Furthermore, the chat conversion unit efficiently synchronizes data using an API for linking with other applications. For example, the chat content is sent to other applications in real time and utilized for task management and project management. In this way, linking the chat content with other applications makes task management and project management more efficient.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The telephone answering unit can also learn the user's tone of voice and speaking style and generate responses that match the user's personality. For example, it can learn the words and phrases that the user normally uses and reflect them in the responses. The telephone answering unit also generates responses that match the user's personality based on the user's tone of voice and speaking style. For example, if the user prefers casual language, it generates casual responses. Furthermore, the telephone answering unit generates responses that match the user's personality based on the user's tone of voice and speaking style, thereby achieving more natural communication. In this way, user satisfaction can be improved by generating responses that match the user's personality.

[0048] The telephone response unit can also acquire the user's health data and generate a response according to the user's health condition. For example, it can monitor the user's heart rate and blood pressure, and adjust the response if an abnormality is detected. The telephone response unit can also provide health advice based on the user's health data. For example, if the user is feeling stressed, it can suggest ways to relax. Furthermore, the telephone response unit can support the user's health management by generating a response according to the user's health condition based on the user's health data. In this way, the user's health can be maintained by generating a response according to the user's health condition.

[0049] The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points. For example, it can extract only the important points from a long call and notify the user of them. The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points, thereby achieving efficient information sharing. For example, it can summarize the contents of a meeting and notify the participants. The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points, thereby preventing important information from being missed. In this way, efficient information sharing can be achieved by summarizing the contents of a call in real time and notifying the user of the main points.

[0050] The telephone answering unit can also provide relevant information based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the telephone answering unit can provide the latest information on that sport. The telephone answering unit can also suggest relevant events and services based on the user's hobbies and interests. For example, if the user is interested in music, the telephone answering unit can provide concert information. Furthermore, the telephone answering unit can improve user satisfaction by providing customized information based on the user's hobbies and interests. This can improve user satisfaction by providing relevant information based on the user's hobbies and interests.

[0051] The telephone response unit can also acquire the user's location information and provide information based on the location. For example, if the user is in a specific location, it provides information related to that location. The telephone response unit can also suggest nearby stores and services based on the user's location information. For example, if the user is looking for a restaurant, it will suggest nearby restaurants. Furthermore, the telephone response unit can improve user convenience by providing customized information based on the user's location information. This can improve user convenience by providing related information based on the user's location information.

[0052] The telephone answering unit can also predict future behavior based on the user's past behavioral data and make appropriate suggestions. For example, if the user has participated in a specific event in the past, a similar event can be suggested. The telephone answering unit can also predict future behavior based on the user's past behavioral data and make appropriate suggestions, thereby improving user convenience. For example, if the user has visited a specific restaurant in the past, a similar restaurant can be suggested. The telephone answering unit can also predict future behavior based on the user's past behavioral data and make customized suggestions, thereby improving user satisfaction. In this way, by predicting future behavior based on the user's past behavioral data and making appropriate suggestions, user convenience can be improved.

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

[0054] Step 1: The telephone answering unit answers the phone on behalf of the user. For example, if the user is out or in a meeting and cannot answer the phone, the telephone answering unit automatically receives the call and listens to the caller's message. The generation AI uses speech recognition technology to understand what the caller is saying and generates an appropriate response. For example, it might respond with something like, "Hello, this is an automated answering system. How can I help you?" Step 2: The reservation unit calls multiple restaurants to check availability and make the reservation. For example, if a user wants to make a reservation at a restaurant, the reservation unit calls the specified restaurant, checks availability, and makes the reservation. The generation AI makes the call based on information such as the restaurant and date and time desired by the user and makes the reservation. Step 3: The schedule confirmation unit checks the user's schedule. For example, if the user is in a meeting and cannot answer the phone, the schedule confirmation unit checks the user's schedule, determines when the meeting will end, and responds at that time. The generation AI generates a response based on the user's schedule information. Step 4: The chat conversion unit converts the contents of the call into text and displays it in chat format. For example, if the user wants to check the contents of a phone conversation later, the chat conversion unit converts the contents of the call into text and displays it in chat format. The generation AI converts the contents of the call into text based on instructions to convert the contents of the call into text.

[0055] (Example 2) The telephone answering system according to the embodiment of the present invention is a system that answers telephone calls on behalf of the user, and the generation AI handles reservations, schedule confirmation, and converts the contents of the call into chat. This reduces the burden on the user and enables the system to process tasks efficiently.

[0056] A telephone answering system according to an embodiment includes a telephone answering unit, a reservation unit, a schedule confirmation unit, and a chat conversion unit. The telephone answering unit answers telephone calls on behalf of a user. For example, if a user is out or in a meeting and cannot answer the phone, the telephone answering unit automatically receives the call and asks the caller for their business. The generation AI uses speech recognition technology to understand what the caller is saying and generates an appropriate response. For example, the generation AI responds with a response such as, "Hello, this is an automated response system. How can I help you?" The reservation unit calls multiple restaurants to check availability and makes a reservation. For example, if a user requests a restaurant reservation, the reservation unit calls the specified restaurant, checks availability, and makes the reservation. The generation AI makes a call based on information such as the user's desired restaurant and date and time and makes the reservation. The schedule confirmation unit checks the user's schedule. For example, if a user is in a meeting and cannot answer the phone, the schedule confirmation unit checks the user's schedule, determines when the meeting ends, and responds at that time. The generation AI generates a response based on the user's schedule information. The chat conversion unit converts the call content into text and displays it in chat format. For example, if a user wants to check the contents of a telephone conversation later, the chat conversion unit converts the contents of the call into text and displays it in chat format. The generation AI converts the contents of the call into text based on an instruction to convert the contents of the call into text. This allows the telephone answering system according to the embodiment to reduce the burden on the user and process tasks efficiently. For example, the user can respond to important calls without missing them even while in a meeting. It also eliminates the need to call multiple stores. Furthermore, by checking the contents of the call in chat format, the user can review the contents later.

[0057] The telephone answering unit uses the emotion estimation function to analyze the emotions of the other party in real time and respond with an appropriate tone and language. The telephone answering unit, for example, uses the emotion estimation function to analyze the emotions of the other party in real time from the tone of voice and language of the other party. For example, if the other party is angry, the telephone answering unit responds in a calm tone to soothe the other party's emotions. The telephone answering unit also uses the emotion estimation function to respond with gentle language if the other party is sad. For example, the telephone answering unit selects an appropriate tone and language depending on the other party's emotions. Furthermore, the telephone answering unit uses the emotion estimation function to respond with a bright tone if the other party is happy. For example, the telephone answering unit adjusts the tone and language of the response to match the other party's emotions. In this way, the quality of communication is improved by responding according to the other party's emotions.

[0058] The telephone answering unit learns the user's past call history and generates the optimal response for each caller. For example, the telephone answering unit analyzes the user's past call history and learns the optimal response pattern for each caller. For example, polite language is used for certain callers and casual language for others. The telephone answering unit also understands the preferences and tendencies of the caller based on the past call history and generates the optimal response. For example, a response is generated based on the caller's frequently used phrases and topics. Furthermore, the telephone answering unit predicts the caller's emotions and reactions based on the past call history and generates the optimal response. For example, the content and tone of the response are adjusted based on how the caller has responded in the past. In this way, optimal responses are generated based on the past call history, improving the response to each caller.

[0059] The telephone answering unit translates the contents of a call in real time, making it possible to handle calls from parties that speak different languages. The telephone answering unit, for example, translates the contents of a call in real time and makes it possible to handle calls from parties that speak different languages. For example, real-time translation is performed between a user speaking Japanese and a party that speaks English, achieving smooth communication. The telephone answering unit also translates the contents of a call in real time and makes it possible to handle multiple languages. For example, it makes it possible to handle calls from parties that speak multiple languages, such as French and Spanish. Furthermore, the telephone answering unit translates the contents of a call in real time and makes it possible to handle technical terms and industry jargon. For example, it translates the contents of a call that include technical terms from specific fields, such as medicine or law. This makes it possible to handle calls from parties that speak different languages, making international communication smoother.

[0060] The telephone answering unit not only answers telephone calls but also automatically responds to emails and messaging apps. The telephone answering unit not only answers telephone calls but also automatically responds to emails and messaging apps. For example, the telephone answering unit automatically generates and sends a reply to an email received by a user. The telephone answering unit also automatically responds to messaging apps. For example, the telephone answering unit automatically generates and sends a reply to a message received by a user. Furthermore, the telephone answering unit supports multiple email accounts and messaging apps. For example, if a user uses multiple email accounts or messaging apps, an automatic response is provided for each account. This reduces the communication burden on the user by automatically responding to emails and messaging apps as well as phone calls.

[0061] The telephone answering unit makes suggestions and reminders taking into consideration the user's preferences and past behavioral patterns. The telephone answering unit makes suggestions and reminders taking into consideration the user's preferences and past behavioral patterns, for example. For example, it suggests making a reservation at a restaurant that the user frequently visits. The telephone answering unit also makes reminders based on the user's past behavioral patterns. For example, it reminds the user of tasks and events that they regularly perform. Furthermore, the telephone answering unit makes customized suggestions based on the user's preferences and behavioral patterns. For example, it suggests events and services that the user might be interested in. In this way, by making suggestions and reminders that take into consideration the user's preferences and behavioral patterns, convenience for the user is improved.

[0062] The telephone answering unit uses the emotion estimation function to generate a response according to the user's emotional state, thereby reducing stress. The telephone answering unit, for example, uses the emotion estimation function to generate a response according to the user's emotional state, thereby reducing stress. For example, if the user is feeling stressed, the telephone answering unit responds using gentle language. The telephone answering unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the response. For example, if the user is tired, the telephone answering unit provides a concise response. Furthermore, the telephone answering unit uses the emotion estimation function to make suggestions and reminders that take the user's emotional state into consideration. For example, it makes suggestions that will help the user relax. In this way, stress is reduced by providing a response according to the user's emotional state.

[0063] The reservation unit uses the emotion estimation function to analyze the emotions of the store staff member and make the reservation at the optimal timing. The reservation unit, for example, uses the emotion estimation function to analyze the emotions from the tone of voice and language of the store staff member and make the reservation at the optimal timing. For example, if the staff member appears busy, the reservation unit waits a little while before making the reservation. The reservation unit also uses the emotion estimation function to analyze the staff member's emotional state in real time and adjust the timing of the reservation. For example, the reservation is made during a time period when the staff member is relaxed. Furthermore, the reservation unit uses the emotion estimation function to suggest a reservation that takes into account the staff member's emotional state. For example, the reservation unit suggests a time period when the staff member is most available. In this way, the reservation success rate is improved by making reservations taking into account the emotions of the store staff member.

[0064] The reservation unit learns the user's past reservation history and suggests the most suitable restaurant based on their preferences and tendencies. For example, the reservation unit analyzes the user's past reservation history, learns their preferences and tendencies, and suggests the most suitable restaurant. For example, it prioritizes suggestions of restaurants that the user frequently visits. The reservation unit also understands the user's preferences and tendencies based on the past reservation history and makes customized suggestions. For example, it suggests restaurants that offer the user's favorite dishes and services. Furthermore, the reservation unit predicts the user's preferences and tendencies based on the past reservation history and suggests new restaurants. For example, it suggests new restaurants that the user might be interested in. In this way, user satisfaction is improved by suggesting the most suitable restaurant based on the user's past reservation history.

[0065] The reservation unit analyzes store congestion status and reviews in real time to select the optimal reservation destination. The reservation unit, for example, analyzes store congestion status in real time to select the optimal reservation destination. For example, reservations are made during less crowded times. The reservation unit also analyzes store reviews in real time to select stores with high ratings. For example, stores with high user ratings are selected preferentially. Furthermore, the reservation unit suggests the optimal reservation destination based on the congestion status and reviews. For example, it suggests stores where the user can spend time comfortably. In this way, the optimal reservation destination can be selected by analyzing store congestion status and reviews in real time.

[0066] The reservation unit not only makes reservations but also automatically handles cancellation and change procedures. For example, the reservation unit not only makes reservations but also automatically handles cancellation and change procedures. For example, if a user changes their plans, the reservation unit automatically cancels the reservation and makes a new reservation. Furthermore, because the reservation unit automatically handles cancellation and change procedures, it saves the user the trouble of having to do it manually. For example, the user simply issues a cancellation or change instruction, and the reservation unit automatically handles the procedure. Furthermore, because the reservation unit quickly handles cancellation and change procedures, the user can smoothly change their plans. For example, the reservation unit handles cancellation and change procedures in real time and notifies the user immediately. In this way, not only reservations but also cancellation and change procedures are handled automatically, saving the user the trouble of having to do it manually.

[0067] The reservation unit manages multiple reservations collectively and proposes an optimal schedule. The reservation unit, for example, manages multiple reservations collectively and proposes an optimal schedule. For example, if a user has multiple reservations, the reservation unit manages them collectively and proposes an optimal schedule. The reservation unit also adjusts multiple reservations to match the user's schedule. For example, it adjusts reservations to match the user's free time. Furthermore, because the reservation unit manages multiple reservations collectively, the user can efficiently manage their schedule. For example, the reservation unit automatically adjusts the schedule and notifies the user. In this way, by managing multiple reservations collectively and proposing an optimal schedule, the user's schedule management becomes more efficient.

[0068] The reservation unit uses the emotion estimation function to select a reservation destination that will satisfy the user most and make a reservation. The reservation unit, for example, uses the emotion estimation function to select a reservation destination that will satisfy the user most and make a reservation. For example, it selects a store that will provide high satisfaction based on the user's past emotion data. The reservation unit also uses the emotion estimation function to analyze the user's emotional state in real time and select the optimal reservation destination. For example, it selects a store where the user can relax. Furthermore, the reservation unit uses the emotion estimation function to suggest a reservation that takes the user's emotional state into consideration. For example, it suggests a store that will provide high satisfaction to the user. In this way, by selecting the optimal reservation destination taking the user's emotions into consideration, user satisfaction is improved.

[0069] The schedule checking unit uses the emotion estimation function to generate a response at optimal timing, taking into account the user's emotional state. The schedule checking unit, for example, uses the emotion estimation function to generate a response at optimal timing, taking into account the user's emotional state. For example, if the user is feeling stressed, the response is delayed. The schedule checking unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the timing of the response. For example, the response is generated during a time period when the user is relaxed. Furthermore, the schedule checking unit uses the emotion estimation function to suggest a response that takes into account the user's emotional state. For example, the response is generated at a timing that will not cause the user to feel stressed. In this way, the response is generated at optimal timing, taking into account the user's emotional state, thereby reducing the user's stress.

[0070] The schedule confirmation unit updates the user's schedule in real time and responds immediately if any changes occur. The schedule confirmation unit, for example, updates the user's schedule in real time and responds immediately if any changes occur. For example, if the time of a meeting is changed, the schedule confirmation unit automatically adjusts the response. The schedule confirmation unit also monitors the user's schedule in real time and immediately notifies the user if any changes occur. For example, if the user changes their plans, the schedule confirmation unit immediately notifies the user. Furthermore, since the schedule confirmation unit updates the user's schedule in real time, the user can smoothly manage their schedule. For example, the schedule confirmation unit automatically updates the schedule and notifies the user. In this way, by updating the user's schedule in real time and responding immediately if any changes occur, schedule management becomes more efficient.

[0071] The schedule confirmation unit suggests optimal break times and reminders based on the user's schedule. The schedule confirmation unit suggests optimal break times based on the user's schedule, for example. For example, suggesting a break after a long meeting. The schedule confirmation unit also suggests reminders based on the user's schedule. For example, setting a reminder before an important task or event. Furthermore, the schedule confirmation unit makes customized suggestions based on the user's schedule. For example, suggesting optimal break times and reminders based on the user's work patterns and health data. In this way, by suggesting optimal break times and reminders based on the user's schedule, the user's efficiency is improved.

[0072] The schedule confirmation unit not only manages schedules, but also manages tasks and projects. The schedule confirmation unit, for example, not only manages schedules but also manages tasks. For example, it automatically organizes and prioritizes the user's tasks. The schedule confirmation unit also manages projects. For example, it manages the progress of the user's projects and suggests necessary tasks. Furthermore, the schedule confirmation unit integrates schedule management, task management, and project management. For example, it manages the progress of tasks and projects based on the user's schedule. This improves the user's work efficiency by managing not only schedules but also tasks and projects.

[0073] The schedule confirmation unit suggests the optimal means of transportation and route based on the user's schedule. The schedule confirmation unit suggests the optimal means of transportation based on the user's schedule, for example. For example, it suggests a train or taxi depending on the location of a meeting. The schedule confirmation unit also suggests the optimal route based on the user's schedule. For example, it suggests the shortest route based on traffic information. Furthermore, the schedule confirmation unit customizes and suggests means of transportation and routes based on the user's schedule. For example, it suggests the optimal means of transportation and route based on the user's preferences and past travel history. In this way, by suggesting the optimal means of transportation and route based on the user's schedule, travel efficiency is improved.

[0074] The schedule confirmation unit uses the emotion estimation function to adjust the schedule to reduce the user's stress level. The schedule confirmation unit, for example, uses the emotion estimation function to adjust the schedule to reduce the user's stress level. For example, if the user is feeling stressed, the schedule confirmation unit increases break time. The schedule confirmation unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the schedule. For example, it proposes a schedule that allows the user to relax. Furthermore, the schedule confirmation unit uses the emotion estimation function to propose a schedule that takes the user's emotional state into consideration. For example, it proposes a schedule that does not cause the user stress. In this way, the schedule confirmation unit adjusts the schedule to reduce the user's stress level, thereby improving the user's health and efficiency.

[0075] The chat conversion unit uses an emotion estimation function to reflect the emotional nuances of the call content in the text. The chat conversion unit, for example, uses the emotion estimation function to reflect the emotional nuances of the call content in the text. For example, if the other party is angry, the emotion is reflected in the text. The chat conversion unit also uses the emotion estimation function to analyze the emotional nuances of the call content in real time and reflect them in the text. For example, if the other party is sad, the emotion is reflected in the text. The chat conversion unit also uses the emotion estimation function to generate text that takes into account the emotional nuances of the call content. For example, if the other party is happy, the emotion is reflected in the text. In this way, by reflecting the emotional nuances of the call content in the text, the emotional nuances can be understood when checking it later.

[0076] The chat conversion unit summarizes the contents of the call and extracts and displays only the important points. For example, the chat conversion unit summarizes the contents of the call and extracts and displays only the important points. For example, it displays a concise summary of the main points of the conversation. The chat conversion unit also summarizes the contents of the call in real time and extracts the important points. For example, it picks out and displays particularly important information from the conversation. Furthermore, the chat conversion unit efficiently summarizes the contents of the call using an algorithm for summarizing the contents of the call. For example, it automatically summarizes the contents of the call using natural language processing technology. In this way, the contents of the call can be summarized and only the important points can be extracted and displayed, allowing for efficient understanding of the information.

[0077] The chat conversion unit automatically classifies the content of the call and provides related information as a link. The chat conversion unit, for example, automatically classifies the content of the call and provides related information as a link. For example, websites and materials related to the content of the conversation are displayed as links. The chat conversion unit also classifies the content of the call in real time and provides related information. For example, information related to topics mentioned in the conversation is displayed as a link. Furthermore, the chat conversion unit efficiently classifies the content of the call using an algorithm for classifying the content of the call. For example, the chat conversion unit automatically classifies the content of the call using machine learning technology. This automatically classifies the content of the call and provides related information as a link, making it easier to search for and refer to information.

[0078] The chat conversion unit not only converts the contents of phone calls into chats, but also converts the contents of voice memos and video calls into text. The chat conversion unit, for example, not only converts the contents of phone calls into chats, but also converts the contents of voice memos into text. For example, it automatically converts voice memos recorded by a user into text. The chat conversion unit also converts the contents of video calls into text. For example, it converts the contents of conversations during video calls into text in real time. Furthermore, the chat conversion unit efficiently converts the contents of voice memos and video calls into text using an algorithm for converting the contents of voice memos and video calls into text. For example, it automatically converts the contents of voice memos and video calls into text using voice recognition technology or video analysis technology. This converts not only the contents of phone calls but also the contents of voice memos and video calls into text, making it convenient for checking later.

[0079] The chat conversion unit links the chat content with other applications and utilizes it for task management and project management. For example, the chat conversion unit links the chat content with a task management application and automatically generates tasks. For example, tasks mentioned in the conversation are automatically added to the task management application. The chat conversion unit also links with a project management application to manage the progress of a project. For example, the project progress mentioned in the conversation is automatically reflected in the project management application. Furthermore, the chat conversion unit efficiently synchronizes data using an API for linking with other applications. For example, the chat content is sent to other applications in real time and utilized for task management and project management. In this way, linking the chat content with other applications makes task management and project management more efficient.

[0080] The chat conversion unit uses the emotion estimation function to analyze the emotional tone of the call content and provide feedback to the user. The chat conversion unit, for example, uses the emotion estimation function to analyze the emotional tone of the call content and provide feedback to the user. For example, the chat conversion unit provides feedback on what emotions the other party was feeling during the conversation. The chat conversion unit also uses the emotion estimation function to analyze the emotional tone of the call content in real time and provide feedback. For example, if the other party is angry, the emotion is fed back to the user. The chat conversion unit also uses the emotion estimation function to provide feedback that takes into account the emotional tone of the call content. For example, if the other party is happy, the emotion is fed back to the user. In this way, by analyzing the emotional tone of the call content and providing feedback to the user, the quality of communication is improved.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The telephone answering unit can also learn the user's tone of voice and speaking style and generate responses that match the user's personality. For example, it can learn the words and phrases that the user normally uses and reflect them in the responses. The telephone answering unit also generates responses that match the user's personality based on the user's tone of voice and speaking style. For example, if the user prefers casual language, it generates casual responses. Furthermore, the telephone answering unit generates responses that match the user's personality based on the user's tone of voice and speaking style, thereby achieving more natural communication. In this way, user satisfaction can be improved by generating responses that match the user's personality.

[0083] The telephone answering unit can also use the emotion estimation function to play music that corresponds to the user's emotional state. For example, if the user is feeling stressed, relaxing music is played. The telephone answering unit also uses the emotion estimation function to analyze the user's emotional state in real time and select appropriate music. For example, if the user is tired, soothing music is played. Furthermore, the telephone answering unit uses the emotion estimation function to suggest music that takes the user's emotional state into consideration. For example, if the user wants to cheer up, up-tempo music is suggested. In this way, the user's mood can be improved by playing music that corresponds to the user's emotional state.

[0084] The telephone response unit can also acquire the user's health data and generate a response according to the user's health condition. For example, it can monitor the user's heart rate and blood pressure, and adjust the response if an abnormality is detected. The telephone response unit can also provide health advice based on the user's health data. For example, if the user is feeling stressed, it can suggest ways to relax. Furthermore, the telephone response unit can support the user's health management by generating a response according to the user's health condition based on the user's health data. In this way, the user's health can be maintained by generating a response according to the user's health condition.

[0085] The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points. For example, it can extract only the important points from a long call and notify the user of them. The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points, thereby achieving efficient information sharing. For example, it can summarize the contents of a meeting and notify the participants. The telephone answering unit can also summarize the contents of a call in real time and notify the user of the main points, thereby preventing important information from being missed. In this way, efficient information sharing can be achieved by summarizing the contents of a call in real time and notifying the user of the main points.

[0086] The telephone answering unit can also set reminders according to the user's emotional state. For example, if the user is feeling stressed, it sets a reminder to relax. The telephone answering unit also uses the emotion estimation function to analyze the user's emotional state in real time and set an appropriate reminder. For example, if the user is tired, it sets a reminder to take a break. Furthermore, the telephone answering unit can support the user's health management by using the emotion estimation function to set reminders that take the user's emotional state into consideration. In this way, the user's health can be maintained by setting reminders according to the user's emotional state.

[0087] The telephone answering unit can also provide relevant information based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the telephone answering unit can provide the latest information on that sport. The telephone answering unit can also suggest relevant events and services based on the user's hobbies and interests. For example, if the user is interested in music, the telephone answering unit can provide concert information. Furthermore, the telephone answering unit can improve user satisfaction by providing customized information based on the user's hobbies and interests. This can improve user satisfaction by providing relevant information based on the user's hobbies and interests.

[0088] The telephone answering unit can also use the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is feeling stressed, it provides advice to relax. The telephone answering unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide appropriate feedback. For example, if the user is tired, it provides advice to take a rest. Furthermore, the telephone answering unit can support the user's health management by using the emotion estimation function to provide feedback that takes the user's emotional state into consideration. In this way, the user's health can be maintained by providing feedback according to the user's emotional state.

[0089] The telephone response unit can also acquire the user's location information and provide information based on the location. For example, if the user is in a specific location, it provides information related to that location. The telephone response unit can also suggest nearby stores and services based on the user's location information. For example, if the user is looking for a restaurant, it will suggest nearby restaurants. Furthermore, the telephone response unit can improve user convenience by providing customized information based on the user's location information. This can improve user convenience by providing related information based on the user's location information.

[0090] The telephone answering unit can also use the emotion estimation function to provide news and articles that correspond to the user's emotional state. For example, if the user is feeling stressed, it provides articles with relaxing content. The telephone answering unit also uses the emotion estimation function to analyze the user's emotional state in real time and select appropriate news and articles. For example, if the user is tired, it provides articles with soothing content. Furthermore, the telephone answering unit uses the emotion estimation function to suggest news and articles that take the user's emotional state into consideration. For example, if the user wants to cheer up, it provides articles with positive content. In this way, it is possible to improve the user's mood by providing news and articles that correspond to the user's emotional state.

[0091] The telephone answering unit can also predict future behavior based on the user's past behavioral data and make appropriate suggestions. For example, if the user has participated in a specific event in the past, a similar event can be suggested. The telephone answering unit can also predict future behavior based on the user's past behavioral data and make appropriate suggestions, thereby improving user convenience. For example, if the user has visited a specific restaurant in the past, a similar restaurant can be suggested. The telephone answering unit can also predict future behavior based on the user's past behavioral data and make customized suggestions, thereby improving user satisfaction. In this way, by predicting future behavior based on the user's past behavioral data and making appropriate suggestions, user convenience can be improved.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The telephone answering unit answers the phone on behalf of the user. For example, if the user is out or in a meeting and cannot answer the phone, the telephone answering unit automatically receives the call and listens to the caller's message. The generation AI uses speech recognition technology to understand what the caller is saying and generates an appropriate response. For example, it might respond with something like, "Hello, this is an automated answering system. How can I help you?" Step 2: The reservation unit calls multiple restaurants to check availability and make the reservation. For example, if a user wants to make a reservation at a restaurant, the reservation unit calls the specified restaurant, checks availability, and makes the reservation. The generation AI makes the call based on information such as the restaurant and date and time desired by the user and makes the reservation. Step 3: The schedule confirmation unit checks the user's schedule. For example, if the user is in a meeting and cannot answer the phone, the schedule confirmation unit checks the user's schedule, determines when the meeting will end, and responds at that time. The generation AI generates a response based on the user's schedule information. Step 4: The chat conversion unit converts the contents of the call into text and displays it in chat format. For example, if the user wants to check the contents of a phone conversation later, the chat conversion unit converts the contents of the call into text and displays it in chat format. The generation AI converts the contents of the call into text based on instructions to convert the contents of the call into text.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a telephone answering unit that answers telephone calls on behalf of a user; The reservation department calls multiple stores to check availability and make reservations. a schedule confirmation unit that confirms a user's schedule; A chat conversion unit converts the contents of the call into text and displays it in a chat format. A system characterized by:

2. The telephone answering unit The call content is translated in real time, making it possible to accommodate calls from different languages.

2. The system of claim 1.

3. The reservation unit Analyze the store's congestion status and reviews in real time to select the best reservation location 2. The system of claim 1.

4. The schedule confirmation unit Update the user's schedule in real time and respond immediately to any changes 2. The system of claim 1.

5. The chat conversion unit Reflecting the emotional nuances of the call content in the text 2. The system of claim 1.

6. The telephone answering unit Analyze the other person's emotions in real time and respond with the appropriate tone and language 2. The system of claim 1.

7. The reservation unit Analyze the emotions of the store staff and make reservations at the optimal time 2. The system of claim 1.

8. The schedule confirmation unit Taking into account the emotional state of the user, the response is generated at an optimal time.

2. The system of claim 1.

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