system

Abstract

The system according to this embodiment aims to enable users who are not comfortable with telephone communication to smoothly convey information. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit allows the user to input the content they wish to convey by phone in advance as text. The generation unit makes a phone call to a designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The display unit displays the conversation content as text on the application in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for users who are not good at communicating by phone to smoothly convey information.

[0005] The system according to the embodiment aims to enable users who are not good at communicating by phone to smoothly convey information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit allows the user to input the content they wish to convey by phone in advance as text. The generation unit makes a phone call to a designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The display unit displays the conversation content as text on the application in real time. [Effects of the Invention]

[0007] The system according to this embodiment allows information to be conveyed smoothly even to users who are not comfortable communicating by telephone. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The AI ​​phone application "Substitute Call," according to an embodiment of the present invention, is a system that combines generative AI and AI speech recognition technology. This system is a service for people who have difficulty with verbal conversation, such as those with hearing impairments or stuttering. First, the user inputs the content they want to convey by phone as text on the application. Next, the generative AI makes a phone call to the designated number, and the AI ​​and the person on the other end of the line converse based on the entered text. The conversation content is displayed as text on the application in real time, allowing the user to check the progress of the conversation. Furthermore, if the other party asks an unexpected question, the user inputs the answer on the application, and the generative AI speaks the answer to the other party. This mechanism provides an environment in which people with hearing impairments, stuttering, and other difficulties with verbal conversation can use the phone without hassle or stress. It also eliminates the hassle and privacy issues associated with using a third party and enables highly real-time conversations. For example, when a user makes a hospital appointment, they input their symptoms and cause as text in advance, and the generative AI makes a phone call to the hospital to make the appointment. The conversation content is displayed as text on the application in real time, allowing the user to check the progress. For questions from the other party, the user enters their answers in the app, and the AI ​​generates responses to the other party. This allows the user to complete reservations smoothly. In the case of emergency calls, the user enters the situation at the scene as text in advance, and the AI ​​generates the call. The conversation is displayed in real time on the app, allowing the user to check the progress. For questions from the other party, the user enters their answers in the app, and the AI ​​generates responses to the other party. This allows the user to make calls quickly and accurately. As a result, the AI ​​phone app "Substitute Call" can provide an environment where people with disabilities in verbal communication, such as those with hearing impairments or stutters, can use the phone without hassle or stress.

[0029] The AI ​​phone application "Substitute Call" according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit allows the user to input the content they wish to convey by phone in advance as text. The content the user wishes to convey by phone may include, but is not limited to, symptoms and causes for hospital appointments, or on-site situations for reporting to emergency services. The reception unit provides, for example, an interface for the user to input text on the application. The generation unit makes a phone call to a designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The generation unit converts the entered text into speech using, for example, a generation AI and conducts a conversation with the designated recipient. The generation unit generates a conversation based on the entered text when the generation AI receives a prompt such as "Please convey this content by phone." The display unit displays the conversation content as text on the application in real time. The display unit provides, for example, an interface for displaying the conversation content generated by the generation unit as text in real time. The display unit allows the user to check the progress of the conversation. As a result, the AI ​​phone app "Substitute Call" according to this embodiment can allow the user to pre-enter the content they want to convey by phone as text, make a call to a designated recipient, and display the conversation content as text on the app in real time.

[0030] The reception system allows users to pre-enter the information they wish to convey over the phone. This information may include, but is not limited to, symptoms and causes for hospital appointments, or on-site situations for contacting emergency services. The reception system provides an interface for users to input text within the app. Specifically, it provides an input form designed for intuitive operation. This input form is divided into categories such as symptoms, causes, and on-site situations to allow users to easily enter the necessary information. It also includes a preview function and an auto-save function to facilitate checking and correcting entered information. Furthermore, the reception system analyzes the text entered by the user and displays prompts for supplementary information as needed. For example, when entering symptoms for a hospital appointment, prompts for additional information such as symptom details and onset date can be displayed to collect more accurate information. In this way, the reception system supports users in accurately and efficiently entering the information they wish to convey over the phone.

[0031] The generation unit makes a phone call to a designated recipient based on information received by the reception unit and conducts a conversation based on the entered text. For example, the generation unit uses a generation AI to convert the entered text into speech and conduct a conversation with the designated recipient. The generation unit receives a prompt such as "Please convey this information over the phone" and generates a conversation based on the entered text. Specifically, the generation unit uses natural language processing technology to convert the text entered by the user into natural-sounding speech. The generation AI understands the context and nuances of the text and generates speech with appropriate intonation and intonation. Furthermore, the generation unit also has the function to analyze the flow of the conversation with the designated recipient in real time and generate appropriate responses. For example, in a phone call for a hospital appointment, it generates appropriate answers to the caller's questions, enabling a smooth conversation. The generation unit also constantly monitors the progress of the conversation and provides feedback to the user as needed. As a result, the generation unit can conduct a natural conversation with the designated recipient based on the text entered by the user.

[0032] The display unit displays the conversation content as text on the app in real time. For example, the display unit provides an interface for displaying the conversation content generated by the generation unit as text in real time. Specifically, the display unit converts the audio generated by the generation unit into text, making it available for the user to review on the app. The display unit displays the conversation content chronologically so that the user can grasp the progress of the conversation at a glance. The display unit also includes keyword search and highlighting functions to allow the user to easily search the conversation content. Furthermore, the display unit has a function to highlight important parts of the conversation content. For example, in a phone call to make a hospital appointment, important information such as the appointment date and time and the details of the consultation are highlighted, allowing the user to quickly find the information they need. As a result, the display unit allows the user to view the conversation content generated by the generation unit in real time and quickly and accurately grasp the necessary information.

[0033] The input section allows the user to input answers to unexpected questions from the other party. The input section provides, for example, an interface that allows the user to freely input text on the app. The input section can also provide an interface that allows the user to select an answer from a set of options. For example, the input section can have a generating AI prepare pre-written phrases according to the flow of the conversation, and the user can select from these options. This allows the user to input answers to unexpected questions from the other party. Some or all of the above processing in the input section may be performed using AI, or not using AI. For example, the input section can input text data entered by the user into a generating AI, which can then generate an answer.

[0034] The selection unit allows the AI ​​to prepare pre-defined phrases based on the flow of conversation, which the user can then select. For example, the selection unit can use a generative AI to analyze the flow of conversation and generate appropriate pre-defined phrases. For instance, the selection unit can receive a prompt such as "Please select the appropriate answer to this question" and generate a pre-defined phrase. The selection unit can also provide an interface that allows the user to choose a pre-defined phrase from a set of options. For example, the selection unit can display the pre-defined phrases generated by the generative AI as options for the user to select. This allows the AI ​​to prepare pre-defined phrases based on the flow of conversation, which the user can then select. Some or all of the above-described processes in the selection unit may be performed using AI, or they may not. For example, the selection unit can present pre-defined phrases generated by the generative AI to the user, who can then select one.

[0035] The response unit allows the AI ​​to speak and answer to the other party. For example, the response unit can have a generating AI convert user input into speech and speak it to the other party as an answer. For example, the response unit can have a generating AI receive a prompt such as "Please convey this to the other party" and generate speech based on the input text. The response unit can also have a generating AI generate an appropriate answer to the other party's question, convert it into speech, and convey it to the other party. For example, the response unit can have a generating AI analyze the other party's question, generate an appropriate answer, convert it into speech, and convey it to the other party. This allows the AI ​​to speak and answer to the other party. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can convey the speech data generated by the generating AI to the other party.

[0036] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest content that the user will use during specific time periods based on their past input history. For example, the reception desk can predict and suggest content that the user will use during specific time periods based on their past input history. This allows the reception desk to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then suggest the optimal input method.

[0037] The reception desk can filter input content based on the user's current situation and areas of interest when text is entered. For example, if a user is making a hospital appointment, the reception desk will filter the input content based on past medical history and current symptoms. For example, if a user is making a call to an emergency service, the reception desk will filter the input content based on the situation and urgency of the situation. The reception desk can also filter input content based on past transaction history and current areas of interest when a user is making a business inquiry. For example, if a user is making a business inquiry, the reception desk will filter the input content based on past transaction history and current areas of interest. This allows the reception desk to filter input content based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's current situation and areas of interest data into a generating AI, which can then filter the input content.

[0038] The reception desk can prioritize accepting highly relevant input content when a user is entering text, taking into account their geographical location. For example, if a user is making a hospital appointment, the reception desk will prioritize suggesting the hospital closest to their current location. For example, if a user is making a call to an emergency service, the reception desk will suggest the most appropriate emergency service based on their current location. The reception desk can also suggest the most relevant business information based on the user's current location when a user is making a business inquiry. For example, if a user is making a business inquiry, the reception desk will suggest the most relevant business information based on their current location. This allows the reception desk to prioritize accepting highly relevant input content, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI, which can then prioritize accepting highly relevant input content.

[0039] The reception desk can analyze the user's social media activity when text is entered and suggest relevant input content. For example, if a user makes a hospital appointment, the reception desk can analyze health-related posts on social media and suggest relevant input content. For example, if a user makes a call to an emergency service, the reception desk can analyze emergency-related posts on social media and suggest relevant input content. The reception desk can also analyze business-related posts on social media and suggest relevant input content when a user makes a business inquiry. For example, if a user makes a business inquiry, the reception desk can analyze business-related posts on social media and suggest relevant input content. This allows the reception desk to analyze the user's social media activity and suggest relevant input content. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then suggest relevant input content.

[0040] The generation unit can generate optimal conversation content by referring to the past response history of the specified recipient when generating a conversation. For example, if the specified recipient is a hospital, the generation unit can generate optimal conversation content by referring to past reservation history. For example, if the specified recipient is an emergency agency, the generation unit can generate optimal conversation content by referring to past notification history. The generation unit can also generate optimal conversation content by referring to past inquiry history if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit can generate optimal conversation content by referring to past inquiry history. This allows the generation unit to generate optimal conversation content by referring to the past response history of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past response history data of the specified recipient into a generation AI, and the generation AI can generate optimal conversation content.

[0041] The generation unit can apply different generation algorithms depending on the industry and service content of the specified recipient when generating conversations. For example, if the specified recipient is a hospital, the generation unit will apply a medical-related generation algorithm to generate conversation content. For example, if the specified recipient is an emergency organization, the generation unit will apply an emergency response-related generation algorithm to generate conversation content. The generation unit can also apply a business-related generation algorithm if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit will apply a business-related generation algorithm to generate conversation content. This allows for the application of different generation algorithms depending on the industry and service content of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the specified recipient's industry and service content into a generation AI, and the generation AI can apply different generation algorithms to generate conversation content.

[0042] The generation unit can determine the priority of conversations based on the response time of the specified recipient when generating conversations. For example, if the specified recipient is a hospital, the generation unit can determine the priority of appointments based on the response time. For example, if the specified recipient is an emergency agency, the generation unit can determine the priority of notifications based on the response time. The generation unit can also determine the priority of inquiries based on the response time if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit can determine the priority of inquiries based on the response time. This allows the generation unit to determine the priority of conversations based on the response time of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the response time data of the specified recipient into a generation AI, and the generation AI can determine the priority of conversations.

[0043] The generation unit can optimize conversation content by referring to relevant literature for the specified destination when generating conversations. For example, if the destination is a hospital, the generation unit can optimize conversation content by referring to medical-related literature. For example, if the destination is an emergency organization, the generation unit can optimize conversation content by referring to emergency response-related literature. The generation unit can also optimize conversation content by referring to business-related literature if the destination is a business. For example, if the destination is a business, the generation unit can optimize conversation content by referring to business-related literature. This allows for the optimization of conversation content by referring to relevant literature for the specified destination. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevant literature data for the specified destination into the generation AI, and the generation AI can optimize the conversation content.

[0044] The display unit can select the optimal display method by referring to the user's past browsing history when displaying information. For example, the display unit can select the optimal display method based on information that the user has frequently viewed in the past. For example, the display unit can prioritize displaying highly relevant information from the user's past browsing history. The display unit can also analyze the user's past browsing history and select the most efficient display method. For example, the display unit can analyze the user's past browsing history and select the most efficient display method. This allows the display unit to select the optimal display method by referring to the user's past browsing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past browsing history data into a generating AI, which can then select the optimal display method.

[0045] The display unit can customize the displayed content based on the user's device information when displaying information. For example, if the user is using a smartphone, the display unit provides content that is adapted to the screen size. For example, if the user is using a tablet, the display unit provides content optimized for a larger screen. The display unit can also provide concise and highly visible content if the user is using a smartwatch. For example, if the user is using a smartwatch, the display unit provides concise and highly visible content. This allows the display content to be customized based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI, which can then customize the displayed content.

[0046] The display unit can prioritize displaying highly relevant content by considering the user's geographical location information. For example, if a user makes a hospital reservation, the display unit will prioritize displaying information about the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the display unit will display information about the most suitable emergency service based on the user's current location. The display unit can also display the most relevant business information based on the user's current location when a user makes a business inquiry. For example, if a user makes a business inquiry, the display unit will display the most relevant business information based on the user's current location. This allows the display unit to prioritize displaying highly relevant content by considering the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information data into a generating AI, which can then prioritize displaying highly relevant content.

[0047] The display unit can analyze the user's social media activity and suggest relevant display content when displaying information. For example, if a user makes a hospital appointment, the display unit can analyze health-related posts on social media and suggest relevant display content. For example, if a user makes a call to an emergency service, the display unit can analyze emergency-related posts on social media and suggest relevant display content. The display unit can also analyze business-related posts on social media and suggest relevant display content when a user makes a business inquiry. For example, if a user makes a business inquiry, the display unit can analyze business-related posts on social media and suggest relevant display content. This allows the display unit to analyze the user's social media activity and suggest relevant display content. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI, which can then suggest relevant display content.

[0048] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as suggestions content that the user has frequently entered in the past. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest content that the user will use during specific time periods based on their past input history. For example, the input unit can predict and suggest content that the user will use during specific time periods based on their past input history. This allows the input unit to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history data into a generating AI, which can then suggest the optimal input method.

[0049] The input unit can prioritize accepting highly relevant input content while considering the user's geographical location information. For example, if a user makes a hospital reservation, the input unit will prioritize suggesting the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the input unit will suggest the most appropriate emergency service based on the user's current location. The input unit can also suggest the most relevant business information based on the user's current location when the user makes a business inquiry. For example, if a user makes a business inquiry, the input unit will suggest the most relevant business information based on the user's current location. This allows the input unit to prioritize accepting highly relevant input content while considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI, which can then prioritize accepting highly relevant input content.

[0050] The selection unit can suggest the optimal option by referring to the user's past selection history when a selection is made. For example, the selection unit suggests the optimal option based on what the user has frequently selected in the past. For example, the selection unit prioritizes displaying highly relevant options from the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the most efficient option. For example, the selection unit analyzes the user's past selection history and suggests the most efficient option. This allows the selection unit to suggest the optimal option by referring to the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection history data into a generating AI, which can then suggest the optimal option.

[0051] The selection unit can prioritize displaying the most relevant options when a user makes a selection, taking into account the user's geographical location. For example, if a user makes a hospital appointment, the selection unit will prioritize displaying the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the selection unit will display the most suitable emergency service options based on the user's current location. The selection unit can also display the most relevant business information options based on the user's current location when a user makes a business inquiry. For example, if a user makes a business inquiry, the selection unit will display the most relevant business information options based on the user's current location. This allows the selection unit to prioritize displaying the most relevant options, taking into account the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location data into a generating AI, which can then prioritize displaying the most relevant options.

[0052] The response unit can generate the optimal response content by referring to the specified recipient's past response history when responding. For example, if the specified recipient is a hospital, the response unit can generate the optimal response content by referring to past reservation history. For example, if the specified recipient is an emergency agency, the response unit can generate the optimal response content by referring to past notification history. The response unit can also generate the optimal response content by referring to past inquiry history if the specified recipient is a business. For example, if the specified recipient is a business, the response unit can generate the optimal response content by referring to past inquiry history. This allows the response unit to generate the optimal response content by referring to the specified recipient's past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the specified recipient's past response history data into a generating AI, and the generating AI can generate the optimal response content.

[0053] The response unit can customize the response content based on the industry and service content of the specified recipient. For example, if the specified recipient is a hospital, the response unit will generate medical-related response content. For example, if the specified recipient is an emergency agency, the response unit will generate emergency response-related response content. The response unit can also generate business-related response content if the specified recipient is a business. For example, if the specified recipient is a business, the response unit will generate business-related response content. This allows the response content to be customized based on the industry and service content of the specified recipient. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on the specified recipient's industry and service content into a generating AI, which can then customize the response content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze user input and suggest appropriate templates based on that input. For example, if a user makes a hospital appointment, the reception desk can suggest an appropriate template based on the symptoms and cause. If a user makes a call to an emergency service, the reception desk can suggest an appropriate template based on the situation at the scene. Furthermore, if a user makes a business inquiry, the reception desk can suggest an appropriate template based on the content of the inquiry. This allows users to create input quickly and accurately. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input user input data into a generating AI, which can then suggest an appropriate template.

[0056] The generation unit can pre-generate conversation scenarios based on user input. For example, if a user makes a hospital appointment, the generation unit generates a conversation scenario based on the symptoms and cause. If a user makes a call to an emergency service, the generation unit generates a conversation scenario based on the situation at the scene. Furthermore, if a user makes a business inquiry, the generation unit can also generate a conversation scenario based on the content of the inquiry. In this way, the generation unit can pre-generate conversation scenarios based on user input. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input user input data into a generation AI, which can then generate a conversation scenario.

[0057] The response unit can pre-generate response content based on user input. For example, when a user makes a hospital appointment, the response unit generates a response based on the symptoms and cause. When a user makes a call to an emergency service, the response unit generates a response based on the situation at the scene. Furthermore, when a user makes a business inquiry, the response unit can also generate a response based on the inquiry. In this way, the response unit can pre-generate response content based on user input. Some or all of the above-described processes in the response unit may be performed using AI, or not. For example, the response unit can input user input data into a generating AI, which can then generate the response content.

[0058] The generation unit can adjust the tone of the conversation based on the user's input. For example, if the user is making a hospital appointment, the generation unit will speak in a calm tone based on the symptoms and cause. If the user is making a call to an emergency service, the generation unit will speak in a quick and concise tone based on the situation at the scene. Also, if the user is making a business inquiry, the generation unit can speak in a cheerful tone based on the content of the inquiry. In this way, the generation unit can adjust the tone of the conversation based on the user's input. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's input data into a generation AI, which can then adjust the tone of the conversation.

[0059] The response unit can determine the priority of responses based on the user's input. For example, when a user makes a hospital appointment, the response unit prioritizes generating important responses based on the symptoms and cause. When a user makes a call to an emergency service, the response unit prioritizes generating the most important responses based on the situation at the scene. Furthermore, when a user makes a business inquiry, the response unit can also prioritize generating important responses based on the inquiry. In this way, the response unit can determine the priority of responses based on the user's input. Some or all of the above processing in the response unit may be performed using AI, or not. For example, the response unit can input user input data into a generating AI, which can then determine the priority of responses.

[0060] The generation unit can adjust the length of the conversation based on the user's input. For example, if a user makes a hospital appointment, the generation unit will have a short, concise conversation based on the symptoms and cause. If a user makes a call to an emergency service, the generation unit will have a quick and concise conversation based on the situation at the scene. If a user makes a business inquiry, the generation unit can also have a longer conversation that includes detailed explanations based on the inquiry. In this way, the generation unit can adjust the length of the conversation based on the user's input. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's input data into a generation AI, which can then adjust the length of the conversation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk pre-enters the information the user wants to convey over the phone. The app provides an interface for users to enter text, allowing them to input information such as symptoms and causes for hospital appointments, or the situation at the scene for contacting emergency services. Step 2: The generation unit makes a phone call to the designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The generation unit uses a generation AI to convert the entered text into speech and conducts a conversation with the designated recipient. The generation AI receives a prompt such as "Please convey this information over the phone" and generates a conversation based on the entered text. Step 3: The display unit displays the conversation content as text on the app in real time. It provides an interface that displays the conversation content generated by the generation unit as text in real time, allowing the user to check the progress of the conversation.

[0063] (Example of form 2) The AI ​​phone application "Substitute Call," according to an embodiment of the present invention, is a system that combines generative AI and AI speech recognition technology. This system is a service for people who have difficulty with verbal conversation, such as those with hearing impairments or stuttering. First, the user inputs the content they want to convey by phone as text on the application. Next, the generative AI makes a phone call to the designated number, and the AI ​​and the person on the other end of the line converse based on the entered text. The conversation content is displayed as text on the application in real time, allowing the user to check the progress of the conversation. Furthermore, if the other party asks an unexpected question, the user inputs the answer on the application, and the generative AI speaks the answer to the other party. This mechanism provides an environment in which people with hearing impairments, stuttering, and other difficulties with verbal conversation can use the phone without hassle or stress. It also eliminates the hassle and privacy issues associated with using a third party and enables highly real-time conversations. For example, when a user makes a hospital appointment, they input their symptoms and cause as text in advance, and the generative AI makes a phone call to the hospital to make the appointment. The conversation content is displayed as text on the application in real time, allowing the user to check the progress. For questions from the other party, the user enters their answers in the app, and the AI ​​generates responses to the other party. This allows the user to complete reservations smoothly. In the case of emergency calls, the user enters the situation at the scene as text in advance, and the AI ​​generates the call. The conversation is displayed in real time on the app, allowing the user to check the progress. For questions from the other party, the user enters their answers in the app, and the AI ​​generates responses to the other party. This allows the user to make calls quickly and accurately. As a result, the AI ​​phone app "Substitute Call" can provide an environment where people with disabilities in verbal communication, such as those with hearing impairments or stutters, can use the phone without hassle or stress.

[0064] The AI ​​phone application "Substitute Call" according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit allows the user to input the content they wish to convey by phone in advance as text. The content the user wishes to convey by phone may include, but is not limited to, symptoms and causes for hospital appointments, or on-site situations for reporting to emergency services. The reception unit provides, for example, an interface for the user to input text on the application. The generation unit makes a phone call to a designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The generation unit converts the entered text into speech using, for example, a generation AI and conducts a conversation with the designated recipient. The generation unit generates a conversation based on the entered text when the generation AI receives a prompt such as "Please convey this content by phone." The display unit displays the conversation content as text on the application in real time. The display unit provides, for example, an interface for displaying the conversation content generated by the generation unit as text in real time. The display unit allows the user to check the progress of the conversation. As a result, the AI ​​phone app "Substitute Call" according to this embodiment can allow the user to pre-enter the content they want to convey by phone as text, make a call to a designated recipient, and display the conversation content as text on the app in real time.

[0065] The reception system allows users to pre-enter the information they wish to convey over the phone. This information may include, but is not limited to, symptoms and causes for hospital appointments, or on-site situations for contacting emergency services. The reception system provides an interface for users to input text within the app. Specifically, it provides an input form designed for intuitive operation. This input form is divided into categories such as symptoms, causes, and on-site situations to allow users to easily enter the necessary information. It also includes a preview function and an auto-save function to facilitate checking and correcting entered information. Furthermore, the reception system analyzes the text entered by the user and displays prompts for supplementary information as needed. For example, when entering symptoms for a hospital appointment, prompts for additional information such as symptom details and onset date can be displayed to collect more accurate information. In this way, the reception system supports users in accurately and efficiently entering the information they wish to convey over the phone.

[0066] The generation unit makes a phone call to a designated recipient based on information received by the reception unit and conducts a conversation based on the entered text. For example, the generation unit uses a generation AI to convert the entered text into speech and conduct a conversation with the designated recipient. The generation unit receives a prompt such as "Please convey this information over the phone" and generates a conversation based on the entered text. Specifically, the generation unit uses natural language processing technology to convert the text entered by the user into natural-sounding speech. The generation AI understands the context and nuances of the text and generates speech with appropriate intonation and intonation. Furthermore, the generation unit also has the function to analyze the flow of the conversation with the designated recipient in real time and generate appropriate responses. For example, in a phone call for a hospital appointment, it generates appropriate answers to the caller's questions, enabling a smooth conversation. The generation unit also constantly monitors the progress of the conversation and provides feedback to the user as needed. As a result, the generation unit can conduct a natural conversation with the designated recipient based on the text entered by the user.

[0067] The display unit displays the conversation content as text on the app in real time. For example, the display unit provides an interface for displaying the conversation content generated by the generation unit as text in real time. Specifically, the display unit converts the audio generated by the generation unit into text, making it available for the user to review on the app. The display unit displays the conversation content chronologically so that the user can grasp the progress of the conversation at a glance. The display unit also includes keyword search and highlighting functions to allow the user to easily search the conversation content. Furthermore, the display unit has a function to highlight important parts of the conversation content. For example, in a phone call to make a hospital appointment, important information such as the appointment date and time and the details of the consultation are highlighted, allowing the user to quickly find the information they need. As a result, the display unit allows the user to view the conversation content generated by the generation unit in real time and quickly and accurately grasp the necessary information.

[0068] The input section allows the user to input answers to unexpected questions from the other party. The input section provides, for example, an interface that allows the user to freely input text on the app. The input section can also provide an interface that allows the user to select an answer from a set of options. For example, the input section can have a generating AI prepare pre-written phrases according to the flow of the conversation, and the user can select from these options. This allows the user to input answers to unexpected questions from the other party. Some or all of the above processing in the input section may be performed using AI, or not using AI. For example, the input section can input text data entered by the user into a generating AI, which can then generate an answer.

[0069] The selection unit allows the AI ​​to prepare pre-defined phrases based on the flow of conversation, which the user can then select. For example, the selection unit can use a generative AI to analyze the flow of conversation and generate appropriate pre-defined phrases. For instance, the selection unit can receive a prompt such as "Please select the appropriate answer to this question" and generate a pre-defined phrase. The selection unit can also provide an interface that allows the user to choose a pre-defined phrase from a set of options. For example, the selection unit can display the pre-defined phrases generated by the generative AI as options for the user to select. This allows the AI ​​to prepare pre-defined phrases based on the flow of conversation, which the user can then select. Some or all of the above-described processes in the selection unit may be performed using AI, or they may not. For example, the selection unit can present pre-defined phrases generated by the generative AI to the user, who can then select one.

[0070] The response unit allows the AI ​​to speak and answer to the other party. For example, the response unit can have a generating AI convert user input into speech and speak it to the other party as an answer. For example, the response unit can have a generating AI receive a prompt such as "Please convey this to the other party" and generate speech based on the input text. The response unit can also have a generating AI generate an appropriate answer to the other party's question, convert it into speech, and convey it to the other party. For example, the response unit can have a generating AI analyze the other party's question, generate an appropriate answer, convert it into speech, and convey it to the other party. This allows the AI ​​to speak and answer to the other party. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can convey the speech data generated by the generating AI to the other party.

[0071] The reception desk can estimate the user's emotions and adjust the text input interface based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input to allow for quick text input if the user is in a hurry. For example, if the reception desk prioritizes voice input to allow for quick text input if the user is in a hurry, the reception desk can prioritize voice input to allow for quick text input. This allows the text input interface to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and adjust the interface.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions content that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest content that the user will use during specific time periods based on their past input history. For example, the reception desk can predict and suggest content that the user will use during specific time periods based on their past input history. This allows the reception desk to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI, which can then suggest the optimal input method.

[0073] The reception desk can filter input content based on the user's current situation and areas of interest when text is entered. For example, if a user is making a hospital appointment, the reception desk will filter the input content based on past medical history and current symptoms. For example, if a user is making a call to an emergency service, the reception desk will filter the input content based on the situation and urgency of the situation. The reception desk can also filter input content based on past transaction history and current areas of interest when a user is making a business inquiry. For example, if a user is making a business inquiry, the reception desk will filter the input content based on past transaction history and current areas of interest. This allows the reception desk to filter input content based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's current situation and areas of interest data into a generating AI, which can then filter the input content.

[0074] The reception desk can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is nervous, the reception desk may prompt them to prioritize entering important information. For example, if the user is relaxed, the reception desk may prompt them to enter detailed information. The reception desk may also prompt the user to prioritize entering the most important information if they are in a hurry. For example, if the reception desk is in a hurry, the reception desk may prompt them to prioritize entering the most important information. This allows the system to prioritize input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotions and determine the priority of the input.

[0075] The reception desk can prioritize accepting highly relevant input content when a user is entering text, taking into account their geographical location. For example, if a user is making a hospital appointment, the reception desk will prioritize suggesting the hospital closest to their current location. For example, if a user is making a call to an emergency service, the reception desk will suggest the most appropriate emergency service based on their current location. The reception desk can also suggest the most relevant business information based on the user's current location when a user is making a business inquiry. For example, if a user is making a business inquiry, the reception desk will suggest the most relevant business information based on their current location. This allows the reception desk to prioritize accepting highly relevant input content, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI, which can then prioritize accepting highly relevant input content.

[0076] The reception desk can analyze the user's social media activity when text is entered and suggest relevant input content. For example, if a user makes a hospital appointment, the reception desk can analyze health-related posts on social media and suggest relevant input content. For example, if a user makes a call to an emergency service, the reception desk can analyze emergency-related posts on social media and suggest relevant input content. The reception desk can also analyze business-related posts on social media and suggest relevant input content when a user makes a business inquiry. For example, if a user makes a business inquiry, the reception desk can analyze business-related posts on social media and suggest relevant input content. This allows the reception desk to analyze the user's social media activity and suggest relevant input content. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then suggest relevant input content.

[0077] The generation unit can estimate the user's emotions and adjust the tone of the generated conversation based on the estimated emotions. For example, if the user is nervous, the generation AI can speak in a calm tone. For example, if the user is relaxed, the generation AI can speak in a cheerful tone. The generation unit can also speak in a quick and concise tone if the user is in a hurry. For example, if the user is in a hurry, the generation AI can speak in a quick and concise tone. This allows the tone of the generated conversation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can estimate the emotions and adjust the tone of the conversation.

[0078] The generation unit can generate optimal conversation content by referring to the past response history of the specified recipient when generating a conversation. For example, if the specified recipient is a hospital, the generation unit can generate optimal conversation content by referring to past reservation history. For example, if the specified recipient is an emergency agency, the generation unit can generate optimal conversation content by referring to past notification history. The generation unit can also generate optimal conversation content by referring to past inquiry history if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit can generate optimal conversation content by referring to past inquiry history. This allows the generation unit to generate optimal conversation content by referring to the past response history of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past response history data of the specified recipient into a generation AI, and the generation AI can generate optimal conversation content.

[0079] The generation unit can apply different generation algorithms depending on the industry and service content of the specified recipient when generating conversations. For example, if the specified recipient is a hospital, the generation unit will apply a medical-related generation algorithm to generate conversation content. For example, if the specified recipient is an emergency organization, the generation unit will apply an emergency response-related generation algorithm to generate conversation content. The generation unit can also apply a business-related generation algorithm if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit will apply a business-related generation algorithm to generate conversation content. This allows for the application of different generation algorithms depending on the industry and service content of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the specified recipient's industry and service content into a generation AI, and the generation AI can apply different generation algorithms to generate conversation content.

[0080] The generation unit can estimate the user's emotions and adjust the length of the generated conversation based on the estimated emotions. For example, if the user is nervous, the generation AI can produce a short, to-the-point conversation. For example, if the user is relaxed, the generation AI can produce a longer conversation with more detailed explanations. The generation unit can also produce a quick and concise conversation if the user is in a hurry. For example, if the user is in a hurry, the generation AI can produce a quick and concise conversation. This allows the length of the generated conversation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the length of the conversation.

[0081] The generation unit can determine the priority of conversations based on the response time of the specified recipient when generating conversations. For example, if the specified recipient is a hospital, the generation unit can determine the priority of appointments based on the response time. For example, if the specified recipient is an emergency agency, the generation unit can determine the priority of notifications based on the response time. The generation unit can also determine the priority of inquiries based on the response time if the specified recipient is a business. For example, if the specified recipient is a business, the generation unit can determine the priority of inquiries based on the response time. This allows the generation unit to determine the priority of conversations based on the response time of the specified recipient. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the response time data of the specified recipient into a generation AI, and the generation AI can determine the priority of conversations.

[0082] The generation unit can optimize conversation content by referring to relevant literature for the specified destination when generating conversations. For example, if the destination is a hospital, the generation unit can optimize conversation content by referring to medical-related literature. For example, if the destination is an emergency organization, the generation unit can optimize conversation content by referring to emergency response-related literature. The generation unit can also optimize conversation content by referring to business-related literature if the destination is a business. For example, if the destination is a business, the generation unit can optimize conversation content by referring to business-related literature. This allows for the optimization of conversation content by referring to relevant literature for the specified destination. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevant literature data for the specified destination into the generation AI, and the generation AI can optimize the conversation content.

[0083] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the user is in a hurry. For example, if the display unit provides a concise display method if the user is in a hurry, the display unit provides a concise display method. This allows the display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the display method.

[0084] The display unit can select the optimal display method by referring to the user's past browsing history when displaying information. For example, the display unit can select the optimal display method based on information that the user has frequently viewed in the past. For example, the display unit can prioritize displaying highly relevant information from the user's past browsing history. The display unit can also analyze the user's past browsing history and select the most efficient display method. For example, the display unit can analyze the user's past browsing history and select the most efficient display method. This allows the display unit to select the optimal display method by referring to the user's past browsing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past browsing history data into a generating AI, which can then select the optimal display method.

[0085] The display unit can customize the displayed content based on the user's device information when displaying information. For example, if the user is using a smartphone, the display unit provides content that is adapted to the screen size. For example, if the user is using a tablet, the display unit provides content optimized for a larger screen. The display unit can also provide concise and highly visible content if the user is using a smartwatch. For example, if the user is using a smartwatch, the display unit provides concise and highly visible content. This allows the display content to be customized based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI, which can then customize the displayed content.

[0086] The display unit can estimate the user's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the user is tense, the display unit will prioritize displaying important information. For example, if the user is relaxed, the display unit will display detailed information. The display unit can also prioritize displaying the most important information if the user is in a hurry. For example, if the user is in a hurry, the display unit will prioritize displaying the most important information. This allows the display unit to determine the priority of the displayed content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the displayed content.

[0087] The display unit can prioritize displaying highly relevant content by considering the user's geographical location information. For example, if a user makes a hospital reservation, the display unit will prioritize displaying information about the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the display unit will display information about the most suitable emergency service based on the user's current location. The display unit can also display the most relevant business information based on the user's current location when a user makes a business inquiry. For example, if a user makes a business inquiry, the display unit will display the most relevant business information based on the user's current location. This allows the display unit to prioritize displaying highly relevant content by considering the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information data into a generating AI, which can then prioritize displaying highly relevant content.

[0088] The display unit can analyze the user's social media activity and suggest relevant display content when displaying information. For example, if a user makes a hospital appointment, the display unit can analyze health-related posts on social media and suggest relevant display content. For example, if a user makes a call to an emergency service, the display unit can analyze emergency-related posts on social media and suggest relevant display content. The display unit can also analyze business-related posts on social media and suggest relevant display content when a user makes a business inquiry. For example, if a user makes a business inquiry, the display unit can analyze business-related posts on social media and suggest relevant display content. This allows the display unit to analyze the user's social media activity and suggest relevant display content. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI, which can then suggest relevant display content.

[0089] The input unit can estimate the user's emotions and adjust the input interface based on the estimated emotions. For example, if the user is nervous, the input unit can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. The input unit can also prioritize voice input and allow for quick text input if the user is in a hurry. For example, if the input unit prioritizes voice input and allows for quick text input if the user is in a hurry, the input unit can prioritize voice input and allow for quick text input. This allows the input interface to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not using AI. For example, the input unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the input interface.

[0090] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as suggestions content that the user has frequently entered in the past. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest content that the user will use during specific time periods based on their past input history. For example, the input unit can predict and suggest content that the user will use during specific time periods based on their past input history. This allows the input unit to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history data into a generating AI, which can then suggest the optimal input method.

[0091] The input unit can estimate the user's emotions and determine the priority of input content based on the estimated emotions. For example, if the user is nervous, the input unit may prompt the user to prioritize entering important information. For example, if the user is relaxed, the input unit may prompt the user to enter detailed information. The input unit can also prompt the user to prioritize entering the most important information if the user is in a hurry. For example, if the input unit is in a hurry, the input unit may prompt the user to prioritize entering the most important information. This allows the system to determine the priority of input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of input content.

[0092] The input unit can prioritize accepting highly relevant input content while considering the user's geographical location information. For example, if a user makes a hospital reservation, the input unit will prioritize suggesting the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the input unit will suggest the most appropriate emergency service based on the user's current location. The input unit can also suggest the most relevant business information based on the user's current location when the user makes a business inquiry. For example, if a user makes a business inquiry, the input unit will suggest the most relevant business information based on the user's current location. This allows the input unit to prioritize accepting highly relevant input content while considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI, which can then prioritize accepting highly relevant input content.

[0093] The selection unit can estimate the user's emotions and adjust how the options are displayed based on the estimated emotions. For example, if the user is nervous, the selection unit can provide simple and easily visible options. For example, if the user is relaxed, the selection unit can provide options containing detailed information. The selection unit can also provide concise options if the user is in a hurry. For example, if the selection unit is in a hurry, the selection unit can provide concise options. This allows the display of options to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust how the options are displayed.

[0094] The selection unit can suggest the optimal option by referring to the user's past selection history when a selection is made. For example, the selection unit suggests the optimal option based on what the user has frequently selected in the past. For example, the selection unit prioritizes displaying highly relevant options from the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the most efficient option. For example, the selection unit analyzes the user's past selection history and suggests the most efficient option. This allows the selection unit to suggest the optimal option by referring to the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection history data into a generating AI, which can then suggest the optimal option.

[0095] The selection unit can estimate the user's emotions and determine the priority of options based on the estimated emotions. For example, if the user is nervous, the selection unit will prioritize displaying important options. For example, if the user is relaxed, the selection unit will display detailed options. Also, if the user is in a hurry, the selection unit can prioritize displaying the most important options. For example, if the user is in a hurry, the selection unit will prioritize displaying the most important options. This allows the system to determine the priority of options based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of options.

[0096] The selection unit can prioritize displaying the most relevant options when a user makes a selection, taking into account the user's geographical location. For example, if a user makes a hospital appointment, the selection unit will prioritize displaying the hospital closest to the user's current location. For example, if a user makes a call to an emergency service, the selection unit will display the most suitable emergency service options based on the user's current location. The selection unit can also display the most relevant business information options based on the user's current location when a user makes a business inquiry. For example, if a user makes a business inquiry, the selection unit will display the most relevant business information options based on the user's current location. This allows the selection unit to prioritize displaying the most relevant options, taking into account the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location data into a generating AI, which can then prioritize displaying the most relevant options.

[0097] The response unit can estimate the user's emotions and adjust the response based on the estimated emotions. For example, if the user is tense, the generating AI will respond in a calm tone. For example, if the user is relaxed, the generating AI will respond in a cheerful tone. The response unit can also respond in a quick and concise tone if the user is in a hurry. For example, if the user is in a hurry, the generating AI will respond in a quick and concise tone. This allows the response to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into the generating AI, which can estimate the emotions and adjust the response.

[0098] The response unit can generate the optimal response content by referring to the specified recipient's past response history when responding. For example, if the specified recipient is a hospital, the response unit can generate the optimal response content by referring to past reservation history. For example, if the specified recipient is an emergency agency, the response unit can generate the optimal response content by referring to past notification history. The response unit can also generate the optimal response content by referring to past inquiry history if the specified recipient is a business. For example, if the specified recipient is a business, the response unit can generate the optimal response content by referring to past inquiry history. This allows the response unit to generate the optimal response content by referring to the specified recipient's past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the specified recipient's past response history data into a generating AI, and the generating AI can generate the optimal response content.

[0099] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is nervous, the response unit will prioritize generating important responses. For example, if the user is relaxed, the response unit will generate detailed responses. The response unit can also prioritize generating the most important responses if the user is in a hurry. For example, if the user is in a hurry, the response unit will prioritize generating the most important responses. This allows the response unit to determine the priority of responses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of responses.

[0100] The response unit can customize the response content based on the industry and service content of the specified recipient. For example, if the specified recipient is a hospital, the response unit will generate medical-related response content. For example, if the specified recipient is an emergency agency, the response unit will generate emergency response-related response content. The response unit can also generate business-related response content if the specified recipient is a business. For example, if the specified recipient is a business, the response unit will generate business-related response content. This allows the response content to be customized based on the industry and service content of the specified recipient. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on the specified recipient's industry and service content into a generating AI, which can then customize the response content.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can analyze user input and suggest appropriate templates based on that input. For example, if a user makes a hospital appointment, the reception desk can suggest an appropriate template based on the symptoms and cause. If a user makes a call to an emergency service, the reception desk can suggest an appropriate template based on the situation at the scene. Furthermore, if a user makes a business inquiry, the reception desk can suggest an appropriate template based on the content of the inquiry. This allows users to create input quickly and accurately. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input user input data into a generating AI, which can then suggest an appropriate template.

[0103] The generation unit can pre-generate conversation scenarios based on user input. For example, if a user makes a hospital appointment, the generation unit generates a conversation scenario based on the symptoms and cause. If a user makes a call to an emergency service, the generation unit generates a conversation scenario based on the situation at the scene. Furthermore, if a user makes a business inquiry, the generation unit can also generate a conversation scenario based on the content of the inquiry. In this way, the generation unit can pre-generate conversation scenarios based on user input. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input user input data into a generation AI, which can then generate a conversation scenario.

[0104] The display unit can estimate the user's emotions and adjust the font size and color of the displayed content based on the estimated emotions. For example, if the user is tense, the display unit provides a font size and color that is easy to read. If the user is relaxed, the display unit provides a font size and color that includes detailed information. If the user is in a hurry, the display unit can also provide a font size and color that highlights the main points. This allows the font size and color of the displayed content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the font size and color of the displayed content.

[0105] The response unit can pre-generate response content based on user input. For example, when a user makes a hospital appointment, the response unit generates a response based on the symptoms and cause. When a user makes a call to an emergency service, the response unit generates a response based on the situation at the scene. Furthermore, when a user makes a business inquiry, the response unit can also generate a response based on the inquiry. In this way, the response unit can pre-generate response content based on user input. Some or all of the above-described processes in the response unit may be performed using AI, or not. For example, the response unit can input user input data into a generating AI, which can then generate the response content.

[0106] The reception desk can estimate the user's emotions and prompt them to confirm their input based on those emotions. For example, if the user is nervous, the reception desk may prompt them to confirm important information. If the user is relaxed, the reception desk may prompt them to confirm detailed information. Also, if the user is in a hurry, the reception desk may prompt them to confirm the most important information. In this way, the reception desk can prompt the user to confirm their input based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotion and prompt the user to confirm their input.

[0107] The generation unit can adjust the tone of the conversation based on the user's input. For example, if the user is making a hospital appointment, the generation unit will speak in a calm tone based on the symptoms and cause. If the user is making a call to an emergency service, the generation unit will speak in a quick and concise tone based on the situation at the scene. Also, if the user is making a business inquiry, the generation unit can speak in a cheerful tone based on the content of the inquiry. In this way, the generation unit can adjust the tone of the conversation based on the user's input. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's input data into a generation AI, which can then adjust the tone of the conversation.

[0108] The display unit can estimate the user's emotions and adjust the layout of the displayed content based on the estimated emotions. For example, if the user is nervous, the display unit provides a simple and highly visible layout. If the user is relaxed, the display unit provides a layout that includes detailed information. Also, if the user is in a hurry, the display unit can provide a layout that gets straight to the point. In this way, the layout of the displayed content can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the layout of the displayed content.

[0109] The response unit can determine the priority of responses based on the user's input. For example, when a user makes a hospital appointment, the response unit prioritizes generating important responses based on the symptoms and cause. When a user makes a call to an emergency service, the response unit prioritizes generating the most important responses based on the situation at the scene. Furthermore, when a user makes a business inquiry, the response unit can also prioritize generating important responses based on the inquiry. In this way, the response unit can determine the priority of responses based on the user's input. Some or all of the above processing in the response unit may be performed using AI, or not. For example, the response unit can input user input data into a generating AI, which can then determine the priority of responses.

[0110] The reception desk can estimate the user's emotions and provide feedback on the input based on the estimated emotions. For example, if the user is nervous, the reception desk will provide concise and clear feedback. If the user is relaxed, the reception desk will provide detailed feedback. Also, if the user is in a hurry, the reception desk can provide quick and to-the-point feedback. This allows for the provision of feedback on the input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which will estimate the emotions and provide feedback on the input.

[0111] The generation unit can adjust the length of the conversation based on the user's input. For example, if a user makes a hospital appointment, the generation unit will have a short, concise conversation based on the symptoms and cause. If a user makes a call to an emergency service, the generation unit will have a quick and concise conversation based on the situation at the scene. If a user makes a business inquiry, the generation unit can also have a longer conversation that includes detailed explanations based on the inquiry. In this way, the generation unit can adjust the length of the conversation based on the user's input. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's input data into a generation AI, which can then adjust the length of the conversation.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk pre-enters the information the user wants to convey over the phone. The app provides an interface for users to enter text, allowing them to input information such as symptoms and causes for hospital appointments, or the situation at the scene for contacting emergency services. Step 2: The generation unit makes a phone call to the designated recipient based on the information received by the reception unit and conducts a conversation based on the entered text. The generation unit uses a generation AI to convert the entered text into speech and conducts a conversation with the designated recipient. The generation AI receives a prompt such as "Please convey this information over the phone" and generates a conversation based on the entered text. Step 3: The display unit displays the conversation content as text on the app in real time. It provides an interface that displays the conversation content generated by the generation unit as text in real time, allowing the user to check the progress of the conversation.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input text on the application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to convert the input text into speech and conduct a conversation with the designated party. The display unit is implemented by the control unit 46A of the smart device 14 and provides an interface to display the conversation content generated by the generation unit as text in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input text on the application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to convert the input text into speech and conduct a conversation with the designated person. The display unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface to display the conversation content generated by the generation unit as text in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input text on the application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to convert the input text into speech and conduct a conversation with the designated party. The display unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface to display the conversation content generated by the generation unit as text in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input text on the application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to convert the input text into speech and engage in conversation with the designated party. The display unit is implemented by the control unit 46A of the robot 414 and provides an interface to display the conversation content generated by the generation unit as text in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception desk allows users to pre-enter the content they want to convey by phone as text, Based on the information received by the reception unit, the generation unit makes a phone call to the designated recipient and conducts a conversation based on the entered text. It includes a display unit that displays the conversation content as text on the app in real time. A system characterized by the following features. (Note 2) It features an input section where the user enters their response to unexpected questions from the other party. The system described in Appendix 1, characterized by the features described herein. (Note 3) Based on the flow of the conversation, the AI ​​prepares pre-defined phrases, and the user can select from these. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features an AI that speaks and responds to the other party. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the text input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering text, the input content is filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter text, the system prioritizes accepting input that is highly relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When you enter text, the system analyzes your social media activity and suggests relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the tone of the conversation generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a conversation, the system references the specified recipient's past response history to generate the most suitable conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating conversations, different generation algorithms are applied depending on the specified industry and service content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the conversation generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a conversation, the conversation priority is determined based on the response time of the specified recipient. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating conversations, the system optimizes the conversation content by referring to the specified relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying content, customize the displayed content based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the user's emotions and determines the priority of displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying content, the system prioritizes showing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying content, the system analyzes the user's social media activity and suggests relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned input unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned input unit is When users input data, the system prioritizes accepting input that is highly relevant to their geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned selection unit is It estimates the user's emotions and adjusts how options are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned selection unit is When making a selection, the system refers to the user's past selection history to suggest the most suitable option. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned selection unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned selection unit is When making a selection, the system prioritizes displaying the most relevant options, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 31) The response unit is It estimates the user's emotions and adjusts the response based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The response unit is When responding, the system generates the optimal response by referring to the specified recipient's past response history. The system described in Appendix 4, characterized by the features described herein. (Note 33) The response unit is It estimates the user's emotions and prioritizes responses based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The response unit is When responding, the response content will be customized based on the specified industry and service content. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk allows users to pre-enter the content they want to convey by phone as text, Based on the information received by the reception unit, the generation unit makes a phone call to the designated recipient and conducts a conversation based on the entered text. It includes a display unit that displays the conversation content as text on the app in real time. A system characterized by the following features.

2. It features an input section where the user enters their response to unexpected questions from the other party. The system according to feature 1.

3. Based on the flow of the conversation, the AI ​​prepares pre-defined phrases, and the system includes a selection section for the user to choose from. The system according to feature 1.

4. It features an AI response unit that speaks and answers to the other party. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the text input interface based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering text, the input content is filtered based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When users enter text, the system prioritizes accepting input that is highly relevant to their geographical location. The system according to feature 1.

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