system

Abstract

The system according to this embodiment aims to automate routine communication and date arrangements for users. [Solution] The system according to the embodiment comprises a communication unit, an adjustment unit, a selection unit, and a decision-making unit. The communication unit handles routine communication from the user. The adjustment unit adjusts the date schedule based on the routine communication handled by the communication unit. The selection unit selects and reserves a date location based on the date and time adjusted by the adjustment unit. The decision-making unit receives important decisions from the user.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that busy businesspersons or users tired of matching apps spend a lot of time on stereotyped communication and date arrangements.

[0005] The system according to the embodiment aims to automate stereotyped communication and date arrangements of users. [[ID=...]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a communication unit, a coordination unit, a selection unit, and a decision-making unit. The communication unit handles routine communication from the user. The coordination unit schedules a date based on the routine communication handled by the communication unit. The selection unit selects and reserves a date location based on the date and time of the date arranged by the coordination unit. The decision-making unit receives important decisions from the user. [Effects of the Invention]

[0007] The system according to this embodiment can automate routine communication and date arrangements for users. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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. ١ shows an example of the configuration of a data processing system ١٠ according to the first embodiment.

[0017] As shown in FIG. ١, the data processing system ١٠ includes a data processing device ١٢ and a smart device ١٤. An example of the data processing device ١٢ is a server.

[0018] The data processing device ١٢ includes a computer ٢٢, a database ٢٤, and a communication I / F ٢٦. The computer ٢٢ includes a processor ٢٨, a RAM ٣٠, and a storage ٣٢. The processor ٢٨, the RAM ٣٠, and the storage ٣٢ are connected to a bus ٣٤. Also, the database ٢٤ and the communication I / F ٢٦ are connected to the bus ٣٤. The communication I / F ٢٦ is connected to a network ٥٤. Examples of the network ٥٤ include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[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 autonomous AI agent service according to an embodiment of the present invention is a system designed to solve the problems of time constraints and the hassle of routine communication faced by busy business people and users who are tired of dating apps. This system automates routine interactions, date scheduling, and location selection and booking on behalf of the user, allowing the user to focus on important decisions and the actual date. This enables efficient and high-quality encounters. For example, a user can entrust routine communication on a dating app to the AI ​​agent. The AI ​​agent acts as a surrogate for the user, autonomously performing routine conversations such as greetings and self-introductions. For example, the AI ​​agent for user A might say "Nice to meet you!" and the AI ​​agent for user B might reply "Nice to meet you!" This frees the user from tedious routine interactions. Next, the AI ​​agent schedules a date. The AI ​​agent works with a calendar app to understand the user's availability. Based on the user's availability, it then suggests a date and time for the date. For example, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." This frees users from the hassle of scheduling. Furthermore, the AI ​​agent selects and reserves a date location. The AI ​​agent collaborates with gourmet websites to suggest date locations that suit the user's preferences. For example, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." The AI ​​agent then makes a reservation at that restaurant. This frees users from the hassle of choosing and reserving a date location. In this way, users are only involved in important decisions (accepting / rejecting a match, finalizing the date plan, and actually attending the date), freeing them from tedious, routine interactions and adjustments. This allows them to enjoy efficient and high-quality encounters. Thus, autonomous AI agent services can save users time and free them from tedious, routine communication and date scheduling.

[0029] The autonomous AI agent service according to this embodiment comprises a communication unit, a coordination unit, a selection unit, and a decision-making unit. The communication unit acts on behalf of the user in routine communication. For example, the communication unit performs routine conversations such as greetings and self-introductions on behalf of the user. For example, the communication unit may have User A's AI agent say "Nice to meet you!" and User B's AI agent respond "Nice to meet you!" The communication unit can also ask and answer general questions on behalf of the user. For example, the communication unit may have User A's AI agent ask "What do you do for a living?" and User B's AI agent respond "I work in IT." Furthermore, the communication unit can also engage in conversations about hobbies and interests on behalf of the user. For example, the communication unit may have User A's AI agent ask "What are your hobbies?" and User B's AI agent respond "Watching movies." The coordination unit schedules a date based on the routine communication performed by the communication unit. The scheduling unit, for example, works with a calendar app to understand the user's availability and suggests a date and time for a date. For example, the scheduling unit's AI agent for User A might suggest, "How about 3 PM next Saturday?" and User B's AI agent might respond, "That time works for me." The scheduling unit can also suggest multiple possible dates and times based on the user's availability. For example, it might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the most suitable date and time based on the user's schedule. For example, it analyzes the user's availability and suggests the most suitable date and time. The selection unit selects and reserves a date location based on the date and time arranged by the scheduling unit. The selection unit, for example, works with a gourmet website to suggest a date location that suits the user's preferences. For example, the selection unit's AI agent for User A might suggest, "How about an Italian restaurant?" and User B's AI agent might respond, "That sounds good."The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. The decision-making unit receives important decisions from the user. For example, the decision-making unit accepts / rejects of matching, final confirmation of the date plan, and participation in the actual date. For example, when the user approves a match, the decision-making unit asks, "Do you want to match with this user?" and the user responds, "Yes." Also, when the user finalizes the date plan, the decision-making unit asks, "Do you want to confirm this plan?" and the user responds, "Yes." Furthermore, when the user accepts participation in the actual date, the decision-making unit asks, "Do you want to participate in this date?" and the user responds, "Yes." As a result, the autonomous AI agent service according to this embodiment can automate routine communication with users, scheduling dates, selecting and booking date locations, and receiving important decisions, thereby enabling efficient and high-quality encounters.

[0030] The Communication Department handles routine communication for users. Specifically, it performs standard conversations such as greetings and self-introductions on behalf of the user. For example, User A's AI agent might say "Nice to meet you!" and User B's AI agent might respond "Nice to meet you too!" The Communication Department can also ask and answer general questions on behalf of the user. For example, User A's AI agent might ask "What do you do for a living?" and User B's AI agent might respond "I work in IT." Furthermore, the Communication Department can engage in conversations about hobbies and interests on behalf of the user. For example, User A's AI agent might ask "What are your hobbies?" and User B's AI agent might respond "Watching movies." This allows users to save their own time while facilitating smooth communication with other users. The Communication Department utilizes natural language processing technology to accurately understand user intent and generate appropriate responses. For example, it can use generative AI to refer to the user's past conversation history and profile information to generate more personalized responses. This allows the communication department to create natural conversations tailored to each user's personality and preferences. Furthermore, the communication department can manage simultaneous conversations with multiple users. For example, even when user A is conversing with multiple people at the same time, it can accurately grasp the context of each conversation and provide appropriate responses. This allows users to efficiently navigate multiple encounters. Additionally, the communication department can collect user feedback and continuously improve the quality of conversations. For instance, user evaluations of conversation content and responses can improve the accuracy and naturalness of the AI ​​agent's responses. This allows the communication department to increase user satisfaction and provide higher-quality encounters.

[0031] The scheduling unit arranges dates based on standardized communication handled by the communication unit. Specifically, it works with calendar apps to understand the user's availability and suggests date and time options. For example, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." The scheduling unit can also suggest multiple date and time options based on the user's availability. For example, it might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the most suitable date and time based on the user's schedule. For example, it can analyze the user's availability and suggest the most appropriate date and time. The scheduling unit uses AI to efficiently manage the user's schedule and select the optimal date and time. For example, it can analyze the user's past schedule data and behavioral patterns to identify the time slot that is most convenient for the user. This allows the scheduling unit to achieve flexible scheduling tailored to the user's schedule. The scheduling unit can also adjust dates based on the user's priority and importance. For example, if a user has an important meeting or appointment, the system will suggest a date time that avoids those times. This allows users to prioritize important appointments while still being able to arrange a date. Furthermore, the scheduling system can collect user feedback and continuously improve the accuracy and efficiency of scheduling. For instance, users can evaluate the suggested dates and times to improve the accuracy of the AI ​​agent's suggestions. This allows the scheduling system to increase user satisfaction and provide more efficient scheduling.

[0032] The selection unit selects and reserves a date location based on the date and time arranged by the coordination unit. Specifically, it works with gourmet websites to suggest date locations that match the user's preferences. For example, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. The selection unit uses AI to analyze the user's preferences and past preference history to select the optimal date location. For example, it can refer to ratings and reviews of restaurants the user has visited in the past to suggest a place that matches the user's preferences. This allows the selection unit to provide personalized suggestions tailored to the user's preferences. The selection unit can also suggest easily accessible locations considering the user's current location and mode of transportation. For example, if the user is using public transportation, it will suggest a restaurant close to the station. This reduces the burden of travel for users, allowing them to enjoy their dates. Furthermore, the selection team can collect user feedback and continuously improve the accuracy and quality of its suggestions. For example, by having users rate restaurants they have visited, the accuracy of the AI ​​agent's suggestions can be improved. This allows the selection team to increase user satisfaction and provide higher-quality date location selections.

[0033] The decision-making department receives important decisions from users. Specifically, it accepts user approval / rejection of matching, final confirmation of date plans, and participation in actual dates. For example, when a user approves a matching, it asks, "Do you want to match with this user?" and the user responds, "Yes." Similarly, when a user confirms a date plan, it asks, "Do you want to finalize this plan?" and the user responds, "Yes." Furthermore, when a user accepts participation in an actual date, it asks, "Do you want to participate in this date?" and the user responds, "Yes." The decision-making department uses AI to efficiently manage user decisions and support important decisions. For example, it can analyze a user's past decision history and behavior patterns to support the user in making the most appropriate decisions. This allows the decision-making department to receive important user decisions quickly and accurately. In addition, the decision-making department can collect user feedback and continuously improve the accuracy and efficiency of its decisions. For example, by evaluating the decisions made by users, the accuracy of the AI ​​agent's decision support can be improved. This allows the decision-making unit to enhance user satisfaction and provide higher-quality decision-making support. Furthermore, the decision-making unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. As a result, the decision-making unit can quickly and reliably support users in making important decisions, enabling efficient and high-quality encounters.

[0034] The communication unit can perform routine conversations such as greetings and self-introductions on behalf of the user. For example, the communication unit can say "Nice to meet you!" on behalf of the user. For instance, the communication unit's AI agent for user A might say "Nice to meet you!", and the AI ​​agent for user B might respond "Nice to meet you!". The communication unit can also introduce itself on behalf of the user. For example, the communication unit's AI agent for user A might introduce itself by saying "I work in the IT field," and the AI ​​agent for user B might respond by saying "I work in the medical field." Furthermore, the communication unit can ask and answer general questions on behalf of the user. For example, the communication unit's AI agent for user A might ask "What do you do for a living?", and the AI ​​agent for user B might respond by saying "I work in the IT field." This frees the user from tedious routine conversations. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or not using generative AI. For example, when the communications department needs to greet or introduce themselves on behalf of a user, they input prompts into a generation AI, which then generates appropriate greetings and self-introductions.

[0035] The scheduling unit can work with a calendar app to understand the user's availability and suggest a date and time for a date. For example, the scheduling unit can work with a calendar app to understand the user's availability. For example, the scheduling unit can retrieve availability data from the user's calendar app and suggest a date and time for a date. The scheduling unit can also suggest multiple possible dates and times based on the user's availability. For example, the scheduling unit might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the optimal date and time to match the user's schedule. For example, the scheduling unit can analyze the user's availability and suggest the most suitable date and time. This frees the user from the hassle of scheduling. Some or all of the above processes in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input availability data obtained from the calendar app into the AI, which then suggests the optimal date and time.

[0036] The selection unit can work with gourmet websites to suggest and book date locations that match the user's preferences. For example, the selection unit's AI agent for user A might suggest, "How about an Italian restaurant?", and the AI ​​agent for user B might respond, "That sounds good." The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. This frees the user from the hassle of choosing and booking a date location. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit inputs data obtained from gourmet websites into a generative AI, and the generative AI suggests the most suitable date location.

[0037] The decision-making unit can accept / reject user matching, finalize date plans, and accept participation in actual dates. For example, when approving a user matching, the decision-making unit asks, "Do you want to match with this user?" and the user responds, "Yes." Similarly, when finalizing a date plan, the unit asks, "Do you want to confirm this plan?" and the user responds, "Yes." Furthermore, when accepting participation in an actual date, the unit asks, "Do you want to participate in this date?" and the user responds, "Yes." This ensures that the user is only involved in important decisions. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, when approving a user matching, the decision-making unit may input a prompt to the AI, which then performs the appropriate confirmation.

[0038] The communication unit can analyze a user's past communication history and select the optimal conversation pattern. For example, the communication unit can prioritize using phrases that the user has used frequently in the past. For instance, the communication unit might use the phrase "Hello, it's nice to talk to you again," which the AI ​​agent for user A has used frequently in the past. The communication unit can also construct new conversations based on successful conversation patterns from the user's past. For example, the communication unit might construct a new conversation based on the successful conversation pattern "Let's go back to our previous topic," which the AI ​​agent for user A has used in the past. Furthermore, the communication unit can adjust the conversation to avoid topics that the user has avoided in the past. For example, the communication unit might adjust the conversation to avoid topics that the AI ​​agent for user A has avoided in the past, such as "Let's talk about something else this time." This allows for optimal conversations based on the user's past history. Some or all of the above processing in the communication unit may be performed using AI, or not. For example, the communication unit could input the user's past communication history into the AI, which would then select the optimal conversation pattern.

[0039] The communication department can customize conversation content based on the user's current interests and trends during communication. For example, the communication department can incorporate topics the user has recently been interested in into the conversation. For instance, the AI ​​agent for User A might ask, "What do you think about recent movies?" based on the user's interests. The communication department can also advance the conversation based on trends the user is following. For example, the AI ​​agent for User A might say, "Let's talk about recent trends," based on the trends the user is following. Furthermore, the communication department can construct a conversation based on keywords the user has recently searched for. For example, the AI ​​agent for User A might say, "Let's talk about the keywords you recently searched for." This allows for conversations based on the user's interests and trends. Some or all of the above processing in the communication department may be performed using AI, or not. For example, the communication department could input the user's interests and trends into the AI, which would then customize the optimal conversation content.

[0040] The communication department can select highly relevant topics during communication, taking into account the user's geographical location. For example, the communication department can provide topics related to the user's current location. For instance, the AI ​​agent for user A might say, "There's a great restaurant near where you are now." The communication department can also advance the conversation based on places the user has visited in the past. For example, the AI ​​agent for user A might say, "Let's talk about places you've visited before." Furthermore, the communication department can provide information related to travel destinations the user is planning. For example, the AI ​​agent for user A might say, "Let's talk about your next travel destination." This ensures that highly relevant topics are provided based on the user's geographical location. Some or all of the above processing in the communication department may be performed using AI, or not. For example, the communication department could input the user's geographical location into the AI, which would then select highly relevant topics.

[0041] The communications department can analyze a user's social media activity and provide relevant topics during communication. For example, the communications department can guide the conversation based on the user's recent posts. For instance, the communications department's AI agent for user A might say, "Let's talk about your recent posts." The communications department can also incorporate topics from accounts the user follows. For example, the communications department's AI agent for user A might say, "Let's talk about the accounts you follow." Furthermore, the communications department can provide topics from online communities the user participates in. For example, the communications department's AI agent for user A might say, "Let's talk about the communities you participate in." This ensures that relevant topics are provided based on the user's social media activity. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department could input the user's social media activity into an AI, which would then provide relevant topics.

[0042] The scheduling unit can suggest the optimal date by referring to the user's past schedule history during scheduling. For example, the scheduling unit can prioritize suggesting dates that the user has previously preferred. For example, the scheduling unit might suggest that User A's AI agent "suggests dates that the user has previously preferred." The scheduling unit can also analyze the user's past schedule patterns and suggest the optimal date. For example, the scheduling unit might suggest that User A's AI agent "suggests the optimal date based on past schedule patterns." Furthermore, the scheduling unit can suggest avoiding dates that the user has previously avoided. For example, the scheduling unit might suggest that User A's AI agent "suggests avoiding dates that the user has previously avoided." This results in the suggestion of the optimal date based on the user's past schedule history. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's past schedule history into the AI, and the AI ​​suggests the optimal date.

[0043] The scheduling unit can customize the schedule based on the user's current lifestyle and priorities during scheduling. For example, if the user is busy, the scheduling unit can suggest a shorter schedule. For example, the AI ​​agent for User A might say, "I suggest a shorter schedule." The scheduling unit can also suggest a more relaxed schedule if the user wants to relax. For example, the AI ​​agent for User A might say, "I suggest a more relaxed schedule." Furthermore, if the user has plans to attend a specific event, the scheduling unit can suggest avoiding the dates before and after that event. For example, the AI ​​agent for User A might say, "I suggest avoiding the dates before and after a specific event." This customizes the schedule based on the user's current lifestyle and priorities. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's current lifestyle and priorities into the AI, and the AI ​​customizes the optimal schedule.

[0044] The scheduling unit can propose an optimal itinerary during scheduling, taking into account the user's geographical location. For example, the scheduling unit can propose an itinerary with minimal travel based on the user's current location. For example, the scheduling unit's AI agent for user A might say, "I propose an itinerary with minimal travel." The scheduling unit can also propose a convenient itinerary based on places the user has visited in the past. For example, the scheduling unit's AI agent for user A might say, "I propose a convenient itinerary based on places you have visited in the past." Furthermore, the scheduling unit can propose an appropriate itinerary based on the travel destination the user is planning. For example, the scheduling unit's AI agent for user A might say, "I propose an appropriate itinerary based on your travel destination." This results in the proposal of an optimal itinerary based on the user's geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimal itinerary.

[0045] The scheduling unit can analyze the user's social media activity and suggest relevant dates during scheduling. For example, the scheduling unit can suggest appropriate dates based on the user's recent posts. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest appropriate dates based on your recent posts." The scheduling unit can also suggest dates based on events from accounts the user follows. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest dates based on events from accounts you follow." Furthermore, the scheduling unit can also suggest dates based on events from online communities the user participates in. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest dates based on events from communities you participate in." This results in the suggestion of relevant dates based on the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's social media activity into the AI, and the AI ​​suggests relevant dates.

[0046] The selection unit can suggest the most suitable location by referring to the user's past dating history during the selection process. For example, the selection unit can prioritize suggesting places the user has previously enjoyed visiting. For example, the selection unit might suggest, "User A's AI agent will suggest places the user has previously enjoyed visiting." The selection unit can also analyze the user's past dating history and suggest the most suitable location. For example, the selection unit might suggest, "User A's AI agent will suggest the most suitable location based on past dating history." Furthermore, the selection unit can suggest avoiding places the user has previously avoided. For example, the selection unit might suggest, "User A's AI agent will suggest avoiding places the user has previously avoided." This results in the suggestion of the most suitable location based on the user's past dating history. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's past dating history into the AI, and the AI ​​suggests the most suitable location.

[0047] The selection unit can customize locations based on the user's current preferences and dietary restrictions during the selection process. For example, if the user is vegetarian, the selection unit will suggest vegetarian-friendly restaurants. For example, the AI ​​agent for User A might say, "I suggest a vegetarian-friendly restaurant." The selection unit can also suggest restaurants that serve a specific type of cuisine if the user prefers it. For example, the AI ​​agent for User A might say, "I suggest a restaurant that serves that specific type of cuisine." Furthermore, if the user has allergies, the selection unit can suggest allergy-friendly restaurants. For example, the AI ​​agent for User A might say, "I suggest an allergy-friendly restaurant." This customizes locations based on the user's current preferences and dietary restrictions. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's preferences and dietary restrictions into the AI, and the AI ​​customizes the optimal location.

[0048] The selection unit can suggest the optimal location by considering the user's geographical location information during the selection process. For example, the selection unit can suggest nearby restaurants based on the user's current location. For example, the selection unit's AI agent for user A might say, "I will suggest nearby restaurants." The selection unit can also suggest convenient locations based on places the user has visited in the past. For example, the selection unit's AI agent for user A might say, "I will suggest convenient locations based on places you have visited in the past." Furthermore, the selection unit can suggest appropriate locations based on the travel destination the user is planning. For example, the selection unit's AI agent for user A might say, "I will suggest appropriate locations based on your travel destination." This results in the suggestion of the optimal location based on the user's geographical location information. 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 inputs the user's geographical location information into the AI, and the AI ​​suggests the optimal location.

[0049] The selection unit can analyze the user's social media activity and suggest relevant locations during the selection process. For example, the selection unit can suggest appropriate locations based on the user's recent posts. For instance, the AI ​​agent for user A might suggest, "We will suggest appropriate locations based on your recent posts." The selection unit can also suggest locations based on the topics of accounts the user follows. For example, the AI ​​agent for user A might suggest, "We will suggest locations based on the topics of accounts you follow." Furthermore, the selection unit can suggest locations based on the topics of online communities the user participates in. For example, the AI ​​agent for user A might suggest, "We will suggest locations based on the topics of communities you participate in." This results in the suggestion of relevant locations based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's social media activity into the AI, and the AI ​​suggests relevant locations.

[0050] The decision-making unit can select the optimal acceptance method by referring to the user's past decision history when accepting a decision. For example, the decision-making unit may prioritize providing an acceptance method that the user has preferred to use in the past. For example, the decision-making unit may provide that User A's AI agent will "accept the request using a method that the user has preferred to use in the past." The decision-making unit can also analyze the user's past decision history and propose the optimal acceptance method. For example, the decision-making unit may provide that User A's AI agent will "accept the request using the most optimal method based on the user's past decision history." Furthermore, the decision-making unit may also provide a method that avoids an acceptance method that the user has avoided in the past. For example, the decision-making unit may provide that User A's AI agent will "accept the request while avoiding methods that the user has avoided in the past." This ensures that the optimal acceptance method is provided based on the user's past decision history. Some or all of the above processing in the decision-making unit may be performed using AI, or not using AI. For example, the decision-making unit may input the user's past decision history into the AI, and the AI ​​will select the optimal acceptance method.

[0051] The decision-making unit can select the optimal reception method when receiving a request, taking into account the user's device information. For example, if the user is using a smartphone, the decision-making unit can provide a reception method optimized for touch operation. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for smartphones." The decision-making unit can also provide a reception method optimized for a larger screen if the user is using a tablet. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for tablets." Furthermore, if the user is using a PC, the decision-making unit can provide a reception method optimized for keyboard input. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for PCs." This ensures that the optimal reception method is provided based on the user's device information. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit inputs the user's device information into the AI, and the AI ​​selects the optimal reception method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The communications department can analyze a user's past communication history and select the optimal conversation pattern. For example, it can prioritize the use of phrases the user has used frequently in the past. It can also construct new conversations based on successful conversation patterns from the user's past. Furthermore, it can adjust the conversation to avoid topics the user has avoided in the past. This allows for optimal conversations based on the user's past history.

[0054] The scheduling unit can suggest the optimal schedule by referring to the user's past schedule history. For example, it can prioritize suggesting dates the user has previously preferred. It can analyze the user's past schedule patterns and suggest the optimal schedule. Furthermore, it can suggest avoiding dates the user has avoided in the past. As a result, the optimal schedule is suggested based on the user's past schedule history.

[0055] The selection function can customize locations based on the user's current preferences and dietary restrictions. For example, if the user is vegetarian, it can suggest vegetarian-friendly restaurants. If the user prefers a specific type of cuisine, it can suggest restaurants that serve that cuisine. Furthermore, if the user has allergies, it can suggest allergy-friendly restaurants. This ensures that locations are customized based on the user's current preferences and dietary restrictions.

[0056] The selection unit can suggest the optimal location by considering the user's geographical location. For example, it can suggest nearby restaurants based on the user's current location. It can suggest convenient locations based on places the user has visited in the past. Furthermore, it can suggest appropriate locations based on the travel destination the user is planning. In this way, the optimal location is suggested based on the user's geographical location.

[0057] The decision-making unit can select the optimal reception method by considering the user's device information. For example, if the user is using a smartphone, it can provide a reception method optimized for touch operation. If the user is using a tablet, it can provide a reception method optimized for a larger screen. Furthermore, if the user is using a PC, it can provide a reception method optimized for keyboard input. This ensures that the optimal reception method is provided based on the user's device information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The communication department handles routine communication for the user. For example, it will greet and introduce itself, ask and answer general questions, and engage in conversations about hobbies and interests on behalf of the user. Specifically, User A's AI agent will say "Nice to meet you!" and User B's AI agent will respond "Nice to meet you too!" It will also ask "What do you do for a living?" and respond "I work in IT." Furthermore, it will ask "What are your hobbies?" and respond "Watching movies." Step 2: The scheduling unit arranges the date based on standardized communication handled by the communication unit. For example, it works with a calendar app to understand the user's availability and suggests date and time for the date. Specifically, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." It might also suggest multiple alternative dates and times, such as, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, it automatically selects the most suitable date and time based on the user's schedule. Step 3: The selection unit selects and reserves a date location based on the date and time arranged by the adjustment unit. For example, it may work with a gourmet website to suggest date locations that suit the user's preferences. Specifically, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." It might also suggest multiple options, such as, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, it may suggest the most suitable location based on the user's past preference history. Step 4: The decision-making section receives important decisions from the user. For example, it accepts the user's approval / rejection of a match, final confirmation of the date plan, and participation in the actual date. Specifically, when the user approves a match, it asks "Do you want to match with this user?" and the user responds "Yes." Also, when the user makes a final confirmation of the date plan, it asks "Do you want to confirm this plan?" and the user responds "Yes." Furthermore, when the user accepts participation in the actual date, it asks "Do you want to participate in this date?" and the user responds "Yes."

[0060] (Example of form 2) The autonomous AI agent service according to an embodiment of the present invention is a system designed to solve the problems of time constraints and the hassle of routine communication faced by busy business people and users who are tired of dating apps. This system automates routine interactions, date scheduling, and location selection and booking on behalf of the user, allowing the user to focus on important decisions and the actual date. This enables efficient and high-quality encounters. For example, a user can entrust routine communication on a dating app to the AI ​​agent. The AI ​​agent acts as a surrogate for the user, autonomously performing routine conversations such as greetings and self-introductions. For example, the AI ​​agent for user A might say "Nice to meet you!" and the AI ​​agent for user B might reply "Nice to meet you!" This frees the user from tedious routine interactions. Next, the AI ​​agent schedules a date. The AI ​​agent works with a calendar app to understand the user's availability. Based on the user's availability, it then suggests a date and time for the date. For example, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." This frees users from the hassle of scheduling. Furthermore, the AI ​​agent selects and reserves a date location. The AI ​​agent collaborates with gourmet websites to suggest date locations that suit the user's preferences. For example, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." The AI ​​agent then makes a reservation at that restaurant. This frees users from the hassle of choosing and reserving a date location. In this way, users are only involved in important decisions (accepting / rejecting a match, finalizing the date plan, and actually attending the date), freeing them from tedious, routine interactions and adjustments. This allows them to enjoy efficient and high-quality encounters. Thus, autonomous AI agent services can save users time and free them from tedious, routine communication and date scheduling.

[0061] The autonomous AI agent service according to this embodiment comprises a communication unit, a coordination unit, a selection unit, and a decision-making unit. The communication unit acts on behalf of the user in routine communication. For example, the communication unit performs routine conversations such as greetings and self-introductions on behalf of the user. For example, the communication unit may have User A's AI agent say "Nice to meet you!" and User B's AI agent respond "Nice to meet you!" The communication unit can also ask and answer general questions on behalf of the user. For example, the communication unit may have User A's AI agent ask "What do you do for a living?" and User B's AI agent respond "I work in IT." Furthermore, the communication unit can also engage in conversations about hobbies and interests on behalf of the user. For example, the communication unit may have User A's AI agent ask "What are your hobbies?" and User B's AI agent respond "Watching movies." The coordination unit schedules a date based on the routine communication performed by the communication unit. The scheduling unit, for example, works with a calendar app to understand the user's availability and suggests a date and time for a date. For example, the scheduling unit's AI agent for User A might suggest, "How about 3 PM next Saturday?" and User B's AI agent might respond, "That time works for me." The scheduling unit can also suggest multiple possible dates and times based on the user's availability. For example, it might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the most suitable date and time based on the user's schedule. For example, it analyzes the user's availability and suggests the most suitable date and time. The selection unit selects and reserves a date location based on the date and time arranged by the scheduling unit. The selection unit, for example, works with a gourmet website to suggest a date location that suits the user's preferences. For example, the selection unit's AI agent for User A might suggest, "How about an Italian restaurant?" and User B's AI agent might respond, "That sounds good."The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. The decision-making unit receives important decisions from the user. For example, the decision-making unit accepts / rejects of matching, final confirmation of the date plan, and participation in the actual date. For example, when the user approves a match, the decision-making unit asks, "Do you want to match with this user?" and the user responds, "Yes." Also, when the user finalizes the date plan, the decision-making unit asks, "Do you want to confirm this plan?" and the user responds, "Yes." Furthermore, when the user accepts participation in the actual date, the decision-making unit asks, "Do you want to participate in this date?" and the user responds, "Yes." As a result, the autonomous AI agent service according to this embodiment can automate routine communication with users, scheduling dates, selecting and booking date locations, and receiving important decisions, thereby enabling efficient and high-quality encounters.

[0062] The Communication Department handles routine communication for users. Specifically, it performs standard conversations such as greetings and self-introductions on behalf of the user. For example, User A's AI agent might say "Nice to meet you!" and User B's AI agent might respond "Nice to meet you too!" The Communication Department can also ask and answer general questions on behalf of the user. For example, User A's AI agent might ask "What do you do for a living?" and User B's AI agent might respond "I work in IT." Furthermore, the Communication Department can engage in conversations about hobbies and interests on behalf of the user. For example, User A's AI agent might ask "What are your hobbies?" and User B's AI agent might respond "Watching movies." This allows users to save their own time while facilitating smooth communication with other users. The Communication Department utilizes natural language processing technology to accurately understand user intent and generate appropriate responses. For example, it can use generative AI to refer to the user's past conversation history and profile information to generate more personalized responses. This allows the communication department to create natural conversations tailored to each user's personality and preferences. Furthermore, the communication department can manage simultaneous conversations with multiple users. For example, even when user A is conversing with multiple people at the same time, it can accurately grasp the context of each conversation and provide appropriate responses. This allows users to efficiently navigate multiple encounters. Additionally, the communication department can collect user feedback and continuously improve the quality of conversations. For instance, user evaluations of conversation content and responses can improve the accuracy and naturalness of the AI ​​agent's responses. This allows the communication department to increase user satisfaction and provide higher-quality encounters.

[0063] The scheduling unit arranges dates based on standardized communication handled by the communication unit. Specifically, it works with calendar apps to understand the user's availability and suggests date and time options. For example, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." The scheduling unit can also suggest multiple date and time options based on the user's availability. For example, it might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the most suitable date and time based on the user's schedule. For example, it can analyze the user's availability and suggest the most appropriate date and time. The scheduling unit uses AI to efficiently manage the user's schedule and select the optimal date and time. For example, it can analyze the user's past schedule data and behavioral patterns to identify the time slot that is most convenient for the user. This allows the scheduling unit to achieve flexible scheduling tailored to the user's schedule. The scheduling unit can also adjust dates based on the user's priority and importance. For example, if a user has an important meeting or appointment, the system will suggest a date time that avoids those times. This allows users to prioritize important appointments while still being able to arrange a date. Furthermore, the scheduling system can collect user feedback and continuously improve the accuracy and efficiency of scheduling. For instance, users can evaluate the suggested dates and times to improve the accuracy of the AI ​​agent's suggestions. This allows the scheduling system to increase user satisfaction and provide more efficient scheduling.

[0064] The selection unit selects and reserves a date location based on the date and time arranged by the coordination unit. Specifically, it works with gourmet websites to suggest date locations that match the user's preferences. For example, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. The selection unit uses AI to analyze the user's preferences and past preference history to select the optimal date location. For example, it can refer to ratings and reviews of restaurants the user has visited in the past to suggest a place that matches the user's preferences. This allows the selection unit to provide personalized suggestions tailored to the user's preferences. The selection unit can also suggest easily accessible locations considering the user's current location and mode of transportation. For example, if the user is using public transportation, it will suggest a restaurant close to the station. This reduces the burden of travel for users, allowing them to enjoy their dates. Furthermore, the selection team can collect user feedback and continuously improve the accuracy and quality of its suggestions. For example, by having users rate restaurants they have visited, the accuracy of the AI ​​agent's suggestions can be improved. This allows the selection team to increase user satisfaction and provide higher-quality date location selections.

[0065] The decision-making department receives important decisions from users. Specifically, it accepts user approval / rejection of matching, final confirmation of date plans, and participation in actual dates. For example, when a user approves a matching, it asks, "Do you want to match with this user?" and the user responds, "Yes." Similarly, when a user confirms a date plan, it asks, "Do you want to finalize this plan?" and the user responds, "Yes." Furthermore, when a user accepts participation in an actual date, it asks, "Do you want to participate in this date?" and the user responds, "Yes." The decision-making department uses AI to efficiently manage user decisions and support important decisions. For example, it can analyze a user's past decision history and behavior patterns to support the user in making the most appropriate decisions. This allows the decision-making department to receive important user decisions quickly and accurately. In addition, the decision-making department can collect user feedback and continuously improve the accuracy and efficiency of its decisions. For example, by evaluating the decisions made by users, the accuracy of the AI ​​agent's decision support can be improved. This allows the decision-making unit to enhance user satisfaction and provide higher-quality decision-making support. Furthermore, the decision-making unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. As a result, the decision-making unit can quickly and reliably support users in making important decisions, enabling efficient and high-quality encounters.

[0066] The communication unit can perform routine conversations such as greetings and self-introductions on behalf of the user. For example, the communication unit can say "Nice to meet you!" on behalf of the user. For instance, the communication unit's AI agent for user A might say "Nice to meet you!", and the AI ​​agent for user B might respond "Nice to meet you!". The communication unit can also introduce itself on behalf of the user. For example, the communication unit's AI agent for user A might introduce itself by saying "I work in the IT field," and the AI ​​agent for user B might respond by saying "I work in the medical field." Furthermore, the communication unit can ask and answer general questions on behalf of the user. For example, the communication unit's AI agent for user A might ask "What do you do for a living?", and the AI ​​agent for user B might respond by saying "I work in the IT field." This frees the user from tedious routine conversations. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or not using generative AI. For example, when the communications department needs to greet or introduce themselves on behalf of a user, they input prompts into a generation AI, which then generates appropriate greetings and self-introductions.

[0067] The scheduling unit can work with a calendar app to understand the user's availability and suggest a date and time for a date. For example, the scheduling unit can work with a calendar app to understand the user's availability. For example, the scheduling unit can retrieve availability data from the user's calendar app and suggest a date and time for a date. The scheduling unit can also suggest multiple possible dates and times based on the user's availability. For example, the scheduling unit might suggest, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, the scheduling unit can automatically select the optimal date and time to match the user's schedule. For example, the scheduling unit can analyze the user's availability and suggest the most suitable date and time. This frees the user from the hassle of scheduling. Some or all of the above processes in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input availability data obtained from the calendar app into the AI, which then suggests the optimal date and time.

[0068] The selection unit can work with gourmet websites to suggest and book date locations that match the user's preferences. For example, the selection unit's AI agent for user A might suggest, "How about an Italian restaurant?", and the AI ​​agent for user B might respond, "That sounds good." The selection unit can also suggest multiple locations based on the user's preferences. For example, it might suggest, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, the selection unit can suggest the most suitable location based on the user's past preference history. For example, it might make suggestions based on data from restaurants the user has visited in the past. This frees the user from the hassle of choosing and booking a date location. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit inputs data obtained from gourmet websites into a generative AI, and the generative AI suggests the most suitable date location.

[0069] The decision-making unit can accept / reject user matching, finalize date plans, and accept participation in actual dates. For example, when approving a user matching, the decision-making unit asks, "Do you want to match with this user?" and the user responds, "Yes." Similarly, when finalizing a date plan, the unit asks, "Do you want to confirm this plan?" and the user responds, "Yes." Furthermore, when accepting participation in an actual date, the unit asks, "Do you want to participate in this date?" and the user responds, "Yes." This ensures that the user is only involved in important decisions. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, when approving a user matching, the decision-making unit may input a prompt to the AI, which then performs the appropriate confirmation.

[0070] The communication department can estimate the user's emotions and adjust the tone and content of standard conversations based on the estimated emotions. For example, if the user is nervous, the AI ​​can greet them in a gentle tone to help them relax. For instance, the AI ​​agent for User A might say, "Hello, let's relax and talk," in a gentle tone. The communication department can also introduce itself in an energetic tone if the user is excited. For example, the AI ​​agent for User A might say, "Hello, let's have a lively conversation!" Furthermore, if the user is tired, the AI ​​can engage in simple and short conversations. For example, the AI ​​agent for User A might say, "Hello, how was your day?" This allows for appropriate conversations tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without AI. For example, when estimating a user's emotions, the communications department may input a prompt to the AI, which may then perform an appropriate emotion estimation.

[0071] The communication unit can analyze a user's past communication history and select the optimal conversation pattern. For example, the communication unit can prioritize using phrases that the user has used frequently in the past. For instance, the communication unit might use the phrase "Hello, it's nice to talk to you again," which the AI ​​agent for user A has used frequently in the past. The communication unit can also construct new conversations based on successful conversation patterns from the user's past. For example, the communication unit might construct a new conversation based on the successful conversation pattern "Let's go back to our previous topic," which the AI ​​agent for user A has used in the past. Furthermore, the communication unit can adjust the conversation to avoid topics that the user has avoided in the past. For example, the communication unit might adjust the conversation to avoid topics that the AI ​​agent for user A has avoided in the past, such as "Let's talk about something else this time." This allows for optimal conversations based on the user's past history. Some or all of the above processing in the communication unit may be performed using AI, or not. For example, the communication unit could input the user's past communication history into the AI, which would then select the optimal conversation pattern.

[0072] The communication department can customize conversation content based on the user's current interests and trends during communication. For example, the communication department can incorporate topics the user has recently been interested in into the conversation. For instance, the AI ​​agent for User A might ask, "What do you think about recent movies?" based on the user's interests. The communication department can also advance the conversation based on trends the user is following. For example, the AI ​​agent for User A might say, "Let's talk about recent trends," based on the trends the user is following. Furthermore, the communication department can construct a conversation based on keywords the user has recently searched for. For example, the AI ​​agent for User A might say, "Let's talk about the keywords you recently searched for." This allows for conversations based on the user's interests and trends. Some or all of the above processing in the communication department may be performed using AI, or not. For example, the communication department could input the user's interests and trends into the AI, which would then customize the optimal conversation content.

[0073] The communication unit can estimate the user's emotions and adjust the timing of the conversation based on the estimated emotions. For example, if the user is relaxed, the communication unit can start the conversation immediately. For instance, the AI ​​agent for User A might start the conversation immediately with "Hello, are you free to talk now?" The communication unit can also wait for an appropriate time to start the conversation if the user is busy. For example, the AI ​​agent for User A might start the conversation at an appropriate time with "Sorry to bother you, are you free to talk now?" Furthermore, if the user is nervous, the communication unit can wait a while before starting the conversation. For example, the AI ​​agent for User A might start the conversation after waiting a while with "Let's talk after we've had a moment." This ensures that the conversation starts at an appropriate time according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without AI. For example, when estimating a user's emotions, the communications department may input a prompt to the AI, which may then perform an appropriate emotion estimation.

[0074] The communication department can select highly relevant topics during communication, taking into account the user's geographical location. For example, the communication department can provide topics related to the user's current location. For instance, the AI ​​agent for user A might say, "There's a great restaurant near where you are now." The communication department can also advance the conversation based on places the user has visited in the past. For example, the AI ​​agent for user A might say, "Let's talk about places you've visited before." Furthermore, the communication department can provide information related to travel destinations the user is planning. For example, the AI ​​agent for user A might say, "Let's talk about your next travel destination." This ensures that highly relevant topics are provided based on the user's geographical location. Some or all of the above processing in the communication department may be performed using AI, or not. For example, the communication department could input the user's geographical location into the AI, which would then select highly relevant topics.

[0075] The communications department can analyze a user's social media activity and provide relevant topics during communication. For example, the communications department can guide the conversation based on the user's recent posts. For instance, the communications department's AI agent for user A might say, "Let's talk about your recent posts." The communications department can also incorporate topics from accounts the user follows. For example, the communications department's AI agent for user A might say, "Let's talk about the accounts you follow." Furthermore, the communications department can provide topics from online communities the user participates in. For example, the communications department's AI agent for user A might say, "Let's talk about the communities you participate in." This ensures that relevant topics are provided based on the user's social media activity. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department could input the user's social media activity into an AI, which would then provide relevant topics.

[0076] The adjustment unit can estimate the user's emotions and adjust the suggested date schedule based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit can suggest a relaxing date. For example, the adjustment unit's AI agent for User A might suggest, "I suggest a relaxing date." The adjustment unit can also suggest an earlier date if the user is excited. For example, the adjustment unit's AI agent for User A might suggest, "I suggest an earlier date." Furthermore, if the user is tired, the adjustment unit can suggest a date that allows for rest. For example, the adjustment unit's AI agent for User A might suggest, "I suggest a date that allows for rest." This enables appropriate scheduling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, when the adjustment unit estimates the user's emotions, it inputs prompts to the AI, which then performs an appropriate emotion estimation.

[0077] The scheduling unit can suggest the optimal date by referring to the user's past schedule history during scheduling. For example, the scheduling unit can prioritize suggesting dates that the user has previously preferred. For example, the scheduling unit might suggest that User A's AI agent "suggests dates that the user has previously preferred." The scheduling unit can also analyze the user's past schedule patterns and suggest the optimal date. For example, the scheduling unit might suggest that User A's AI agent "suggests the optimal date based on past schedule patterns." Furthermore, the scheduling unit can suggest avoiding dates that the user has previously avoided. For example, the scheduling unit might suggest that User A's AI agent "suggests avoiding dates that the user has previously avoided." This results in the suggestion of the optimal date based on the user's past schedule history. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's past schedule history into the AI, and the AI ​​suggests the optimal date.

[0078] The scheduling unit can customize the schedule based on the user's current lifestyle and priorities during scheduling. For example, if the user is busy, the scheduling unit can suggest a shorter schedule. For example, the AI ​​agent for User A might say, "I suggest a shorter schedule." The scheduling unit can also suggest a more relaxed schedule if the user wants to relax. For example, the AI ​​agent for User A might say, "I suggest a more relaxed schedule." Furthermore, if the user has plans to attend a specific event, the scheduling unit can suggest avoiding the dates before and after that event. For example, the AI ​​agent for User A might say, "I suggest avoiding the dates before and after a specific event." This customizes the schedule based on the user's current lifestyle and priorities. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's current lifestyle and priorities into the AI, and the AI ​​customizes the optimal schedule.

[0079] The scheduling unit can estimate the user's emotions and determine scheduling priorities based on the estimated emotions. For example, if the user is stressed, the scheduling unit will prioritize relaxing dates. For example, the AI ​​agent for User A might suggest, "Prioritize relaxing dates." The scheduling unit can also prioritize earlier dates if the user is excited. For example, the AI ​​agent for User A might suggest, "Prioritize earlier dates." Furthermore, if the user is tired, the scheduling unit can prioritize dates that allow for rest. For example, the AI ​​agent for User A might suggest, "Prioritize dates that allow for rest." This allows scheduling to be done with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, when the adjustment unit estimates the user's emotions, it inputs prompts to the AI, which then performs an appropriate emotion estimation.

[0080] The scheduling unit can propose an optimal itinerary during scheduling, taking into account the user's geographical location. For example, the scheduling unit can propose an itinerary with minimal travel based on the user's current location. For example, the scheduling unit's AI agent for user A might say, "I propose an itinerary with minimal travel." The scheduling unit can also propose a convenient itinerary based on places the user has visited in the past. For example, the scheduling unit's AI agent for user A might say, "I propose a convenient itinerary based on places you have visited in the past." Furthermore, the scheduling unit can propose an appropriate itinerary based on the travel destination the user is planning. For example, the scheduling unit's AI agent for user A might say, "I propose an appropriate itinerary based on your travel destination." This results in the proposal of an optimal itinerary based on the user's geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimal itinerary.

[0081] The scheduling unit can analyze the user's social media activity and suggest relevant dates during scheduling. For example, the scheduling unit can suggest appropriate dates based on the user's recent posts. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest appropriate dates based on your recent posts." The scheduling unit can also suggest dates based on events from accounts the user follows. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest dates based on events from accounts you follow." Furthermore, the scheduling unit can also suggest dates based on events from online communities the user participates in. For example, the scheduling unit's AI agent for user A might suggest, "We will suggest dates based on events from communities you participate in." This results in the suggestion of relevant dates based on the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit inputs the user's social media activity into the AI, and the AI ​​suggests relevant dates.

[0082] The selection unit can estimate the user's emotions and adjust the suggested date locations based on the estimated emotions. For example, if the user wants to relax, the selection unit might suggest a quiet cafe. For example, the AI ​​agent for User A might suggest, "I suggest a quiet cafe where you can relax." The selection unit can also suggest a lively restaurant if the user is excited. For example, the AI ​​agent for User A might suggest, "I suggest a lively restaurant." Furthermore, if the user is tired, the selection unit might suggest a nearby restaurant. For example, the AI ​​agent for User A might suggest, "I suggest a nearby restaurant." This ensures that an appropriate date location is suggested according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, when the selection unit estimates the user's emotions, it inputs prompts into the AI, which then performs an appropriate emotion estimation.

[0083] The selection unit can suggest the most suitable location by referring to the user's past dating history during the selection process. For example, the selection unit can prioritize suggesting places the user has previously enjoyed visiting. For example, the selection unit might suggest, "User A's AI agent will suggest places the user has previously enjoyed visiting." The selection unit can also analyze the user's past dating history and suggest the most suitable location. For example, the selection unit might suggest, "User A's AI agent will suggest the most suitable location based on past dating history." Furthermore, the selection unit can suggest avoiding places the user has previously avoided. For example, the selection unit might suggest, "User A's AI agent will suggest avoiding places the user has previously avoided." This results in the suggestion of the most suitable location based on the user's past dating history. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's past dating history into the AI, and the AI ​​suggests the most suitable location.

[0084] The selection unit can customize locations based on the user's current preferences and dietary restrictions during the selection process. For example, if the user is vegetarian, the selection unit will suggest vegetarian-friendly restaurants. For example, the AI ​​agent for User A might say, "I suggest a vegetarian-friendly restaurant." The selection unit can also suggest restaurants that serve a specific type of cuisine if the user prefers it. For example, the AI ​​agent for User A might say, "I suggest a restaurant that serves that specific type of cuisine." Furthermore, if the user has allergies, the selection unit can suggest allergy-friendly restaurants. For example, the AI ​​agent for User A might say, "I suggest an allergy-friendly restaurant." This customizes locations based on the user's current preferences and dietary restrictions. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's preferences and dietary restrictions into the AI, and the AI ​​customizes the optimal location.

[0085] The selection unit can suggest the optimal location by considering the user's geographical location information during the selection process. For example, the selection unit can suggest nearby restaurants based on the user's current location. For example, the selection unit's AI agent for user A might say, "I will suggest nearby restaurants." The selection unit can also suggest convenient locations based on places the user has visited in the past. For example, the selection unit's AI agent for user A might say, "I will suggest convenient locations based on places you have visited in the past." Furthermore, the selection unit can suggest appropriate locations based on the travel destination the user is planning. For example, the selection unit's AI agent for user A might say, "I will suggest appropriate locations based on your travel destination." This results in the suggestion of the optimal location based on the user's geographical location information. 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 inputs the user's geographical location information into the AI, and the AI ​​suggests the optimal location.

[0086] The selection unit can analyze the user's social media activity and suggest relevant locations during the selection process. For example, the selection unit can suggest appropriate locations based on the user's recent posts. For instance, the AI ​​agent for user A might suggest, "We will suggest appropriate locations based on your recent posts." The selection unit can also suggest locations based on the topics of accounts the user follows. For example, the AI ​​agent for user A might suggest, "We will suggest locations based on the topics of accounts you follow." Furthermore, the selection unit can suggest locations based on the topics of online communities the user participates in. For example, the AI ​​agent for user A might suggest, "We will suggest locations based on the topics of communities you participate in." This results in the suggestion of relevant locations based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit inputs the user's social media activity into the AI, and the AI ​​suggests relevant locations.

[0087] The decision-making unit can estimate the user's emotions and adjust the decision-making method based on the estimated emotions. For example, if the user is nervous, the decision-making unit can provide a simple and easy-to-understand decision-making method. For example, the AI ​​agent for user A might say, "We will process your request in a simple and easy-to-understand way." The decision-making unit can also provide a decision-making method that includes detailed information if the user is relaxed. For example, the AI ​​agent for user A might say, "We will process your request in a way that includes detailed information." Furthermore, if the user is in a hurry, the decision-making unit can provide a decision-making method that allows for quick decision-making. For example, the AI ​​agent for user A might say, "We will process your request in a way that allows for quick decision-making." This ensures that an appropriate decision-making method is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision-making unit may be performed using AI, for example, or without AI. For example, when the decision-making unit estimates the user's emotions, it inputs prompts into the AI, which then performs an appropriate emotion estimation.

[0088] The decision-making unit can select the optimal acceptance method by referring to the user's past decision history when accepting a decision. For example, the decision-making unit may prioritize providing an acceptance method that the user has preferred to use in the past. For example, the decision-making unit may provide that User A's AI agent will "accept the request using a method that the user has preferred to use in the past." The decision-making unit can also analyze the user's past decision history and propose the optimal acceptance method. For example, the decision-making unit may provide that User A's AI agent will "accept the request using the most optimal method based on the user's past decision history." Furthermore, the decision-making unit may also provide a method that avoids an acceptance method that the user has avoided in the past. For example, the decision-making unit may provide that User A's AI agent will "accept the request while avoiding methods that the user has avoided in the past." This ensures that the optimal acceptance method is provided based on the user's past decision history. Some or all of the above processing in the decision-making unit may be performed using AI, or not using AI. For example, the decision-making unit may input the user's past decision history into the AI, and the AI ​​will select the optimal acceptance method.

[0089] The decision-making unit can estimate the user's emotions and determine the priority of decisions based on those emotions. For example, if the user is stressed, the decision-making unit may postpone important decisions. For instance, the AI ​​agent for User A may decide to "postpone important decisions." The decision-making unit can also prioritize important decisions if the user is relaxed. For example, the AI ​​agent for User A may decide to "prioritize important decisions." Furthermore, if the user is in a hurry, the decision-making unit may prioritize items that can be decided quickly. For example, the AI ​​agent for User A may decide to "prioritize items that can be decided quickly." This allows decisions to be made with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, when the decision-making unit estimates the user's emotions, it inputs a prompt to the AI, which then performs an appropriate emotion estimation.

[0090] The decision-making unit can select the optimal reception method when receiving a request, taking into account the user's device information. For example, if the user is using a smartphone, the decision-making unit can provide a reception method optimized for touch operation. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for smartphones." The decision-making unit can also provide a reception method optimized for a larger screen if the user is using a tablet. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for tablets." Furthermore, if the user is using a PC, the decision-making unit can provide a reception method optimized for keyboard input. For example, the AI ​​agent for user A might say, "We will perform the reception using a method optimized for PCs." This ensures that the optimal reception method is provided based on the user's device information. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit inputs the user's device information into the AI, and the AI ​​selects the optimal reception method.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The communication department can estimate the user's emotions and adjust the conversation based on those estimates. For example, if the user is nervous, it can greet them in a gentle tone to help them relax. If the user is excited, it can introduce itself in an energetic tone. Furthermore, if the user is tired, it can engage in simple and short conversations. This allows for appropriate conversations tailored to the user's emotions.

[0093] The adjustment unit can estimate the user's emotions and adjust the date schedule based on those emotions. For example, if the user is feeling stressed, it can suggest a relaxing date. If the user is excited, it can suggest an earlier date. Furthermore, if the user is tired, it can suggest a date that allows for rest. This allows for appropriate scheduling tailored to the user's emotions.

[0094] The selection unit can estimate the user's emotions and suggest date locations based on those estimates. For example, if the user wants to relax, it can suggest a quiet cafe. If the user is excited, it can suggest a lively restaurant. Furthermore, if the user is tired, it can suggest a nearby restaurant. This ensures that appropriate date locations are suggested according to the user's emotions.

[0095] The decision-making unit can estimate the user's emotions and adjust the decision-making process based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand decision-making process. If the user is relaxed, it can provide a decision-making process that includes detailed information. Furthermore, if the user is in a hurry, it can provide a decision-making process that allows for quick decision-making. This ensures that an appropriate decision-making process is provided according to the user's emotions.

[0096] The decision-making unit can estimate the user's emotions and determine the priority of decisions based on those emotions. For example, if the user is nervous, important decisions can be postponed. If the user is relaxed, important decisions can be prioritized. Furthermore, if the user is in a hurry, items that can be decided quickly can be prioritized. This allows decisions to be made with appropriate priorities according to the user's emotions.

[0097] The communications department can analyze a user's past communication history and select the optimal conversation pattern. For example, it can prioritize the use of phrases the user has used frequently in the past. It can also construct new conversations based on successful conversation patterns from the user's past. Furthermore, it can adjust the conversation to avoid topics the user has avoided in the past. This allows for optimal conversations based on the user's past history.

[0098] The scheduling unit can suggest the optimal schedule by referring to the user's past schedule history. For example, it can prioritize suggesting dates the user has previously preferred. It can analyze the user's past schedule patterns and suggest the optimal schedule. Furthermore, it can suggest avoiding dates the user has avoided in the past. As a result, the optimal schedule is suggested based on the user's past schedule history.

[0099] The selection function can customize locations based on the user's current preferences and dietary restrictions. For example, if the user is vegetarian, it can suggest vegetarian-friendly restaurants. If the user prefers a specific type of cuisine, it can suggest restaurants that serve that cuisine. Furthermore, if the user has allergies, it can suggest allergy-friendly restaurants. This ensures that locations are customized based on the user's current preferences and dietary restrictions.

[0100] The selection unit can suggest the optimal location by considering the user's geographical location. For example, it can suggest nearby restaurants based on the user's current location. It can suggest convenient locations based on places the user has visited in the past. Furthermore, it can suggest appropriate locations based on the travel destination the user is planning. In this way, the optimal location is suggested based on the user's geographical location.

[0101] The decision-making unit can select the optimal reception method by considering the user's device information. For example, if the user is using a smartphone, it can provide a reception method optimized for touch operation. If the user is using a tablet, it can provide a reception method optimized for a larger screen. Furthermore, if the user is using a PC, it can provide a reception method optimized for keyboard input. This ensures that the optimal reception method is provided based on the user's device information.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The communication department handles routine communication for the user. For example, it will greet and introduce itself, ask and answer general questions, and engage in conversations about hobbies and interests on behalf of the user. Specifically, User A's AI agent will say "Nice to meet you!" and User B's AI agent will respond "Nice to meet you too!" It will also ask "What do you do for a living?" and respond "I work in IT." Furthermore, it will ask "What are your hobbies?" and respond "Watching movies." Step 2: The scheduling unit arranges the date based on standardized communication handled by the communication unit. For example, it works with a calendar app to understand the user's availability and suggests date and time for the date. Specifically, User A's AI agent might suggest, "How about 3 PM next Saturday?", and User B's AI agent might respond, "That time works for me." It might also suggest multiple alternative dates and times, such as, "How about 3 PM next Saturday or 10 AM Sunday?" Furthermore, it automatically selects the most suitable date and time based on the user's schedule. Step 3: The selection unit selects and reserves a date location based on the date and time arranged by the adjustment unit. For example, it may work with a gourmet website to suggest date locations that suit the user's preferences. Specifically, User A's AI agent might suggest, "How about an Italian restaurant?", and User B's AI agent might respond, "That sounds good." It might also suggest multiple options, such as, "How about an Italian restaurant or a Japanese restaurant?" Furthermore, it may suggest the most suitable location based on the user's past preference history. Step 4: The decision-making section receives important decisions from the user. For example, it accepts the user's approval / rejection of a match, final confirmation of the date plan, and participation in the actual date. Specifically, when the user approves a match, it asks "Do you want to match with this user?" and the user responds "Yes." Also, when the user makes a final confirmation of the date plan, it asks "Do you want to confirm this plan?" and the user responds "Yes." Furthermore, when the user accepts participation in the actual date, it asks "Do you want to participate in this date?" and the user responds "Yes."

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the communication unit, adjustment unit, selection unit, and decision-making unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the communication unit is implemented by the control unit 46A of the smart device 14 and conducts routine conversations on behalf of the user. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and coordinates the date schedule in cooperation with a calendar application. The selection unit is implemented by the control unit 46A of the smart device 14 and selects and reserves a date location in cooperation with a gourmet website. The decision-making unit is implemented by the specific processing unit 290 of the data processing device 12 and receives important decisions from the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the communication unit, adjustment unit, selection unit, and decision-making unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the smart glasses 214 and performs routine conversations on behalf of the user. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and coordinates the date schedule in cooperation with a calendar application. The selection unit is implemented by the control unit 46A of the smart glasses 214 and selects and reserves a date location in cooperation with a gourmet website. The decision-making unit is implemented by the specific processing unit 290 of the data processing unit 12 and receives important decisions from the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the communication unit, adjustment unit, selection unit, and decision-making unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the headset terminal 314 and conducts routine conversations on behalf of the user. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and coordinates the date schedule in cooperation with a calendar application. The selection unit is implemented by the control unit 46A of the headset terminal 314 and selects and reserves a date location in cooperation with a gourmet website. The decision-making unit is implemented by the specific processing unit 290 of the data processing unit 12 and receives important decisions from the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the communication unit, adjustment unit, selection unit, and decision-making unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the communication unit is implemented by the control unit 46A of the robot 414 and performs routine conversations on behalf of the user. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and coordinates the date schedule in cooperation with a calendar application. The selection unit is implemented by the control unit 46A of the robot 414 and selects and reserves a date location in cooperation with a gourmet website. The decision-making unit is implemented by the specific processing unit 290 of the data processing unit 12 and receives important decisions from the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) The communications department handles routine communication with users, The coordination unit arranges the date based on the standardized communication performed by the aforementioned communication unit, A selection unit selects and reserves a date location based on the date and time of the date adjusted by the aforementioned adjustment unit, It comprises a decision receiving unit that receives important decisions from the user. A system characterized by the following features. (Note 2) The aforementioned communications department, It performs standard conversations such as greetings and self-introductions on behalf of the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, It integrates with calendar apps to understand the user's availability and suggests dates and times for dates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is We collaborate with gourmet websites to suggest and book date locations that match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The receiving unit that makes the aforementioned determination: This process involves approving / rejecting user matches, finalizing date plans, and accepting participation in actual dates. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned communications department, It estimates the user's emotions and adjusts the tone and content of standard conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned communications department, Analyze the user's past communication history and select the optimal conversation pattern. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned communications department, During communication, the conversation content is customized based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned communications department, It estimates the user's emotions and adjusts the timing of conversation initiation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned communications department, When communicating, the system selects highly relevant topics by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned communications department, When communicating, we analyze the user's social media activity and provide relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 12) The adjustment unit is, The system estimates the user's emotions and adjusts the suggested date scheduling based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, During scheduling, we refer to the user's past schedule history to suggest the optimal date. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, During scheduling, the schedule is customized based on the user's current life circumstances and priorities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, It estimates the user's emotions and prioritizes scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, During scheduling, we will propose the optimal date considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, During scheduling, we analyze the user's social media activity and suggest relevant dates. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is It estimates the user's emotions and adjusts the suggested date locations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is During the selection process, we refer to the user's past dating history to suggest the most suitable location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is During the selection process, the location is customized based on the user's current preferences and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is During the selection process, we will propose the optimal location considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is During the selection process, we analyze users' social media activity and suggest relevant locations. The system described in Appendix 1, characterized by the features described herein. (Note 23) The receiving unit that makes the aforementioned determination: The system estimates the user's emotions and adjusts the decision-making process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The receiving unit that makes the aforementioned determination: When a decision is submitted, the system selects the most suitable submission method by referring to the user's past decision history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The receiving unit that makes the aforementioned determination: It estimates the user's emotions and determines the priority of decisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The receiving unit that makes the aforementioned determination: When processing a request, the system selects the most suitable processing method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0176] 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 communications department handles routine communication with users, The coordination unit arranges the date based on the standardized communication performed by the aforementioned communication unit, A selection unit selects and reserves a date location based on the date and time of the date adjusted by the aforementioned adjustment unit, It comprises a decision receiving unit that receives important decisions from the user. A system characterized by the following features.

2. The aforementioned communications department, It performs standard conversations such as greetings and self-introductions on behalf of the user. The system according to feature 1.

3. The adjustment unit is, It integrates with calendar apps to understand the user's availability and suggests dates and times for dates. The system according to feature 1.

4. The aforementioned selection unit is We collaborate with gourmet websites to suggest and book date locations that match the user's preferences. The system according to feature 1.

5. The receiving unit that makes the aforementioned determination: This process involves approving / rejecting user matches, finalizing date plans, and accepting participation in actual dates. The system according to feature 1.

6. The aforementioned communications department, It estimates the user's emotions and adjusts the tone and content of standard conversations based on those estimated emotions. The system according to feature 1.

7. The aforementioned communications department, Analyze the user's past communication history and select the optimal conversation pattern. The system according to feature 1.

8. The aforementioned communications department, During communication, the conversation content is customized based on the user's current interests and trends. The system according to feature 1.

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