system

Abstract

The system according to this embodiment aims to reduce the effort required of users and facilitate efficient matching in matchmaking and dating services. [Solution] The system according to the embodiment comprises a reception unit, an agency unit, a response unit, and a confirmation unit. The reception unit registers the profile. The agency unit uses the profile registered by the reception unit to have an AI agent perform tasks such as sending "likes," exchanging messages, and scheduling dates. The response unit provides responses in accordance with the user's tone and character during the interactions conducted by the agency unit. The confirmation unit checks the progress and results of the interactions conducted by the response unit.

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 the chatbot's 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, in matchmaking and dating services, there is a problem that the user has to perform "likes", message exchanges, and date scheduling by themselves, which is time-consuming.

[0005] The system according to the embodiment aims to reduce the user's effort and promote matching efficiently in matchmaking and dating services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an agency unit, a response unit, and a confirmation unit. The reception unit registers the profile. The agency unit uses the profile registered by the reception unit to have an AI agent perform tasks such as sending "likes," exchanging messages, and scheduling dates. The response unit provides responses in accordance with the user's tone and character during the interactions conducted by the agency unit. The confirmation unit checks the progress and results of the interactions conducted by the response unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the effort required from users and efficiently facilitate matching in matchmaking and dating services. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 matchmaking and dating support system according to an embodiment of the present invention is a system that utilizes an AI agent to reduce the mental burden of matchmaking and dating services. In this system, the user registers a profile, and the AI ​​agent handles tasks such as sending "likes," exchanging messages, and scheduling dates. The AI ​​agent responds in a manner that matches the user's tone and character, and conducts interviews on their behalf. The user only needs to check the progress and results, and can intervene as needed at points they deem important. This mechanism reduces the mental burden on the user, allowing them to efficiently find their ideal partner. Furthermore, because the AI ​​agent handles interactions with multiple people in parallel, the user can proceed with matchmaking and dating without becoming fatigued. For example, when a user registers a profile, the AI ​​agent automatically sends a "like" and starts exchanging messages. The AI ​​agent analyzes the user's past interaction history and generates the most appropriate message. In addition, the AI ​​agent manages the user's schedule and schedules dates. This allows the user to efficiently proceed with matchmaking and dating. As a result, the matchmaking and dating support system reduces the mental burden on the user and allows them to efficiently find their ideal partner.

[0029] The matchmaking / dating support system according to this embodiment comprises a reception unit, an agency unit, a response unit, and a confirmation unit. The reception unit allows users to register their profiles. The profiles include, for example, name, age, hobbies, occupation, etc., but are not limited to these examples. The reception unit, for example, stores the information entered by the user in a database so that it can be used by the AI ​​agent. The agency unit allows the AI ​​agent to act on behalf of the user, such as sending "likes," exchanging messages, and scheduling dates. For example, the agency unit allows the AI ​​agent to send "likes" and exchange messages on behalf of the user. The agency unit can also allow the AI ​​agent to manage the user's schedule and schedule dates. The response unit provides responses in accordance with the user's tone and character during interactions conducted by the agency unit. For example, the response unit allows the AI ​​agent to analyze the user's past interaction history and generate the most appropriate message. The response unit can also allow the AI ​​agent to generate messages that match the user's tone and character. The confirmation unit checks the progress and results of interactions conducted by the response unit. The verification unit, for example, allows an AI agent to report the progress to the user, and the user can intervene as needed. As a result, the matchmaking / dating support system according to this embodiment reduces the user's mental burden and allows them to efficiently find their ideal partner.

[0030] The reception desk allows users to register their profiles. These profiles may include, but are not limited to, name, age, hobbies, and occupation. Specifically, users enter their profile information through a dedicated application or website. In addition to name, age, hobbies, and occupation, users can register detailed information such as height, weight, birthplace, education level, annual income, marital status, whether they have children, whether they have pets, favorite movies and music, and how they spend their holidays. This information is crucial for users to find their ideal partner. The reception desk stores the entered information in a database, making it available to AI agents. The database is securely managed to prevent the leakage of personal information. Users can update their profile information at any time to reflect the latest information. This allows the reception desk to efficiently collect detailed user profile information and provide a foundation for AI agents to perform optimal matching.

[0031] The proxy service uses AI agents to handle tasks such as sending "likes," exchanging messages, and scheduling dates. Specifically, the AI ​​agents select the most suitable partners based on the user's profile information and past communication history, and send "likes" to them. The AI ​​agents automatically find partners that match the user's preferences and criteria, and approach them efficiently. In addition, during message exchanges, the AI ​​agents generate messages that match the user's tone and personality, facilitating smooth communication. Furthermore, when scheduling dates, the AI ​​agents manage the user's schedule, coordinate with the other party, and propose the best date. This eliminates the hassle of cumbersome communication and scheduling, allowing users to efficiently meet their ideal partner. The proxy service plays a role in reducing the user's burden by automating a series of processes on behalf of the user with AI agents.

[0032] The response unit, in interactions conducted by the proxy unit, provides responses that are in line with the user's tone and character. Specifically, the AI ​​agent analyzes the user's past interaction history to understand the user's communication style and preferences. For example, it learns the words, expressions, and sense of humor that the user usually uses, and generates the most appropriate messages based on that. The AI ​​agent utilizes natural language processing technology to generate messages that match the user's tone and character, facilitating smooth communication. Furthermore, the response unit can accurately understand the user's emotions and intentions and provide appropriate responses accordingly. For example, if the user shows interest in the other party, it will ask questions that will pique that interest; conversely, if the user has lost interest, it will guide them to end the interaction at an appropriate time. In this way, the response unit supports the user's communication and plays a role in fostering smooth relationships with others.

[0033] The verification unit checks the progress and results of interactions conducted by the response unit. Specifically, the AI ​​agent periodically reports the progress of the interaction to the user, allowing the user to understand the situation. For example, the AI ​​agent may report to the user, "The interaction with Mr. / Ms. XX is progressing smoothly," or "The date with Mr. / Ms. XX has been set." It also provides an interface for checking the progress and results so that the user can intervene in the interaction as needed. The user can review the messages and interaction content generated by the AI ​​agent and make corrections or additional instructions as needed. In this way, the verification unit plays a role in supporting the user in understanding the status of the interaction and intervening at the appropriate time. Furthermore, the verification unit analyzes the results of the interaction and provides feedback to improve the user's success rate and satisfaction. For example, based on the results of past interactions, it analyzes which approaches are more likely to succeed and which messages are effective, and provides advice to the user. In this way, the verification unit plays a role in supporting the user's dating and marriage-seeking activities and improving their success rate.

[0034] The parallel communication function allows for simultaneous interaction with multiple people. For example, the parallel communication function allows an AI agent to exchange messages with multiple users simultaneously. Furthermore, the parallel communication function allows an AI agent to simultaneously schedule multiple dates. For instance, the AI ​​agent can exchange messages with multiple users at the same time and provide appropriate responses to each user. It can also simultaneously schedule multiple dates and propose dates that fit each user's schedule. This allows users to engage in dating and relationship activities without becoming overwhelmed by the ability to interact with multiple people in parallel.

[0035] The intervention unit can intervene at times it deems important. For example, it can intervene when a user is making a date and provide appropriate advice. It can also intervene when a user asks an important question and provide an appropriate answer. For instance, when a user is making a date, the AI ​​agent can provide appropriate advice, allowing the user to make the arrangement smoothly. Similarly, when a user asks an important question, the AI ​​agent can provide an appropriate answer, enabling the user to respond appropriately. This allows for more appropriate responses by intervening at times the user deems important.

[0036] The creation unit can create a personal AI for each user and have the AIs communicate with each other on their behalf. For example, the creation unit can customize the personal AI based on the user's data, creating an AI that matches the user's tone of voice and personality. Furthermore, by having the AIs communicate with each other on their behalf, the creation unit eliminates the need for the user to interact directly. For example, by customizing the personal AI based on the user's data, creating an AI that matches the user's tone of voice and personality. Furthermore, by having the AIs communicate with each other on their behalf, the user eliminates the need for the user to interact directly. This allows for more personalized responses by creating a personal AI for each user.

[0037] The proxy function can provide responses that match the user's tone of voice and personality. For example, the proxy function's AI agent can analyze the user's past interaction history and generate the most appropriate message. The proxy function's AI agent can also generate messages that match the user's tone of voice and personality. For example, the AI ​​agent can analyze the user's past interaction history and generate the most appropriate message. The AI ​​agent can also generate messages that match the user's tone of voice and personality. This allows for more natural interactions by providing responses that match the user's tone of voice and personality.

[0038] The verification unit can check the progress and results. For example, the verification unit can have an AI agent report the progress to the user, allowing the user to intervene as needed. The verification unit can also have an AI agent report the results to the user, allowing the user to decide on the next step. This makes it easier for the user to understand the situation by checking the progress and results.

[0039] The reception desk can analyze a user's past profile registration history and select the optimal registration method. For example, the reception desk can analyze the content of a user's past profile registrations and suggest the most suitable method. It can also prioritize suggesting registration methods the user has used in the past (manual, voice input, etc.). Furthermore, based on the user's past registration history, the reception desk can suggest the most suitable registration method for a specific time period. This allows the reception desk to suggest the optimal registration method by analyzing the user's past profile registration history.

[0040] The reception desk can filter profiles based on the user's current lifestyle and areas of interest during the profile registration process. For example, the reception desk can filter profile content based on the user's current lifestyle (work, hobbies, etc.). It can also filter profile content based on the user's areas of interest (sports, music, etc.). Furthermore, the reception desk can suggest the most suitable profile registration method based on the user's lifestyle and areas of interest. This allows for more appropriate profile registration by filtering based on the user's lifestyle and areas of interest.

[0041] The reception desk can prioritize registering highly relevant information when a user registers their profile, taking into account their geographical location. For example, the reception desk can prioritize registering highly relevant information based on the user's current location. It can also prioritize registering highly relevant information based on the user's past location. Furthermore, the reception desk can analyze the user's location information and suggest the optimal profile registration method. For example, it can prioritize registering highly relevant information based on the user's current location. It can also prioritize registering highly relevant information based on the user's past location. Furthermore, it can analyze the user's location information and suggest the optimal profile registration method. As a result, highly relevant information is prioritized when the user's geographical location is taken into consideration.

[0042] The reception desk can analyze a user's social media activity during profile registration and register relevant information. For example, the reception desk can analyze a user's social media activity and register relevant information in their profile. It can also suggest the most suitable profile registration method based on the user's social media posts. Furthermore, the reception desk can analyze a user's social media friendships and register relevant information in their profile. This ensures that relevant information is registered in the profile by analyzing the user's social media activity.

[0043] The proxy service can adjust the level of detail provided based on the importance of the communication during the proxy process. For example, the proxy service can provide detailed information for important communication, and concise information for general communication. Furthermore, the proxy service can respond quickly to urgent communication. This allows for more appropriate responses by adjusting the level of detail based on the importance of the communication.

[0044] The proxy function can apply different proxy algorithms depending on the category of the interaction during the proxy process. For example, when arranging a date, the proxy function applies a schedule management algorithm. It can also apply a natural language processing algorithm when exchanging messages. Furthermore, it can apply a recommendation system algorithm when exchanging "likes." This allows for more appropriate responses by applying different proxy algorithms depending on the category of the interaction.

[0045] The proxy service can determine the priority of the proxy service based on the submission timing of the correspondence. For example, the proxy service will prioritize urgent correspondence. It can also prioritize important correspondence. Furthermore, it can prioritize general correspondence. This allows for more appropriate responses by determining the priority of the proxy service based on the submission timing of the correspondence.

[0046] The proxy unit can adjust the order of proxy actions based on the relevance of the interactions. For example, the proxy unit can prioritize handling highly relevant interactions. It can also postpone handling less relevant interactions. Furthermore, the proxy unit can analyze the relevance of interactions and handle them in the optimal order. For example, it can prioritize handling highly relevant interactions. It can also postpone handling less relevant interactions. Furthermore, it can analyze the relevance of interactions and handle them in the optimal order. This allows for more appropriate responses by adjusting the order of proxy actions based on the relevance of the interactions.

[0047] The response unit can analyze the user's past interaction history to select the optimal response method when responding. For example, the response unit can analyze the user's past interaction history and propose the optimal response method. It can also prioritize suggesting response methods the user has preferred in the past. Furthermore, the response unit can suggest the optimal response method for a specific time period based on the user's past interaction history. This allows the system to propose the optimal response method by analyzing past interaction history.

[0048] The response unit can customize its response methods based on the user's current life circumstances. For example, it can customize its response methods based on the user's current life circumstances (work, hobbies, etc.). It can also customize its response methods based on the user's areas of interest (sports, music, etc.). Furthermore, it can suggest the most appropriate response method based on the user's life circumstances and areas of interest. This allows for more appropriate responses by customizing the response methods according to the user's life circumstances.

[0049] The response unit can select the optimal response method when responding, taking into account the user's geographical location information. For example, the response unit can propose the optimal response method based on the user's current location. It can also propose the optimal response method based on the user's past location information. Furthermore, the response unit can analyze the user's location information and propose the optimal response method. This allows the system to propose the optimal response method by considering the user's geographical location information.

[0050] The response unit can analyze the user's social media activity and suggest appropriate response methods when responding. For example, the response unit can analyze the user's social media activity and suggest the most suitable response method. It can also suggest the most suitable response method based on the content of the user's social media posts. Furthermore, the response unit can analyze the user's social media friendships and suggest the most suitable response method. This allows the system to suggest the most appropriate response method by analyzing the user's social media activity.

[0051] The verification unit can select the optimal verification method by considering the user's device information during verification. For example, if the user is using a smartphone, the verification unit provides a verification method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can provide a verification method optimized for larger screens. Also, if the user is using a smartwatch, the verification unit can provide a concise and highly visible verification method. This allows the system to propose the optimal verification method by considering the user's device information.

[0052] The parallel processing unit can select the optimal parallel processing method by referring to the user's past interaction history during parallel processing. For example, the parallel processing unit can refer to the user's past interaction history and propose the optimal parallel processing method. Furthermore, the parallel processing unit can prioritize suggesting parallel processing methods that the user has preferred in the past. Additionally, the parallel processing unit can propose the optimal parallel processing method for a specific time period based on the user's past interaction history. This allows the optimal parallel processing method to be proposed by referring to past interaction history.

[0053] The parallel processing unit can select the optimal parallel processing method by considering the user's device information during parallel processing. For example, if the user is using a smartphone, the parallel processing unit provides a parallel processing method that matches the screen size. Furthermore, if the user is using a tablet, the parallel processing unit can provide a parallel processing method optimized for larger screens. Also, if the user is using a smartwatch, the parallel processing unit can provide a concise and highly visible parallel processing method. This allows the system to propose the optimal parallel processing method by considering the user's device information.

[0054] The intervention unit can select the optimal intervention method by referring to the user's past intervention history during an intervention. For example, the intervention unit can refer to the user's past intervention history and propose the optimal intervention method. It can also prioritize suggesting intervention methods the user has preferred in the past. Furthermore, the intervention unit can suggest the optimal intervention method for a specific time period based on the user's past intervention history. This allows the system to propose the optimal intervention method by referring to past intervention history.

[0055] The intervention unit can select the optimal intervention method by considering the user's device information during intervention. For example, if the user is using a smartphone, the intervention unit will provide an intervention method that matches the screen size. Furthermore, if the user is using a tablet, the intervention unit can provide an intervention method optimized for larger screens. Also, if the user is using a smartwatch, the intervention unit can provide a concise and highly visible intervention method. This allows the system to propose the optimal intervention method by considering the user's device information.

[0056] The creation unit can select the optimal creation method by referring to the user's past interaction history when creating a personal AI. For example, the creation unit can refer to the user's past interaction history to create the optimal personal AI. Furthermore, the creation unit can prioritize suggesting interaction methods that the user has preferred in the past. Additionally, the creation unit can create a personal AI optimized for a specific time period based on the user's past interaction history. This allows for the creation of an optimal personal AI by referring to past interaction history.

[0057] The creation unit can select the optimal creation method when creating a personal AI, taking into account the user's device information. For example, if the user is using a smartphone, the creation unit will create a personal AI that is adapted to the screen size. Furthermore, if the user is using a tablet, the creation unit can create a personal AI optimized for a larger screen. Also, if the user is using a smartwatch, the creation unit can create a concise and highly visible personal AI. This allows for the creation of an optimal personal AI by considering the user's device information.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The matchmaking and dating support system can also include a feedback section. This feedback section allows users to provide feedback on the AI ​​agent's responses. For example, it can evaluate whether the user is satisfied with the AI ​​agent's messages and use that evaluation to improve the AI ​​agent's responses. The feedback section can also allow users to provide feedback on the outcome of a date, which can then be used to adjust the next date. Furthermore, the feedback section can allow users to provide feedback on the AI ​​agent's overall performance, which can then be used to improve the entire system. By incorporating user feedback, this system enables more satisfying matchmaking and dating support.

[0060] The matchmaking and dating support system can also be equipped with a learning component. This learning component allows the AI ​​agent to learn the user's behavior patterns and preferences, enabling more personalized responses. For example, the learning component can learn the tone and content of messages the user prefers and generate messages accordingly. It can also learn the user's preferred date locations and activities and suggest dates based on that. Furthermore, the learning component can learn from the user's past successes and failures and suggest the optimal approach based on that. This enables responses tailored to the user's behavior patterns and preferences, resulting in more effective matchmaking and dating support.

[0061] The matchmaking and dating support system can also be equipped with a notification function. This notification function can alert users to important events and messages. For example, it can notify users when they receive a new "like." It can also notify users when they receive a message. Furthermore, it can send reminder notifications to help users remember their dates. This allows users to smoothly proceed with their matchmaking and dating activities without missing important events or messages.

[0062] The matchmaking and dating support system can also include an analytics department. This department can analyze user activity data and evaluate the progress and success rate of matchmaking and dating. For example, it can analyze the number of "likes" a user sends and receives to calculate the success rate. It can also analyze the content of messages exchanged by the user and suggest effective communication methods. Furthermore, it can analyze the results of a user's dates and suggest improvements for the next date. This allows users to understand their matchmaking and dating progress and take more effective approaches.

[0063] The matchmaking and dating support system can also include a recommendation function. This function can recommend the most suitable partner to the user. For example, it can recommend compatible partners based on the user's profile information and past interaction history. It can also recommend partners with shared hobbies and interests based on the user's interests and interests. Furthermore, it can recommend partners who are more likely to be successful based on the user's dating success rate. This makes it easier for users to find their ideal partner and allows them to efficiently pursue matchmaking and dating.

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

[0065] Step 1: The reception desk registers the user's profile. The profile includes name, age, hobbies, occupation, etc. The reception desk saves the information entered by the user to a database so that it can be used by the AI ​​agent. Step 2: The proxy service uses AI agents to handle tasks such as sending "likes," exchanging messages, and scheduling dates. The proxy service uses AI agents to send "likes" and exchange messages on behalf of the user. The AI ​​agents can also manage the user's schedule and schedule dates. Step 3: The response unit provides responses tailored to the user's tone and character during interactions conducted by the proxy unit. The response unit uses an AI agent to analyze the user's past interaction history and generate the most appropriate message. The AI ​​agent can also generate messages that match the user's tone and character. Step 4: The verification unit checks the progress and results of the interaction conducted by the response unit. The verification unit allows the AI ​​agent to report the progress to the user, and the user can intervene as needed.

[0066] (Example of form 2) The matchmaking and dating support system according to an embodiment of the present invention is a system that utilizes an AI agent to reduce the mental burden of matchmaking and dating services. In this system, the user registers a profile, and the AI ​​agent handles tasks such as sending "likes," exchanging messages, and scheduling dates. The AI ​​agent responds in a manner that matches the user's tone and character, and conducts interviews on their behalf. The user only needs to check the progress and results, and can intervene as needed at points they deem important. This mechanism reduces the mental burden on the user, allowing them to efficiently find their ideal partner. Furthermore, because the AI ​​agent handles interactions with multiple people in parallel, the user can proceed with matchmaking and dating without becoming fatigued. For example, when a user registers a profile, the AI ​​agent automatically sends a "like" and starts exchanging messages. The AI ​​agent analyzes the user's past interaction history and generates the most appropriate message. In addition, the AI ​​agent manages the user's schedule and schedules dates. This allows the user to efficiently proceed with matchmaking and dating. As a result, the matchmaking and dating support system reduces the mental burden on the user and allows them to efficiently find their ideal partner.

[0067] The matchmaking / dating support system according to this embodiment comprises a reception unit, an agency unit, a response unit, and a confirmation unit. The reception unit allows users to register their profiles. The profiles include, for example, name, age, hobbies, occupation, etc., but are not limited to these examples. The reception unit, for example, stores the information entered by the user in a database so that it can be used by the AI ​​agent. The agency unit allows the AI ​​agent to act on behalf of the user, such as sending "likes," exchanging messages, and scheduling dates. For example, the agency unit allows the AI ​​agent to send "likes" and exchange messages on behalf of the user. The agency unit can also allow the AI ​​agent to manage the user's schedule and schedule dates. The response unit provides responses in accordance with the user's tone and character during interactions conducted by the agency unit. For example, the response unit allows the AI ​​agent to analyze the user's past interaction history and generate the most appropriate message. The response unit can also allow the AI ​​agent to generate messages that match the user's tone and character. The confirmation unit checks the progress and results of interactions conducted by the response unit. The verification unit, for example, allows an AI agent to report the progress to the user, and the user can intervene as needed. As a result, the matchmaking / dating support system according to this embodiment reduces the user's mental burden and allows them to efficiently find their ideal partner.

[0068] The reception desk allows users to register their profiles. These profiles may include, but are not limited to, name, age, hobbies, and occupation. Specifically, users enter their profile information through a dedicated application or website. In addition to name, age, hobbies, and occupation, users can register detailed information such as height, weight, birthplace, education level, annual income, marital status, whether they have children, whether they have pets, favorite movies and music, and how they spend their holidays. This information is crucial for users to find their ideal partner. The reception desk stores the entered information in a database, making it available to AI agents. The database is securely managed to prevent the leakage of personal information. Users can update their profile information at any time to reflect the latest information. This allows the reception desk to efficiently collect detailed user profile information and provide a foundation for AI agents to perform optimal matching.

[0069] The proxy service uses AI agents to handle tasks such as sending "likes," exchanging messages, and scheduling dates. Specifically, the AI ​​agents select the most suitable partners based on the user's profile information and past communication history, and send "likes" to them. The AI ​​agents automatically find partners that match the user's preferences and criteria, and approach them efficiently. In addition, during message exchanges, the AI ​​agents generate messages that match the user's tone and personality, facilitating smooth communication. Furthermore, when scheduling dates, the AI ​​agents manage the user's schedule, coordinate with the other party, and propose the best date. This eliminates the hassle of cumbersome communication and scheduling, allowing users to efficiently meet their ideal partner. The proxy service plays a role in reducing the user's burden by automating a series of processes on behalf of the user with AI agents.

[0070] The response unit, in interactions conducted by the proxy unit, provides responses that are in line with the user's tone and character. Specifically, the AI ​​agent analyzes the user's past interaction history to understand the user's communication style and preferences. For example, it learns the words, expressions, and sense of humor that the user usually uses, and generates the most appropriate messages based on that. The AI ​​agent utilizes natural language processing technology to generate messages that match the user's tone and character, facilitating smooth communication. Furthermore, the response unit can accurately understand the user's emotions and intentions and provide appropriate responses accordingly. For example, if the user shows interest in the other party, it will ask questions that will pique that interest; conversely, if the user has lost interest, it will guide them to end the interaction at an appropriate time. In this way, the response unit supports the user's communication and plays a role in fostering smooth relationships with others.

[0071] The verification unit checks the progress and results of interactions conducted by the response unit. Specifically, the AI ​​agent periodically reports the progress of the interaction to the user, allowing the user to understand the situation. For example, the AI ​​agent may report to the user, "The interaction with Mr. / Ms. XX is progressing smoothly," or "The date with Mr. / Ms. XX has been set." It also provides an interface for checking the progress and results so that the user can intervene in the interaction as needed. The user can review the messages and interaction content generated by the AI ​​agent and make corrections or additional instructions as needed. In this way, the verification unit plays a role in supporting the user in understanding the status of the interaction and intervening at the appropriate time. Furthermore, the verification unit analyzes the results of the interaction and provides feedback to improve the user's success rate and satisfaction. For example, based on the results of past interactions, it analyzes which approaches are more likely to succeed and which messages are effective, and provides advice to the user. In this way, the verification unit plays a role in supporting the user's dating and marriage-seeking activities and improving their success rate.

[0072] The parallel communication function allows for simultaneous interaction with multiple people. For example, the parallel communication function allows an AI agent to exchange messages with multiple users simultaneously. Furthermore, the parallel communication function allows an AI agent to simultaneously schedule multiple dates. For instance, the AI ​​agent can exchange messages with multiple users at the same time and provide appropriate responses to each user. It can also simultaneously schedule multiple dates and propose dates that fit each user's schedule. This allows users to engage in dating and relationship activities without becoming overwhelmed by the ability to interact with multiple people in parallel.

[0073] The intervention unit can intervene at times it deems important. For example, it can intervene when a user is making a date and provide appropriate advice. It can also intervene when a user asks an important question and provide an appropriate answer. For instance, when a user is making a date, the AI ​​agent can provide appropriate advice, allowing the user to make the arrangement smoothly. Similarly, when a user asks an important question, the AI ​​agent can provide an appropriate answer, enabling the user to respond appropriately. This allows for more appropriate responses by intervening at times the user deems important.

[0074] The creation unit can create a personal AI for each user and have the AIs communicate with each other on their behalf. For example, the creation unit can customize the personal AI based on the user's data, creating an AI that matches the user's tone of voice and personality. Furthermore, by having the AIs communicate with each other on their behalf, the creation unit eliminates the need for the user to interact directly. For example, by customizing the personal AI based on the user's data, creating an AI that matches the user's tone of voice and personality. Furthermore, by having the AIs communicate with each other on their behalf, the user eliminates the need for the user to interact directly. This allows for more personalized responses by creating a personal AI for each user.

[0075] The proxy function can provide responses that match the user's tone of voice and personality. For example, the proxy function's AI agent can analyze the user's past interaction history and generate the most appropriate message. The proxy function's AI agent can also generate messages that match the user's tone of voice and personality. For example, the AI ​​agent can analyze the user's past interaction history and generate the most appropriate message. The AI ​​agent can also generate messages that match the user's tone of voice and personality. This allows for more natural interactions by providing responses that match the user's tone of voice and personality.

[0076] The verification unit can check the progress and results. For example, the verification unit can have an AI agent report the progress to the user, allowing the user to intervene as needed. The verification unit can also have an AI agent report the results to the user, allowing the user to decide on the next step. This makes it easier for the user to understand the situation by checking the progress and results.

[0077] The reception desk can estimate the user's emotions and adjust the timing of profile registration based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prompt them to register their profile during a time when they can relax. Alternatively, if the user is relaxed, the reception desk can prompt them to register their profile immediately. Furthermore, if the user is busy, the reception desk can prompt them to register their profile during a time when they have free time. This allows for more appropriate registration by adjusting the timing of profile registration 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.

[0078] The reception desk can analyze a user's past profile registration history and select the optimal registration method. For example, the reception desk can analyze the content of a user's past profile registrations and suggest the most suitable method. It can also prioritize suggesting registration methods the user has used in the past (manual, voice input, etc.). Furthermore, based on the user's past registration history, the reception desk can suggest the most suitable registration method for a specific time period. This allows the reception desk to suggest the optimal registration method by analyzing the user's past profile registration history.

[0079] The reception desk can filter profiles based on the user's current lifestyle and areas of interest during the profile registration process. For example, the reception desk can filter profile content based on the user's current lifestyle (work, hobbies, etc.). It can also filter profile content based on the user's areas of interest (sports, music, etc.). Furthermore, the reception desk can suggest the most suitable profile registration method based on the user's lifestyle and areas of interest. This allows for more appropriate profile registration by filtering based on the user's lifestyle and areas of interest.

[0080] The reception desk can estimate the user's emotions and determine the priority of the profile to be registered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize registering important information. If the user is relaxed, the reception desk can also prioritize registering detailed information. If the user is in a hurry, the reception desk can also prioritize registering only the essential information. By prioritizing the profile according to the user's emotions, more appropriate information is registered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can prioritize registering highly relevant information when a user registers their profile, taking into account their geographical location. For example, the reception desk can prioritize registering highly relevant information based on the user's current location. It can also prioritize registering highly relevant information based on the user's past location. Furthermore, the reception desk can analyze the user's location information and suggest the optimal profile registration method. For example, it can prioritize registering highly relevant information based on the user's current location. It can also prioritize registering highly relevant information based on the user's past location. Furthermore, it can analyze the user's location information and suggest the optimal profile registration method. As a result, highly relevant information is prioritized when the user's geographical location is taken into consideration.

[0082] The reception desk can analyze a user's social media activity during profile registration and register relevant information. For example, the reception desk can analyze a user's social media activity and register relevant information in their profile. It can also suggest the most suitable profile registration method based on the user's social media posts. Furthermore, the reception desk can analyze a user's social media friendships and register relevant information in their profile. This ensures that relevant information is registered in the profile by analyzing the user's social media activity.

[0083] The proxy unit can estimate the user's emotions and adjust its expression based on the estimated emotions. For example, if the user is relaxed, the proxy unit will use softer expressions. If the user is tense, the proxy unit can use simpler and clearer expressions. If the user is excited, the proxy unit can use more energetic expressions. This allows for more appropriate expressions by adjusting the proxy's expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The proxy service can adjust the level of detail provided based on the importance of the communication during the proxy process. For example, the proxy service can provide detailed information for important communication, and concise information for general communication. Furthermore, the proxy service can respond quickly to urgent communication. This allows for more appropriate responses by adjusting the level of detail based on the importance of the communication.

[0085] The proxy function can apply different proxy algorithms depending on the category of the interaction during the proxy process. For example, when arranging a date, the proxy function applies a schedule management algorithm. It can also apply a natural language processing algorithm when exchanging messages. Furthermore, it can apply a recommendation system algorithm when exchanging "likes." This allows for more appropriate responses by applying different proxy algorithms depending on the category of the interaction.

[0086] The proxy unit can estimate the user's emotions and adjust the length of the message based on the estimated emotions. For example, if the user is relaxed, the proxy unit will deliver a longer message. If the user is in a hurry, the proxy unit can deliver a short, concise message. If the user is excited, the proxy unit can deliver a lively message. For example, if the user is relaxed, the proxy unit will deliver a longer message. If the user is in a hurry, the proxy unit can deliver a short, concise message. If the user is excited, the proxy unit can deliver a lively message. This allows for a more appropriate response by adjusting the length of the message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The proxy service can determine the priority of the proxy service based on the submission timing of the correspondence. For example, the proxy service will prioritize urgent correspondence. It can also prioritize important correspondence. Furthermore, it can prioritize general correspondence. This allows for more appropriate responses by determining the priority of the proxy service based on the submission timing of the correspondence.

[0088] The proxy unit can adjust the order of proxy actions based on the relevance of the interactions. For example, the proxy unit can prioritize handling highly relevant interactions. It can also postpone handling less relevant interactions. Furthermore, the proxy unit can analyze the relevance of interactions and handle them in the optimal order. For example, it can prioritize handling highly relevant interactions. It can also postpone handling less relevant interactions. Furthermore, it can analyze the relevance of interactions and handle them in the optimal order. This allows for more appropriate responses by adjusting the order of proxy actions based on the relevance of the interactions.

[0089] The response unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is relaxed, the response unit will respond in a gentle tone. If the user is tense, the response unit can respond in a simple and clear tone. If the user is excited, the response unit can respond in a lively tone. For example, if the user is relaxed, the response unit will respond in a gentle tone. If the user is tense, the response unit can respond in a simple and clear tone. If the user is excited, the response unit can respond in a lively tone. This allows for more appropriate responses by adjusting the response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The response unit can analyze the user's past interaction history to select the optimal response method when responding. For example, the response unit can analyze the user's past interaction history and propose the optimal response method. It can also prioritize suggesting response methods the user has preferred in the past. Furthermore, the response unit can suggest the optimal response method for a specific time period based on the user's past interaction history. This allows the system to propose the optimal response method by analyzing past interaction history.

[0091] The response unit can customize its response methods based on the user's current life circumstances. For example, it can customize its response methods based on the user's current life circumstances (work, hobbies, etc.). It can also customize its response methods based on the user's areas of interest (sports, music, etc.). Furthermore, it can suggest the most appropriate response method based on the user's life circumstances and areas of interest. This allows for more appropriate responses by customizing the response methods according to the user's life circumstances.

[0092] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize important responses. If the user is relaxed, the response unit can also prioritize detailed responses. If the user is in a hurry, the response unit can also prioritize the minimum necessary responses. This allows for more appropriate responses by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The response unit can select the optimal response method when responding, taking into account the user's geographical location information. For example, the response unit can propose the optimal response method based on the user's current location. It can also propose the optimal response method based on the user's past location information. Furthermore, the response unit can analyze the user's location information and propose the optimal response method. This allows the system to propose the optimal response method by considering the user's geographical location information.

[0094] The response unit can analyze the user's social media activity and suggest appropriate response methods when responding. For example, the response unit can analyze the user's social media activity and suggest the most suitable response method. It can also suggest the most suitable response method based on the content of the user's social media posts. Furthermore, the response unit can analyze the user's social media friendships and suggest the most suitable response method. This allows the system to suggest the most appropriate response method by analyzing the user's social media activity.

[0095] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is relaxed, the verification unit can provide a detailed verification method. If the user is tense, the verification unit can also provide a simple and clear verification method. If the user is excited, the verification unit can also provide a lively verification method. This allows for more appropriate verification by adjusting the verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The verification unit can estimate the user's emotions and determine the priority of verifications based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize important verifications. If the user is relaxed, the verification unit can also prioritize detailed verifications. If the user is in a hurry, the verification unit can also prioritize the minimum necessary verifications. This allows for more appropriate verifications by determining the priority of verifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The verification unit can select the optimal verification method by considering the user's device information during verification. For example, if the user is using a smartphone, the verification unit provides a verification method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can provide a verification method optimized for larger screens. Also, if the user is using a smartwatch, the verification unit can provide a concise and highly visible verification method. This allows the system to propose the optimal verification method by considering the user's device information.

[0098] The parallel processing unit can estimate the user's emotions and adjust the parallel processing method based on the estimated emotions. For example, if the user is relaxed, the parallel processing unit can handle multiple interactions simultaneously. If the user is tense, the parallel processing unit can handle interactions one at a time. If the user is excited, the parallel processing unit can handle lively interactions simultaneously. This allows for a more appropriate response by adjusting the parallel processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The parallel processing unit can select the optimal parallel processing method by referring to the user's past interaction history during parallel processing. For example, the parallel processing unit can refer to the user's past interaction history and propose the optimal parallel processing method. Furthermore, the parallel processing unit can prioritize suggesting parallel processing methods that the user has preferred in the past. Additionally, the parallel processing unit can propose the optimal parallel processing method for a specific time period based on the user's past interaction history. This allows the optimal parallel processing method to be proposed by referring to past interaction history.

[0100] The parallel processing unit can estimate the user's emotions and determine the priority of parallel processing based on the estimated emotions. For example, if the user is stressed, the parallel processing unit will prioritize important parallel processing. If the user is relaxed, the parallel processing unit can also prioritize detailed parallel processing. If the user is in a hurry, the parallel processing unit can also prioritize the minimum necessary parallel processing. This allows for more appropriate responses by determining the priority of parallel processing 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.

[0101] The parallel processing unit can select the optimal parallel processing method by considering the user's device information during parallel processing. For example, if the user is using a smartphone, the parallel processing unit provides a parallel processing method that matches the screen size. Furthermore, if the user is using a tablet, the parallel processing unit can provide a parallel processing method optimized for larger screens. Also, if the user is using a smartwatch, the parallel processing unit can provide a concise and highly visible parallel processing method. This allows the system to propose the optimal parallel processing method by considering the user's device information.

[0102] The intervention unit can estimate the user's emotions and adjust the timing of its intervention based on the estimated emotions. For example, if the user is relaxed, the intervention unit will intervene at the appropriate time. It can also intervene earlier if the user is tense, or later if the user is excited. This allows for a more appropriate response by adjusting the timing of intervention 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.

[0103] The intervention unit can select the optimal intervention method by referring to the user's past intervention history during an intervention. For example, the intervention unit can refer to the user's past intervention history and propose the optimal intervention method. It can also prioritize suggesting intervention methods the user has preferred in the past. Furthermore, the intervention unit can suggest the optimal intervention method for a specific time period based on the user's past intervention history. This allows the system to propose the optimal intervention method by referring to past intervention history.

[0104] The intervention unit can estimate the user's emotions and determine the priority of interventions based on the estimated emotions. For example, if the user is stressed, the intervention unit will prioritize important interventions. If the user is relaxed, the intervention unit can also prioritize detailed interventions. If the user is in a hurry, the intervention unit can also prioritize minimal interventions. This allows for more appropriate responses by determining the priority of interventions 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.

[0105] The intervention unit can select the optimal intervention method by considering the user's device information during intervention. For example, if the user is using a smartphone, the intervention unit will provide an intervention method that matches the screen size. Furthermore, if the user is using a tablet, the intervention unit can provide an intervention method optimized for larger screens. Also, if the user is using a smartwatch, the intervention unit can provide a concise and highly visible intervention method. This allows the system to propose the optimal intervention method by considering the user's device information.

[0106] The creation unit can estimate the user's emotions and adjust how the personal AI is created based on the estimated emotions. For example, if the user is relaxed, the creation unit can create a personal AI with a gentle tone. If the user is tense, the creation unit can also create a personal AI with a simple and clear tone. If the user is excited, the creation unit can also create a personal AI with a lively tone. This allows for more appropriate responses by adjusting how the personal AI is created according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The creation unit can select the optimal creation method by referring to the user's past interaction history when creating a personal AI. For example, the creation unit can refer to the user's past interaction history to create the optimal personal AI. Furthermore, the creation unit can prioritize suggesting interaction methods that the user has preferred in the past. Additionally, the creation unit can create a personal AI optimized for a specific time period based on the user's past interaction history. This allows for the creation of an optimal personal AI by referring to past interaction history.

[0108] The creation unit can estimate the user's emotions and determine the priority of personal AI based on the estimated emotions. For example, if the user is stressed, the creation unit will prioritize creating important personal AI. It can also prioritize creating detailed personal AI if the user is relaxed. Furthermore, if the user is in a hurry, the creation unit can prioritize creating only the essential personal AI. This allows for more appropriate responses by prioritizing personal AI 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.

[0109] The creation unit can select the optimal creation method when creating a personal AI, taking into account the user's device information. For example, if the user is using a smartphone, the creation unit will create a personal AI that is adapted to the screen size. Furthermore, if the user is using a tablet, the creation unit can create a personal AI optimized for a larger screen. Also, if the user is using a smartwatch, the creation unit can create a concise and highly visible personal AI. This allows for the creation of an optimal personal AI by considering the user's device information.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The matchmaking and dating support system can also include a feedback section. This feedback section allows users to provide feedback on the AI ​​agent's responses. For example, it can evaluate whether the user is satisfied with the AI ​​agent's messages and use that evaluation to improve the AI ​​agent's responses. The feedback section can also allow users to provide feedback on the outcome of a date, which can then be used to adjust the next date. Furthermore, the feedback section can allow users to provide feedback on the AI ​​agent's overall performance, which can then be used to improve the entire system. By incorporating user feedback, this system enables more satisfying matchmaking and dating support.

[0112] The matchmaking and dating support system can also be equipped with a learning component. This learning component allows the AI ​​agent to learn the user's behavior patterns and preferences, enabling more personalized responses. For example, the learning component can learn the tone and content of messages the user prefers and generate messages accordingly. It can also learn the user's preferred date locations and activities and suggest dates based on that. Furthermore, the learning component can learn from the user's past successes and failures and suggest the optimal approach based on that. This enables responses tailored to the user's behavior patterns and preferences, resulting in more effective matchmaking and dating support.

[0113] The matchmaking and dating support system can also be equipped with a notification function. This notification function can alert users to important events and messages. For example, it can notify users when they receive a new "like." It can also notify users when they receive a message. Furthermore, it can send reminder notifications to help users remember their dates. This allows users to smoothly proceed with their matchmaking and dating activities without missing important events or messages.

[0114] The matchmaking and dating support system can also include an analytics department. This department can analyze user activity data and evaluate the progress and success rate of matchmaking and dating. For example, it can analyze the number of "likes" a user sends and receives to calculate the success rate. It can also analyze the content of messages exchanged by the user and suggest effective communication methods. Furthermore, it can analyze the results of a user's dates and suggest improvements for the next date. This allows users to understand their matchmaking and dating progress and take more effective approaches.

[0115] The matchmaking and dating support system can also include a recommendation function. This function can recommend the most suitable partner to the user. For example, it can recommend compatible partners based on the user's profile information and past interaction history. It can also recommend partners with shared hobbies and interests based on the user's interests and interests. Furthermore, it can recommend partners who are more likely to be successful based on the user's dating success rate. This makes it easier for users to find their ideal partner and allows them to efficiently pursue matchmaking and dating.

[0116] The matchmaking and dating support system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust its response based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit can generate messages to help them relax. If the user is relaxed, the unit can also suggest a more proactive approach. Furthermore, if the user is agitated, the unit can provide advice to help them calm down. This enables responses tailored to the user's emotions, resulting in more effective matchmaking and dating support.

[0117] The matchmaking and dating support system can also be equipped with a stress reduction unit. This unit can estimate the user's stress level and take actions to alleviate that stress. For example, if the user is experiencing high stress levels, the stress reduction unit can provide relaxing content. It can also offer stress-reducing advice if the user is experiencing stress. Furthermore, if the user is experiencing stress, the stress reduction unit can temporarily slow down the matchmaking and dating process. This reduces the user's stress, allowing them to proceed with matchmaking and dating in a more relaxed state.

[0118] The matchmaking and dating support system can also include a motivation maintenance unit. This unit can take actions to maintain the user's motivation. For example, if a user is losing motivation for matchmaking or dating, the unit can provide encouraging messages. It can also boost motivation by allowing users to share their success stories. Furthermore, the unit can provide support to help users set goals and work towards them. This helps maintain user motivation and allows them to continue their matchmaking and dating efforts.

[0119] The matchmaking and dating support system can also include a reflection section. This section allows users to review past interactions and date results, providing feedback for self-improvement. For example, the reflection section helps users review past message exchanges and identify areas for improvement. It can also help users review past date results and suggest improvements for future dates. Furthermore, the reflection section allows users to conduct self-assessments and provides advice for self-improvement based on those assessments. This enables users to improve themselves and pursue matchmaking and dating more effectively.

[0120] The matchmaking and dating support system can also include an engagement section. This section can take steps to increase user engagement in matchmaking and dating. For example, it can provide messages encouraging users to actively engage in matchmaking and dating. It can also increase engagement by allowing users to share their progress. Furthermore, it can provide support to help users set goals for matchmaking and dating and work towards them. This increases user engagement in matchmaking and dating, leading to more effective matchmaking and dating.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk registers the user's profile. The profile includes name, age, hobbies, occupation, etc. The reception desk saves the information entered by the user to a database so that it can be used by the AI ​​agent. Step 2: The proxy service uses AI agents to handle tasks such as sending "likes," exchanging messages, and scheduling dates. The proxy service uses AI agents to send "likes" and exchange messages on behalf of the user. The AI ​​agents can also manage the user's schedule and schedule dates. Step 3: The response unit provides responses tailored to the user's tone and character during interactions conducted by the proxy unit. The response unit uses an AI agent to analyze the user's past interaction history and generate the most appropriate message. The AI ​​agent can also generate messages that match the user's tone and character. Step 4: The verification unit checks the progress and results of the interaction conducted by the response unit. The verification unit allows the AI ​​agent to report the progress to the user, and the user can intervene as needed.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, proxy unit, response unit, confirmation unit, parallel unit, intervention unit, and creation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user registers their profile. The proxy unit is implemented by the specific processing unit 290 of the data processing device 12, where the AI ​​agent handles tasks such as liking posts, exchanging messages, and scheduling dates. The response unit is implemented by the control unit 46A of the smart device 14, where it responds in a manner consistent with the user's tone and character. The confirmation unit is implemented by the specific processing unit 290 of the data processing device 12, where it checks the progress and results of the interaction. The parallel unit is implemented by the control unit 46A of the smart device 14, where it handles interactions with multiple people in parallel. The intervention unit is implemented by the specific processing unit 290 of the data processing device 12, where it intervenes at points deemed important. The creation unit is implemented, for example, by the control unit 46A of the smart device 14, which creates a personal AI for each user and has the AIs communicate with each other on their behalf. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, proxy unit, response unit, confirmation unit, parallel unit, intervention unit, and creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user registers their profile. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12, where the AI ​​agent handles tasks such as liking posts, exchanging messages, and scheduling dates. The response unit is implemented by the control unit 46A of the smart glasses 214, where it responds in a manner appropriate to the user's tone and character. The confirmation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it checks the progress and results of the interaction. The parallel unit is implemented by the control unit 46A of the smart glasses 214, where it handles interactions with multiple people in parallel. The intervention unit is implemented by the specific processing unit 290 of the data processing unit 12, where it intervenes at points deemed important. The creation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which creates a personal AI for each user and has the AIs communicate with each other on their behalf. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, proxy unit, response unit, confirmation unit, parallel unit, intervention unit, and creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user registers their profile. The proxy unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the AI ​​agent handles tasks such as liking posts, exchanging messages, and scheduling dates. The response unit is implemented by, for example, the control unit 46A of the headset terminal 314, where it responds in a manner consistent with the user's tone and character. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it checks the progress and results of the interaction. The parallel unit is implemented by, for example, the control unit 46A of the headset terminal 314, where it handles interactions with multiple people in parallel. The intervention unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it intervenes at points deemed important. The creation unit is implemented, for example, by the control unit 46A of the headset terminal 314, which creates a personal AI for each user and has the AIs communicate with each other on their behalf. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, proxy unit, response unit, confirmation unit, parallel unit, intervention unit, and creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user registers their profile. The proxy unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the AI ​​agent handles tasks such as liking posts, exchanging messages, and scheduling dates. The response unit is implemented by, for example, the control unit 46A of the robot 414, where it responds in a manner appropriate to the user's tone and character. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it checks the progress and results of the interaction. The parallel unit is implemented by, for example, the control unit 46A of the robot 414, where it interacts with multiple people in parallel. The intervention unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it intervenes at points deemed important. The creation unit is implemented, for example, by the control unit 46A of the robot 414, which creates a personal AI for each user and has the AIs communicate with each other on their behalf. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The reception desk where you register your profile, Based on the profiles registered by the aforementioned reception department, the AI ​​agent acts as an agent, handling tasks such as sending "likes," exchanging messages, and scheduling dates. The aforementioned proxy unit provides responses that are in line with the user's tone and character during the interaction, The system includes a confirmation unit that checks the progress and results of the exchange conducted by the aforementioned response unit. A system characterized by the following features. (Note 2) It also includes a parallel processing unit for handling interactions with multiple people simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will further develop intervention teams to intervene in situations we deem important. The system described in Appendix 1, characterized by the features described herein. (Note 4) It further includes a creation unit that creates a personal AI for each user and has the AIs communicate with each other on their behalf. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned agency unit, The system will respond in a manner that matches the user's tone and personality. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is Check the progress and results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of profile registration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past profile registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When registering a profile, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of profiles to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When registering a profile, the system prioritizes registering highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a user registers their profile, the system analyzes their social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned agency unit, It estimates the user's emotions and adjusts the way it expresses itself based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned agency unit, When acting as an intermediary, the level of detail provided will be adjusted based on the importance of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned agency unit, When acting as an intermediary, different intermediary algorithms are applied depending on the category of the interaction. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned agency unit, It estimates the user's emotions and adjusts the length of the proxy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned agency unit, When acting as an agent, the priority of the agent will be determined based on the timing of submission of communications. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned agency unit, When acting as an agent, the order of actions is adjusted based on the relevance of the interactions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response unit is, It estimates the user's emotions and adjusts its response method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response unit is, When responding, the system analyzes the user's past interaction history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response unit is, When responding, the method of response is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response unit is, It estimates the user's emotions and determines the priority of responses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response unit is, When responding, the system selects the most appropriate response method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned response unit is, When responding, the system analyzes the user's social media activity and suggests appropriate response methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is During verification, the optimal verification method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned parallel section is It estimates the user's emotions and adjusts the parallel processing method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned parallel section is During parallel processing, the system selects the optimal parallel processing method by referring to the user's past interaction history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned parallel section is The system estimates the user's emotions and determines the priority of parallel processing based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned parallel section is During parallel processing, the optimal parallel processing method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned intervention unit is It estimates the user's emotions and adjusts the timing of interventions based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned intervention unit is During intervention, the system selects the optimal intervention method by referring to the user's past intervention history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned intervention unit is The system estimates the user's emotions and determines the priority of interventions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned intervention unit is During intervention, the optimal intervention method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned creation unit, It estimates the user's emotions and adjusts how the personal AI is created based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned creation unit, When creating a personal AI, the system selects the optimal creation method by referring to the user's past interaction history. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned creation unit, It estimates the user's emotions and determines the priority of the personal AI based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned creation unit, When creating a personal AI, the optimal creation method is selected by considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you register your profile, Based on the profile registered by the aforementioned reception department, an AI agent acts as an intermediary, handling tasks such as exchanging messages and scheduling dates. The aforementioned proxy unit provides responses that are in line with the user's tone and character during the interaction, The system includes a confirmation unit that checks the progress and results of the exchange conducted by the aforementioned response unit. A system characterized by the following features.

2. It also includes a parallel processing unit for handling interactions with multiple people simultaneously. The system according to feature 1.

3. We will further develop intervention teams to intervene in situations we deem important. The system according to feature 1.

4. It further includes a creation unit that creates a personal AI for each user and has the AIs communicate with each other on their behalf. The system according to feature 1.

5. The aforementioned agency unit, The system will respond in a manner that matches the user's tone and personality. The system according to feature 1.

6. The aforementioned verification unit is Check the progress and results. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of profile registration based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned reception unit is When registering a profile, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of profiles to register based on the estimated user emotions. The system according to feature 1.

11. The aforementioned reception unit is When registering a profile, the system prioritizes registering highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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