System

Abstract

To provide a system that automates interactions in marriage hunting services and reduces the burden on users.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, a provision unit and a transmission unit. The reception unit receives user profile information. The generation unit generates a message based on information received by the reception unit. The provision unit provides the message generated by the generation unit to the user in a form that can be modified by the user. The transmission unit transmits the message provided by the provision unit based on the user's request.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, issues with matchmaking services included the burden of communication and the barrier of actually meeting the person in question.

[0005] The system according to the embodiment aims to automate transactions in matchmaking services and reduce the burden on users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a transmission unit. The reception unit receives profile information of a user. The generation unit generates a message based on the information received by the reception unit. The provision unit provides the message generated by the generation unit to the user in a form that can be modified by the user. The transmission unit transmits the message provided by the provision unit based on a request from the user. Effect of the Invention

[0007] The system according to the embodiment can automate transactions in matchmaking services, reducing the burden on users. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The matchmaking support system according to the embodiment of the present invention is a system that entrusts some of the interactions in matchmaking services such as matching apps to a generation AI. This matchmaking support system has a mechanism that accepts user profile information, generates a message based on the accepted information, provides the generated message in a form that can be modified by the user, and transmits it at the user's request. For example, information such as name, age, hobbies, and occupation is input as the user's profile information. This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a message. For example, a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" is generated. The generated message is provided in a form that can be modified by the user. For example, the user can change or add part of the message. Furthermore, the generation AI analyzes the other party's reaction and generates the next message. For example, if the other party replies with "My hobby is reading," the generation AI generates the next message such as "What kind of books do you like?" In this way, support is provided so that the interaction proceeds smoothly. The generation AI also analyzes the other party's profile information and generates a message tailored to the other party. For example, if the other person's hobby is sports, a message such as "What sports have you been playing lately?" can be generated. This makes it possible to interest the other person. This system reduces the burden of communication before actually meeting the matchmaker. For example, even if a person is busy at work and does not have time to think of a message to send, the generation AI can automatically generate a message, allowing for efficient communication. Also, even if the person is not good at communication, the generation AI will support them, allowing them to proceed with their matchmaking with peace of mind. In this way, the matchmaking support system reduces the burden on the matchmaker and allows for efficient communication.

[0029] The matchmaking support system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a transmission unit. The reception unit receives profile information of a user. The profile information of a user includes, for example, a name, an age, a hobby, an occupation, and the like, but is not limited to such examples. For example, the reception unit stores the information input by the user in a database and provides it to the generation unit. The generation unit generates a message based on the information received by the reception unit using a generation AI. The generation AI generates a message using, for example, a text generation AI (for example, LLM). The generation AI analyzes the profile information of the user and generates an appropriate message. For example, the generation AI generates a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" The provision unit provides the message generated by the generation unit in a form that can be modified by the user. The user can, for example, change or add a part of the message. The provision unit saves the message modified by the user and provides it to the transmission unit. The transmission unit transmits the message provided by the provision unit at the request of the user. For example, the transmission unit transmits the message to the other party and feeds back the transmission result to the user. As a result, the matchmaking support system according to the embodiment can reduce the burden on matchmakers by accepting user profile information, generating messages, providing the messages in an editable manner, and sending the messages.

[0030] The reception unit receives the profile information of the user. The profile information of the user includes, but is not limited to, for example, name, age, hobbies, and occupation. The reception unit, for example, stores the information input by the user in a database and provides it to the generation unit. Specifically, the reception unit receives information provided by the user through a dedicated input form in real time and stores it in a database. This input form is accessible through a web browser or a mobile application, and the user can easily input information. The input information is stored in a database with security measures and is encrypted to prevent leakage of personal information. Furthermore, the reception unit has a check function for confirming the accuracy of the information input by the user, and performs validation to prevent input errors in, for example, age and occupation. This allows the reception unit to accurately and safely receive the profile information of the user and provide it to the next processing step. In addition, the reception unit also supports cases where the user wants to update the profile information, allowing the user to modify and add information at any time. This makes it possible to always provide services based on the latest information.

[0031] The generation unit uses the generation AI to generate a message based on the information received by the reception unit. The generation AI generates a message using, for example, a text generation AI (e.g., LLM). The generation AI analyzes the user's profile information and generates an appropriate message. Specifically, the generation AI generates an individually customized message based on information such as the user's name, hobbies, and occupation. For example, if the user's hobby is "watching movies," the generation AI generates a message such as "Hello, Mr. X. Your hobby is watching movies, isn't it? What movie have you seen recently?" The generation AI makes full use of natural language processing technology to create a message that includes content and questions that are likely to interest the user. In addition, the generation AI can learn from past message exchanges and the user's reactions and build a feedback loop to generate more effective messages. This allows the generation unit to quickly and accurately generate individually optimized messages by utilizing the user's profile information. The generation AI can also provide messages that match the user's preferences by generating multiple message candidates and allowing the user to select one.

[0032] The providing unit provides the message generated by the generating unit in a form that can be modified by the user. For example, the user can change or add a part of the message. The providing unit saves the message modified by the user and provides it to the sending unit. Specifically, the providing unit displays the generated message on the user's device and provides an interface that allows the user to easily edit the message. This interface allows intuitive operation, and the user can modify the message by dragging and dropping or by entering text. Furthermore, the providing unit also has a function of saving the history of messages modified by the user and making it possible to refer to them later. This allows the user to refer to the history when creating a new message based on past messages. In addition, the providing unit analyzes the content of the message modified by the user and provides feedback to the generating AI to improve the accuracy of message generation from the next time onwards. As a result, the providing unit provides an environment in which the user can freely customize the generated message, thereby increasing the user's satisfaction.

[0033] The sending unit sends the message provided by the providing unit in response to a user's request. For example, the sending unit sends the message to the other party and feeds back the sending result to the user. Specifically, when the user presses the send button, the sending unit sends the message to the designated other party and notifies the user in real time whether the sending was successful or not. The sending unit supports multiple communication means, and can send messages through, for example, email, SMS, chat applications, and the like. Furthermore, the sending unit also has a function of tracking the status of the sent message and checking whether the other party has received the message and opened it. This allows the user to understand the sending status of the message and to resend or follow up as necessary. In addition, the sending unit provides an analysis function based on the sending result, allowing the user to evaluate which message was effective. This allows the sending unit to not only reliably deliver the message generated by the user to the other party, but also to evaluate its effectiveness and use it to create messages from the next time onwards.

[0034] The generation unit can generate a message using a generation AI. The generation unit generates a message using, for example, a generation AI. The generation AI generates a message using, for example, a text generation AI (for example, LLM). The generation AI analyzes the user's profile information and generates an appropriate message. For example, the generation AI generates a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" The generation AI can also generate a message that will interest the other person based on the user's profile information. For example, if the other person's hobby is sports, a message such as "What kind of sports have you been playing recently?" is generated. In this way, the generation AI is used to automate the generation of messages. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's profile information and outputs a message.

[0035] The providing unit can provide the user with a plurality of message options generated by the generating unit. The providing unit, for example, provides the user with a plurality of message options generated by the generating unit. The plurality of message options include, for example, messages with different tones and contents, but are not limited to such examples. The providing unit displays a plurality of message options so that the user can select from the options. For example, the providing unit displays the message options generated by the generating unit in a list format so that the user can select. As a result, by providing a plurality of message options, the user can select from the options. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide message options using an AI model that uses the message options generated by the generating unit as input and provides them to the user.

[0036] The generation unit can analyze the other party's reaction and generate the next message based on the analysis result. The generation unit can, for example, analyze the other party's reaction and generate the next message based on the analysis result. The other party's reaction includes, for example, a text message, an image message, and the like, but is not limited to such examples. The generation unit can use text analysis technology and sentiment analysis technology to analyze the other party's reaction. For example, the generation unit can analyze the other party's text message and generate the next message based on the content. The generation unit can also analyze the other party's emotion and generate the next message based on the emotion. For example, if the other party replies, "My hobby is reading," the generation unit generates the next message such as, "What kind of books do you like?". In this way, the next message is generated based on the other party's reaction, and the exchange proceeds smoothly. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate the next message using a generation AI model that inputs the other party's reaction and outputs the next message.

[0037] The generation unit can analyze the profile information of the other party and generate a message tailored to the other party. The generation unit can, for example, analyze the profile information of the other party and generate a message tailored to the other party. The profile information of the other party includes, for example, name, age, hobbies, occupation, etc., but is not limited to such examples. The generation unit can use text analysis technology or data mining technology to analyze the profile information of the other party. For example, if the other party's hobby is sports, the generation unit generates a message such as "What kind of sports have you been playing recently?". The generation unit can also generate a work-related message based on the other party's occupation. For example, if the other party is a doctor, the generation unit generates a message such as "What are the recent topics in the medical field?". This makes it possible to generate a message tailored to the other party and interest the other party. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the other party's profile information and outputs a message tailored to the other party.

[0038] The reception unit can analyze the user's past profile information and select an appropriate input method. The reception unit, for example, analyzes the user's past profile information and selects an appropriate input method. The past profile information includes, for example, previous input contents, past activity history, etc., but is not limited to such examples. The reception unit can cause the user to input profile information in a simple question format based on information previously input by the user. For example, the reception unit can cause the user to input profile information by presenting options based on information previously input by the user. In addition, the reception unit can also cause the user to input profile information by suggesting voice input based on information previously input by the user. In this way, the optimal input method can be provided by analyzing the past profile information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs past profile information and outputs an optimal input method.

[0039] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting profile information. The reception unit performs filtering based on the user's current living situation and field of interest when inputting profile information, for example. Living situations include, but are not limited to, for example, occupation, home environment, daily schedule, etc. Fields of interest include, but are not limited to, for example, hobbies, topics of interest, etc. The reception unit can display related questions based on the field of interest when the user inputs the current living situation. For example, the reception unit displays related options based on the current living situation when the user inputs a hobby. In addition, the reception unit can also display related information based on the current living situation when the user inputs an occupation. In this way, by filtering based on the user's living situation and field of interest, highly relevant information can be provided. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's living situation and field of interest and outputs related information.

[0040] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting profile information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting profile information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The reception unit can display related information based on the geographical location information when the user inputs a current location. For example, the reception unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the reception unit can also display related information based on the geographical location information when the user inputs an occupation. This improves the user's convenience by providing highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the geographical location information of the user and outputs related information.

[0041] The reception unit can analyze the social media activity of the user and input related information when the profile information is input. The reception unit can analyze the social media activity of the user and input related information when the profile information is input, for example, when the profile information is input. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. The reception unit can display related information based on the social media activity when the user inputs a hobby. For example, the reception unit displays related information based on the social media activity when the user inputs an occupation. The reception unit can also display related information based on the social media activity when the user inputs a current living situation. This makes it more efficient to input the profile information of the user by providing related information based on the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the social media activity of the user and outputs related information.

[0042] The generation unit can adjust the level of detail of the message based on the user's importance when generating the message. For example, the generation unit adjusts the level of detail of the message based on the user's importance when generating the message. The user's importance includes, for example, the user's activity, past interactions, etc., but is not limited to such examples. The generation unit can generate a detailed message when the user interacts with an important person. For example, the generation unit generates a concise message when the user interacts with a casual person. In addition, the generation unit can also generate a message with an appropriate level of detail when the user interacts with a person they have met for the first time. As a result, an appropriate message can be generated by adjusting the level of detail of the message based on the user's importance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's importance and outputs the level of detail of the message.

[0043] The generation unit can apply different generation algorithms depending on the user's category when generating a message. For example, the generation unit applies different generation algorithms depending on the user's category when generating a message. The user's category includes, for example, age group, interests, occupation, etc., but is not limited to such examples. If the user belongs to a business category, the generation unit can apply a business-oriented generation algorithm. For example, if the user belongs to a hobby category, the generation unit applies a hobby-oriented generation algorithm. In addition, if the user belongs to a friend category, the generation unit can also apply a friend-oriented generation algorithm. In this way, a more appropriate message can be generated by applying a generation algorithm depending on the user's category. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's category and outputs a generation algorithm.

[0044] The generation unit can determine the priority of messages based on the user's submission time when generating messages. The generation unit, for example, determines the priority of messages based on the user's submission time when generating messages. The submission time includes, for example, the submission date and time, the submission frequency, etc., but is not limited to such examples. The generation unit can generate a message that replies quickly when the user is in a hurry. For example, the generation unit generates a detailed message when the user has time. In addition, the generation unit can also generate a message that matches the deadline when the user needs to reply within a specific deadline. This allows messages to be generated at an appropriate time by determining the priority of messages based on the user's submission time. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's submission time and outputs the priority of messages.

[0045] The generation unit can adjust the order of messages based on the relevance of the user when generating messages. The generation unit, for example, adjusts the order of messages based on the relevance of the user when generating messages. Relevance includes, for example, the user's interests and past interactions, but is not limited to such examples. When the user interacts with an important person, the generation unit can preferentially generate a message for that person. For example, when the user interacts with multiple people, the generation unit preferentially generates a message for a person with a high relevance. In addition, when the user interacts with a specific person, the generation unit can also generate a message for that person first. Thereby, by adjusting the order of messages based on the relevance of the user, important messages can be preferentially generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the relevance of the user and outputs the order of messages.

[0046] The providing unit can select an appropriate providing method by referring to the user's past operation history when providing a message. For example, the providing unit selects an appropriate providing method by referring to the user's past operation history when providing a message. The past operation history includes, for example, a click history, a browsing history, and the like, but is not limited to such examples. The providing unit can select an optimal providing method based on a providing method used by the user in the past. For example, the providing unit selects the most effective providing method from the user's past operation history. In addition, the providing unit can also analyze the user's past operation history and propose an optimal providing method. As a result, the optimal providing method can be selected by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that inputs the user's past operation history and outputs an optimal providing method.

[0047] The providing unit can customize the means of providing based on the user's current living situation when providing a message. For example, the providing unit customizes the means of providing based on the user's current living situation when providing a message. Living situations include, for example, occupation, home environment, daily schedule, etc., but are not limited to such examples. The providing unit can provide a concise message when the user is busy. For example, the providing unit provides a detailed message when the user is relaxed. In addition, the providing unit can also provide a message tailored to a specific situation when the user is in a specific situation. This allows the message to be provided in an optimal manner for the user by customizing the means of providing based on the current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can customize the means using an AI model that inputs the user's living situation and outputs the means of providing.

[0048] The providing unit can select an appropriate providing method in consideration of the geographical location information of the user when providing a message. For example, the providing unit selects an appropriate providing method in consideration of the geographical location information of the user when providing a message. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The providing unit can display related information based on the geographical location information when the user inputs a current location. For example, the providing unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the providing unit can also display related information based on the geographical location information when the user inputs an occupation. In this way, by selecting an optimal providing method based on the geographical location information, it is possible to provide information that is highly relevant to the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that inputs the geographical location information of the user and outputs an optimal providing method.

[0049] The providing unit can analyze the social media activity of the user and suggest a means of providing the message when providing the message. For example, the providing unit analyzes the social media activity of the user and suggests a means of providing the message when providing the message. The social media activity includes, for example, the content of the post, the number of followers, the number of likes, and the like, but is not limited to such examples. The providing unit can display related information based on the social media activity when the user inputs a hobby. For example, the providing unit displays related information based on the social media activity when the user inputs an occupation. The providing unit can also display related information based on the social media activity when the user inputs a current living situation. This makes it possible to provide a message in an optimal manner for the user by suggesting a means of providing based on the social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can suggest a means using an AI model that inputs the social media activity of the user and outputs a means of providing.

[0050] The transmission unit can select an appropriate transmission method by referring to the user's past transmission history when transmitting a message. For example, the transmission unit selects an appropriate transmission method by referring to the user's past transmission history when transmitting a message. The past transmission history includes, for example, the transmission date and time, the transmission content, etc., but is not limited to such examples. The transmission unit can select an optimal transmission method based on the transmission method used by the user in the past. For example, the transmission unit selects the most effective transmission method from the user's past transmission history. In addition, the transmission unit can also analyze the user's past transmission history and propose the optimal transmission method. In this way, the optimal transmission method can be selected by referring to the past transmission history. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select a transmission method using an AI model that inputs the user's past transmission history and outputs the optimal transmission method.

[0051] The sending unit can customize the means of transmission based on the user's current living situation when sending a message. The sending unit, for example, customizes the means of transmission based on the user's current living situation when sending a message. Living situations include, for example, occupation, home environment, daily schedule, etc., but are not limited to such examples. The sending unit can send a concise message when the user is busy. For example, the sending unit can send a detailed message when the user is relaxed. In addition, the sending unit can also send a message tailored to the situation when the user is in a specific situation. This allows the message to be sent in an optimal manner for the user by customizing the means of transmission based on the current living situation. Part or all of the above-mentioned processing in the sending unit may be performed, for example, using AI, or may be performed without using AI. For example, the sending unit can customize the means using an AI model that inputs the user's living situation and outputs the means of transmission.

[0052] The transmission unit can select an appropriate transmission method in consideration of the geographical location information of the user when transmitting a message. For example, the transmission unit selects an appropriate transmission method in consideration of the geographical location information of the user when transmitting a message. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The transmission unit can display related information based on the geographical location information when the user inputs a current location. For example, the transmission unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the transmission unit can also display related information based on the geographical location information when the user inputs an occupation. In this way, by selecting an optimal transmission method based on the geographical location information, it is possible to transmit information that is highly relevant to the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select a transmission method using an AI model that inputs the geographical location information of the user and outputs an optimal transmission method.

[0053] The transmission unit can analyze the social media activity of the user and suggest a means of transmission when sending a message. For example, the transmission unit analyzes the social media activity of the user and suggests a means of transmission when sending a message. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. The transmission unit can display related information based on the social media activity when the user inputs a hobby. For example, the transmission unit displays related information based on the social media activity when the user inputs an occupation. The transmission unit can also display related information based on the social media activity when the user inputs a current living situation. In this way, by suggesting a means of transmission based on the social media activity, it is possible to send a message in a manner optimal for the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can suggest a means using an AI model that inputs the social media activity of the user and outputs a means of transmission.

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

[0055] The matchmaking support system can further analyze the user's past message history and generate messages based on the user's communication style. For example, the system can analyze the language and tone used by the user in the past and adjust the style of the generated message based on the analysis. If the user prefers casual language, the generated message will also be generated in a casual tone. On the other hand, if the user prefers formal language, the generated message will also be generated in a formal tone. This allows for more natural interactions by providing messages that match the user's communication style.

[0056] The providing unit can analyze the user's past message revision history and learn the style and content of messages that the user prefers. For example, it can analyze what revisions the user has made in the past and suggest messages to be generated based on that. If the user prefers a specific phrase, messages containing that phrase can be preferentially suggested. Also, if the user tends to avoid a specific topic, messages that do not include that topic can be suggested. This makes it possible to provide a service with a higher level of satisfaction by providing messages that match the user's preferences.

[0057] The providing unit can suggest messages based on relevant local events and activities taking into account the geographic location information of the user. For example, if the user is in a particular area, messages including information about events and activities held in the area can be suggested. If the user is interested in sporting events, messages related to sporting events held in the area can be suggested. Also, if the user is interested in cultural events, messages related to cultural events held in the area can be suggested. This allows for more interesting interactions by providing relevant messages based on the geographic location information of the user.

[0058] The reception unit can analyze the user's past input history of profile information and provide information in a format that is easy for the user to input. For example, if the user has preferred input in multiple choice format in the past, input in multiple choice format can be provided preferentially. Also, if the user has preferred input in free text format in the past, input in free text format can be provided preferentially. This allows for more comfortable input of profile information by providing an input format that matches the user's preferences.

[0059] The sending unit can analyze the user's past message sending history and learn the user's preferred sending timing. For example, if the user has tended to send messages at a specific time of day in the past, the sending unit can send messages according to that time of day. Also, if the user has tended to send messages on a specific day of the week in the past, the sending unit can send messages according to that day of the week. This allows for more effective message sending by providing sending timings that match the user's preferences.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives the profile information of the user. The profile information of the user includes, for example, the name, age, hobbies, occupation, etc. The reception unit stores the information input by the user in a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to generate a message based on the information received by the reception unit. The generation AI generates a message using, for example, a text generation AI (e.g., LLM) and analyzes the user's profile information to generate an appropriate message. Step 3: The providing unit provides the message generated by the generating unit in a form that can be modified by the user. The user can change or add parts of the message. The providing unit stores the message modified by the user and provides it to the sending unit. Step 4: The sending unit sends the message provided by the providing unit in response to a user request. The sending unit sends the message to the other party and provides feedback of the sending result to the user.

[0062] (Example 2) The matchmaking support system according to the embodiment of the present invention is a system that entrusts some of the interactions in matchmaking services such as matching apps to a generation AI. This matchmaking support system has a mechanism that accepts user profile information, generates a message based on the accepted information, provides the generated message in a form that can be modified by the user, and transmits it at the user's request. For example, information such as name, age, hobbies, and occupation is input as the user's profile information. This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a message. For example, a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" is generated. The generated message is provided in a form that can be modified by the user. For example, the user can change or add part of the message. Furthermore, the generation AI analyzes the other party's reaction and generates the next message. For example, if the other party replies with "My hobby is reading," the generation AI generates the next message such as "What kind of books do you like?" In this way, support is provided so that the interaction proceeds smoothly. The generation AI also analyzes the other party's profile information and generates a message tailored to the other party. For example, if the other person's hobby is sports, a message such as "What sports have you been playing lately?" can be generated. This makes it possible to interest the other person. This system reduces the burden of communication before actually meeting the matchmaker. For example, even if a person is busy at work and does not have time to think of a message to send, the generation AI can automatically generate a message, allowing for efficient communication. Also, even if the person is not good at communication, the generation AI will support them, allowing them to proceed with their matchmaking with peace of mind. In this way, the matchmaking support system reduces the burden on the matchmaker and allows for efficient communication.

[0063] The matchmaking support system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a transmission unit. The reception unit receives profile information of a user. The profile information of a user includes, for example, a name, an age, a hobby, an occupation, and the like, but is not limited to such examples. For example, the reception unit stores the information input by the user in a database and provides it to the generation unit. The generation unit generates a message based on the information received by the reception unit using a generation AI. The generation AI generates a message using, for example, a text generation AI (for example, LLM). The generation AI analyzes the profile information of the user and generates an appropriate message. For example, the generation AI generates a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" The provision unit provides the message generated by the generation unit in a form that can be modified by the user. The user can, for example, change or add a part of the message. The provision unit saves the message modified by the user and provides it to the transmission unit. The transmission unit transmits the message provided by the provision unit at the request of the user. For example, the transmission unit transmits the message to the other party and feeds back the transmission result to the user. As a result, the matchmaking support system according to the embodiment can reduce the burden on matchmakers by accepting user profile information, generating messages, providing the messages in an editable manner, and sending the messages.

[0064] The reception unit receives the profile information of the user. The profile information of the user includes, but is not limited to, for example, name, age, hobbies, and occupation. The reception unit, for example, stores the information input by the user in a database and provides it to the generation unit. Specifically, the reception unit receives information provided by the user through a dedicated input form in real time and stores it in a database. This input form is accessible through a web browser or a mobile application, and the user can easily input information. The input information is stored in a database with security measures and is encrypted to prevent leakage of personal information. Furthermore, the reception unit has a check function for confirming the accuracy of the information input by the user, and performs validation to prevent input errors in, for example, age and occupation. This allows the reception unit to accurately and safely receive the profile information of the user and provide it to the next processing step. In addition, the reception unit also supports cases where the user wants to update the profile information, allowing the user to modify and add information at any time. This makes it possible to always provide services based on the latest information.

[0065] The generation unit uses the generation AI to generate a message based on the information received by the reception unit. The generation AI generates a message using, for example, a text generation AI (e.g., LLM). The generation AI analyzes the user's profile information and generates an appropriate message. Specifically, the generation AI generates an individually customized message based on information such as the user's name, hobbies, and occupation. For example, if the user's hobby is "watching movies," the generation AI generates a message such as "Hello, Mr. X. Your hobby is watching movies, isn't it? What movie have you seen recently?" The generation AI makes full use of natural language processing technology to create a message that includes content and questions that are likely to interest the user. In addition, the generation AI can learn from past message exchanges and the user's reactions and build a feedback loop to generate more effective messages. This allows the generation unit to quickly and accurately generate individually optimized messages by utilizing the user's profile information. The generation AI can also provide messages that match the user's preferences by generating multiple message candidates and allowing the user to select one.

[0066] The providing unit provides the message generated by the generating unit in a form that can be modified by the user. For example, the user can change or add a part of the message. The providing unit saves the message modified by the user and provides it to the sending unit. Specifically, the providing unit displays the generated message on the user's device and provides an interface that allows the user to easily edit the message. This interface allows intuitive operation, and the user can modify the message by dragging and dropping or by entering text. Furthermore, the providing unit also has a function of saving the history of messages modified by the user and making it possible to refer to them later. This allows the user to refer to the history when creating a new message based on past messages. In addition, the providing unit analyzes the content of the message modified by the user and provides feedback to the generating AI to improve the accuracy of message generation from the next time onwards. As a result, the providing unit provides an environment in which the user can freely customize the generated message, thereby increasing the user's satisfaction.

[0067] The sending unit sends the message provided by the providing unit in response to a user's request. For example, the sending unit sends the message to the other party and feeds back the sending result to the user. Specifically, when the user presses the send button, the sending unit sends the message to the designated other party and notifies the user in real time whether the sending was successful or not. The sending unit supports multiple communication means, and can send messages through, for example, email, SMS, chat applications, and the like. Furthermore, the sending unit also has a function of tracking the status of the sent message and checking whether the other party has received the message and opened it. This allows the user to understand the sending status of the message and to resend or follow up as necessary. In addition, the sending unit provides an analysis function based on the sending result, allowing the user to evaluate which message was effective. This allows the sending unit to not only reliably deliver the message generated by the user to the other party, but also to evaluate its effectiveness and use it to create messages from the next time onwards.

[0068] The generation unit can generate a message using a generation AI. The generation unit generates a message using, for example, a generation AI. The generation AI generates a message using, for example, a text generation AI (for example, LLM). The generation AI analyzes the user's profile information and generates an appropriate message. For example, the generation AI generates a message such as "Hello, Mr. X. I'm glad to talk to you. What are your hobbies?" The generation AI can also generate a message that will interest the other person based on the user's profile information. For example, if the other person's hobby is sports, a message such as "What kind of sports have you been playing recently?" is generated. In this way, the generation AI is used to automate the generation of messages. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's profile information and outputs a message.

[0069] The providing unit can provide the user with a plurality of message options generated by the generating unit. The providing unit, for example, provides the user with a plurality of message options generated by the generating unit. The plurality of message options include, for example, messages with different tones and contents, but are not limited to such examples. The providing unit displays a plurality of message options so that the user can select from the options. For example, the providing unit displays the message options generated by the generating unit in a list format so that the user can select. As a result, by providing a plurality of message options, the user can select from the options. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide message options using an AI model that uses the message options generated by the generating unit as input and provides them to the user.

[0070] The generation unit can analyze the other party's reaction and generate the next message based on the analysis result. The generation unit can, for example, analyze the other party's reaction and generate the next message based on the analysis result. The other party's reaction includes, for example, a text message, an image message, and the like, but is not limited to such examples. The generation unit can use text analysis technology and sentiment analysis technology to analyze the other party's reaction. For example, the generation unit can analyze the other party's text message and generate the next message based on the content. The generation unit can also analyze the other party's emotion and generate the next message based on the emotion. For example, if the other party replies, "My hobby is reading," the generation unit generates the next message such as, "What kind of books do you like?". In this way, the next message is generated based on the other party's reaction, and the exchange proceeds smoothly. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate the next message using a generation AI model that inputs the other party's reaction and outputs the next message.

[0071] The generation unit can analyze the profile information of the other party and generate a message tailored to the other party. The generation unit can, for example, analyze the profile information of the other party and generate a message tailored to the other party. The profile information of the other party includes, for example, name, age, hobbies, occupation, etc., but is not limited to such examples. The generation unit can use text analysis technology or data mining technology to analyze the profile information of the other party. For example, if the other party's hobby is sports, the generation unit generates a message such as "What kind of sports have you been playing recently?". The generation unit can also generate a work-related message based on the other party's occupation. For example, if the other party is a doctor, the generation unit generates a message such as "What are the recent topics in the medical field?". This makes it possible to generate a message tailored to the other party and interest the other party. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the other party's profile information and outputs a message tailored to the other party.

[0072] The reception unit can estimate the user's emotion and adjust the input timing of the profile information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input timing of the profile information based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, when the user is feeling stressed, the reception unit prompts the user to input the profile information during a time period when the user can relax. In addition, when the user is relaxed, the reception unit can prompt the user to input the profile information immediately. Furthermore, when the user is busy, the reception unit can prompt the user to input the profile information during a time period when the user is not busy. In this way, the input timing of the profile information can be adjusted according to the user's emotion, thereby reducing the burden on the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past profile information and select an appropriate input method. The reception unit, for example, analyzes the user's past profile information and selects an appropriate input method. The past profile information includes, for example, previous input contents, past activity history, etc., but is not limited to such examples. The reception unit can cause the user to input profile information in a simple question format based on information previously input by the user. For example, the reception unit can cause the user to input profile information by presenting options based on information previously input by the user. In addition, the reception unit can also cause the user to input profile information by suggesting voice input based on information previously input by the user. In this way, the optimal input method can be provided by analyzing the past profile information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can select an input method using an AI model that inputs past profile information and outputs an optimal input method.

[0074] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting profile information. The reception unit performs filtering based on the user's current living situation and field of interest when inputting profile information, for example. Living situations include, but are not limited to, for example, occupation, home environment, daily schedule, etc. Fields of interest include, but are not limited to, for example, hobbies, topics of interest, etc. The reception unit can display related questions based on the field of interest when the user inputs the current living situation. For example, the reception unit displays related options based on the current living situation when the user inputs a hobby. In addition, the reception unit can also display related information based on the current living situation when the user inputs an occupation. In this way, by filtering based on the user's living situation and field of interest, highly relevant information can be provided. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's living situation and field of interest and outputs related information.

[0075] The reception unit can estimate the user's emotion and determine the priority of the profile information to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the profile information to be input based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, when the user is feeling stressed, the reception unit allows the user to input important information first. Furthermore, when the user is relaxed, the reception unit can also allow the user to input detailed information first. Furthermore, when the user is in a hurry, the reception unit can also allow the user to input the most important information first. In this way, by determining the priority of the profile information according to the user's emotion, important information can be input preferentially. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0076] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting profile information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting profile information. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The reception unit can display related information based on the geographical location information when the user inputs a current location. For example, the reception unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the reception unit can also display related information based on the geographical location information when the user inputs an occupation. This improves the user's convenience by providing highly relevant information based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the geographical location information of the user and outputs related information.

[0077] The reception unit can analyze the social media activity of the user and input related information when the profile information is input. The reception unit can analyze the social media activity of the user and input related information when the profile information is input, for example, when the profile information is input. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. The reception unit can display related information based on the social media activity when the user inputs a hobby. For example, the reception unit displays related information based on the social media activity when the user inputs an occupation. The reception unit can also display related information based on the social media activity when the user inputs a current living situation. This makes it more efficient to input the profile information of the user by providing related information based on the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can provide information using an AI model that inputs the social media activity of the user and outputs related information.

[0078] The generation unit can estimate the user's emotion and adjust the expression method of the message based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the message based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, the generation unit generates a message using a casual expression when the user is relaxed. In addition, the generation unit can also generate a message using a polite expression when the user is nervous. Furthermore, the generation unit can also generate a message using an energetic expression when the user is excited. In this way, a more appropriate message can be generated by adjusting the expression method of the message according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0079] The generation unit can adjust the level of detail of the message based on the user's importance when generating the message. For example, the generation unit adjusts the level of detail of the message based on the user's importance when generating the message. The user's importance includes, for example, the user's activity, past interactions, etc., but is not limited to such examples. The generation unit can generate a detailed message when the user interacts with an important person. For example, the generation unit generates a concise message when the user interacts with a casual person. In addition, the generation unit can also generate a message with an appropriate level of detail when the user interacts with a person they have met for the first time. As a result, an appropriate message can be generated by adjusting the level of detail of the message based on the user's importance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's importance and outputs the level of detail of the message.

[0080] The generation unit can apply different generation algorithms depending on the user's category when generating a message. For example, the generation unit applies different generation algorithms depending on the user's category when generating a message. The user's category includes, for example, age group, interests, occupation, etc., but is not limited to such examples. If the user belongs to a business category, the generation unit can apply a business-oriented generation algorithm. For example, if the user belongs to a hobby category, the generation unit applies a hobby-oriented generation algorithm. In addition, if the user belongs to a friend category, the generation unit can also apply a friend-oriented generation algorithm. In this way, a more appropriate message can be generated by applying a generation algorithm depending on the user's category. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's category and outputs a generation algorithm.

[0081] The generation unit can estimate the user's emotion and adjust the length of the message based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the message based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, the generation unit generates a longer message when the user is relaxed. Furthermore, the generation unit can also generate a shorter message when the user is nervous. Furthermore, the generation unit can also generate a message of a moderate length when the user is excited. In this way, a more appropriate message can be generated by adjusting the length of the message according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The generation unit can determine the priority of messages based on the user's submission time when generating messages. The generation unit, for example, determines the priority of messages based on the user's submission time when generating messages. The submission time includes, for example, the submission date and time, the submission frequency, etc., but is not limited to such examples. The generation unit can generate a message that replies quickly when the user is in a hurry. For example, the generation unit generates a detailed message when the user has time. In addition, the generation unit can also generate a message that matches the deadline when the user needs to reply within a specific deadline. This allows messages to be generated at an appropriate time by determining the priority of messages based on the user's submission time. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the user's submission time and outputs the priority of messages.

[0083] The generation unit can adjust the order of messages based on the relevance of the user when generating messages. The generation unit, for example, adjusts the order of messages based on the relevance of the user when generating messages. Relevance includes, for example, the user's interests and past interactions, but is not limited to such examples. When the user interacts with an important person, the generation unit can preferentially generate a message for that person. For example, when the user interacts with multiple people, the generation unit preferentially generates a message for a person with a high relevance. In addition, when the user interacts with a specific person, the generation unit can also generate a message for that person first. Thereby, by adjusting the order of messages based on the relevance of the user, important messages can be preferentially generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a message using a generation AI model that inputs the relevance of the user and outputs the order of messages.

[0084] The providing unit can estimate the user's emotion and adjust the message providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the message providing method based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, the providing unit uses a casual providing method when the user is relaxed. Furthermore, the providing unit can also use a polite providing method when the user is nervous. Furthermore, the providing unit can also use an energetic providing method when the user is excited. In this way, the message can be provided in a more appropriate manner by adjusting the message providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The providing unit can select an appropriate providing method by referring to the user's past operation history when providing a message. For example, the providing unit selects an appropriate providing method by referring to the user's past operation history when providing a message. The past operation history includes, for example, a click history, a browsing history, and the like, but is not limited to such examples. The providing unit can select an optimal providing method based on a providing method used by the user in the past. For example, the providing unit selects the most effective providing method from the user's past operation history. In addition, the providing unit can also analyze the user's past operation history and propose an optimal providing method. As a result, the optimal providing method can be selected by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that inputs the user's past operation history and outputs an optimal providing method.

[0086] The providing unit can customize the means of providing based on the user's current living situation when providing a message. For example, the providing unit customizes the means of providing based on the user's current living situation when providing a message. Living situations include, for example, occupation, home environment, daily schedule, etc., but are not limited to such examples. The providing unit can provide a concise message when the user is busy. For example, the providing unit provides a detailed message when the user is relaxed. In addition, the providing unit can also provide a message tailored to a specific situation when the user is in a specific situation. This allows the message to be provided in an optimal manner for the user by customizing the means of providing based on the current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can customize the means using an AI model that inputs the user's living situation and outputs the means of providing.

[0087] The providing unit can estimate the user's emotion and determine the order of message provision based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the order of message provision based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, when the user is relaxed, the providing unit provides important messages first. Also, when the user is nervous, the providing unit can provide simple messages first. Furthermore, when the user is excited, the providing unit can provide messages in a moderate order. In this way, by determining the order of message provision according to the user's emotion, important messages can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The providing unit can select an appropriate providing method in consideration of the geographical location information of the user when providing a message. For example, the providing unit selects an appropriate providing method in consideration of the geographical location information of the user when providing a message. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The providing unit can display related information based on the geographical location information when the user inputs a current location. For example, the providing unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the providing unit can also display related information based on the geographical location information when the user inputs an occupation. In this way, by selecting an optimal providing method based on the geographical location information, it is possible to provide information that is highly relevant to the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that inputs the geographical location information of the user and outputs an optimal providing method.

[0089] The providing unit can analyze the social media activity of the user and suggest a means of providing the message when providing the message. For example, the providing unit analyzes the social media activity of the user and suggests a means of providing the message when providing the message. The social media activity includes, for example, the content of the post, the number of followers, the number of likes, and the like, but is not limited to such examples. The providing unit can display related information based on the social media activity when the user inputs a hobby. For example, the providing unit displays related information based on the social media activity when the user inputs an occupation. The providing unit can also display related information based on the social media activity when the user inputs a current living situation. This makes it possible to provide a message in an optimal manner for the user by suggesting a means of providing based on the social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can suggest a means using an AI model that inputs the social media activity of the user and outputs a means of providing.

[0090] The transmission unit can estimate the user's emotion and adjust the timing of sending the message based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and adjusts the timing of sending the message based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, when the user is relaxed, the transmission unit can immediately send the message. Also, when the user is nervous, the transmission unit can send the message at an appropriate timing. Furthermore, when the user is busy, the transmission unit can send the message during an unoccupied time period. In this way, the timing of sending the message can be adjusted according to the user's emotion, so that the message can be sent at an appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0091] The transmission unit can select an appropriate transmission method by referring to the user's past transmission history when transmitting a message. For example, the transmission unit selects an appropriate transmission method by referring to the user's past transmission history when transmitting a message. The past transmission history includes, for example, the transmission date and time, the transmission content, etc., but is not limited to such examples. The transmission unit can select an optimal transmission method based on the transmission method used by the user in the past. For example, the transmission unit selects the most effective transmission method from the user's past transmission history. In addition, the transmission unit can also analyze the user's past transmission history and propose the optimal transmission method. In this way, the optimal transmission method can be selected by referring to the past transmission history. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select a transmission method using an AI model that inputs the user's past transmission history and outputs the optimal transmission method.

[0092] The sending unit can customize the means of transmission based on the user's current living situation when sending a message. The sending unit, for example, customizes the means of transmission based on the user's current living situation when sending a message. Living situations include, for example, occupation, home environment, daily schedule, etc., but are not limited to such examples. The sending unit can send a concise message when the user is busy. For example, the sending unit can send a detailed message when the user is relaxed. In addition, the sending unit can also send a message tailored to the situation when the user is in a specific situation. This allows the message to be sent in an optimal manner for the user by customizing the means of transmission based on the current living situation. Part or all of the above-mentioned processing in the sending unit may be performed, for example, using AI, or may be performed without using AI. For example, the sending unit can customize the means using an AI model that inputs the user's living situation and outputs the means of transmission.

[0093] The transmission unit can estimate the user's emotion and determine the transmission priority of messages based on the estimated user's emotion. The transmission unit, for example, estimates the user's emotion and determines the transmission priority of messages based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology can be used for estimating the emotion. For example, when the user is relaxed, the transmission unit transmits important messages first. Also, when the user is nervous, the transmission unit can transmit simple messages first. Furthermore, when the user is excited, the transmission unit can transmit messages in a moderate order. In this way, important messages can be preferentially transmitted by determining the transmission priority of messages according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0094] The transmission unit can select an appropriate transmission method in consideration of the geographical location information of the user when transmitting a message. For example, the transmission unit selects an appropriate transmission method in consideration of the geographical location information of the user when transmitting a message. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. The transmission unit can display related information based on the geographical location information when the user inputs a current location. For example, the transmission unit displays related options based on the geographical location information when the user inputs a hobby. In addition, the transmission unit can also display related information based on the geographical location information when the user inputs an occupation. In this way, by selecting an optimal transmission method based on the geographical location information, it is possible to transmit information that is highly relevant to the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select a transmission method using an AI model that inputs the geographical location information of the user and outputs an optimal transmission method.

[0095] The transmission unit can analyze the social media activity of the user and suggest a means of transmission when sending a message. For example, the transmission unit analyzes the social media activity of the user and suggests a means of transmission when sending a message. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. The transmission unit can display related information based on the social media activity when the user inputs a hobby. For example, the transmission unit displays related information based on the social media activity when the user inputs an occupation. The transmission unit can also display related information based on the social media activity when the user inputs a current living situation. In this way, by suggesting a means of transmission based on the social media activity, it is possible to send a message in a manner optimal for the user. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can suggest a means using an AI model that inputs the social media activity of the user and outputs a means of transmission.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The matchmaking support system can further analyze the user's past message history and generate messages based on the user's communication style. For example, the system can analyze the language and tone used by the user in the past and adjust the style of the generated message based on the analysis. If the user prefers casual language, the generated message will also be generated in a casual tone. On the other hand, if the user prefers formal language, the generated message will also be generated in a formal tone. This allows for more natural interactions by providing messages that match the user's communication style.

[0098] The generation unit can estimate the user's emotions and adjust the content of the message based on the estimated user's emotions. For example, if the user is relaxed, the generated message will have a more friendly content. If the user is nervous, the generated message will have a more polite and reassuring content. If the user is excited, the generated message will have an energetic and positive content. This enables more effective communication by providing a message according to the user's emotions.

[0099] The providing unit can analyze the user's past message revision history and learn the style and content of messages that the user prefers. For example, it can analyze what revisions the user has made in the past and suggest messages to be generated based on that. If the user prefers a specific phrase, messages containing that phrase can be preferentially suggested. Also, if the user tends to avoid a specific topic, messages that do not include that topic can be suggested. This makes it possible to provide a service with a higher level of satisfaction by providing messages that match the user's preferences.

[0100] The generation unit can estimate the user's emotion and adjust the length of the message based on the estimated user's emotion. For example, if the user is relaxed, the generated message will be longer and more detailed. If the user is nervous, the generated message will be shorter and more concise. If the user is excited, the generated message will be of appropriate length and energetic content. This allows for more appropriate communication by adjusting the length of the message according to the user's emotion.

[0101] The providing unit can suggest messages based on relevant local events and activities taking into account the geographic location information of the user. For example, if the user is in a particular area, messages including information about events and activities held in the area can be suggested. If the user is interested in sporting events, messages related to sporting events held in the area can be suggested. Also, if the user is interested in cultural events, messages related to cultural events held in the area can be suggested. This allows for more interesting interactions by providing relevant messages based on the geographic location information of the user.

[0102] The reception unit can estimate the user's emotions and adjust the input order of profile information based on the estimated user's emotions. For example, if the user is relaxed, the input can be prompted to start with detailed information. If the user is nervous, the input can be prompted to start with simple information. If the user is excited, the input can be prompted to start with interesting information. This allows the input of profile information to be made smoother by adjusting the input order according to the user's emotions.

[0103] The reception unit can analyze the user's past input history of profile information and provide information in a format that is easy for the user to input. For example, if the user has preferred input in multiple choice format in the past, input in multiple choice format can be provided preferentially. Also, if the user has preferred input in free text format in the past, input in free text format can be provided preferentially. This allows for more comfortable input of profile information by providing an input format that matches the user's preferences.

[0104] The providing unit can estimate the user's emotion and adjust the timing of providing the message based on the estimated user's emotion. For example, if the user is relaxed, the message can be provided immediately. Also, if the user is nervous, the message can be provided at an appropriate time. Furthermore, if the user is busy, the message can be provided during a time period when the user is not busy. This allows for more effective communication by adjusting the timing of providing the message according to the user's emotion.

[0105] The sending unit can analyze the user's past message sending history and learn the user's preferred sending timing. For example, if the user has tended to send messages at a specific time of day in the past, the sending unit can send messages according to that time of day. Also, if the user has tended to send messages on a specific day of the week in the past, the sending unit can send messages according to that day of the week. This allows for more effective message sending by providing sending timings that match the user's preferences.

[0106] The transmission unit can estimate the user's emotion and adjust the message transmission method based on the estimated user's emotion. For example, if the user is relaxed, the message can be transmitted by a normal transmission method. If the user is nervous, the message can be transmitted by a polite transmission method. If the user is excited, the message can be transmitted by an energetic transmission method. In this way, by adjusting the transmission method according to the user's emotion, more appropriate message transmission is possible.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit receives the user's profile information. The user's profile information includes, for example, name, age, hobbies, occupation, etc. The reception unit stores the information input by the user in a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to generate a message based on the information received by the reception unit. The generation AI generates a message using, for example, a text generation AI (e.g., LLM) and analyzes the user's profile information to generate an appropriate message. Step 3: The providing unit provides the message generated by the generating unit in a form that can be modified by the user. The user can change or add parts of the message. The providing unit stores the message modified by the user and provides it to the sending unit. Step 4: The sending unit sends the message provided by the providing unit in response to a user request. The sending unit sends the message to the other party and provides feedback of the sending result to the user.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's profile information. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The provision unit is realized by, for example, the control unit 46A of the smart device 14 and provides the generated message in a form that can be modified by the user. The transmission unit is realized by, for example, the communication I / F 44 of the smart device 14 and transmits a message in response to a user request. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's profile information. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates a message using a generation AI. The provision unit is realized by, for example, the control unit 46A of the smart glasses 214 and provides the generated message in a form that can be modified by the user. The transmission unit is realized by, for example, the communication I / F 44 of the smart glasses 214 and transmits a message in response to a user request. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives the user's profile information. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a message using a generation AI. The provision unit is realized by, for example, the control unit 46A of the headset type terminal 314, and provides the generated message in a form that can be modified by the user. The transmission unit is realized by, for example, the communication I / F 44 of the headset type terminal 314, and transmits a message in response to a user request. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a 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.

[0147] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] For example, the reception unit is realized by the microphone 238 of the robot 414, and receives the user's profile information. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a message using a generation AI. The provision unit is realized by, for example, the control unit 46A of the robot 414, and provides the generated message in a form that can be modified by the user. The transmission unit is realized by, for example, the communication I / F 44 of the robot 414, and transmits a message in response to a user request. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A reception unit that receives profile information of a user; a generating unit that generates a message based on the information received by the receiving unit; a providing unit that provides the message generated by the generating unit to the user in a form that can be modified by the user; a sending unit that sends the message provided by the providing unit based on a request from the user. A system characterized by: (Appendix 2) The generation unit is Generate messages using generative AI 2. The system of claim 1, (Appendix 3) The providing unit is providing a user with a plurality of message options generated by the generating unit; 2. The system of claim 1, (Appendix 4) The generation unit is Analyze the other person's reaction and generate the next message based on the analysis results 2. The system of claim 1, (Appendix 5) The generation unit is Analyze the other person's profile information and generate a message tailored to them 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotion and adjust the timing of inputting profile information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past profile information and select the appropriate input method 2. The system of claim 1, (Appendix 8) The reception unit is Filter profile information based on your current life situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is A user's emotions are estimated, and a priority order of profile information to be input is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When filling out profile information, it takes into account the user's geographic location to prioritize the most relevant information 2. The system of claim 1, (Appendix 11) The reception unit is Analyze your social media activity and populate your profile information accordingly 2. The system of claim 1, (Appendix 12) The generation unit is Infer user emotions and adjust message expression based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The generation unit is When generating a message, adjust the message detail based on the user's importance level 2. The system of claim 1, (Appendix 14) The generation unit is When generating messages, different generation algorithms are applied depending on the category of the user. 2. The system of claim 1, (Appendix 15) The generation unit is Infer user emotions and adjust message length based on the estimated user emotions 2. The system of claim 1, (Appendix 16) The generation unit is During message generation, messages are prioritized based on when the user submitted them 2. The system of claim 1, (Appendix 17) The generation unit is Order messages based on user relevance when generating messages 2. The system of claim 1, (Appendix 18) The providing unit is Inferring user emotions and adjusting message delivery methods based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The providing unit is When delivering a message, the appropriate delivery method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 20) The providing unit is When delivering messages, tailor the delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 21) The providing unit is A method for determining the order in which messages are delivered based on a user's estimated emotions 2. The system of claim 1, (Appendix 22) The providing unit is When delivering a message, the appropriate delivery method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The providing unit is When delivering messages, analyze users' social media activity to suggest delivery methods 2. The system of claim 1, (Appendix 24) The transmission unit is A system that estimates a user's emotions and adjusts the timing of sending messages based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The transmission unit is When sending a message, the system selects the appropriate sending method by referring to the user's past sending history. 2. The system of claim 1, (Appendix 26) The transmission unit is When sending a message, customize the delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 27) The transmission unit is A user's emotion is estimated, and a priority for sending messages is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The transmission unit is When sending a message, the appropriate delivery method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The transmission unit is When sending a message, we analyze your social media activity to suggest delivery methods. 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives profile information of a user; a generating unit that generates a message based on the information received by the receiving unit; a providing unit that provides the message generated by the generating unit to the user in a form that can be modified by the user; a transmission unit that transmits the message provided by the provision unit based on a request from the user. A system characterized by:

2. The generation unit is Generate the message using the generation AI 2. The system of claim 1 .

3. The providing unit is providing the user with a plurality of message options generated by the generating unit; 2. The system of claim 1 .

4. The generation unit is Analyze the other person's reaction and generate the next message based on the analysis results 2. The system of claim 1 .

5. The generation unit is Analyze the other person's profile information and generate a message tailored to them 2. The system of claim 1 .

6. The reception unit is The user's emotion is estimated, and the timing of inputting profile information is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

7. The reception unit is Analyzing the user's past profile information and selecting an appropriate input method 2. The system of claim 1 .

8. The reception unit is Filtering profile information based on the user's current life situation and interests 2. The system of claim 1 .

9. The reception unit is A feeling of the user is estimated, and a priority order of profile information to be input is determined based on the estimated feeling of the user.

2. The system of claim 1 .

10. The reception unit is When entering profile information, the user's geographic location information is taken into consideration to preferentially enter information that is highly relevant.

2. The system of claim 1 .

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