system

Abstract

The system according to this embodiment aims to provide emotional support and opportunities for new encounters to people who are experiencing feelings of loneliness. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a proposal unit, and a planning unit. The collection unit collects user information. The generation unit generates an AI character based on the information collected by the collection unit. The proposal unit uses the AI ​​character generated by the generation unit to make suggestions based on the user's emotions and interests. The planning unit creates a plan based on the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sufficient mental care and opportunities for new encounters have not been provided for people with a sense of loneliness, and there is room for improvement.

[0005] The system according to the embodiment aims to provide mental care and opportunities for new encounters for people with a sense of loneliness.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a proposal unit, and a planning unit. The collection unit collects user information. The generation unit generates an AI character based on the information collected by the collection unit. The proposal unit uses the AI ​​character generated by the generation unit to make suggestions based on the user's emotions and interests. The planning unit creates a plan based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide emotional support and opportunities for new encounters to people who are experiencing feelings of loneliness. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that provides emotional care and opportunities for new encounters to people experiencing loneliness. This AI agent system, when a user communicates their hobbies and interests in a conversational format, memorizes that information and automatically creates an AI character suited to the user. This AI character can make suggestions and plans based on the user's emotions and interests. For example, when a user is feeling down, the AI ​​character might suggest a movie, and if the user accepts the suggestion, it will create a concrete plan. The AI ​​character can also learn the user's vital information and understand their health status. This allows for emotional care and reduces feelings of loneliness. Furthermore, the AI ​​character can introduce other users with shared interests and support the building of new relationships. This enables users to lead more fulfilling lives and deepen their social connections. This provides emotional support to users and reduces feelings of loneliness. Furthermore, the AI ​​character introduces users with shared interests, supporting the building of new relationships. This allows users to lead more fulfilling lives and deepen their social connections. In this way, the AI ​​agent system can alleviate users' feelings of loneliness and provide them with a more fulfilling life.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, a proposal unit, and a planning unit. The collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and behavioral history. For example, the collection unit collects information when the user communicates their hobbies and interests in a conversational format. The collection unit can also collect the user's behavioral history. The generation unit generates an AI character based on the information collected by the collection unit. For example, the generation unit automatically creates an AI character suited to the user based on the collected information. The generation unit can generate an AI character based on user information using a generation AI. The proposal unit makes suggestions based on the user's emotions and interests using the AI ​​character generated by the generation unit. For example, if the user is feeling down, the proposal unit has the AI ​​character suggest a movie. The proposal unit can make suggestions based on the user's emotions and interests using a generation AI. The planning unit creates a plan based on the content proposed by the proposal unit. For example, if the user accepts the suggestion, the planning unit creates a specific schedule. The planning department can use generative AI to create a plan based on the proposed content. This allows the AI ​​agent system according to the embodiment to alleviate the user's feelings of loneliness and provide them with a fulfilling life.

[0030] The data collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and behavioral history. For example, the data collection unit collects information when a user communicates their hobbies and interests in a conversational format. The data collection unit can also collect the user's behavioral history. Specifically, the data collection unit collects data from the devices and applications used by the user. For example, it can collect the usage history of applications used on a user's smartphone, web browsing history, and social media posts. This allows for a detailed understanding of the user's interests and concerns. The data collection unit also analyzes text and voice data entered by the user to extract information related to hobbies and interests. For example, by collecting text entered when a user converses with a chatbot or what they say to a voice assistant and analyzing it using natural language processing technology, the user's hobbies and interests can be identified. Furthermore, the data collection unit can also collect the user's location information and movement history. This allows for an understanding of places the user has visited and events they have attended, enabling analysis of the user's behavioral patterns. This information is centrally managed by the data collection department and stored in a database for use by the generation, proposal, and planning departments. It is crucial for the data collection department to implement appropriate security measures in data collection and management to protect user privacy. For example, data encryption and access control should be implemented to prevent unauthorized use of users' personal information. Furthermore, data should be collected only with the user's consent, and users should be able to stop providing data at any time. This allows the data collection department to gain user trust, collect data efficiently and effectively, and improve the overall system performance.

[0031] The generation unit generates AI characters based on information collected by the collection unit. For example, the generation unit automatically creates an AI character tailored to the user based on the collected information. The generation unit can generate AI characters based on user information using the generation AI. Specifically, the generation unit analyzes information such as the user's hobbies, interests, and behavioral history, and designs the appearance and personality of the character to match the user's preferences. For example, if the user is a movie lover, the generation unit will generate a character that has knowledge related to movies and can engage in lively conversations about movies. Also, if the user is a sports lover, the generation unit will generate a character that is knowledgeable about sports and can enjoy talking about sports with the user. The generation unit uses the generation AI to generate the appearance and personality of the AI ​​character based on user information. The generation AI uses deep learning technology to analyze user information and generate the optimal character. For example, it customizes the character's appearance, clothing, and way of speaking based on the user's hobbies and interests. The generation AI also analyzes the user's behavioral history and generates a character's personality and behavioral patterns that match the user's preferences. As a result, the generation unit can generate an AI character that is approachable and relatable to the user. Furthermore, the generation unit provides the generated AI character to the user, who can further customize the character's personality and behavior patterns by interacting with the character. For example, the user can request changes to the character's personality or behavior patterns while interacting with the character. This allows the generation unit to continuously improve the character by incorporating user feedback.

[0032] The suggestion unit uses the generation unit to generate AI characters that make suggestions based on the user's emotions and interests. For example, if the user is feeling down, the AI ​​character might suggest a movie. The suggestion unit uses generational AI to make suggestions based on the user's emotions and interests. Specifically, the suggestion unit analyzes the user's facial expressions, tone of voice, and text content to analyze the user's emotions. For example, it analyzes text and audio data from conversations between the user and the chatbot to identify the user's emotional state. If it determines that the user is feeling down, the AI ​​character will suggest movies, music, activities, etc., that suit the user's preferences. Also, if the user has a particular interest, it will suggest information and events related to that interest. The suggestion unit uses generational AI to make optimal suggestions based on the user's emotions and interests. The generational AI analyzes user information and builds models to identify the user's emotional state and interests. For example, it predicts changes in the user's emotional state and interests based on the user's past behavioral history and conversation history. The generational AI also uses algorithms to generate optimal suggestions based on the user's emotional state and interests. This allows the suggestion department to make appropriate suggestions that align with the user's emotions and interests. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on whether the user accepted the suggestion and their satisfaction level, and use this feedback to improve the suggestion algorithm. This enables the suggestion department to make more valuable suggestions to users and improve user satisfaction.

[0033] The planning department creates plans based on the proposals made by the proposal department. For example, if a user accepts a proposal, the planning department will create a specific schedule. The planning department can use generative AI to create plans based on the proposed content. Specifically, the planning department creates the optimal plan based on the user's schedule and preferences. For example, if a user accepts a movie proposal, the planning department will check the user's schedule and suggest the best showtimes and cinemas. Similarly, if a user accepts a travel proposal, the planning department will plan the travel schedule, accommodations, and sightseeing spots based on the user's wishes. The planning department uses generative AI to analyze user information and build algorithms for creating optimal plans. For example, it builds models to generate optimal plans based on the user's schedule, preferences, and past activity history. Furthermore, the generative AI continuously improves the plan based on user feedback. This allows the planning department to provide the best possible plan for the user and improve user satisfaction. In addition, the planning department can centrally manage user schedules and plans and integrate with other systems and departments. For example, the planning department can integrate with the user's calendar app and task management app to automatically reflect the plan content. Furthermore, the planning department updates the plan details in real time and notifies the user if there are any changes to the user's schedule. This allows the planning department to streamline user schedule management and enrich the user's life.

[0034] The referral AI agent system further includes a referral unit that introduces other users with shared interests. The referral unit introduces other users with shared interests, for example, based on the user's hobbies and interests. The referral unit can introduce other users with shared interests using generative AI. This can support the building of new relationships by introducing other users with shared interests. Some or all of the above processing in the referral unit may be performed using AI, for example, or without AI. For example, the referral unit can introduce other users using an AI model that takes the user's hobbies and interests as input and outputs other users with shared interests.

[0035] The learning unit AI agent system further includes a learning unit that learns the user's vital information. The learning unit collects and learns vital information such as the user's heart rate, blood pressure, and body temperature. The learning unit can learn the user's vital information using generative AI. This allows the system to understand the user's health status and provide mental care by learning the user's vital information. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can understand the user's health status using an AI model that takes the user's vital information as input and outputs the health status.

[0036] The data collection unit can analyze the user's past hobbies and interests and select the optimal collection method. For example, the data collection unit can prioritize collecting hobbies that the user has shown interest in in the past. The data collection unit can also analyze the changes in the user's past hobbies and collect hobbies that match their current interests. The data collection unit can also analyze the frequency of the user's past hobbies and collect the hobbies that the user is most interested in. In this way, the optimal collection method can be selected by analyzing the user's past hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past hobbies and interests history data into a generating AI and have the generating AI select the optimal collection method.

[0037] The data collection unit can filter data on hobbies and interests based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect hobbies related to a project the user is currently working on. The data collection unit can also collect appropriate hobbies based on the user's current lifestyle (e.g., work, family). The data collection unit can also filter hobbies based on the user's current areas of interest. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on hobbies and interests. For example, the data collection unit can collect hobbies and interests that can be done nearby based on the user's current location. The data collection unit can also collect hobbies and interests related to the user's travel destinations. The data collection unit can also collect hobbies and interests based on the characteristics of the area where the user lives. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0039] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting hobbies and interests. For example, the data collection unit can collect hobbies and interests related to accounts that the user follows on social media. The data collection unit can also collect hobbies and interests based on content that the user shares on social media. The data collection unit can also collect relevant hobbies and interests by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0040] The generation unit can adjust the level of detail of the AI ​​character based on the importance of the user's hobbies and interests during generation. For example, the generation unit can generate an AI character with detailed knowledge about the user's hobbies. If the user's interests are broad, the generation unit can also generate an AI character with broad knowledge. If the user's interests are concentrated in a specific field, the generation unit can also generate an AI character specialized in that field. By adjusting the level of detail of the AI ​​character based on the importance of the user's hobbies and interests, a more appropriate AI character can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's hobbies and interests into a generation AI and have the generation AI perform the level of detail adjustment.

[0041] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is a student, the generation unit can generate an AI character specialized in learning support. If the user is a businessman, the generation unit can also generate an AI character that provides business-related information. If the user is elderly, the generation unit can also generate an AI character specialized in health management. By applying different generation algorithms depending on the user's category, a more appropriate AI character can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0042] The generation unit can determine the priority of AI characters based on the user's submission timing during generation. For example, if the user is in a hurry, the generation unit will prioritize AI characters that can be generated quickly. If the user has ample time, the generation unit can also prioritize AI characters that allow for detailed settings. If the user needs an AI character for a specific event, the generation unit can also prioritize the AI ​​character best suited to that event. This allows for the generation of AI characters at a more appropriate time by determining the priority of AI characters based on the user's submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI perform the priority determination.

[0043] The generation unit can adjust the order of AI characters based on user relevance during generation. For example, the generation unit can prioritize generating AI characters related to the user's specific hobbies. The generation unit can also prioritize generating AI characters related to the user's specific interests. The generation unit can also prioritize generating AI characters related to the user's specific categories. By adjusting the order of AI characters based on user relevance, the AI ​​characters can be generated in a more appropriate order. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the order adjustment.

[0044] The proposal unit can make the most suitable proposal by referring to the user's past proposal history. For example, the proposal unit can make the most suitable proposal based on proposals the user has accepted in the past. The proposal unit can also make proposals that are likely to interest the user based on their past proposal history. The proposal unit can also analyze the user's past proposal history and make the most effective proposal. In this way, the proposal unit can make the most suitable proposal by referring to the user's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's past proposal history data into a generating AI and have the generating AI select the most suitable proposal.

[0045] The suggestion unit can make highly relevant suggestions by considering the user's geographical location information. For example, the suggestion unit can suggest activities that can be done nearby based on the user's current location. The suggestion unit can also suggest activities related to the user's travel destination. The suggestion unit can also suggest activities based on the characteristics of the area where the user lives. In this way, by considering the user's geographical location information, highly relevant suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant suggestions.

[0046] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can suggest activities related to accounts the user follows on social media. The suggestion unit can also suggest activities based on content the user has shared on social media. The suggestion unit can also analyze the user's social media activity history and suggest relevant activities. In this way, it can make relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI select relevant suggestions.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit can analyze the user's past hobbies and interests and select the optimal collection method. For example, the data collection unit can prioritize collecting hobbies that the user has shown interest in in the past. The data collection unit can also analyze the changes in the user's past hobbies and collect hobbies that match their current interests. The data collection unit can also analyze the frequency of the user's past hobbies and collect the hobbies that the user is most interested in. In this way, the optimal collection method can be selected by analyzing the user's past hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past hobbies and interests history data into a generating AI and have the generating AI select the optimal collection method.

[0049] The data collection unit can filter data on hobbies and interests based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect hobbies related to a project the user is currently working on. The data collection unit can also collect appropriate hobbies based on the user's current lifestyle (e.g., work, family). The data collection unit can also filter hobbies based on the user's current areas of interest. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0050] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on hobbies and interests. For example, the data collection unit can collect hobbies and interests that can be done nearby based on the user's current location. The data collection unit can also collect hobbies and interests related to the user's travel destinations. The data collection unit can also collect hobbies and interests based on the characteristics of the area where the user lives. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0051] The generation unit can adjust the level of detail of the AI ​​character based on the importance of the user's hobbies and interests during generation. For example, the generation unit can generate an AI character with detailed knowledge about the user's hobbies. If the user's interests are broad, the generation unit can also generate an AI character with broad knowledge. If the user's interests are concentrated in a specific field, the generation unit can also generate an AI character specialized in that field. By adjusting the level of detail of the AI ​​character based on the importance of the user's hobbies and interests, a more appropriate AI character can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's hobbies and interests into a generation AI and have the generation AI perform the level of detail adjustment.

[0052] The proposal unit can make the most suitable proposal by referring to the user's past proposal history. For example, the proposal unit can make the most suitable proposal based on proposals the user has accepted in the past. The proposal unit can also make proposals that are likely to interest the user based on their past proposal history. The proposal unit can also analyze the user's past proposal history and make the most effective proposal. In this way, the proposal unit can make the most suitable proposal by referring to the user's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's past proposal history data into a generating AI and have the generating AI select the most suitable proposal.

[0053] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting hobbies and interests. For example, the data collection unit can collect hobbies and interests related to accounts that the user follows on social media. The data collection unit can also collect hobbies and interests based on content that the user shares on social media. The data collection unit can also collect relevant hobbies and interests by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects user information. User information includes, for example, personal information, hobbies, interests, and behavioral history. The collection unit collects information when the user communicates their hobbies and interests in a conversational format. It can also collect the user's behavioral history. Step 2: The generation unit generates an AI character based on the information collected by the collection unit. The generation unit automatically creates an AI character that suits the user based on the collected information. Using the generated AI, an AI character can be generated based on the user's information. Step 3: The suggestion unit uses the AI ​​character generated by the generation unit to make suggestions based on the user's emotions and interests. For example, if the user is feeling down, the AI ​​character might suggest a movie. By using the generation AI, suggestions can be made based on the user's emotions and interests. Step 4: The planning department creates a plan based on the proposals made by the proposal department. For example, if the user accepts the proposal, a specific schedule is created. A generation AI can be used to create a plan based on the proposed content.

[0056] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that provides emotional care and opportunities for new encounters to people experiencing loneliness. This AI agent system, when a user communicates their hobbies and interests in a conversational format, memorizes that information and automatically creates an AI character suited to the user. This AI character can make suggestions and plans based on the user's emotions and interests. For example, when a user is feeling down, the AI ​​character might suggest a movie, and if the user accepts the suggestion, it will create a concrete plan. The AI ​​character can also learn the user's vital information and understand their health status. This allows for emotional care and reduces feelings of loneliness. Furthermore, the AI ​​character can introduce other users with shared interests and support the building of new relationships. This enables users to lead more fulfilling lives and deepen their social connections. This provides emotional support to users and reduces feelings of loneliness. Furthermore, the AI ​​character introduces users with shared interests, supporting the building of new relationships. This allows users to lead more fulfilling lives and deepen their social connections. In this way, the AI ​​agent system can alleviate users' feelings of loneliness and provide them with a more fulfilling life.

[0057] The AI ​​agent system according to this embodiment comprises a collection unit, a generation unit, a proposal unit, and a planning unit. The collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and behavioral history. For example, the collection unit collects information when the user communicates their hobbies and interests in a conversational format. The collection unit can also collect the user's behavioral history. The generation unit generates an AI character based on the information collected by the collection unit. For example, the generation unit automatically creates an AI character suited to the user based on the collected information. The generation unit can generate an AI character based on user information using a generation AI. The proposal unit makes suggestions based on the user's emotions and interests using the AI ​​character generated by the generation unit. For example, if the user is feeling down, the proposal unit has the AI ​​character suggest a movie. The proposal unit can make suggestions based on the user's emotions and interests using a generation AI. The planning unit creates a plan based on the content proposed by the proposal unit. For example, if the user accepts the suggestion, the planning unit creates a specific schedule. The planning department can use generative AI to create a plan based on the proposed content. This allows the AI ​​agent system according to the embodiment to alleviate the user's feelings of loneliness and provide them with a fulfilling life.

[0058] The data collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and behavioral history. For example, the data collection unit collects information when a user communicates their hobbies and interests in a conversational format. The data collection unit can also collect the user's behavioral history. Specifically, the data collection unit collects data from the devices and applications used by the user. For example, it can collect the usage history of applications used on a user's smartphone, web browsing history, and social media posts. This allows for a detailed understanding of the user's interests and concerns. The data collection unit also analyzes text and voice data entered by the user to extract information related to hobbies and interests. For example, by collecting text entered when a user converses with a chatbot or what they say to a voice assistant and analyzing it using natural language processing technology, the user's hobbies and interests can be identified. Furthermore, the data collection unit can also collect the user's location information and movement history. This allows for an understanding of places the user has visited and events they have attended, enabling analysis of the user's behavioral patterns. This information is centrally managed by the data collection department and stored in a database for use by the generation, proposal, and planning departments. It is crucial for the data collection department to implement appropriate security measures in data collection and management to protect user privacy. For example, data encryption and access control should be implemented to prevent unauthorized use of users' personal information. Furthermore, data should be collected only with the user's consent, and users should be able to stop providing data at any time. This allows the data collection department to gain user trust, collect data efficiently and effectively, and improve the overall system performance.

[0059] The generation unit generates AI characters based on information collected by the collection unit. For example, the generation unit automatically creates an AI character tailored to the user based on the collected information. The generation unit can generate AI characters based on user information using the generation AI. Specifically, the generation unit analyzes information such as the user's hobbies, interests, and behavioral history, and designs the appearance and personality of the character to match the user's preferences. For example, if the user is a movie lover, the generation unit will generate a character that has knowledge related to movies and can engage in lively conversations about movies. Also, if the user is a sports lover, the generation unit will generate a character that is knowledgeable about sports and can enjoy talking about sports with the user. The generation unit uses the generation AI to generate the appearance and personality of the AI ​​character based on user information. The generation AI uses deep learning technology to analyze user information and generate the optimal character. For example, it customizes the character's appearance, clothing, and way of speaking based on the user's hobbies and interests. The generation AI also analyzes the user's behavioral history and generates a character's personality and behavioral patterns that match the user's preferences. As a result, the generation unit can generate an AI character that is approachable and relatable to the user. Furthermore, the generation unit provides the generated AI character to the user, who can further customize the character's personality and behavior patterns by interacting with the character. For example, the user can request changes to the character's personality or behavior patterns while interacting with the character. This allows the generation unit to continuously improve the character by incorporating user feedback.

[0060] The suggestion unit uses the generation unit to generate AI characters that make suggestions based on the user's emotions and interests. For example, if the user is feeling down, the AI ​​character might suggest a movie. The suggestion unit uses generational AI to make suggestions based on the user's emotions and interests. Specifically, the suggestion unit analyzes the user's facial expressions, tone of voice, and text content to analyze the user's emotions. For example, it analyzes text and audio data from conversations between the user and the chatbot to identify the user's emotional state. If it determines that the user is feeling down, the AI ​​character will suggest movies, music, activities, etc., that suit the user's preferences. Also, if the user has a particular interest, it will suggest information and events related to that interest. The suggestion unit uses generational AI to make optimal suggestions based on the user's emotions and interests. The generational AI analyzes user information and builds models to identify the user's emotional state and interests. For example, it predicts changes in the user's emotional state and interests based on the user's past behavioral history and conversation history. The generational AI also uses algorithms to generate optimal suggestions based on the user's emotional state and interests. This allows the suggestion department to make appropriate suggestions that align with the user's emotions and interests. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on whether the user accepted the suggestion and their satisfaction level, and use this feedback to improve the suggestion algorithm. This enables the suggestion department to make more valuable suggestions to users and improve user satisfaction.

[0061] The planning department creates plans based on the proposals made by the proposal department. For example, if a user accepts a proposal, the planning department will create a specific schedule. The planning department can use generative AI to create plans based on the proposed content. Specifically, the planning department creates the optimal plan based on the user's schedule and preferences. For example, if a user accepts a movie proposal, the planning department will check the user's schedule and suggest the best showtimes and cinemas. Similarly, if a user accepts a travel proposal, the planning department will plan the travel schedule, accommodations, and sightseeing spots based on the user's wishes. The planning department uses generative AI to analyze user information and build algorithms for creating optimal plans. For example, it builds models to generate optimal plans based on the user's schedule, preferences, and past activity history. Furthermore, the generative AI continuously improves the plan based on user feedback. This allows the planning department to provide the best possible plan for the user and improve user satisfaction. In addition, the planning department can centrally manage user schedules and plans and integrate with other systems and departments. For example, the planning department can integrate with the user's calendar app and task management app to automatically reflect the plan content. Furthermore, the planning department updates the plan details in real time and notifies the user if there are any changes to the user's schedule. This allows the planning department to streamline user schedule management and enrich the user's life.

[0062] The referral AI agent system further includes a referral unit that introduces other users with shared interests. The referral unit introduces other users with shared interests, for example, based on the user's hobbies and interests. The referral unit can introduce other users with shared interests using generative AI. This can support the building of new relationships by introducing other users with shared interests. Some or all of the above processing in the referral unit may be performed using AI, for example, or without AI. For example, the referral unit can introduce other users using an AI model that takes the user's hobbies and interests as input and outputs other users with shared interests.

[0063] The learning unit AI agent system further includes a learning unit that learns the user's vital information. The learning unit collects and learns vital information such as the user's heart rate, blood pressure, and body temperature. The learning unit can learn the user's vital information using generative AI. This allows the system to understand the user's health status and provide mental care by learning the user's vital information. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can understand the user's health status using an AI model that takes the user's vital information as input and outputs the health status.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting hobbies and interests based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect hobbies and interests at a time when the user can relax. If the user is relaxed, the data collection unit can also collect detailed hobbies and interests. If the user is busy, the data collection unit can also collect hobbies and interests quickly and easily. By adjusting the timing of collecting hobbies and interests according to the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past hobbies and interests and select the optimal collection method. For example, the data collection unit can prioritize collecting hobbies that the user has shown interest in in the past. The data collection unit can also analyze the changes in the user's past hobbies and collect hobbies that match their current interests. The data collection unit can also analyze the frequency of the user's past hobbies and collect the hobbies that the user is most interested in. In this way, the optimal collection method can be selected by analyzing the user's past hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past hobbies and interests history data into a generating AI and have the generating AI select the optimal collection method.

[0066] The data collection unit can filter data on hobbies and interests based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect hobbies related to a project the user is currently working on. The data collection unit can also collect appropriate hobbies based on the user's current lifestyle (e.g., work, family). The data collection unit can also filter hobbies based on the user's current areas of interest. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is depressed, the data collection unit may prioritize collecting hobbies and interests that lift their spirits. If the user is excited, the data collection unit may also prioritize collecting hobbies and interests that enhance their concentration. If the user is tired, the data collection unit may also prioritize collecting hobbies and interests that promote relaxation. By prioritizing the information to collect according to the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on hobbies and interests. For example, the data collection unit can collect hobbies and interests that can be done nearby based on the user's current location. The data collection unit can also collect hobbies and interests related to the user's travel destinations. The data collection unit can also collect hobbies and interests based on the characteristics of the area where the user lives. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0069] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting hobbies and interests. For example, the data collection unit can collect hobbies and interests related to accounts that the user follows on social media. The data collection unit can also collect hobbies and interests based on content that the user shares on social media. The data collection unit can also collect relevant hobbies and interests by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0070] The generation unit can estimate the user's emotions and adjust the method of generating the AI ​​character based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an AI character with a calm personality. If the user is excited, the generation unit can also generate an AI character with an energetic personality. If the user is depressed, the generation unit can also generate an AI character that offers words of encouragement. In this way, by adjusting the method of generating the AI ​​character according to the user's emotions, a more appropriate AI character can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] The generation unit can adjust the level of detail of the AI ​​character based on the importance of the user's hobbies and interests during generation. For example, the generation unit can generate an AI character with detailed knowledge about the user's hobbies. If the user's interests are broad, the generation unit can also generate an AI character with broad knowledge. If the user's interests are concentrated in a specific field, the generation unit can also generate an AI character specialized in that field. By adjusting the level of detail of the AI ​​character based on the importance of the user's hobbies and interests, a more appropriate AI character can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's hobbies and interests into a generation AI and have the generation AI perform the level of detail adjustment.

[0072] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is a student, the generation unit can generate an AI character specialized in learning support. If the user is a businessman, the generation unit can also generate an AI character that provides business-related information. If the user is elderly, the generation unit can also generate an AI character specialized in health management. By applying different generation algorithms depending on the user's category, a more appropriate AI character can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0073] The generation unit can estimate the user's emotions and adjust the appearance and personality of the AI ​​character based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an AI character with a calm appearance and personality. If the user is excited, the generation unit can also generate an AI character with an energetic appearance and personality. If the user is depressed, the generation unit can also generate an AI character with a gentle appearance and personality. In this way, by adjusting the appearance and personality of the AI ​​character according to the user's emotions, a more appropriate AI character can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0074] The generation unit can determine the priority of AI characters based on the user's submission timing during generation. For example, if the user is in a hurry, the generation unit will prioritize AI characters that can be generated quickly. If the user has ample time, the generation unit can also prioritize AI characters that allow for detailed settings. If the user needs an AI character for a specific event, the generation unit can also prioritize the AI ​​character best suited to that event. This allows for the generation of AI characters at a more appropriate time by determining the priority of AI characters based on the user's submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user submission timing data into a generation AI and have the generation AI perform the priority determination.

[0075] The generation unit can adjust the order of AI characters based on user relevance during generation. For example, the generation unit can prioritize generating AI characters related to the user's specific hobbies. The generation unit can also prioritize generating AI characters related to the user's specific interests. The generation unit can also prioritize generating AI characters related to the user's specific categories. By adjusting the order of AI characters based on user relevance, the AI ​​characters can be generated in a more appropriate order. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the order adjustment.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it expresses its suggestions based on those emotions. For example, if the user is depressed, the suggestion unit can offer suggestions that include words of encouragement. If the user is relaxed, the suggestion unit can offer suggestions in a calm manner. If the user is excited, the suggestion unit can offer suggestions in an energetic manner. By adjusting the way it expresses its suggestions according to the user's emotions, it can offer more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The suggestion unit can update its suggestions in real time in response to changes in the user's emotions. For example, if the user's emotions change, the suggestion unit will immediately update the suggestions. The suggestion unit can also change the priority of suggestions in response to changes in the user's emotions. The suggestion unit can also adjust the level of detail of suggestions based on changes in the user's emotions. This allows for more appropriate suggestions to be made by updating the suggestions in real time in response to changes in the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The proposal unit can make the most suitable proposal by referring to the user's past proposal history. For example, the proposal unit can make the most suitable proposal based on proposals the user has accepted in the past. The proposal unit can also make proposals that are likely to interest the user based on their past proposal history. The proposal unit can also analyze the user's past proposal history and make the most effective proposal. In this way, the proposal unit can make the most suitable proposal by referring to the user's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's past proposal history data into a generating AI and have the generating AI select the most suitable proposal.

[0079] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is depressed, the suggestion unit will prioritize suggestions that will lift their mood. If the user is relaxed, the suggestion unit may also prioritize suggestions that will help them relax. If the user is excited, the suggestion unit may also prioritize suggestions that will help them maintain their excitement. By prioritizing suggestions according to the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion unit can make highly relevant suggestions by considering the user's geographical location information. For example, the suggestion unit can suggest activities that can be done nearby based on the user's current location. The suggestion unit can also suggest activities related to the user's travel destination. The suggestion unit can also suggest activities based on the characteristics of the area where the user lives. In this way, by considering the user's geographical location information, highly relevant suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant suggestions.

[0081] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can suggest activities related to accounts the user follows on social media. The suggestion unit can also suggest activities based on content the user has shared on social media. The suggestion unit can also analyze the user's social media activity history and suggest relevant activities. In this way, it can make relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI select relevant suggestions.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The suggestion unit can estimate the user's emotions and adjust the way it expresses its suggestions based on those emotions. For example, if the user is depressed, the suggestion unit can offer suggestions that include words of encouragement. If the user is relaxed, the suggestion unit can offer suggestions in a calm manner. If the user is excited, the suggestion unit can offer suggestions in an energetic manner. By adjusting the way it expresses its suggestions according to the user's emotions, it can offer more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can analyze the user's past hobbies and interests and select the optimal collection method. For example, the data collection unit can prioritize collecting hobbies that the user has shown interest in in the past. The data collection unit can also analyze the changes in the user's past hobbies and collect hobbies that match their current interests. The data collection unit can also analyze the frequency of the user's past hobbies and collect the hobbies that the user is most interested in. In this way, the optimal collection method can be selected by analyzing the user's past hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past hobbies and interests history data into a generating AI and have the generating AI select the optimal collection method.

[0085] The generation unit can estimate the user's emotions and adjust the method of generating the AI ​​character based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an AI character with a calm personality. If the user is excited, the generation unit can also generate an AI character with an energetic personality. If the user is depressed, the generation unit can also generate an AI character that offers words of encouragement. In this way, by adjusting the method of generating the AI ​​character according to the user's emotions, a more appropriate AI character can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] The data collection unit can filter data on hobbies and interests based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect hobbies related to a project the user is currently working on. The data collection unit can also collect appropriate hobbies based on the user's current lifestyle (e.g., work, family). The data collection unit can also filter hobbies based on the user's current areas of interest. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0087] The suggestion unit can update its suggestions in real time in response to changes in the user's emotions. For example, if the user's emotions change, the suggestion unit will immediately update the suggestions. The suggestion unit can also change the priority of suggestions in response to changes in the user's emotions. The suggestion unit can also adjust the level of detail of suggestions based on changes in the user's emotions. This allows for more appropriate suggestions to be made by updating the suggestions in real time in response to changes in the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information on hobbies and interests. For example, the data collection unit can collect hobbies and interests that can be done nearby based on the user's current location. The data collection unit can also collect hobbies and interests related to the user's travel destinations. The data collection unit can also collect hobbies and interests based on the characteristics of the area where the user lives. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0089] The generation unit can adjust the level of detail of the AI ​​character based on the importance of the user's hobbies and interests during generation. For example, the generation unit can generate an AI character with detailed knowledge about the user's hobbies. If the user's interests are broad, the generation unit can also generate an AI character with broad knowledge. If the user's interests are concentrated in a specific field, the generation unit can also generate an AI character specialized in that field. By adjusting the level of detail of the AI ​​character based on the importance of the user's hobbies and interests, a more appropriate AI character can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's hobbies and interests into a generation AI and have the generation AI perform the level of detail adjustment.

[0090] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is depressed, the suggestion unit will prioritize suggestions that will lift their mood. If the user is relaxed, the suggestion unit may also prioritize suggestions that will help them relax. If the user is excited, the suggestion unit may also prioritize suggestions that will help them maintain their excitement. By prioritizing suggestions according to the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The proposal unit can make the most suitable proposal by referring to the user's past proposal history. For example, the proposal unit can make the most suitable proposal based on proposals the user has accepted in the past. The proposal unit can also make proposals that are likely to interest the user based on their past proposal history. The proposal unit can also analyze the user's past proposal history and make the most effective proposal. In this way, the proposal unit can make the most suitable proposal by referring to the user's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's past proposal history data into a generating AI and have the generating AI select the most suitable proposal.

[0092] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting hobbies and interests. For example, the data collection unit can collect hobbies and interests related to accounts that the user follows on social media. The data collection unit can also collect hobbies and interests based on content that the user shares on social media. The data collection unit can also collect relevant hobbies and interests by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection unit collects user information. User information includes, for example, personal information, hobbies, interests, and behavioral history. The collection unit collects information when the user communicates their hobbies and interests in a conversational format. It can also collect the user's behavioral history. Step 2: The generation unit generates an AI character based on the information collected by the collection unit. The generation unit automatically creates an AI character that suits the user based on the collected information. Using the generated AI, an AI character can be generated based on the user's information. Step 3: The suggestion unit uses the AI ​​character generated by the generation unit to make suggestions based on the user's emotions and interests. For example, if the user is feeling down, the AI ​​character might suggest a movie. By using the generation AI, suggestions can be made based on the user's emotions and interests. Step 4: The planning department creates a plan based on the proposals made by the proposal department. For example, if the user accepts the proposal, a specific schedule is created. A generation AI can be used to create a plan based on the proposed content.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, planning unit, introduction unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects user information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an AI character based on the collected information. The proposal unit is implemented by the control unit 46A of the smart device 14 and the generated AI character makes suggestions based on the user's emotions and interests. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a plan based on the suggested content. The introduction unit is implemented by the control unit 46A of the smart device 14 and introduces other users with common interests. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's vital information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, planning unit, introduction unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects user information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an AI character based on the collected information. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and the generated AI character makes suggestions based on the user's emotions and interests. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a plan based on the suggested content. The introduction unit is implemented by the control unit 46A of the smart glasses 214 and introduces other users with common interests. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's vital information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, planning unit, introduction unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects user information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an AI character based on the collected information. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and the generated AI character makes suggestions based on the user's emotions and interests. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a plan based on the proposed content. The introduction unit is implemented by the control unit 46A of the headset terminal 314 and introduces other users with common interests. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's vital information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, planning unit, introduction unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects user information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an AI character based on the collected information. The proposal unit is implemented by the control unit 46A of the robot 414 and the generated AI character makes suggestions based on the user's emotions and interests. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a plan based on the suggested content. The introduction unit is implemented by the control unit 46A of the robot 414 and introduces other users with common interests. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's vital information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) A collection unit that collects user information, A generation unit that generates an AI character based on the information collected by the collection unit, The AI ​​character generated by the generation unit makes suggestions based on the user's emotions and interests, and the suggestion unit The system comprises a planning unit that creates a plan based on the content proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) It also includes a section for introducing other users with similar interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes a learning unit that learns the user's vital information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting information on hobbies and interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past hobbies and interests to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information on hobbies and interests, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information on hobbies and interests, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information about hobbies and interests, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the AI ​​character generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the level of detail of the AI ​​character is adjusted based on the importance of the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the appearance and personality of the AI ​​character based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the priority of AI characters is determined based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the order of AI characters is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, the proposal content is updated in real time in response to changes in the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we refer to the user's past proposal history to make the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, we take the user's geographical location into consideration to provide highly relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, A generation unit generates an AI character based on the information collected by the collection unit, The AI ​​character generated by the generation unit makes suggestions based on the user's emotions and interests, and the suggestion unit The system comprises a planning unit that creates a plan based on the content proposed by the aforementioned proposal unit. A system characterized by the following features.

2. It also includes a section for introducing other users with similar interests. The system according to feature 1.

3. It also includes a learning unit that learns the user's vital information. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting information on hobbies and interests based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past hobbies and interests to select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting information on hobbies and interests, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting information on hobbies and interests, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When gathering information about hobbies and interests, we analyze users' social media activity and collect relevant information. The system according to feature 1.

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