system

Abstract

The system according to this embodiment aims to understand the user's personality and hobbies and propose the most suitable activities based on the time available. [Solution] The system according to the embodiment comprises an understanding unit, a suggestion unit, an information provision unit, and an analysis and generation unit. The understanding unit understands the user's personality and hobbies from conversations with the user. The suggestion unit suggests activities that match the available time based on the information understood by the understanding unit. The information provision unit provides detailed information about the activities suggested by the suggestion unit. The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision 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 performed by at least one processor, the method including 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 as a 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, it is difficult to propose an optimal activity based on the user's personality and hobbies, and there is room for improvement.

[0005] The system according to the embodiment aims to understand the user's personality and hobbies and propose an optimal activity according to the available time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an understanding unit, a suggestion unit, an information provision unit, and an analysis and generation unit. The understanding unit understands the user's personality and hobbies from conversations with the user. The suggestion unit suggests activities that match the available time based on the information understood by the understanding unit. The information provision unit provides detailed information about the activities suggested by the suggestion unit. The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the user's personality and hobbies and suggest the most suitable activities based on the time available. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system in which a generating AI learns the user's personality, preferences, hobbies, and schedule, and makes optimized suggestions for exercise, lessons, and events. This system understands the user's personality and hobbies through conversation and suggests appropriate activities that match the available time. Next, it provides detailed information about each activity (time, fee, location, etc.) so that the user can compare all the information at once. Furthermore, it analyzes and generates activity requests and suggests new activities in real time based on activity data of people around the world. For example, through conversation with the user, the generating AI understands the user's personality and hobbies. At this time, it grasps in detail what kind of activities the user is interested in and what kind of personality the user has. For example, if the user is interested in outdoor activities, it suggests appropriate activities based on that information. Next, the generating AI considers the user's schedule and suggests appropriate activities that match the available time. For example, if the user has two hours of free time on the weekend, it suggests exercise, lessons, or events that are suitable for that time. These suggestions are optimized based on the user's personality and hobbies. Furthermore, it provides detailed information about each activity. For example, it provides information such as the time, fee, and location of the suggested activity so that the user can compare all the information at once. This allows users to select the most suitable activity. Finally, the generative AI analyzes and generates activity requests, and suggests new activities in real time based on activity data from people around the world. For example, it suggests popular activities that other users are participating in, or newly held events. This ensures that users can always select activities based on the latest information. This allows users to find the most suitable activity that suits their personality and hobbies, and to make effective use of their time. Furthermore, comparing detailed information about each activity enables better choices. In addition, because the generative AI suggests new activities in real time, users always have access to the latest information. This means that the AI ​​agent system can suggest the most suitable activity based on the user's personality and hobbies, provide detailed information, and analyze and generate activity requests.

[0029] The AI ​​agent system according to this embodiment comprises an understanding unit, a suggestion unit, an information provision unit, and an analysis and generation unit. The understanding unit understands the user's personality and hobbies from conversations with the user. The understanding unit analyzes conversations with the user using, for example, natural language processing technology to understand the user's personality and hobbies. The suggestion unit suggests activities that match the user's available time based on the information understood by the understanding unit. The suggestion unit suggests, for example, exercise, lessons, or events that match the user's schedule and available time. The information provision unit provides detailed information about the activities suggested by the suggestion unit. The information provision unit collects detailed information about activities from, for example, the internet or partner sources and provides it to the user. The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision unit. The analysis and generation unit suggests new activities in real time based on activity data of people around the world. As a result, the AI ​​agent system can suggest optimal activities based on the user's personality and hobbies, provide detailed information, and analyze and generate activity requests.

[0030] The understanding unit understands the user's personality and hobbies through conversations with them. For example, it uses natural language processing technology to analyze conversations with users and understand their personality and hobbies. Specifically, the understanding unit analyzes the content of the user's statements, word choices, and emotional expressions through dialogue with the user. By using natural language processing technology, it extracts keywords and phrases from the user's statements and uses them to identify the user's interests and concerns. For example, if a user says, "I recently started jogging," the understanding unit extracts the keyword "jogging" and determines that the user is interested in exercise. It can also grasp the user's personality and mood by analyzing the tone of their statements and emotional expressions. For example, if a user says, "I'm very tired today," the understanding unit determines that the user is tired and provides information to suggest activities that can help them relax. Furthermore, the understanding unit accumulates the user's past statement and behavioral history and analyzes long-term trends and patterns to achieve a more accurate understanding. As a result, the understanding unit can deeply understand the user's personality and hobbies and provide a foundation for making optimal suggestions to individual users.

[0031] The suggestion department proposes activities tailored to the user's available time, based on information understood by the understanding department. For example, the suggestion department considers the user's schedule and proposes exercise, lessons, or events that fit within that time. Specifically, the suggestion department integrates with the user's calendar or schedule management app to understand the user's free time and plans. This allows the suggestion department to select the most suitable activity for the user's available time. For example, if a user has one hour of free time, the suggestion department will propose a jogging or yoga session that can be completed within that time. Also, if a user has a block of time on the weekend, the suggestion department will propose workshops or events that can be taken advantage of during that time. The suggestion department customizes the suggestions based on the user's interests and preferences, providing activities that the user will enjoy. Furthermore, the suggestion department collects user feedback and uses it to improve the accuracy and satisfaction of the suggestions. For example, if a user provides feedback such as "it was fun" or "it was helpful" regarding a suggested activity, the suggestion department will use that information to adjust the next suggestion to be more appropriate. In this way, the suggestion department can propose activities that are best suited to the user's lifestyle and preferences, increasing user satisfaction.

[0032] The Information Provision Department provides detailed information about activities proposed by the Proposal Department. For example, the Information Provision Department collects detailed activity information from the internet and partner sources and provides it to users. Specifically, the Information Provision Department collects information related to proposed activities from reliable sources on the internet and provides it in an easy-to-understand format for users. For example, as detailed information for a proposed jogging course, it provides information such as the course distance, estimated time, difficulty level, and surrounding scenery and facilities. It can also obtain information directly from partner fitness clubs and event organizers to provide users with the latest information. The Information Provision Department uses detailed explanations, images, and videos to provide information so that users can obtain sufficient information about the proposed activities. Furthermore, the Information Provision Department continuously improves the quality and content of information based on user feedback. For example, if a user provides feedback such as "it was easy to understand" or "I would like more detailed information," the Information Provision Department reflects that feedback and improves the way information is provided. This allows the Information Provision Department to provide users with reliable and detailed information, deepening their understanding of the proposed activities.

[0033] The analysis and generation unit analyzes and generates activity requests based on information provided by the information provision unit. For example, the analysis and generation unit proposes new activities in real time based on activity data of people around the world. Specifically, the analysis and generation unit utilizes a large-scale database and generates new activities for users by analyzing past activity data and trend information. For example, the analysis and generation unit analyzes activities that are popular in specific regions or seasons and proposes those activities to users. The analysis and generation unit can also generate new activities that match the user's preferences based on the user's past activity history and feedback. Furthermore, the analysis and generation unit uses AI technology to analyze activity requests and propose activities that are best suited to the user's needs. For example, if a user requests "a relaxing activity," the analysis and generation unit will propose relaxing activities such as yoga, meditation, or nature walks based on past data and trend information. Based on data that is updated in real time, the analysis and generation unit can always provide the latest information and generate optimal activities that meet the user's needs. In this way, the analysis and generation unit can propose new activities to users and enrich their lifestyles.

[0034] The understanding unit can understand the user's personality and hobbies from conversations using natural language processing techniques. For example, the understanding unit can analyze the user's utterances using morphological analysis to understand their personality and hobbies. It can also analyze the structure of the user's utterances using grammatical analysis to understand their personality and hobbies. Furthermore, it can analyze the meaning of the user's utterances using semantic analysis to understand their personality and hobbies. This allows for a more accurate understanding of the user's personality and hobbies by using natural language processing techniques. Some or all of the above-described processes in the understanding unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the understanding unit can input the user's utterances into a generative AI, which will analyze the utterances and understand the user's personality and hobbies.

[0035] The suggestion unit can consider the user's schedule and propose activities that match the available time. For example, the suggestion unit can obtain the user's calendar information and propose activities that match the available time. It can also consider the priority of the user's appointments and propose activities that match the available time. Furthermore, the suggestion unit can analyze the user's schedule in real time and propose activities that match the available time. This allows it to propose the most suitable activities for the user's schedule. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's calendar information into a generation AI, and the generation AI can propose activities that match the available time.

[0036] The Information Provision Department can collect detailed information about activities from the internet and partner sources and provide it to users. For example, the Information Provision Department can collect information from the internet and provide detailed information about activities. It can also collect information provided by partner sources and provide detailed information about activities. Furthermore, the Information Provision Department can collect and provide detailed information about activities in response to user requests. This allows users to select the most suitable activities by collecting and providing detailed information about activities. Some or all of the above processing in the Information Provision Department may be performed using a generative AI, or it may be performed without a generative AI. For example, the Information Provision Department can input information from the internet into a generative AI, which can then collect and provide detailed information about activities.

[0037] The analysis and generation unit can suggest new activities in real time based on activity data from people around the world. For example, the analysis and generation unit can analyze social media posts and suggest popular activities. It can also analyze location data and suggest popular activities in a specific region. Furthermore, the analysis and generation unit can suggest new activities in real time in response to user requests. This allows users to always select activities based on the latest information by suggesting new activities in real time based on activity data from people around the world. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input social media posts into a generation AI, which can then suggest popular activities.

[0038] The suggestion unit can propose the most suitable activities based on the user's personality and hobbies. For example, the suggestion unit can analyze the user's personality traits and propose the most suitable activities. It can also analyze the user's hobbies and propose the most suitable activities. Furthermore, the suggestion unit can comprehensively analyze the user's personality and hobbies and propose the most suitable activities. By proposing the most suitable activities based on the user's personality and hobbies, user satisfaction can be improved. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's personality traits into a generative AI, which can then propose the most suitable activities.

[0039] The understanding unit can analyze a user's past conversation history and track changes in their personality and interests. For example, the understanding unit can analyze a user's past conversation history to detect changes in their personality and interests. It can also predict current changes in personality and interests based on what the user has said in the past. Furthermore, the understanding unit can periodically analyze a user's conversation history and track changes in personality and interests in real time. This allows for tracking changes in personality and interests by analyzing the user's past conversation history, enabling more appropriate suggestions. Some or all of the above processing in the understanding unit may be performed using generative AI, or it may be performed without generative AI. For example, the understanding unit can input a user's past conversation history into a generative AI, which can then analyze and track changes in personality and interests.

[0040] The understanding unit can understand the user's temporary interests and concerns by considering the context of the conversation. For example, the understanding unit can analyze the temporary interests and concerns the user shows during the conversation and suggest activities based on that information. The understanding unit can also analyze the context of the conversation and understand topics that the user is temporarily interested in. Furthermore, the understanding unit can learn the user's temporary interests and concerns and reflect them in activity suggestions in real time. This allows for a better understanding of the user's temporary interests and concerns by considering the context of the conversation, enabling more appropriate suggestions. Some or all of the above processing in the understanding unit may be performed using generative AI, or it may be performed without generative AI. For example, the understanding unit can input the context of the conversation into a generative AI, which can then analyze and understand the temporary interests and concerns.

[0041] The understanding unit can analyze a user's social media activity to help understand their personality and interests. For example, it can analyze a user's social media posts to understand their personality and interests. It can also analyze accounts a user follows and groups they participate in to grasp their interests and preferences. Furthermore, it can track changes in a user's personality and interests based on their social media activity history. This allows for a better understanding of a user's personality and interests through the analysis of their social media activity. Some or all of the above processing in the understanding unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the understanding unit can input a user's social media posts into a generative AI, which can then analyze and understand the user's personality and interests.

[0042] The understanding unit can understand region-specific hobbies and activities by considering the user's geographical location information. For example, the understanding unit can understand popular hobbies and activities in a region based on the user's current location. The understanding unit can also analyze information about places the user has visited to grasp region-specific interests and concerns. Furthermore, the understanding unit can suggest region-specific events and activities based on the user's geographical location information. This allows for a better understanding of region-specific hobbies and activities and more appropriate suggestions by considering the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the understanding unit can input the user's geographical location information into a generative AI, which can then analyze and understand region-specific hobbies and activities.

[0043] The suggestion unit can select the most suitable activity by referring to the user's past activity history when making a suggestion. For example, the suggestion unit can suggest similar activities based on activities the user has participated in in the past. The suggestion unit can also analyze the user's past activity history and suggest new activities that might interest them. Furthermore, the suggestion unit can prioritize suggesting activities that the user has enjoyed in the past. In this way, the optimal activity can be selected by referring to the user's past activity history. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past activity history into a generative AI, which can then select the most suitable activity.

[0044] The suggestion unit can propose activities that take into account the user's current health and physical condition. For example, if the user is tired, the suggestion unit can propose relaxing activities. It can also propose activities that include moderate exercise if the user is seeking healthy exercise. Furthermore, if the user is unwell, the suggestion unit can propose activities that are not strenuous. This allows for the suggestion of more appropriate activities by considering the user's current health and physical condition. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's health data into a generative AI, which can then propose activities that take into account the user's health and physical condition.

[0045] The suggestion unit can propose region-specific activities by considering the user's geographical location information when making suggestions. For example, the suggestion unit can propose popular activities in a given region based on the user's current location. It can also propose region-specific events based on information about places the user has visited. Furthermore, the suggestion unit can propose region-specific hobbies and activities based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to propose region-specific activities. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then analyze and propose region-specific activities.

[0046] The suggestion unit can analyze a user's social media activity and suggest relevant activities when making suggestions. For example, the suggestion unit can analyze a user's social media posts and suggest activities that might interest them. It can also suggest relevant activities based on accounts the user follows and groups the user participates in. Furthermore, it can suggest activities that might interest the user based on their social media activity history. In this way, relevant activities can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input a user's social media posts into a generative AI, which can then analyze and suggest relevant activities.

[0047] The information provision unit can provide optimal information by referring to the user's past selection history when providing information. For example, the information provision unit can provide relevant information based on the user's past selected activities. The information provision unit can also analyze the user's past selection history and provide information that is likely to be of interest. Furthermore, the information provision unit can prioritize providing information that the user has liked in the past. In this way, optimal information can be provided by referring to the user's past selection history. Some or all of the above processing in the information provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provision unit can input the user's past selection history into a generative AI, and the generative AI can provide optimal information.

[0048] The information provider can customize information when providing it, taking into account the user's current interests and trends. For example, the information provider can provide information related to topics the user is currently interested in. It can also provide information aligned with trends based on the user's current interests. Furthermore, the information provider can learn the user's interests and trends and customize information in real time. This allows for the provision of more appropriate information by considering the user's current interests and trends. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input the user's current interests and trends into a generative AI, which can then customize the information.

[0049] The information provision unit can provide region-specific information by considering the user's geographical location when providing information. For example, the information provision unit can provide information on popular topics in a given region based on the user's current location. It can also provide region-specific information based on information about places the user has visited. Furthermore, the information provision unit can provide information on region-specific events and activities based on the user's geographical location. In this way, region-specific information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information provision unit can input the user's geographical location information into a generation AI, which can then analyze and provide region-specific information.

[0050] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can analyze a user's social media posts and provide information that might be of interest to the user. The information provision unit can also provide relevant information based on the accounts the user follows and the groups the user participates in. Furthermore, the information provision unit can provide information that might be of interest based on the user's social media activity history. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provision unit can input a user's social media posts into a generative AI, which can then analyze and provide relevant information.

[0051] The analysis and generation unit can generate new activities by referring to the user's past activity data during analysis and generation. For example, the analysis and generation unit can generate new activities based on activities the user has participated in in the past. The analysis and generation unit can also analyze the user's past activity data and generate new activities that are likely to interest the user. Furthermore, the analysis and generation unit can generate new activities by referring to activities the user has enjoyed in the past. In this way, new activities can be generated by referring to the user's past activity data. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input the user's past activity data into a generation AI, and the generation AI can generate new activities.

[0052] The analysis and generation unit can generate activities while considering the user's current lifestyle and trends during analysis and generation. For example, the analysis and generation unit can generate suitable activities based on the user's current lifestyle. It can also generate activities that are of interest to the user by considering the user's current trends. Furthermore, the analysis and generation unit can learn the user's lifestyle and trends and generate activities in real time. This allows for the generation of more appropriate activities by considering the user's current lifestyle and trends. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input the user's lifestyle and trends into a generation AI, which can then generate activities.

[0053] The analysis and generation unit can generate region-specific activities by considering the user's geographical location information during analysis and generation. For example, the analysis and generation unit can generate popular activities in a given region based on the user's current location. It can also generate region-specific events based on information about places the user has visited. Furthermore, the analysis and generation unit can generate region-specific hobbies and activities based on the user's geographical location information. In this way, region-specific activities can be generated by considering the user's geographical location information. Some or all of the above-described processes in the analysis and generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis and generation unit can input the user's geographical location information into a generation AI, which can then generate region-specific activities.

[0054] The analysis and generation unit can analyze a user's social media activity and generate relevant activities during the analysis and generation process. For example, the analysis and generation unit can analyze a user's social media posts and generate activities that are likely to be of interest. The analysis and generation unit can also generate relevant activities based on accounts that the user follows and groups that the user participates in. Furthermore, the analysis and generation unit can generate activities that are likely to be of interest based on the user's social media activity history. In this way, relevant activities can be generated by analyzing a user's social media activity. Some or all of the above-described processes in the analysis and generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis and generation unit can input a user's social media posts into a generation AI, and the generation AI can generate relevant activities.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The suggestion function can learn the user's preferred activity trends based on their past activity history, thereby improving the accuracy of its suggestions. For example, it can analyze the frequency of outdoor activities the user has participated in in the past and prioritize suggesting outdoor activities. It can also analyze the types of events the user has participated in in the past and suggest similar events. Furthermore, it can consider the time of day the user has preferred for activities in the past and suggest activities suitable for the same time slot. In this way, it can suggest more appropriate activities based on the user's past activity history.

[0057] The analysis and generation unit can generate new activities that a user might be interested in, based on the user's past activity data. For example, it can analyze data from sports events the user has participated in in the past and suggest new sports events. It can also generate similar cultural events based on data from cultural events the user has participated in in the past. Furthermore, it can analyze the user's past preferred activities and generate new activities that will pique their interest. In this way, new activities can be generated based on the user's past activity data.

[0058] The understanding unit can analyze a user's social media activity to help understand their personality and interests. For example, it can analyze a user's social media posts to understand their personality and hobbies. It can also analyze the accounts a user follows and the groups they participate in to understand their interests and passions. Furthermore, it can track changes in a user's personality and interests based on their social media activity history. In this way, analyzing a user's social media activity can help to understand their personality and interests.

[0059] The information provision department can provide optimal information by referring to the user's past selection history. For example, it can provide relevant information based on the user's past activities. It can also analyze the user's past selection history and provide information that is likely to be of interest. Furthermore, it can prioritize providing information that the user has previously preferred. In this way, by referring to the user's past selection history, the system can provide optimal information.

[0060] The suggestion function can propose activities that take into account the user's current health and physical condition. For example, if the user is tired, it can suggest relaxing activities. If the user is seeking healthy exercise, it can also suggest activities that include moderate exercise. Furthermore, if the user is feeling unwell, it can suggest activities that are not strenuous. In this way, by considering the user's current health and physical condition, it can suggest more appropriate activities.

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

[0062] Step 1: The understanding unit understands the user's personality and hobbies from conversations with the user. The understanding unit analyzes conversations with the user using, for example, natural language processing technology to understand the user's personality and hobbies. Step 2: The suggestion unit proposes activities that fit the available time based on the information understood by the understanding unit. For example, the suggestion unit considers the user's schedule and proposes exercise, lessons, or events that fit the available time. Step 3: The Information Provision Department provides detailed information about the activities proposed by the Proposal Department. The Information Provision Department collects detailed information about the activities from sources such as the internet and partners, and provides it to users. Step 4: The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision unit. For example, the analysis and generation unit proposes new activities in real time based on activity data of people around the world.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system in which a generating AI learns the user's personality, preferences, hobbies, and schedule, and makes optimized suggestions for exercise, lessons, and events. This system understands the user's personality and hobbies through conversation and suggests appropriate activities that match the available time. Next, it provides detailed information about each activity (time, fee, location, etc.) so that the user can compare all the information at once. Furthermore, it analyzes and generates activity requests and suggests new activities in real time based on activity data of people around the world. For example, through conversation with the user, the generating AI understands the user's personality and hobbies. At this time, it grasps in detail what kind of activities the user is interested in and what kind of personality the user has. For example, if the user is interested in outdoor activities, it suggests appropriate activities based on that information. Next, the generating AI considers the user's schedule and suggests appropriate activities that match the available time. For example, if the user has two hours of free time on the weekend, it suggests exercise, lessons, or events that are suitable for that time. These suggestions are optimized based on the user's personality and hobbies. Furthermore, it provides detailed information about each activity. For example, it provides information such as the time, fee, and location of the suggested activity so that the user can compare all the information at once. This allows users to select the most suitable activity. Finally, the generative AI analyzes and generates activity requests, and suggests new activities in real time based on activity data from people around the world. For example, it suggests popular activities that other users are participating in, or newly held events. This ensures that users can always select activities based on the latest information. This allows users to find the most suitable activity that suits their personality and hobbies, and to make effective use of their time. Furthermore, comparing detailed information about each activity enables better choices. In addition, because the generative AI suggests new activities in real time, users always have access to the latest information. This means that the AI ​​agent system can suggest the most suitable activity based on the user's personality and hobbies, provide detailed information, and analyze and generate activity requests.

[0064] The AI ​​agent system according to this embodiment comprises an understanding unit, a suggestion unit, an information provision unit, and an analysis and generation unit. The understanding unit understands the user's personality and hobbies from conversations with the user. The understanding unit analyzes conversations with the user using, for example, natural language processing technology to understand the user's personality and hobbies. The suggestion unit suggests activities that match the user's available time based on the information understood by the understanding unit. The suggestion unit suggests, for example, exercise, lessons, or events that match the user's schedule and available time. The information provision unit provides detailed information about the activities suggested by the suggestion unit. The information provision unit collects detailed information about activities from, for example, the internet or partner sources and provides it to the user. The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision unit. The analysis and generation unit suggests new activities in real time based on activity data of people around the world. As a result, the AI ​​agent system can suggest optimal activities based on the user's personality and hobbies, provide detailed information, and analyze and generate activity requests.

[0065] The understanding unit understands the user's personality and hobbies through conversations with them. For example, it uses natural language processing technology to analyze conversations with users and understand their personality and hobbies. Specifically, the understanding unit analyzes the content of the user's statements, word choices, and emotional expressions through dialogue with the user. By using natural language processing technology, it extracts keywords and phrases from the user's statements and uses them to identify the user's interests and concerns. For example, if a user says, "I recently started jogging," the understanding unit extracts the keyword "jogging" and determines that the user is interested in exercise. It can also grasp the user's personality and mood by analyzing the tone of their statements and emotional expressions. For example, if a user says, "I'm very tired today," the understanding unit determines that the user is tired and provides information to suggest activities that can help them relax. Furthermore, the understanding unit accumulates the user's past statement and behavioral history and analyzes long-term trends and patterns to achieve a more accurate understanding. As a result, the understanding unit can deeply understand the user's personality and hobbies and provide a foundation for making optimal suggestions to individual users.

[0066] The suggestion department proposes activities tailored to the user's available time, based on information understood by the understanding department. For example, the suggestion department considers the user's schedule and proposes exercise, lessons, or events that fit within that time. Specifically, the suggestion department integrates with the user's calendar or schedule management app to understand the user's free time and plans. This allows the suggestion department to select the most suitable activity for the user's available time. For example, if a user has one hour of free time, the suggestion department will propose a jogging or yoga session that can be completed within that time. Also, if a user has a block of time on the weekend, the suggestion department will propose workshops or events that can be taken advantage of during that time. The suggestion department customizes the suggestions based on the user's interests and preferences, providing activities that the user will enjoy. Furthermore, the suggestion department collects user feedback and uses it to improve the accuracy and satisfaction of the suggestions. For example, if a user provides feedback such as "it was fun" or "it was helpful" regarding a suggested activity, the suggestion department will use that information to adjust the next suggestion to be more appropriate. In this way, the suggestion department can propose activities that are best suited to the user's lifestyle and preferences, increasing user satisfaction.

[0067] The Information Provision Department provides detailed information about activities proposed by the Proposal Department. For example, the Information Provision Department collects detailed activity information from the internet and partner sources and provides it to users. Specifically, the Information Provision Department collects information related to proposed activities from reliable sources on the internet and provides it in an easy-to-understand format for users. For example, as detailed information for a proposed jogging course, it provides information such as the course distance, estimated time, difficulty level, and surrounding scenery and facilities. It can also obtain information directly from partner fitness clubs and event organizers to provide users with the latest information. The Information Provision Department uses detailed explanations, images, and videos to provide information so that users can obtain sufficient information about the proposed activities. Furthermore, the Information Provision Department continuously improves the quality and content of information based on user feedback. For example, if a user provides feedback such as "it was easy to understand" or "I would like more detailed information," the Information Provision Department reflects that feedback and improves the way information is provided. This allows the Information Provision Department to provide users with reliable and detailed information, deepening their understanding of the proposed activities.

[0068] The analysis and generation unit analyzes and generates activity requests based on information provided by the information provision unit. For example, the analysis and generation unit proposes new activities in real time based on activity data of people around the world. Specifically, the analysis and generation unit utilizes a large-scale database and generates new activities for users by analyzing past activity data and trend information. For example, the analysis and generation unit analyzes activities that are popular in specific regions or seasons and proposes those activities to users. The analysis and generation unit can also generate new activities that match the user's preferences based on the user's past activity history and feedback. Furthermore, the analysis and generation unit uses AI technology to analyze activity requests and propose activities that are best suited to the user's needs. For example, if a user requests "a relaxing activity," the analysis and generation unit will propose relaxing activities such as yoga, meditation, or nature walks based on past data and trend information. Based on data that is updated in real time, the analysis and generation unit can always provide the latest information and generate optimal activities that meet the user's needs. In this way, the analysis and generation unit can propose new activities to users and enrich their lifestyles.

[0069] The understanding unit can understand the user's personality and hobbies from conversations using natural language processing techniques. For example, the understanding unit can analyze the user's utterances using morphological analysis to understand their personality and hobbies. It can also analyze the structure of the user's utterances using grammatical analysis to understand their personality and hobbies. Furthermore, it can analyze the meaning of the user's utterances using semantic analysis to understand their personality and hobbies. This allows for a more accurate understanding of the user's personality and hobbies by using natural language processing techniques. Some or all of the above-described processes in the understanding unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the understanding unit can input the user's utterances into a generative AI, which will analyze the utterances and understand the user's personality and hobbies.

[0070] The suggestion unit can consider the user's schedule and propose activities that match the available time. For example, the suggestion unit can obtain the user's calendar information and propose activities that match the available time. It can also consider the priority of the user's appointments and propose activities that match the available time. Furthermore, the suggestion unit can analyze the user's schedule in real time and propose activities that match the available time. This allows it to propose the most suitable activities for the user's schedule. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's calendar information into a generation AI, and the generation AI can propose activities that match the available time.

[0071] The Information Provision Department can collect detailed information about activities from the internet and partner sources and provide it to users. For example, the Information Provision Department can collect information from the internet and provide detailed information about activities. It can also collect information provided by partner sources and provide detailed information about activities. Furthermore, the Information Provision Department can collect and provide detailed information about activities in response to user requests. This allows users to select the most suitable activities by collecting and providing detailed information about activities. Some or all of the above processing in the Information Provision Department may be performed using a generative AI, or it may be performed without a generative AI. For example, the Information Provision Department can input information from the internet into a generative AI, which can then collect and provide detailed information about activities.

[0072] The analysis and generation unit can suggest new activities in real time based on activity data from people around the world. For example, the analysis and generation unit can analyze social media posts and suggest popular activities. It can also analyze location data and suggest popular activities in a specific region. Furthermore, the analysis and generation unit can suggest new activities in real time in response to user requests. This allows users to always select activities based on the latest information by suggesting new activities in real time based on activity data from people around the world. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input social media posts into a generation AI, which can then suggest popular activities.

[0073] The suggestion unit can propose the most suitable activities based on the user's personality and hobbies. For example, the suggestion unit can analyze the user's personality traits and propose the most suitable activities. It can also analyze the user's hobbies and propose the most suitable activities. Furthermore, the suggestion unit can comprehensively analyze the user's personality and hobbies and propose the most suitable activities. By proposing the most suitable activities based on the user's personality and hobbies, user satisfaction can be improved. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's personality traits into a generative AI, which can then propose the most suitable activities.

[0074] The understanding unit can estimate the user's emotions and improve the accuracy of its understanding of personality and hobbies based on the estimated emotions. For example, the understanding unit can analyze the emotions a user shows during a conversation and deepen its understanding of personality and hobbies based on changes in those emotions. It can also analyze the emotions a user shows towards a specific topic and understand their interest in that topic. Furthermore, the understanding unit can learn the user's emotional patterns and update its understanding of personality and hobbies in response to changes in those emotions. This improves the accuracy of its understanding of personality and hobbies based on the user's emotions, enabling more accurate suggestions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the understanding unit may be performed using or without a generative AI. For example, the understanding unit can input user emotion data into a generative AI, which can then estimate emotions and improve the accuracy of its understanding of personality and hobbies.

[0075] The understanding unit can analyze a user's past conversation history and track changes in their personality and interests. For example, the understanding unit can analyze a user's past conversation history to detect changes in their personality and interests. It can also predict current changes in personality and interests based on what the user has said in the past. Furthermore, the understanding unit can periodically analyze a user's conversation history and track changes in personality and interests in real time. This allows for tracking changes in personality and interests by analyzing the user's past conversation history, enabling more appropriate suggestions. Some or all of the above processing in the understanding unit may be performed using generative AI, or it may be performed without generative AI. For example, the understanding unit can input a user's past conversation history into a generative AI, which can then analyze and track changes in personality and interests.

[0076] The understanding unit can understand the user's temporary interests and concerns by considering the context of the conversation. For example, the understanding unit can analyze the temporary interests and concerns the user shows during the conversation and suggest activities based on that information. The understanding unit can also analyze the context of the conversation and understand topics that the user is temporarily interested in. Furthermore, the understanding unit can learn the user's temporary interests and concerns and reflect them in activity suggestions in real time. This allows for a better understanding of the user's temporary interests and concerns by considering the context of the conversation, enabling more appropriate suggestions. Some or all of the above processing in the understanding unit may be performed using generative AI, or it may be performed without generative AI. For example, the understanding unit can input the context of the conversation into a generative AI, which can then analyze and understand the temporary interests and concerns.

[0077] The understanding unit can estimate the user's emotions and adjust the conversation based on those emotions. For example, if the user is stressed, the understanding unit can switch to a relaxing topic. It can also continue with an interesting topic if the user is excited. Furthermore, if the user is tired, the understanding unit can conduct a concise and easy-to-understand conversation. This allows for more appropriate conversation by adjusting the conversation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 understanding unit may be performed using or without a generative AI. For example, the understanding unit can input user emotion data into a generative AI, which can then estimate the emotion and adjust the conversation accordingly.

[0078] The understanding unit can analyze a user's social media activity to help understand their personality and interests. For example, it can analyze a user's social media posts to understand their personality and interests. It can also analyze accounts a user follows and groups they participate in to grasp their interests and preferences. Furthermore, it can track changes in a user's personality and interests based on their social media activity history. This allows for a better understanding of a user's personality and interests through the analysis of their social media activity. Some or all of the above processing in the understanding unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the understanding unit can input a user's social media posts into a generative AI, which can then analyze and understand the user's personality and interests.

[0079] The understanding unit can understand region-specific hobbies and activities by considering the user's geographical location information. For example, the understanding unit can understand popular hobbies and activities in a region based on the user's current location. The understanding unit can also analyze information about places the user has visited to grasp region-specific interests and concerns. Furthermore, the understanding unit can suggest region-specific events and activities based on the user's geographical location information. This allows for a better understanding of region-specific hobbies and activities and more appropriate suggestions by considering the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the understanding unit can input the user's geographical location information into a generative AI, which can then analyze and understand region-specific hobbies and activities.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions in a calm manner. If the user is in a hurry, the suggestion unit can present concise and quick suggestions. Furthermore, if the user is excited, the suggestion unit can present suggestions in an engaging manner. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotion and adjust the way suggestions are presented.

[0081] The suggestion unit can select the most suitable activity by referring to the user's past activity history when making a suggestion. For example, the suggestion unit can suggest similar activities based on activities the user has participated in in the past. The suggestion unit can also analyze the user's past activity history and suggest new activities that might interest them. Furthermore, the suggestion unit can prioritize suggesting activities that the user has enjoyed in the past. In this way, the optimal activity can be selected by referring to the user's past activity history. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past activity history into a generative AI, which can then select the most suitable activity.

[0082] The suggestion unit can propose activities that take into account the user's current health and physical condition. For example, if the user is tired, the suggestion unit can propose relaxing activities. It can also propose activities that include moderate exercise if the user is seeking healthy exercise. Furthermore, if the user is unwell, the suggestion unit can propose activities that are not strenuous. This allows for the suggestion of more appropriate activities by considering the user's current health and physical condition. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's health data into a generative AI, which can then propose activities that take into account the user's health and physical condition.

[0083] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting relaxing activities. Similarly, if the user is excited, the suggestion unit can prioritize suggesting activities that pique their interest. Furthermore, if the user is tired, the suggestion unit can prioritize suggesting activities that allow them to rest. This allows for more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of suggestions.

[0084] The suggestion unit can propose region-specific activities by considering the user's geographical location information when making suggestions. For example, the suggestion unit can propose popular activities in a given region based on the user's current location. It can also propose region-specific events based on information about places the user has visited. Furthermore, the suggestion unit can propose region-specific hobbies and activities based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to propose region-specific activities. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then analyze and propose region-specific activities.

[0085] The suggestion unit can analyze a user's social media activity and suggest relevant activities when making suggestions. For example, the suggestion unit can analyze a user's social media posts and suggest activities that might interest them. It can also suggest relevant activities based on accounts the user follows and groups the user participates in. Furthermore, it can suggest activities that might interest the user based on their social media activity history. In this way, relevant activities can be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input a user's social media posts into a generative AI, which can then analyze and suggest relevant activities.

[0086] The information provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible display method. If the user is relaxed, the information provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a display method that gets straight to the point. By adjusting the way information is displayed based on the user's emotions, more appropriate information can be provided. 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 above processing in the information provider may be performed using the generative AI or not. For example, the information provider can input user emotion data into the generative AI, which can then estimate the emotions and adjust the way information is displayed.

[0087] The information provision unit can provide optimal information by referring to the user's past selection history when providing information. For example, the information provision unit can provide relevant information based on the user's past selected activities. The information provision unit can also analyze the user's past selection history and provide information that is likely to be of interest. Furthermore, the information provision unit can prioritize providing information that the user has liked in the past. In this way, optimal information can be provided by referring to the user's past selection history. Some or all of the above processing in the information provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provision unit can input the user's past selection history into a generative AI, and the generative AI can provide optimal information.

[0088] The information provider can customize information when providing it, taking into account the user's current interests and trends. For example, the information provider can provide information related to topics the user is currently interested in. It can also provide information aligned with trends based on the user's current interests. Furthermore, the information provider can learn the user's interests and trends and customize information in real time. This allows for the provision of more appropriate information by considering the user's current interests and trends. Some or all of the above processing in the information provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provider can input the user's current interests and trends into a generative AI, which can then customize the information.

[0089] The information provider can estimate the user's emotions and prioritize information based on those emotions. For example, if the user is stressed, the information provider can prioritize providing information that helps them relax. Similarly, if the user is excited, the information provider can prioritize providing information that will pique their interest. Furthermore, if the user is tired, the information provider can prioritize providing information that will help them rest. This allows for more appropriate information delivery by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the information provider may be performed using or without a generative AI. For example, the information provider can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of the information.

[0090] The information provision unit can provide region-specific information by considering the user's geographical location when providing information. For example, the information provision unit can provide information on popular topics in a given region based on the user's current location. It can also provide region-specific information based on information about places the user has visited. Furthermore, the information provision unit can provide information on region-specific events and activities based on the user's geographical location. In this way, region-specific information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information provision unit can input the user's geographical location information into a generation AI, which can then analyze and provide region-specific information.

[0091] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can analyze a user's social media posts and provide information that might be of interest to the user. The information provision unit can also provide relevant information based on the accounts the user follows and the groups the user participates in. Furthermore, the information provision unit can provide information that might be of interest based on the user's social media activity history. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the information provision unit can input a user's social media posts into a generative AI, which can then analyze and provide relevant information.

[0092] The analysis and generation unit can estimate the user's emotions and improve the accuracy of analysis and generation based on the estimated emotions. For example, if the user is relaxed, the analysis and generation unit can perform a detailed analysis and produce highly accurate generation. If the user is in a hurry, the analysis and generation unit can perform a rapid analysis and produce generation in a short time. Furthermore, if the user is excited, the analysis and generation unit can perform an engaging analysis and produce visually stimulating generation. This improves the accuracy of analysis and generation based on the user's emotions, enabling more accurate analysis and generation. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the analysis and generation unit may be performed using the generation AI or not. For example, the analysis and generation unit can input user emotion data into the generation AI, which can then estimate the emotions and improve the accuracy of analysis and generation.

[0093] The analysis and generation unit can generate new activities by referring to the user's past activity data during analysis and generation. For example, the analysis and generation unit can generate new activities based on activities the user has participated in in the past. The analysis and generation unit can also analyze the user's past activity data and generate new activities that are likely to interest the user. Furthermore, the analysis and generation unit can generate new activities by referring to activities the user has enjoyed in the past. In this way, new activities can be generated by referring to the user's past activity data. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input the user's past activity data into a generation AI, and the generation AI can generate new activities.

[0094] The analysis and generation unit can generate activities while considering the user's current lifestyle and trends during analysis and generation. For example, the analysis and generation unit can generate suitable activities based on the user's current lifestyle. It can also generate activities that are of interest to the user by considering the user's current trends. Furthermore, the analysis and generation unit can learn the user's lifestyle and trends and generate activities in real time. This allows for the generation of more appropriate activities by considering the user's current lifestyle and trends. Some or all of the above processing in the analysis and generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis and generation unit can input the user's lifestyle and trends into a generation AI, which can then generate activities.

[0095] The analysis and generation unit can estimate the user's emotions and determine the priority of activities to generate based on the estimated emotions. For example, if the user is stressed, the analysis and generation unit can prioritize generating relaxing activities. It can also prioritize generating engaging activities if the user is excited. Furthermore, if the user is tired, it can prioritize generating restful activities. This allows for the generation of more appropriate activities by prioritizing activities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis and generation unit may be performed using or without a generation AI. For example, the analysis and generation unit can input user emotion data into a generation AI, which can then estimate the emotions and determine the priority of activities to generate.

[0096] The analysis and generation unit can generate region-specific activities by considering the user's geographical location information during analysis and generation. For example, the analysis and generation unit can generate popular activities in a given region based on the user's current location. It can also generate region-specific events based on information about places the user has visited. Furthermore, the analysis and generation unit can generate region-specific hobbies and activities based on the user's geographical location information. In this way, region-specific activities can be generated by considering the user's geographical location information. Some or all of the above-described processes in the analysis and generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis and generation unit can input the user's geographical location information into a generation AI, which can then generate region-specific activities.

[0097] The analysis and generation unit can analyze a user's social media activity and generate relevant activities during the analysis and generation process. For example, the analysis and generation unit can analyze a user's social media posts and generate activities that are likely to be of interest. The analysis and generation unit can also generate relevant activities based on accounts that the user follows and groups that the user participates in. Furthermore, the analysis and generation unit can generate activities that are likely to be of interest based on the user's social media activity history. In this way, relevant activities can be generated by analyzing a user's social media activity. Some or all of the above-described processes in the analysis and generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis and generation unit can input a user's social media posts into a generation AI, and the generation AI can generate relevant activities.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The understanding unit can analyze the user's voice tone and speaking patterns to estimate the user's emotions and stress levels. For example, if the user speaks in a high-pitched voice, it can estimate that the user is excited and suggest relaxing activities. Similarly, if the user speaks slowly, it can estimate that the user is tired and suggest activities that allow for rest. Furthermore, if the user speaks quickly, it can estimate that the user is in a hurry and suggest activities that can be completed in a short time. In this way, by analyzing the user's voice tone and speaking patterns, more appropriate activities can be suggested.

[0100] The suggestion function can learn the user's preferred activity trends based on their past activity history, thereby improving the accuracy of its suggestions. For example, it can analyze the frequency of outdoor activities the user has participated in in the past and prioritize suggesting outdoor activities. It can also analyze the types of events the user has participated in in the past and suggest similar events. Furthermore, it can consider the time of day the user has preferred for activities in the past and suggest activities suitable for the same time slot. In this way, it can suggest more appropriate activities based on the user's past activity history.

[0101] The information delivery system can customize how information is displayed based on the user's current mood and emotions. For example, if the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a concise display method that gets straight to the point. Furthermore, if the user is excited, it can provide a visually stimulating display method. By customizing how information is displayed based on the user's current mood and emotions, it becomes possible to provide more appropriate information.

[0102] The analysis and generation unit can generate new activities that a user might be interested in, based on the user's past activity data. For example, it can analyze data from sports events the user has participated in in the past and suggest new sports events. It can also generate similar cultural events based on data from cultural events the user has participated in in the past. Furthermore, it can analyze the user's past preferred activities and generate new activities that will pique their interest. In this way, new activities can be generated based on the user's past activity data.

[0103] The understanding unit can analyze a user's social media activity to help understand their personality and interests. For example, it can analyze a user's social media posts to understand their personality and hobbies. It can also analyze the accounts a user follows and the groups they participate in to understand their interests and passions. Furthermore, it can track changes in a user's personality and interests based on their social media activity history. In this way, analyzing a user's social media activity can help to understand their personality and interests.

[0104] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, it can prioritize suggesting relaxing activities. If the user is excited, it can prioritize suggesting activities that pique their interest. Furthermore, if the user is tired, it can prioritize suggesting activities that allow them to rest. By prioritizing suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0105] The information provision department can provide optimal information by referring to the user's past selection history. For example, it can provide relevant information based on the user's past activities. It can also analyze the user's past selection history and provide information that is likely to be of interest. Furthermore, it can prioritize providing information that the user has previously preferred. In this way, by referring to the user's past selection history, the system can provide optimal information.

[0106] The analysis and generation unit can estimate the user's emotions and determine the priority of activities to generate based on those estimated emotions. For example, if the user is stressed, it can prioritize generating relaxing activities. If the user is excited, it can prioritize generating activities that pique their interest. Furthermore, if the user is tired, it can prioritize generating activities that allow them to rest. By prioritizing activities based on the user's emotions, it is possible to generate more appropriate activities.

[0107] The suggestion function can propose activities that take into account the user's current health and physical condition. For example, if the user is tired, it can suggest relaxing activities. If the user is seeking healthy exercise, it can also suggest activities that include moderate exercise. Furthermore, if the user is feeling unwell, it can suggest activities that are not strenuous. In this way, by considering the user's current health and physical condition, it can suggest more appropriate activities.

[0108] The information provision department can estimate the user's emotions and prioritize information based on those emotions. For example, if a user is stressed, it can prioritize providing information that helps them relax. If a user is excited, it can prioritize providing information that will pique their interest. Furthermore, if a user is tired, it can prioritize providing information that allows them to rest. By prioritizing information based on the user's emotions, it becomes possible to provide more appropriate information.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The understanding unit understands the user's personality and hobbies from conversations with the user. The understanding unit analyzes conversations with the user using, for example, natural language processing technology to understand the user's personality and hobbies. Step 2: The suggestion unit proposes activities that fit the available time based on the information understood by the understanding unit. For example, the suggestion unit considers the user's schedule and proposes exercise, lessons, or events that fit the available time. Step 3: The Information Provision Department provides detailed information about the activities proposed by the Proposal Department. The Information Provision Department collects detailed information about the activities from sources such as the internet and partners, and provides it to users. Step 4: The analysis and generation unit analyzes and generates activity requests based on the information provided by the information provision unit. For example, the analysis and generation unit proposes new activities in real time based on activity data of people around the world.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the understanding unit, proposal unit, information provision unit, and analysis / generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the understanding unit is implemented by the control unit 46A of the smart device 14, which analyzes conversations with the user to understand their personality and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, which considers the user's schedule and proposes activities that fit their available time. The information provision unit is implemented by the control unit 46A of the smart device 14, which provides detailed information about the proposed activities. The analysis / generation unit is implemented by the specific processing unit 290 of the data processing device 12, which proposes new activities in real time based on activity data of people around the world. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0130] Each of the multiple elements described above, including the understanding unit, suggestion unit, information provision unit, and analysis / generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the understanding unit is implemented by the control unit 46A of the smart glasses 214, which analyzes conversations with the user and understands their personality and hobbies. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which considers the user's schedule and suggests activities that fit their available time. The information provision unit is implemented by the control unit 46A of the smart glasses 214, which provides detailed information about the suggested activities. The analysis / generation unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests new activities in real time based on activity data of people around the world. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[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 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.

[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 (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).

[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] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the understanding unit, suggestion unit, information provision unit, and analysis / generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the headset terminal 314, which analyzes conversations with the user and understands their personality and hobbies. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which considers the user's schedule and suggests activities that fit their available time. The information provision unit is implemented by the control unit 46A of the headset terminal 314, which provides detailed information about the suggested activities. The analysis / generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests new activities in real time based on activity data of people around the world. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the understanding unit, proposal unit, information provision unit, and analysis / generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the robot 414, which analyzes conversations with the user and understands their personality and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes activities that take the user's schedule into consideration and match their available time. The information provision unit is implemented by the control unit 46A of the robot 414, which provides detailed information about the proposed activities. The analysis / generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes new activities in real time based on activity data of people around the world. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) An understanding unit that understands the user's personality and hobbies from conversations, Based on the information understood by the aforementioned understanding unit, the proposal unit proposes activities that are appropriate for the allotted time, An information provision department provides detailed information on the activities proposed by the aforementioned proposal department, The system includes an analysis and generation unit that analyzes and generates activity requests based on the information provided by the aforementioned information provision unit. A system characterized by the following features. (Note 2) The aforementioned understanding unit is, Using natural language processing technology, we understand the user's personality and hobbies from their conversations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We take the user's schedule into consideration and propose activities that fit their available time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information provision unit, We collect detailed information about our activities from the internet and our partners and provide it to users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The analysis generation unit, Based on activity data from people around the world, we propose new activities in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest the most suitable activities based on the user's personality and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned understanding unit is, It estimates the user's emotions and improves the accuracy of understanding their personality and hobbies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned understanding unit is, Analyze the user's past conversation history to track changes in their personality and interests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned understanding unit is, Understanding the user's temporary interests and concerns by considering the context of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned understanding unit is, It estimates the user's emotions and adjusts the conversation flow based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned understanding unit is, Analyzing users' social media activity helps us understand their personality and interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned understanding unit is, Understanding region-specific hobbies and activities by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned proposal section is, When making a proposal, the system selects the most suitable activity by referring to the user's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, we propose activities that take into account the user's current health and physical condition. 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 determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we suggest region-specific activities that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant activities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, When providing information, we refer to the user's past selection history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, When providing information, customize the information to take into account the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, It estimates the user's emotions and prioritizes information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, When providing information, we will provide region-specific information that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The analysis generation unit, It estimates user emotions and improves the accuracy of analysis and generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The analysis generation unit, When generating analysis, new activities are generated by referencing the user's past activity data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The analysis generation unit, When generating analysis, activities are generated while considering the user's current lifestyle and trends. The system described in Appendix 1, characterized by the features described herein. (Note 28) The analysis generation unit, It estimates the user's emotions and determines the priority of activities to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The analysis generation unit, When generating analysis, region-specific activities are generated by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The analysis generation unit, During analysis and generation, the system analyzes the user's social media activity and generates relevant activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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. An understanding unit that understands the user's personality and hobbies from conversations, Based on the information understood by the aforementioned understanding unit, the proposal unit proposes activities that are appropriate for the allotted time, An information provision department provides detailed information on the activities proposed by the aforementioned proposal department, The system includes an analysis and generation unit that analyzes and generates activity requests based on the information provided by the aforementioned information provision unit. A system characterized by the following features.

2. The aforementioned understanding unit is, Using natural language processing technology, we understand the user's personality and hobbies from their conversations. The system according to feature 1.

3. The aforementioned proposal section is, We take the user's schedule into consideration and propose activities that fit their available time. The system according to feature 1.

4. The aforementioned information provision unit, We collect detailed information about our activities from the internet and our partners and provide it to users. The system according to feature 1.

5. The analysis generation unit, Based on activity data from people around the world, we propose new activities in real time. The system according to feature 1.

6. The aforementioned proposal section is, We suggest the most suitable activities based on the user's personality and hobbies. The system according to feature 1.

7. The aforementioned understanding unit is, It estimates the user's emotions and improves the accuracy of understanding their personality and hobbies based on those estimated emotions. The system according to feature 1.

8. The aforementioned understanding unit is, Analyze the user's past conversation history to track changes in their personality and interests. The system according to feature 1.

9. The aforementioned understanding unit is, Understanding the user's temporary interests and concerns by considering the context of the conversation. The system according to feature 1.

10. The aforementioned understanding unit is, It estimates the user's emotions and adjusts the conversation flow based on those estimated emotions. The system according to feature 1.

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