system

Abstract

The system according to this embodiment aims to achieve effective advertising proposals while maintaining user trust. [Solution] The system according to the embodiment comprises a setting unit, a visualization unit, and a selection unit. The setting unit sets the scope of information sharing. The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the setting unit. The selection unit selects whether or not to receive advertising proposals based on the information sharing and decision-making processes visualized by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that Internet advertisements are likely to be a factor that damages users' trust, and there are concerns about information overload and privacy infringement.

[0005] The system according to the embodiment aims to realize effective advertisement proposals while maintaining users' trust.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a setting unit, a visualization unit, and a selection unit. The setting unit sets the scope of information sharing. The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the setting unit. The selection unit selects whether or not to receive advertising proposals based on the information sharing and decision-making processes visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide effective advertising suggestions while maintaining user trust. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal AI advertising agent system according to an embodiment of the present invention is a system in which a user-dedicated AI agent and an advertising-dedicated AI agent work together to provide advertising suggestions that are optimal for the user's needs. This system allows users to finely configure the scope of information sharing and choose whether or not to receive advertising suggestions while protecting their privacy through a sequential approval system and personal information abstraction filters. Furthermore, transparency is enhanced by visualizing the information sharing and decision-making processes. A mechanism is also incorporated to return a portion of advertising revenue to the user, strengthening user engagement. For example, a user agrees to share information with the advertising-dedicated AI agent through their dedicated AI agent. At that time, they can finely configure what information to share. For example, users can control the information they share, such as categories of interest, budget, and past purchase history. While the dedicated AI agent is familiar with the user, when communicating with the advertising-dedicated AI agent, it adjusts the sequential approval system and personal information abstraction filters to ensure appropriate information sharing with consideration for privacy. Next, the information sharing and decision-making processes are made visible to the user, ensuring transparency and trust. Users can always check how their information is being shared and what suggestions are being made. When a user is looking to solve a problem or find a product, a dedicated AI agent receives suggestions from an advertising-specific AI agent, based on the user's settings. If there are suggestions, the dedicated AI agent notifies the user, and they can choose whether or not to accept them. If the user accepts a suggestion, the dedicated AI agent, the advertising-specific AI agent, and the service provider's agent work together to automatically complete the purchase or service usage procedures. This reduces information overload and privacy concerns for the user, allowing them to receive personalized advertising suggestions with peace of mind. Furthermore, revenue sharing creates incentives, motivating users to participate more actively in information sharing. For advertisers, the benefits include highly accurate targeting with user consent, improving advertising effectiveness. By utilizing generative AI, appropriate suggestions can be made at the right time, building trust between users and advertisers.This allows the personal AI advertising agent system to provide advertising suggestions that are best suited to the user's needs.

[0029] The personal AI advertising agent system according to this embodiment comprises a setting unit, a visualization unit, and a selection unit. The setting unit sets the scope of information sharing. The setting unit can set details such as the type of information the user shares, the target audience, and the purpose of sharing. For example, the setting unit can set information such as categories of interest to the user, budget, and past purchase history. The setting unit may include AI processing and can automatically suggest the scope of information sharing using AI. The visualization unit visualizes the information sharing and decision-making process based on the scope of information sharing set by the setting unit. The visualization unit can visualize information in the form of graphs, charts, dashboards, etc. The visualization unit may include AI processing and can visualize the information sharing and decision-making process in real time using AI. The selection unit selects whether to receive advertising suggestions based on the information sharing and decision-making process visualized by the visualization unit. The selection unit can set specific methods and criteria for the user to choose whether to receive advertising suggestions. The selection unit may include AI processing and can assist the user in making their selection using AI. As a result, the personal AI advertising agent system according to this embodiment can provide advertising suggestions that are best suited to the user's needs.

[0030] The settings section allows users to define the scope of information sharing. Specifically, it allows users to configure details such as the type of information they share, the target audience, and the purpose of sharing. For example, users can set categories of interest to receive only relevant advertisements. They can also maximize the cost-effectiveness of advertisements by setting a budget. By setting past purchase history, advertisements are suggested based on the user's purchasing trends. Furthermore, the settings section can include AI processing, and it is possible to automatically suggest the scope of information sharing using AI. For example, the AI ​​analyzes the user's past behavior data and interests to suggest the optimal scope of information sharing. This allows users to receive effective advertisement suggestions without any effort. The settings section is designed to be intuitively operable through a user interface, and users can easily change settings. For example, they can adjust the scope of information sharing using drag-and-drop operations or sliders. The settings section also includes features to protect user privacy, allowing for fine-grained control over the scope of information shared. This allows users to use the system with peace of mind.

[0031] The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the settings unit. Specifically, it can visualize information in the form of graphs, charts, dashboards, etc. For example, it can display the performance of relevant advertisements in a graph based on categories of interest and budget set by the user. This allows the user to grasp the effectiveness of advertisements at a glance. The visualization unit also includes AI processing and can use AI to visualize the information sharing and decision-making processes in real time. For example, the AI ​​analyzes user behavior data in real time and predicts the effectiveness of advertisements. This allows the user to check in real time how effective their current advertising strategy is. Furthermore, the visualization unit provides an intuitive interface that users can easily use to filter information and view detailed data. For example, users can check the performance of advertisements by narrowing it down to a specific period or category. In addition, the visualization unit can collect user feedback and continuously improve the accuracy and ease of use of the visualizations. This makes the visualization unit a powerful tool for users to make effective decisions.

[0032] The selection section allows users to choose whether or not to receive ad suggestions based on the information sharing and decision-making processes visualized by the visualization section. Specifically, users can set specific methods and criteria for choosing whether or not to receive ad suggestions. For example, users can choose whether or not to receive ad suggestions based on specific categories or budgets. Users can also set the frequency and timing of ad suggestions. The selection section can include AI processing and use AI to assist user choices. For example, AI can analyze the user's past selection data and automatically select the most suitable ad suggestions. This allows users to receive effective ad suggestions without any effort. The selection section is designed to be intuitively operable through a user interface, allowing users to easily change their selections. For example, users can adjust the conditions for receiving ad suggestions using checkboxes or sliders. The selection section can also collect user feedback and continuously improve the accuracy and ease of use of its selections. This makes the selection section a powerful tool for users to receive the most suitable ad suggestions.

[0033] The adjustment unit adjusts the sequential approval process and the personal information abstraction filter. For example, the adjustment unit can set specific methods and criteria for the sequential approval process. For example, it can set the approval steps and the roles of the approvers. The adjustment unit can also set specific methods and criteria for the personal information abstraction filter. For example, it can set the level of abstraction and the scope of application of the filter. The adjustment unit may also include AI processing and can use AI to automatically adjust the sequential approval process and the personal information abstraction filter. This enables appropriate information sharing while protecting privacy.

[0034] The revenue sharing section returns a portion of the advertising revenue to the users. The revenue sharing section can, for example, set the method and criteria for distributing the advertising revenue. For instance, it can set the percentage of revenue distributed and the timing of the distribution. The revenue sharing section may also include AI processing, and can use AI to automatically suggest methods for distributing advertising revenue. This enhances user engagement.

[0035] The settings section allows users to configure information such as categories of interest, budget, and past purchase history. For example, the settings section allows users to set categories of interest. For example, the settings section allows users to set a budget. For example, the settings section allows users to set their past purchase history. This enables information sharing tailored to the user's needs.

[0036] The visualization unit can make the information sharing and decision-making processes visible to users. For example, the visualization unit can display the information sharing process as a graph. For example, the visualization unit can display the decision-making process as a chart. For example, the visualization unit can display the information sharing and decision-making processes on a dashboard. This increases transparency and builds user trust.

[0037] The selection section allows users to choose whether or not to receive ad suggestions. The selection section can, for example, set specific methods and criteria for users to choose whether or not to receive ad suggestions. For example, the selection section can set the timing for receiving ad suggestions. For example, the selection section can set the content of the ad suggestions. This allows users to choose whether or not to receive ad suggestions.

[0038] The settings unit can analyze the user's past behavior history and automatically suggest the optimal scope of information sharing. For example, the settings unit can suggest the optimal scope of information sharing based on information the user has frequently shared in the past. For example, the settings unit can suggest information to share at specific time periods based on the user's past behavior history. For example, the settings unit can analyze the user's past behavior history and suggest the most efficient scope of information sharing. This enables optimal information sharing based on past behavior history.

[0039] The settings unit can customize the scope of information sharing based on the user's current interests and life events. For example, the settings unit can set the scope of information sharing based on topics the user is currently interested in. For example, the settings unit can customize the scope of information sharing based on the user's life events (marriage, moving, etc.). For example, the settings unit can dynamically adjust the scope of information sharing considering the user's current interests and life events. This enables information sharing that is tailored to the user's interests and life events.

[0040] The settings unit can prioritize highly relevant information when setting the scope of information sharing, taking into account the user's geographical location. For example, the settings unit can prioritize sharing highly relevant information based on the user's current location. For example, the settings unit can set the optimal scope of information sharing by considering the user's geographical location. For example, the settings unit can prioritize relevant information based on the user's current location. This enables information sharing based on geographical location information.

[0041] The settings unit can analyze the user's social media activity and set relevant information when defining the scope of information sharing. For example, the settings unit can analyze the user's social media activity and prioritize the sharing of relevant information. For example, the settings unit can define the scope of information sharing based on the user's interests on social media. For example, the settings unit can define the optimal scope of information sharing considering the user's social media activity. This enables information sharing based on social media activity.

[0042] The visualization unit can customize the displayed content according to the user's level of understanding when visualizing information sharing and decision-making processes. For example, the visualization unit can adjust the level of detail of the information according to the user's level of understanding. For example, the visualization unit can display information in a format that is easy for the user to understand. For example, the visualization unit can provide the optimal display method considering the user's level of understanding. This allows for the provision of displayed content tailored to the user's level of understanding.

[0043] The visualization unit can optimize the display method by reflecting past user feedback when visualizing information sharing and decision-making processes. For example, the visualization unit can adjust the display method based on past user feedback. For example, the visualization unit can provide the optimal display method by reflecting past user feedback. For example, the visualization unit can optimize the information display method by considering past user feedback. This allows for the provision of the optimal display method based on past feedback.

[0044] The visualization unit can select the optimal display method by considering the user's device information when visualizing information sharing and decision-making processes. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the visualization unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the visualization unit can provide a concise and highly visible display method. This allows for the provision of the optimal display method based on device information.

[0045] The visualization unit can display content in multiple languages ​​according to the user's language settings when visualizing information sharing and decision-making processes. For example, the visualization unit can automatically set the display content based on the language settings of the user's device. For example, the visualization unit can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the visualization unit can provide the display content in that language. This enables the provision of multilingual display content.

[0046] The selection function can analyze a user's past selection history to provide optimal suggestions when they choose whether or not to receive ad suggestions. For example, the selection function can provide optimal suggestions based on ad suggestions the user has previously accepted. For example, the selection function can provide highly relevant ad suggestions based on the user's past selection history. For example, the selection function can analyze the user's past selection history to provide the most effective ad suggestions. This makes it possible to provide optimal ad suggestions based on past selection history.

[0047] The selection section can customize the content of advertising suggestions based on the user's current situation and needs when the user chooses whether or not to receive them. For example, the selection section can provide optimal advertising suggestions based on the user's current situation. For example, the selection section can customize the content of advertising suggestions based on the user's needs. For example, the selection section can provide advertising suggestions considering the user's current situation and needs. This makes it possible to provide advertising suggestions that are tailored to the user's current situation and needs.

[0048] The selection function can prioritize highly relevant suggestions by considering the user's geographical location when the user chooses whether or not to receive ad suggestions. For example, the selection function can prioritize highly relevant ad suggestions based on the user's current location. For example, the selection function can provide optimal ad suggestions by considering the user's geographical location. For example, the selection function can prioritize relevant ad suggestions based on the user's current location. This enables geographically-based ad suggestions.

[0049] The selection unit can analyze the user's social media activity and make relevant suggestions when the user chooses whether or not to receive advertising suggestions. For example, the selection unit can analyze the user's social media activity and make relevant advertising suggestions. For example, the selection unit can make advertising suggestions based on the user's interests on social media. For example, the selection unit can consider the user's social media activity and make optimal advertising suggestions. This enables advertising suggestions based on social media activity.

[0050] The adjustment unit can analyze the user's past approval history and propose optimal settings when adjusting the sequential approval method and personal information abstraction filter. For example, the adjustment unit can propose the optimal sequential approval method based on the user's past approval history. For example, the adjustment unit can propose the optimal settings for the personal information abstraction filter based on the user's past approval history. For example, the adjustment unit can analyze the user's past approval history and propose the most efficient sequential approval method and personal information abstraction filter settings. This makes it possible to set the optimal sequential approval method and personal information abstraction filter based on past approval history.

[0051] The adjustment unit can optimize settings by considering the user's geographical location when adjusting the sequential approval method and personal information abstraction filter. For example, the adjustment unit can set the optimal sequential approval method based on the user's current location. For example, the adjustment unit can optimize settings for the personal information abstraction filter by considering the user's geographical location. For example, the adjustment unit can optimize the settings for the sequential approval method and personal information abstraction filter based on the user's current location. This makes it possible to set sequential approval methods and personal information abstraction filters based on geographical location information.

[0052] The revenue sharing unit can analyze a user's past revenue sharing history to propose the optimal method for sharing advertising revenue. For example, the revenue sharing unit can propose the optimal method based on a user's past revenue sharing history. For example, the revenue sharing unit can propose a highly relevant method based on a user's past revenue sharing history. For example, the revenue sharing unit can analyze a user's past revenue sharing history and propose the most effective method. This allows for the provision of the optimal advertising revenue sharing method based on past revenue sharing history.

[0053] The revenue sharing unit can determine the optimal method for distributing advertising revenue by considering the user's geographical location. For example, the revenue sharing unit can provide the optimal method based on the user's current location. For example, the revenue sharing unit can optimize the method of distribution by considering the user's geographical location. For example, the revenue sharing unit can provide the relevant method of distribution based on the user's current location. This allows for the provision of advertising revenue distribution methods based on geographical location information.

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

[0055] A personal AI advertising agent system can analyze a user's past behavior history and provide optimal advertising suggestions. For example, it can suggest relevant ads based on products the user has previously purchased. It can also suggest ads based on categories the user has shown interest in in the past. By analyzing the user's past behavior history, it can provide the most effective advertising suggestions. This enables optimal advertising suggestions based on past behavior history.

[0056] The personal AI advertising agent system can customize advertising suggestions based on the user's current situation and needs. For example, it can provide optimal advertising suggestions based on the user's current circumstances. It can customize advertising suggestions based on the user's needs. It can provide advertising suggestions considering the user's current situation and needs. This enables advertising suggestions tailored to the user's current situation and needs.

[0057] The personal AI advertising agent system can provide highly relevant advertising suggestions by considering the user's geographical location. For example, it can provide highly relevant advertising suggestions based on the user's current location. It can provide optimal advertising suggestions by considering the user's geographical location. It can provide relevant advertising suggestions based on the user's current location. This enables advertising suggestions based on geographical location information.

[0058] A personal AI advertising agent system can analyze a user's social media activity and provide relevant advertising suggestions. For example, it can analyze a user's social media activity and provide relevant advertising suggestions. It can also provide advertising suggestions based on the user's interests on social media. Furthermore, it can provide optimal advertising suggestions by considering the user's social media activity. This enables advertising suggestions based on social media activity.

[0059] The personal AI advertising agent system can analyze a user's past selection history to provide optimal advertising suggestions. For example, it can provide optimal suggestions based on advertising suggestions the user has previously accepted. It can provide highly relevant advertising suggestions based on the user's past selection history. By analyzing the user's past selection history, it can provide the most effective advertising suggestions. This enables optimal advertising suggestions based on past selection history.

[0060] The personal AI advertising agent system can analyze a user's past reward history and propose the optimal reward method. For example, it can suggest the optimal reward method based on the user's past reward history. It can suggest highly relevant reward methods based on the user's past reward history. It can analyze the user's past reward history and propose the most effective reward method. This allows for the provision of the optimal advertising revenue reward method based on past reward history.

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

[0062] Step 1: The settings section defines the scope of information sharing. The settings section allows users to configure details such as the type of information to be shared, the target audience, and the purpose of sharing. For example, users can set information such as categories they are interested in, their budget, and their past purchase history. The settings section also includes AI processing and can automatically suggest the scope of information sharing using AI. Step 2: The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the configuration unit. The visualization unit can visualize information in the form of graphs, charts, dashboards, etc. Furthermore, it can include AI processing and use AI to visualize the information sharing and decision-making processes in real time. Step 3: The selection unit chooses whether to receive ad suggestions based on the information sharing and decision-making processes visualized by the visualization unit. The selection unit can set specific methods and criteria for the user to choose whether or not to receive ad suggestions. Furthermore, it can include AI processing and use AI to assist the user's decision.

[0063] (Example of form 2) The personal AI advertising agent system according to an embodiment of the present invention is a system in which a user-dedicated AI agent and an advertising-dedicated AI agent work together to provide advertising suggestions that are optimal for the user's needs. This system allows users to finely configure the scope of information sharing and choose whether or not to receive advertising suggestions while protecting their privacy through a sequential approval system and personal information abstraction filters. Furthermore, transparency is enhanced by visualizing the information sharing and decision-making processes. A mechanism is also incorporated to return a portion of advertising revenue to the user, strengthening user engagement. For example, a user agrees to share information with the advertising-dedicated AI agent through their dedicated AI agent. At that time, they can finely configure what information to share. For example, users can control the information they share, such as categories of interest, budget, and past purchase history. While the dedicated AI agent is familiar with the user, when communicating with the advertising-dedicated AI agent, it adjusts the sequential approval system and personal information abstraction filters to ensure appropriate information sharing with consideration for privacy. Next, the information sharing and decision-making processes are made visible to the user, ensuring transparency and trust. Users can always check how their information is being shared and what suggestions are being made. When a user is looking to solve a problem or find a product, a dedicated AI agent receives suggestions from an advertising-specific AI agent, based on the user's settings. If there are suggestions, the dedicated AI agent notifies the user, and they can choose whether or not to accept them. If the user accepts a suggestion, the dedicated AI agent, the advertising-specific AI agent, and the service provider's agent work together to automatically complete the purchase or service usage procedures. This reduces information overload and privacy concerns for the user, allowing them to receive personalized advertising suggestions with peace of mind. Furthermore, revenue sharing creates incentives, motivating users to participate more actively in information sharing. For advertisers, the benefits include highly accurate targeting with user consent, improving advertising effectiveness. By utilizing generative AI, appropriate suggestions can be made at the right time, building trust between users and advertisers.This allows the personal AI advertising agent system to provide advertising suggestions that are best suited to the user's needs.

[0064] The personal AI advertising agent system according to this embodiment comprises a setting unit, a visualization unit, and a selection unit. The setting unit sets the scope of information sharing. The setting unit can set details such as the type of information the user shares, the target audience, and the purpose of sharing. For example, the setting unit can set information such as categories of interest to the user, budget, and past purchase history. The setting unit may include AI processing and can automatically suggest the scope of information sharing using AI. The visualization unit visualizes the information sharing and decision-making process based on the scope of information sharing set by the setting unit. The visualization unit can visualize information in the form of graphs, charts, dashboards, etc. The visualization unit may include AI processing and can visualize the information sharing and decision-making process in real time using AI. The selection unit selects whether to receive advertising suggestions based on the information sharing and decision-making process visualized by the visualization unit. The selection unit can set specific methods and criteria for the user to choose whether to receive advertising suggestions. The selection unit may include AI processing and can assist the user in making their selection using AI. As a result, the personal AI advertising agent system according to this embodiment can provide advertising suggestions that are best suited to the user's needs.

[0065] The settings section allows users to define the scope of information sharing. Specifically, it allows users to configure details such as the type of information they share, the target audience, and the purpose of sharing. For example, users can set categories of interest to receive only relevant advertisements. They can also maximize the cost-effectiveness of advertisements by setting a budget. By setting past purchase history, advertisements are suggested based on the user's purchasing trends. Furthermore, the settings section can include AI processing, and it is possible to automatically suggest the scope of information sharing using AI. For example, the AI ​​analyzes the user's past behavior data and interests to suggest the optimal scope of information sharing. This allows users to receive effective advertisement suggestions without any effort. The settings section is designed to be intuitively operable through a user interface, and users can easily change settings. For example, they can adjust the scope of information sharing using drag-and-drop operations or sliders. The settings section also includes features to protect user privacy, allowing for fine-grained control over the scope of information shared. This allows users to use the system with peace of mind.

[0066] The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the settings unit. Specifically, it can visualize information in the form of graphs, charts, dashboards, etc. For example, it can display the performance of relevant advertisements in a graph based on categories of interest and budget set by the user. This allows the user to grasp the effectiveness of advertisements at a glance. The visualization unit also includes AI processing and can use AI to visualize the information sharing and decision-making processes in real time. For example, the AI ​​analyzes user behavior data in real time and predicts the effectiveness of advertisements. This allows the user to check in real time how effective their current advertising strategy is. Furthermore, the visualization unit provides an intuitive interface that users can easily use to filter information and view detailed data. For example, users can check the performance of advertisements by narrowing it down to a specific period or category. In addition, the visualization unit can collect user feedback and continuously improve the accuracy and ease of use of the visualizations. This makes the visualization unit a powerful tool for users to make effective decisions.

[0067] The selection section allows users to choose whether or not to receive ad suggestions based on the information sharing and decision-making processes visualized by the visualization section. Specifically, users can set specific methods and criteria for choosing whether or not to receive ad suggestions. For example, users can choose whether or not to receive ad suggestions based on specific categories or budgets. Users can also set the frequency and timing of ad suggestions. The selection section can include AI processing and use AI to assist user choices. For example, AI can analyze the user's past selection data and automatically select the most suitable ad suggestions. This allows users to receive effective ad suggestions without any effort. The selection section is designed to be intuitively operable through a user interface, allowing users to easily change their selections. For example, users can adjust the conditions for receiving ad suggestions using checkboxes or sliders. The selection section can also collect user feedback and continuously improve the accuracy and ease of use of its selections. This makes the selection section a powerful tool for users to receive the most suitable ad suggestions.

[0068] The adjustment unit adjusts the sequential approval process and the personal information abstraction filter. For example, the adjustment unit can set specific methods and criteria for the sequential approval process. For example, it can set the approval steps and the roles of the approvers. The adjustment unit can also set specific methods and criteria for the personal information abstraction filter. For example, it can set the level of abstraction and the scope of application of the filter. The adjustment unit may also include AI processing and can use AI to automatically adjust the sequential approval process and the personal information abstraction filter. This enables appropriate information sharing while protecting privacy.

[0069] The revenue sharing section returns a portion of the advertising revenue to the users. The revenue sharing section can, for example, set the method and criteria for distributing the advertising revenue. For instance, it can set the percentage of revenue distributed and the timing of the distribution. The revenue sharing section may also include AI processing, and can use AI to automatically suggest methods for distributing advertising revenue. This enhances user engagement.

[0070] The settings section allows users to configure information such as categories of interest, budget, and past purchase history. For example, the settings section allows users to set categories of interest. For example, the settings section allows users to set a budget. For example, the settings section allows users to set their past purchase history. This enables information sharing tailored to the user's needs.

[0071] The visualization unit can make the information sharing and decision-making processes visible to users. For example, the visualization unit can display the information sharing process as a graph. For example, the visualization unit can display the decision-making process as a chart. For example, the visualization unit can display the information sharing and decision-making processes on a dashboard. This increases transparency and builds user trust.

[0072] The selection section allows users to choose whether or not to receive ad suggestions. The selection section can, for example, set specific methods and criteria for users to choose whether or not to receive ad suggestions. For example, the selection section can set the timing for receiving ad suggestions. For example, the selection section can set the content of the ad suggestions. This allows users to choose whether or not to receive ad suggestions.

[0073] The settings unit can estimate the user's emotions and dynamically adjust the scope of information sharing based on the estimated emotions. For example, if the user is stressed, the settings unit can minimize the scope of information sharing. For example, if the user is relaxed, the settings unit can expand the scope of information sharing. For example, if the user is in a hurry, the settings unit can share only essential information. This enables information sharing that is tailored to 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.

[0074] The settings unit can analyze the user's past behavior history and automatically suggest the optimal scope of information sharing. For example, the settings unit can suggest the optimal scope of information sharing based on information the user has frequently shared in the past. For example, the settings unit can suggest information to share at specific time periods based on the user's past behavior history. For example, the settings unit can analyze the user's past behavior history and suggest the most efficient scope of information sharing. This enables optimal information sharing based on past behavior history.

[0075] The settings unit can customize the scope of information sharing based on the user's current interests and life events. For example, the settings unit can set the scope of information sharing based on topics the user is currently interested in. For example, the settings unit can customize the scope of information sharing based on the user's life events (marriage, moving, etc.). For example, the settings unit can dynamically adjust the scope of information sharing considering the user's current interests and life events. This enables information sharing that is tailored to the user's interests and life events.

[0076] The settings unit can estimate the user's emotions and determine the priority of information sharing based on the estimated emotions. For example, if the user is stressed, the settings unit can prioritize sharing important information. For example, if the user is relaxed, the settings unit can prioritize sharing detailed information. For example, if the user is in a hurry, the settings unit can prioritize sharing information that is needed quickly. This allows for the determination of information sharing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The settings unit can prioritize highly relevant information when setting the scope of information sharing, taking into account the user's geographical location. For example, the settings unit can prioritize sharing highly relevant information based on the user's current location. For example, the settings unit can set the optimal scope of information sharing by considering the user's geographical location. For example, the settings unit can prioritize relevant information based on the user's current location. This enables information sharing based on geographical location information.

[0078] The settings unit can analyze the user's social media activity and set relevant information when defining the scope of information sharing. For example, the settings unit can analyze the user's social media activity and prioritize the sharing of relevant information. For example, the settings unit can define the scope of information sharing based on the user's interests on social media. For example, the settings unit can define the optimal scope of information sharing considering the user's social media activity. This enables information sharing based on social media activity.

[0079] The visualization unit can estimate the user's emotions and adjust the display method of information sharing and decision-making processes based on the estimated user emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. This allows for the provision of a display method that is tailored to 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.

[0080] The visualization unit can customize the displayed content according to the user's level of understanding when visualizing information sharing and decision-making processes. For example, the visualization unit can adjust the level of detail of the information according to the user's level of understanding. For example, the visualization unit can display information in a format that is easy for the user to understand. For example, the visualization unit can provide the optimal display method considering the user's level of understanding. This allows for the provision of displayed content tailored to the user's level of understanding.

[0081] The visualization unit can optimize the display method by reflecting past user feedback when visualizing information sharing and decision-making processes. For example, the visualization unit can adjust the display method based on past user feedback. For example, the visualization unit can provide the optimal display method by reflecting past user feedback. For example, the visualization unit can optimize the information display method by considering past user feedback. This allows for the provision of the optimal display method based on past feedback.

[0082] The visualization unit can estimate the user's emotions and adjust the display order of information sharing and decision-making processes based on the estimated emotions. For example, if the user is stressed, the visualization unit can prioritize displaying important information. For example, if the user is relaxed, the visualization unit can prioritize displaying detailed information. For example, if the user is in a hurry, the visualization unit can prioritize displaying information that is needed quickly. This provides a display order that is tailored to 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.

[0083] The visualization unit can select the optimal display method by considering the user's device information when visualizing information sharing and decision-making processes. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the visualization unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the visualization unit can provide a concise and highly visible display method. This allows for the provision of the optimal display method based on device information.

[0084] The visualization unit can display content in multiple languages ​​according to the user's language settings when visualizing information sharing and decision-making processes. For example, the visualization unit can automatically set the display content based on the language settings of the user's device. For example, the visualization unit can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the visualization unit can provide the display content in that language. This enables the provision of multilingual display content.

[0085] The selection unit can estimate the user's emotions and adjust how ad suggestions are delivered based on those estimated emotions. For example, if the user is stressed, the selection unit can deliver ad suggestions with a simple interface. For example, if the user is relaxed, the selection unit can deliver detailed ad suggestions. For example, if the user is in a hurry, the selection unit can deliver ad suggestions quickly. This provides a way for users to receive ad suggestions that are tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The selection function can analyze a user's past selection history to provide optimal suggestions when they choose whether or not to receive ad suggestions. For example, the selection function can provide optimal suggestions based on ad suggestions the user has previously accepted. For example, the selection function can provide highly relevant ad suggestions based on the user's past selection history. For example, the selection function can analyze the user's past selection history to provide the most effective ad suggestions. This makes it possible to provide optimal ad suggestions based on past selection history.

[0087] The selection section can customize the content of advertising suggestions based on the user's current situation and needs when the user chooses whether or not to receive them. For example, the selection section can provide optimal advertising suggestions based on the user's current situation. For example, the selection section can customize the content of advertising suggestions based on the user's needs. For example, the selection section can provide advertising suggestions considering the user's current situation and needs. This makes it possible to provide advertising suggestions that are tailored to the user's current situation and needs.

[0088] The selection unit can estimate the user's emotions and prioritize ad suggestions based on those emotions. For example, if the user is stressed, the selection unit can prioritize important ad suggestions. For example, if the user is relaxed, the selection unit can prioritize detailed ad suggestions. For example, if the user is in a hurry, the selection unit can prioritize ad suggestions that are needed quickly. This allows for the prioritization of ad suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The selection function can prioritize highly relevant suggestions by considering the user's geographical location when the user chooses whether or not to receive ad suggestions. For example, the selection function can prioritize highly relevant ad suggestions based on the user's current location. For example, the selection function can provide optimal ad suggestions by considering the user's geographical location. For example, the selection function can prioritize relevant ad suggestions based on the user's current location. This enables geographically-based ad suggestions.

[0090] The selection unit can analyze the user's social media activity and make relevant suggestions when the user chooses whether or not to receive advertising suggestions. For example, the selection unit can analyze the user's social media activity and make relevant advertising suggestions. For example, the selection unit can make advertising suggestions based on the user's interests on social media. For example, the selection unit can consider the user's social media activity and make optimal advertising suggestions. This enables advertising suggestions based on social media activity.

[0091] The adjustment unit can estimate the user's emotions and adjust the sequential approval method and personal information abstraction filter settings based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can simplify the sequential approval method and strengthen the personal information abstraction filter. For example, if the user is relaxed, the adjustment unit can make the sequential approval method more detailed and relax the personal information abstraction filter. For example, if the user is in a hurry, the adjustment unit can expedite the sequential approval method and optimize the personal information abstraction filter. This makes it possible to set the sequential approval method and personal information abstraction filter according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The adjustment unit can analyze the user's past approval history and propose optimal settings when adjusting the sequential approval method and personal information abstraction filter. For example, the adjustment unit can propose the optimal sequential approval method based on the user's past approval history. For example, the adjustment unit can propose the optimal settings for the personal information abstraction filter based on the user's past approval history. For example, the adjustment unit can analyze the user's past approval history and propose the most efficient sequential approval method and personal information abstraction filter settings. This makes it possible to set the optimal sequential approval method and personal information abstraction filter based on past approval history.

[0093] The adjustment unit can estimate the user's emotions and determine the priority of sequential approval methods and personal information abstraction filters based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can prioritize the approval of important information. For example, if the user is relaxed, the adjustment unit can prioritize the approval of detailed information. For example, if the user is in a hurry, the adjustment unit can prioritize the approval of information that is needed quickly. This allows for the determination of priority for sequential approval methods and personal information abstraction filters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The adjustment unit can optimize settings by considering the user's geographical location when adjusting the sequential approval method and personal information abstraction filter. For example, the adjustment unit can set the optimal sequential approval method based on the user's current location. For example, the adjustment unit can optimize settings for the personal information abstraction filter by considering the user's geographical location. For example, the adjustment unit can optimize the settings for the sequential approval method and personal information abstraction filter based on the user's current location. This makes it possible to set sequential approval methods and personal information abstraction filters based on geographical location information.

[0095] The revenue sharing unit can estimate the user's emotions and adjust the method of distributing advertising revenue based on the estimated emotions. For example, if the user is stressed, the revenue sharing unit can simplify the distribution method. For example, if the user is relaxed, the revenue sharing unit can provide a more detailed distribution method. For example, if the user is in a hurry, the revenue sharing unit can provide a quick distribution method. This allows for the distribution of advertising revenue in a way that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The revenue sharing unit can analyze a user's past revenue sharing history to propose the optimal method for sharing advertising revenue. For example, the revenue sharing unit can propose the optimal method based on a user's past revenue sharing history. For example, the revenue sharing unit can propose a highly relevant method based on a user's past revenue sharing history. For example, the revenue sharing unit can analyze a user's past revenue sharing history and propose the most effective method. This allows for the provision of the optimal advertising revenue sharing method based on past revenue sharing history.

[0097] The revenue sharing unit can estimate the user's emotions and determine the priority of advertising revenue sharing based on those estimated emotions. For example, if the user is stressed, the revenue sharing unit can prioritize important rewards. For example, if the user is relaxed, the revenue sharing unit can prioritize detailed rewards. For example, if the user is in a hurry, the revenue sharing unit can prioritize quickly needed rewards. This allows for the determination of advertising revenue sharing priorities that correspond to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The revenue sharing unit can determine the optimal method for distributing advertising revenue by considering the user's geographical location. For example, the revenue sharing unit can provide the optimal method based on the user's current location. For example, the revenue sharing unit can optimize the method of distribution by considering the user's geographical location. For example, the revenue sharing unit can provide the relevant method of distribution based on the user's current location. This allows for the provision of advertising revenue distribution methods based on geographical location information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The personal AI advertising agent system can estimate a user's emotions and adjust the timing of ad recommendations based on those emotions. For example, if a user is stressed, it can refrain from showing ads. If a user is relaxed, it can actively show ads. If a user is in a hurry, it can only show important ads. This allows for optimal timing of ad recommendations tailored to the user's emotions.

[0101] A personal AI advertising agent system can analyze a user's past behavior history and provide optimal advertising suggestions. For example, it can suggest relevant ads based on products the user has previously purchased. It can also suggest ads based on categories the user has shown interest in in the past. By analyzing the user's past behavior history, it can provide the most effective advertising suggestions. This enables optimal advertising suggestions based on past behavior history.

[0102] The personal AI advertising agent system can customize advertising suggestions based on the user's current situation and needs. For example, it can provide optimal advertising suggestions based on the user's current circumstances. It can customize advertising suggestions based on the user's needs. It can provide advertising suggestions considering the user's current situation and needs. This enables advertising suggestions tailored to the user's current situation and needs.

[0103] The personal AI advertising agent system can estimate a user's emotions and prioritize ad suggestions based on those emotions. For example, if a user is stressed, important ad suggestions can be prioritized. If a user is relaxed, detailed ad suggestions can be prioritized. If a user is in a hurry, ad suggestions that are needed quickly can be prioritized. This allows for the prioritization of ad suggestions according to the user's emotions.

[0104] The personal AI advertising agent system can provide highly relevant advertising suggestions by considering the user's geographical location. For example, it can provide highly relevant advertising suggestions based on the user's current location. It can provide optimal advertising suggestions by considering the user's geographical location. It can provide relevant advertising suggestions based on the user's current location. This enables advertising suggestions based on geographical location information.

[0105] A personal AI advertising agent system can analyze a user's social media activity and provide relevant advertising suggestions. For example, it can analyze a user's social media activity and provide relevant advertising suggestions. It can also provide advertising suggestions based on the user's interests on social media. Furthermore, it can provide optimal advertising suggestions by considering the user's social media activity. This enables advertising suggestions based on social media activity.

[0106] The personal AI advertising agent system can estimate a user's emotions and adjust how they receive advertising suggestions based on those emotions. For example, if a user is stressed, it can present advertising suggestions with a simple interface. If a user is relaxed, it can present detailed advertising suggestions. If a user is in a hurry, it can present advertising suggestions quickly. This allows the system to provide advertising suggestions tailored to the user's emotions.

[0107] The personal AI advertising agent system can analyze a user's past selection history to provide optimal advertising suggestions. For example, it can provide optimal suggestions based on advertising suggestions the user has previously accepted. It can provide highly relevant advertising suggestions based on the user's past selection history. By analyzing the user's past selection history, it can provide the most effective advertising suggestions. This enables optimal advertising suggestions based on past selection history.

[0108] The personal AI advertising agent system can estimate a user's emotions and adjust the method of distributing advertising revenue based on those emotions. For example, if a user is stressed, the distribution method can be simplified. If a user is relaxed, a more detailed distribution method can be provided. If a user is in a hurry, a rapid distribution method can be provided. This allows for advertising revenue distribution methods tailored to the user's emotions.

[0109] The personal AI advertising agent system can analyze a user's past reward history and propose the optimal reward method. For example, it can suggest the optimal reward method based on the user's past reward history. It can suggest highly relevant reward methods based on the user's past reward history. It can analyze the user's past reward history and propose the most effective reward method. This allows for the provision of the optimal advertising revenue reward method based on past reward history.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The settings section defines the scope of information sharing. The settings section allows users to configure details such as the type of information to be shared, the target audience, and the purpose of sharing. For example, users can set information such as categories they are interested in, their budget, and their past purchase history. The settings section also includes AI processing and can automatically suggest the scope of information sharing using AI. Step 2: The visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the configuration unit. The visualization unit can visualize information in the form of graphs, charts, dashboards, etc. Furthermore, it can include AI processing and use AI to visualize the information sharing and decision-making processes in real time. Step 3: The selection unit chooses whether to receive ad suggestions based on the information sharing and decision-making processes visualized by the visualization unit. The selection unit can set specific methods and criteria for the user to choose whether or not to receive ad suggestions. Furthermore, it can include AI processing and use AI to assist the user's decision.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the setting unit, visualization unit, selection unit, adjustment unit, and return unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the setting unit is implemented by the control unit 46A of the smart device 14 and sets the type and scope of information shared by the user. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the information sharing and decision-making processes in graphs and charts. The selection unit is implemented by the control unit 46A of the smart device 14 and allows the user to choose whether or not to receive advertising suggestions. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the sequential approval method and personal information abstraction filter. The return unit is implemented by the control unit 46A of the smart device 14 and returns a portion of the advertising revenue to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the setting unit, visualization unit, selection unit, adjustment unit, and return unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is implemented by the control unit 46A of the smart glasses 214 and sets the type and scope of information shared by the user. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the information sharing and decision-making processes in graphs and charts. The selection unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to choose whether or not to receive advertising suggestions. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the sequential approval method and personal information abstraction filter. The return unit is implemented by the control unit 46A of the smart glasses 214 and returns a portion of the advertising revenue to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

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

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

[0142] In 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.

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0146] The data processing system 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.

[0147] Each of the multiple elements described above, including the setting unit, visualization unit, selection unit, adjustment unit, and return unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the setting unit is implemented by the control unit 46A of the headset terminal 314 and sets the type and scope of information shared by the user. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the information sharing and decision-making processes in graphs and charts. The selection unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to choose whether or not to receive advertising suggestions. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the sequential approval method and personal information abstraction filter. The return unit is implemented by the control unit 46A of the headset terminal 314 and returns a portion of the advertising revenue to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the setting unit, visualization unit, selection unit, adjustment unit, and return unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the setting unit is implemented by the control unit 46A of the robot 414 and sets the type and scope of information to be shared by the user. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the information sharing and decision-making processes in graphs and charts. The selection unit is implemented by the control unit 46A of the robot 414 and selects whether the user receives advertising suggestions. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the sequential approval method and personal information abstraction filter. The return unit is implemented by the control unit 46A of the robot 414 and returns a portion of the advertising revenue to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A settings section for setting the scope of information sharing, A visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the aforementioned setting unit, The system includes a selection unit that allows the user to choose whether or not to receive advertising proposals based on the information sharing and decision-making processes visualized by the visualization unit. A system characterized by the following features. (Note 2) It includes an adjustment unit that adjusts sequential approval methods and personal information abstraction filters. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a section that returns a portion of advertising revenue to users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The setting unit is, Set information such as categories of interest, budget, and past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, To make the information sharing and decision-making processes visible to users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is Choose whether or not to receive advertising proposals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The setting unit is, It estimates the user's emotions and dynamically adjusts the scope of information sharing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The setting unit is, It analyzes the user's past behavior history and automatically suggests the optimal scope for information sharing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The setting unit is, When setting the scope of information sharing, customize it based on the user's current interests and life events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The setting unit is, It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The setting unit is, When setting the scope of information sharing, the system prioritizes highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The setting unit is, When setting the scope of information sharing, analyze users' social media activity and set relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned visualization unit, It estimates the user's emotions and adjusts how information sharing and decision-making processes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, When visualizing information sharing and decision-making processes, the displayed content can be customized according to the user's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing information sharing and decision-making processes, the display method is optimized by reflecting past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display order of information sharing and decision-making processes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, When visualizing information sharing and decision-making processes, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing information sharing and decision-making processes, the displayed content can be made multilingual according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is It estimates the user's emotions and adjusts how they receive ad suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When users choose whether or not to receive ad suggestions, we analyze their past selection history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When users choose whether or not to receive ad suggestions, the suggestions are customized based on their current situation and needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and prioritizes ad suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When users choose whether or not to receive ad suggestions, the system prioritizes relevant suggestions by taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When users choose whether or not to receive advertising proposals, the system analyzes their social media activity to provide relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the sequential approval method and personal information abstraction filter settings based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The adjustment unit is, When adjusting sequential approval methods or personal information abstraction filters, we analyze the user's past approval history to suggest the optimal settings. The system described in Appendix 2, characterized by the features described herein. (Note 27) The adjustment unit is, The system estimates the user's emotions and, based on these estimated emotions, determines the priority of sequential approval methods and personal information abstraction filters. The system described in Appendix 2, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting sequential approval methods and personal information abstraction filters, the optimal settings should be made considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The reduction unit is We estimate user sentiment and adjust how advertising revenue is distributed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The reduction unit is When setting up the method for sharing advertising revenue, we analyze the user's past revenue sharing history and propose the optimal method. The system described in Appendix 3, characterized by the features described herein. (Note 31) The reduction unit is, It estimates user sentiment and determines the priority of ad revenue distribution based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The reduction unit is When setting up the method for distributing advertising revenue, we take into account the user's geographical location to determine the optimal distribution method. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A settings section for setting the scope of information sharing, A visualization unit visualizes the information sharing and decision-making processes based on the scope of information sharing set by the aforementioned setting unit, The system includes a selection unit that allows the user to choose whether or not to receive advertising proposals based on the information sharing and decision-making processes visualized by the visualization unit. A system characterized by the following features.

2. It includes an adjustment unit for adjusting sequential approval methods and personal information abstraction filters. The system according to feature 1.

3. It includes a section that returns a portion of advertising revenue to users. The system according to feature 1.

4. The setting unit is, Set information such as categories of interest, budget, and past purchase history. The system according to feature 1.

5. The aforementioned visualization unit, To make the information sharing and decision-making processes visible to users. The system according to feature 1.

6. The aforementioned selection unit is Choose whether or not to receive advertising proposals. The system according to feature 1.

7. The setting unit is, It estimates the user's emotions and dynamically adjusts the scope of information sharing based on those estimated emotions. The system according to feature 1.

8. The setting unit is, It analyzes the user's past behavior history and automatically suggests the optimal scope of information sharing. The system according to feature 1.

9. The setting unit is, When setting the scope of information sharing, customize it based on the user's current interests and life events. The system according to feature 1.

10. The setting unit is, It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system according to feature 1.

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