system

Abstract

The system according to this embodiment aims to improve the user experience by naturally inserting advertisements during conversations with users. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, and an adjustment unit. The analysis unit analyzes the user's hobbies, preferences, behavior, and conversation context in real time. The proposal unit proposes appropriate advertisements based on the information obtained by the analysis unit. The adjustment unit monitors the user's reaction to the advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, unnatural advertisements may be inserted during a conversation with a user, which may damage the user experience.

[0005] The system according to an embodiment aims to naturally insert advertisements during a conversation with a user and improve the user experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, and an adjustment unit. The analysis unit analyzes the user's hobbies, preferences, behavior, and conversational context in real time. The proposal unit proposes appropriate advertisements based on the information obtained by the analysis unit. The adjustment unit monitors the user's response to the advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements. [Effects of the Invention]

[0007] The system according to this embodiment can naturally insert advertisements during conversations with users, thereby improving the user experience. [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) An advertising insertion system according to an embodiment of the present invention is a system that utilizes generative AI to naturally insert advertisements into conversations between a user and a generative AI. The advertising insertion system works as follows: the user initiates a conversation with the generative AI, which analyzes the user's interests, preferences, behavior, and the context of the conversation in real time. Next, the generative AI proposes appropriate advertisements based on the analysis results, and adjusts the content and timing of the advertisements by monitoring the user's response. Furthermore, the generative AI ensures transparency in data usage and protects user privacy. This system allows users to receive highly relevant advertisements without discomfort, and enables advertisers to achieve effective ad delivery. Additionally, utilizing generative AI is expected to significantly improve the efficiency of advertising operations and achieve higher results. For example, when a user initiates a conversation with the generative AI, the advertising insertion system analyzes the user's interests, preferences, behavior, and the context of the conversation in real time. The generative AI uses natural language processing techniques and machine learning algorithms to analyze the user's interests, preferences, behavior, and the context of the conversation. Next, the generative AI proposes appropriate advertisements based on the analysis results. The generative AI selects highly relevant advertisements based on the user's interests, preferences, behavior, and conversational context. For example, if a user is interested in music, the generative AI will suggest music-related advertisements. Furthermore, the generative AI monitors user responses and adjusts the content and timing of advertisements. The generative AI analyzes user responses in real time and optimizes the content and timing of advertisements to maximize their effectiveness. For example, if a user shows interest in an advertisement, the generative AI will explain the advertisement's content in detail. The generative AI also ensures transparency in data usage and protects user privacy. The generative AI properly manages user data and clearly states the purpose and scope of data usage. This allows users to use the system with confidence. As a result, the ad insertion system can suggest appropriate advertisements based on the user's interests, preferences, behavior, and conversational context, improving the user experience.

[0029] The ad insertion system according to this embodiment comprises an analysis unit, a suggestion unit, and an adjustment unit. The analysis unit analyzes the user's hobbies, preferences, behavior, and conversation context in real time. The analysis unit, for example, uses natural language processing technology to analyze the content of the user's conversation and identify the user's hobbies, preferences, and behavior. The analysis unit, for example, uses machine learning algorithms to analyze the user's behavior patterns and estimate the user's interests. The analysis unit, for example, analyzes the user's website browsing history and purchase history to understand the user's behavior. The suggestion unit proposes appropriate advertisements based on the information obtained by the analysis unit. The suggestion unit, for example, selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. The suggestion unit, for example, proposes appropriate advertisements based on the context of the user's conversation. The suggestion unit, for example, customizes the content of advertisements based on the user's interests. The adjustment unit monitors the user's response to the advertisements proposed by the suggestion unit and adjusts the content and timing of the advertisements. The adjustment unit, for example, analyzes the user's response in real time and optimizes the content and timing of the advertisements to maximize their effectiveness. The adjustment unit adjusts the display time of advertisements based on user responses, for example. The adjustment unit also modifies the content of advertisements based on user responses, for example. As a result, the ad insertion system according to this embodiment can suggest appropriate advertisements based on the user's tastes, preferences, behavior, and conversational context, thereby improving the user experience.

[0030] The analytics unit analyzes users' hobbies, preferences, behavior, and conversational context in real time. Specifically, it uses natural language processing technology to analyze the content of user conversations and identify users' hobbies, preferences, and behaviors. Natural language processing technology extracts keywords and phrases from user conversations and uses these to estimate users' interests and concerns. For example, if a user says, "I want to go hiking recently," the analytics unit extracts the keyword "hiking" and determines that the user is interested in outdoor activities. It also uses machine learning algorithms to analyze user behavior patterns and estimate users' interests. Machine learning algorithms learn from users' past behavioral data and predict future interests from current behavior. For example, if a user frequently visits sports-related websites, the analytics unit estimates that the user is interested in sports. Furthermore, it analyzes users' website browsing history and purchase history to understand user behavior. This allows the analytics unit to identify users' hobbies and preferences from their online behavior and use this information for targeted advertising. For example, if a user has a history of purchasing outdoor equipment, the analytics department will determine that the user is interested in outdoor activities and provide information to suggest relevant advertisements.

[0031] The Proposal Department suggests appropriate advertisements based on information obtained by the Analytics Department. Specifically, it selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. For example, if the Analytics Department identifies a user's interest in outdoor activities, the Proposal Department will select advertisements for outdoor equipment and related events. It also suggests appropriate advertisements based on the context of the user's conversation. For example, if a user says, "I want a new camera," the Proposal Department will suggest advertisements for cameras and camera accessories. Furthermore, it customizes the content of advertisements based on the user's interests. For example, if a user is interested in a particular brand, it will prioritize suggesting advertisements related to that brand's products and services. Based on this information, the Proposal Department can select the most relevant and interesting advertisements for the user, thereby improving the user experience. In selecting advertisements, the Proposal Department also considers the user's past reactions and feedback to provide more accurate advertisement suggestions. For example, it learns patterns of advertisements that have had high click-through rates in the past and prioritizes suggesting advertisements with similar patterns. This allows the Proposal Department to provide advertisements optimized for the user's interests and maximize the effectiveness of the advertisements.

[0032] The adjustment department monitors user reactions to advertisements proposed by the proposal department and adjusts the content and timing of the advertisements. Specifically, it analyzes user reactions in real time and optimizes the content and timing of advertisements to maximize their effectiveness. For example, if a user shows a positive reaction to an advertisement, it continues to display similar advertisements, while changing the content of advertisements if the user shows a negative reaction. It also adjusts the display time of advertisements based on user reactions. For example, if a user shows high interest in advertisements during a specific time period, it concentrates the display of advertisements during that time period. Furthermore, it changes the content of advertisements based on user reactions. For example, if a user shows high interest in advertisements in a specific category, it prioritizes the display of advertisements in that category. The adjustment department makes these adjustments in real time to maximize the effectiveness of advertisements. The adjustment department collects user feedback and incorporates it into the adjustments of advertisement content and timing. For example, it improves the content of advertisements based on user feedback and provides more effective advertisements. The adjustment department also adjusts the frequency and location of advertisements to provide the optimal advertising experience for users. This allows the adjustment unit to optimize the content and timing of advertisements based on user responses, thereby maximizing the effectiveness of the advertisements.

[0033] The ad insertion system includes a transparency unit to ensure transparency in data usage. The transparency unit appropriately manages user data and clearly states the purpose and scope of data usage to ensure transparency in data usage. For example, the transparency unit explains the details of data usage to the user and obtains the user's consent. For example, the transparency unit provides the user with a data usage policy to clearly state the purpose and scope of data usage. For example, the transparency unit performs data encryption and access control to appropriately manage user data. This ensures transparency in data usage and protects user privacy. Some or all of the above-described processes in the transparency unit may be performed using AI or not. For example, the transparency unit can input user data into AI and have the AI ​​explain the purpose and scope of data usage.

[0034] The ad insertion system includes an operational efficiency improvement unit that utilizes generative AI to improve the efficiency of ad operations. The operational efficiency improvement unit optimizes ad delivery timing and targeting methods in order to improve the efficiency of ad operations by utilizing generative AI. For example, the operational efficiency improvement unit optimizes ad delivery timing using generative AI. For example, the operational efficiency improvement unit optimizes ad targeting methods using generative AI. For example, the operational efficiency improvement unit develops algorithms to maximize the effectiveness of ads using generative AI. As a result, the efficiency of ad operations is improved by utilizing generative AI. Some or all of the above processes in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can have AI perform the optimization of ad delivery timing and targeting methods.

[0035] The analysis unit can optimize its analysis algorithm by referring to the user's past conversation history during analysis. For example, the analysis unit can analyze keywords that the user has frequently used in the past and evaluate their importance in the current conversation. For example, the analysis unit can analyze the user's past conversation patterns and improve the prediction accuracy in the current conversation. For example, the analysis unit can identify topics that the user has shown interest in in the past and increase their relevance in the current conversation. In this way, by referring to the user's past conversation history, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past conversation history into AI and have the AI ​​perform the optimization of the analysis algorithm.

[0036] The analysis unit can adjust the level of detail of its analysis based on the user's current lifestyle and areas of interest during the analysis process. For example, if the user inputs their current lifestyle, the analysis unit will adjust the level of detail based on that information. For example, if the user has a specific area of ​​interest, the analysis unit will prioritize analyzing information related to that area. For example, if the user's lifestyle changes, the generating AI will adjust the level of detail of the analysis to reflect that change. By adjusting the level of detail of the analysis based on the user's current lifestyle and areas of interest, the analysis unit can suggest more relevant advertisements. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's lifestyle and areas of interest into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0037] The analysis unit can improve the relevance of its analysis by considering the user's geographical location information during the analysis process. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. For example, if the user is traveling, the analysis unit will prioritize analyzing information related to their travel destination. For example, if the user is at home, the analysis unit will prioritize analyzing information around their home. By considering the user's geographical location information, the analysis unit can improve the relevance of its analysis and suggest more appropriate advertisements. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into the AI ​​and have the AI ​​perform processes to improve the relevance of the analysis.

[0038] The analysis unit can analyze a user's social media activity and obtain relevant information during the analysis process. For example, the analysis unit can analyze the content a user frequently posts on social media and obtain relevant information. For example, the analysis unit can analyze information about accounts a user follows on social media and obtain relevant information. For example, the analysis unit can analyze information about groups a user participates in on social media and obtain relevant information. By analyzing a user's social media activity, relevant information can be obtained, and more appropriate advertisements can be suggested. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity data into AI and have AI perform the acquisition of relevant information.

[0039] The proposal unit can adjust the level of detail in its proposals based on the importance of the advertisements. For example, for highly important advertisements, the proposal unit will provide a detailed proposal. For less important advertisements, the proposal unit will provide a concise proposal. For medium-importance advertisements, the proposal unit will provide a proposal with an appropriate level of detail. By adjusting the level of detail in proposals based on the importance of the advertisements, more effective advertisements can be proposed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input advertisement importance data into AI and have the AI ​​adjust the level of detail in the proposals.

[0040] The proposal unit can apply different proposal algorithms depending on the advertisement category when making proposals. For example, in the case of product advertisements, the proposal unit will make proposals that emphasize the features and benefits of the product. For example, in the case of service advertisements, the proposal unit will make proposals that emphasize how to use the service and its benefits. For example, in the case of event advertisements, the proposal unit will make proposals that emphasize the details of the event and how to participate. By applying different proposal algorithms depending on the advertisement category, more effective advertisements can be proposed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input advertisement category data into AI and have the AI ​​perform the application of the proposal algorithm.

[0041] The proposal department can determine the priority of proposals based on the submission timing of the advertisements. For example, the proposal department will prioritize proposals for advertisements with an upcoming submission date. For example, the proposal department will postpone proposals for advertisements with a distant submission date. For example, the proposal department will give appropriate priority to advertisements with a medium submission date. By determining the priority of proposals based on the submission timing of the advertisements, the proposal department can propose more effective advertisements. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input advertisement submission date data into AI and have the AI ​​perform the determination of proposal priority.

[0042] The suggestion unit can adjust the order of suggestions based on the relevance of the advertisements. For example, the suggestion unit will suggest highly relevant advertisements first. For example, the suggestion unit will suggest less relevant advertisements later. For example, the suggestion unit will suggest moderately relevant advertisements in an appropriate order. By adjusting the order of suggestions based on the relevance of the advertisements, the suggestion unit can suggest more effective advertisements. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input advertisement relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0043] The adjustment unit can optimize its adjustment algorithm by referring to the user's past response history during the adjustment process. For example, the adjustment unit can analyze patterns of advertisements in which the user has previously shown a positive response and reflect them in the current advertisement. For example, the adjustment unit can analyze patterns of advertisements in which the user has previously shown a negative response and avoid reflecting them in the current advertisement. For example, the adjustment unit can adjust the optimal timing for displaying advertisements based on the user's past response history. In this way, by referring to the user's past response history, the adjustment algorithm can be optimized and more effective advertisements can be displayed. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past response history data into AI and have the AI ​​perform the optimization of the adjustment algorithm.

[0044] The adjustment unit can customize the means of adjustment based on the user's current lifestyle during the adjustment process. For example, if the user is busy, the adjustment unit may display short, concise advertisements. If the user is relaxed, the adjustment unit may display advertisements with detailed explanations. If the user is participating in a particular event, the adjustment unit may display advertisements related to that event. This allows for the display of more effective advertisements by customizing the means of adjustment based on the user's current lifestyle. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user lifestyle data into AI and have the AI ​​perform the customization of the means of adjustment.

[0045] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, if the user is in a specific region, the adjustment unit will display advertisements related to that region. For example, if the user is traveling, the adjustment unit will display advertisements related to the travel destination. For example, if the user is at home, the adjustment unit will display advertisements around the user's home. By considering the user's geographical location information, the adjustment unit can select the optimal adjustment method and display more effective advertisements. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's geographical location data into the AI ​​and have the AI ​​select the optimal adjustment method.

[0046] The adjustment unit can analyze the user's social media activity during the adjustment process and propose adjustment methods. For example, the adjustment unit can analyze the content that the user frequently posts on social media and display relevant advertisements. For example, the adjustment unit can analyze information about accounts that the user follows on social media and display relevant advertisements. For example, the adjustment unit can analyze information about groups that the user participates in on social media and display relevant advertisements. In this way, by analyzing the user's social media activity, adjustment methods can be proposed and more effective advertisements can be displayed. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's social media activity data into AI and have the AI ​​execute the proposal of adjustment methods.

[0047] The transparency assurance unit can optimize the transparency assurance algorithm by referring to the user's past data usage history when ensuring transparency. For example, the transparency assurance unit can refer to the content of data usage that the user has previously consented to and reflect it in the current transparency assurance. For example, the transparency assurance unit can refer to the content of data usage that the user has previously refused and not reflect it in the current transparency assurance. For example, the transparency assurance unit can propose the optimal transparency assurance method based on the user's past data usage history. In this way, by referring to the user's past data usage history, the transparency assurance algorithm can be optimized, and more effective transparency assurance can be achieved. Some or all of the above processing in the transparency assurance unit may be performed using AI or not. For example, the transparency assurance unit can input the user's past data usage history data into AI and have the AI ​​perform the optimization of the transparency assurance algorithm.

[0048] The transparency assurance unit can select means of ensuring transparency by considering the user's geographical location information when ensuring transparency. For example, if the user is in a specific region, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of that region. For example, if the user is traveling, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of the travel destination. For example, if the user is at home, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of the user's home area. By considering the user's geographical location information, the transparency assurance unit can select means of ensuring transparency and achieve more effective transparency. Some or all of the above processing in the transparency assurance unit may be performed using AI or not. For example, the transparency assurance unit can input the user's geographical location information data into AI and have the AI ​​perform the selection of means of ensuring transparency.

[0049] The operational efficiency improvement unit can optimize the operational algorithm by referring to past operational data when improving operational efficiency. For example, the operational efficiency improvement unit proposes the optimal operational method based on past operational data. For example, the operational efficiency improvement unit identifies patterns for improving operational efficiency from past operational data. For example, the operational efficiency improvement unit analyzes past operational data and optimizes the operational algorithm. By doing so, by referring to past operational data, the operational algorithm can be optimized, enabling more effective advertising operations. Some or all of the above processes in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can input past operational data into AI and have AI perform the optimization of the operational algorithm.

[0050] The operational efficiency improvement unit can select operational efficiency measures by considering the user's geographical location information when improving operational efficiency. For example, if the user is in a specific region, the operational efficiency improvement unit will select operational efficiency improvement measures for that region. For example, if the user is traveling, the operational efficiency improvement unit will select operational efficiency improvement measures for the travel destination. For example, if the user is at home, the operational efficiency improvement unit will select operational efficiency improvement measures for the home area. By considering the user's geographical location information, the operational efficiency improvement unit can select operational efficiency measures and achieve more effective advertising operations. Some or all of the above processing in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can input the user's geographical location information data into AI and have the AI ​​perform the selection of operational efficiency measures.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The suggestion department can refer to a user's past purchase history and suggest advertisements related to products and services the user has previously purchased. For example, if a user has previously purchased cosmetics from a specific brand, the suggestion department will suggest new products or related items from that brand. If a user has previously used travel-related services, the suggestion department will suggest new travel plans and offers. If a user has previously purchased health foods, the suggestion department will suggest new health foods and related health information. This allows for the suggestion of highly relevant advertisements based on the user's past purchase history, thereby improving the user experience.

[0053] The adjustment unit can analyze the user's device usage and optimize the timing of ad display. For example, if a user frequently uses a smartphone, the adjustment unit displays ads optimized for smartphones. If the user uses a computer, the adjustment unit displays ads optimized for computers. If the user uses a tablet, the adjustment unit displays ads optimized for tablets. This optimizes the timing of ad display based on the user's device usage, enabling the display of more effective ads.

[0054] The operational efficiency improvement unit can monitor the effectiveness of advertisements in real time and automatically stop low-performing advertisements. For example, if an advertisement's click-through rate falls below a certain standard, the operational efficiency improvement unit will stop that advertisement. If an advertisement's conversion rate is low, the operational efficiency improvement unit will stop that advertisement. If an advertisement receives many impressions but few responses, the operational efficiency improvement unit will stop that advertisement. In this way, the efficiency of advertising operations can be improved by monitoring the effectiveness of advertisements in real time and automatically stopping low-performing advertisements.

[0055] The suggestion department can analyze a user's social media activity and suggest advertisements related to the influencers and brands the user follows. For example, if a user follows a specific influencer, the suggestion department will suggest advertisements for products and services promoted by that influencer. If a user follows a specific brand, the suggestion department will suggest advertisements for that brand's new products or campaigns. If a user is interested in a specific topic, the suggestion department will suggest advertisements related to that topic. This allows for the suggestion of highly relevant advertisements based on the user's social media activity, thereby improving the user experience.

[0056] The transparency assurance unit can select means to ensure data usage transparency by taking into account the user's geographical location information. For example, if the user is in a specific region, the transparency assurance means can be selected based on the data usage regulations of that region. If the user is traveling, the transparency assurance means can be selected based on the data usage regulations of the travel destination. If the user is at home, the transparency assurance means can be selected based on the data usage regulations of the user's home area. By considering the user's geographical location information, the transparency assurance means can be selected, resulting in more effective transparency assurance.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes the user's hobbies, preferences, behavior, and conversation context in real time. For example, it uses natural language processing technology to analyze the content of the user's conversation and identify the user's hobbies, preferences, and behavior. It also uses machine learning algorithms to analyze the user's behavior patterns and estimate the user's interests. Furthermore, it analyzes the user's website browsing history and purchase history to understand the user's behavior. Step 2: The proposal department proposes appropriate advertisements based on the information obtained by the analysis department. For example, it selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. It also proposes appropriate advertisements based on the context of the user's conversation. Furthermore, it customizes the content of the advertisements based on the user's interests. Step 3: The adjustment unit monitors user responses to the advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements. For example, it analyzes user responses in real time and optimizes the content and timing of the advertisements to maximize their effectiveness. It also adjusts the display time of the advertisements based on user responses. Furthermore, it modifies the content of the advertisements based on user responses.

[0059] (Example of form 2) An advertising insertion system according to an embodiment of the present invention is a system that utilizes generative AI to naturally insert advertisements into conversations between a user and a generative AI. The advertising insertion system works as follows: the user initiates a conversation with the generative AI, which analyzes the user's interests, preferences, behavior, and the context of the conversation in real time. Next, the generative AI proposes appropriate advertisements based on the analysis results, and adjusts the content and timing of the advertisements by monitoring the user's response. Furthermore, the generative AI ensures transparency in data usage and protects user privacy. This system allows users to receive highly relevant advertisements without discomfort, and enables advertisers to achieve effective ad delivery. Additionally, utilizing generative AI is expected to significantly improve the efficiency of advertising operations and achieve higher results. For example, when a user initiates a conversation with the generative AI, the advertising insertion system analyzes the user's interests, preferences, behavior, and the context of the conversation in real time. The generative AI uses natural language processing techniques and machine learning algorithms to analyze the user's interests, preferences, behavior, and the context of the conversation. Next, the generative AI proposes appropriate advertisements based on the analysis results. The generative AI selects highly relevant advertisements based on the user's interests, preferences, behavior, and conversational context. For example, if a user is interested in music, the generative AI will suggest music-related advertisements. Furthermore, the generative AI monitors user responses and adjusts the content and timing of advertisements. The generative AI analyzes user responses in real time and optimizes the content and timing of advertisements to maximize their effectiveness. For example, if a user shows interest in an advertisement, the generative AI will explain the advertisement's content in detail. The generative AI also ensures transparency in data usage and protects user privacy. The generative AI properly manages user data and clearly states the purpose and scope of data usage. This allows users to use the system with confidence. As a result, the ad insertion system can suggest appropriate advertisements based on the user's interests, preferences, behavior, and conversational context, improving the user experience.

[0060] The ad insertion system according to this embodiment comprises an analysis unit, a suggestion unit, and an adjustment unit. The analysis unit analyzes the user's hobbies, preferences, behavior, and conversation context in real time. The analysis unit, for example, uses natural language processing technology to analyze the content of the user's conversation and identify the user's hobbies, preferences, and behavior. The analysis unit, for example, uses machine learning algorithms to analyze the user's behavior patterns and estimate the user's interests. The analysis unit, for example, analyzes the user's website browsing history and purchase history to understand the user's behavior. The suggestion unit proposes appropriate advertisements based on the information obtained by the analysis unit. The suggestion unit, for example, selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. The suggestion unit, for example, proposes appropriate advertisements based on the context of the user's conversation. The suggestion unit, for example, customizes the content of advertisements based on the user's interests. The adjustment unit monitors the user's response to the advertisements proposed by the suggestion unit and adjusts the content and timing of the advertisements. The adjustment unit, for example, analyzes the user's response in real time and optimizes the content and timing of the advertisements to maximize their effectiveness. The adjustment unit adjusts the display time of advertisements based on user responses, for example. The adjustment unit also modifies the content of advertisements based on user responses, for example. As a result, the ad insertion system according to this embodiment can suggest appropriate advertisements based on the user's tastes, preferences, behavior, and conversational context, thereby improving the user experience.

[0061] The analytics unit analyzes users' hobbies, preferences, behavior, and conversational context in real time. Specifically, it uses natural language processing technology to analyze the content of user conversations and identify users' hobbies, preferences, and behaviors. Natural language processing technology extracts keywords and phrases from user conversations and uses these to estimate users' interests and concerns. For example, if a user says, "I want to go hiking recently," the analytics unit extracts the keyword "hiking" and determines that the user is interested in outdoor activities. It also uses machine learning algorithms to analyze user behavior patterns and estimate users' interests. Machine learning algorithms learn from users' past behavioral data and predict future interests from current behavior. For example, if a user frequently visits sports-related websites, the analytics unit estimates that the user is interested in sports. Furthermore, it analyzes users' website browsing history and purchase history to understand user behavior. This allows the analytics unit to identify users' hobbies and preferences from their online behavior and use this information for targeted advertising. For example, if a user has a history of purchasing outdoor equipment, the analytics department will determine that the user is interested in outdoor activities and provide information to suggest relevant advertisements.

[0062] The Proposal Department suggests appropriate advertisements based on information obtained by the Analytics Department. Specifically, it selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. For example, if the Analytics Department identifies a user's interest in outdoor activities, the Proposal Department will select advertisements for outdoor equipment and related events. It also suggests appropriate advertisements based on the context of the user's conversation. For example, if a user says, "I want a new camera," the Proposal Department will suggest advertisements for cameras and camera accessories. Furthermore, it customizes the content of advertisements based on the user's interests. For example, if a user is interested in a particular brand, it will prioritize suggesting advertisements related to that brand's products and services. Based on this information, the Proposal Department can select the most relevant and interesting advertisements for the user, thereby improving the user experience. In selecting advertisements, the Proposal Department also considers the user's past reactions and feedback to provide more accurate advertisement suggestions. For example, it learns patterns of advertisements that have had high click-through rates in the past and prioritizes suggesting advertisements with similar patterns. This allows the Proposal Department to provide advertisements optimized for the user's interests and maximize the effectiveness of the advertisements.

[0063] The adjustment department monitors user reactions to advertisements proposed by the proposal department and adjusts the content and timing of the advertisements. Specifically, it analyzes user reactions in real time and optimizes the content and timing of advertisements to maximize their effectiveness. For example, if a user shows a positive reaction to an advertisement, it continues to display similar advertisements, while changing the content of advertisements if the user shows a negative reaction. It also adjusts the display time of advertisements based on user reactions. For example, if a user shows high interest in advertisements during a specific time period, it concentrates the display of advertisements during that time period. Furthermore, it changes the content of advertisements based on user reactions. For example, if a user shows high interest in advertisements in a specific category, it prioritizes the display of advertisements in that category. The adjustment department makes these adjustments in real time to maximize the effectiveness of advertisements. The adjustment department collects user feedback and incorporates it into the adjustments of advertisement content and timing. For example, it improves the content of advertisements based on user feedback and provides more effective advertisements. The adjustment department also adjusts the frequency and location of advertisements to provide the optimal advertising experience for users. This allows the adjustment unit to optimize the content and timing of advertisements based on user responses, thereby maximizing the effectiveness of the advertisements.

[0064] The ad insertion system includes a transparency unit to ensure transparency in data usage. The transparency unit appropriately manages user data and clearly states the purpose and scope of data usage to ensure transparency in data usage. For example, the transparency unit explains the details of data usage to the user and obtains the user's consent. For example, the transparency unit provides the user with a data usage policy to clearly state the purpose and scope of data usage. For example, the transparency unit performs data encryption and access control to appropriately manage user data. This ensures transparency in data usage and protects user privacy. Some or all of the above-described processes in the transparency unit may be performed using AI or not. For example, the transparency unit can input user data into AI and have the AI ​​explain the purpose and scope of data usage.

[0065] The ad insertion system includes an operational efficiency improvement unit that utilizes generative AI to improve the efficiency of ad operations. The operational efficiency improvement unit optimizes ad delivery timing and targeting methods in order to improve the efficiency of ad operations by utilizing generative AI. For example, the operational efficiency improvement unit optimizes ad delivery timing using generative AI. For example, the operational efficiency improvement unit optimizes ad targeting methods using generative AI. For example, the operational efficiency improvement unit develops algorithms to maximize the effectiveness of ads using generative AI. As a result, the efficiency of ad operations is improved by utilizing generative AI. Some or all of the above processes in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can have AI perform the optimization of ad delivery timing and targeting methods.

[0066] The analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the generating AI analyzes the user's level of excitement and identifies the factors causing the excitement. For example, if the user is depressed, the generating AI refers to the user's past conversation history and analyzes the cause of the depression. For example, if the user is relaxed, the generating AI analyzes the user's level of relaxation and identifies the factors promoting relaxation. By improving the accuracy of the analysis based on the user's emotions, more appropriate advertisements can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0067] The analysis unit can optimize its analysis algorithm by referring to the user's past conversation history during analysis. For example, the analysis unit can analyze keywords that the user has frequently used in the past and evaluate their importance in the current conversation. For example, the analysis unit can analyze the user's past conversation patterns and improve the prediction accuracy in the current conversation. For example, the analysis unit can identify topics that the user has shown interest in in the past and increase their relevance in the current conversation. In this way, by referring to the user's past conversation history, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past conversation history into AI and have the AI ​​perform the optimization of the analysis algorithm.

[0068] The analysis unit can adjust the level of detail of its analysis based on the user's current lifestyle and areas of interest during the analysis process. For example, if the user inputs their current lifestyle, the analysis unit will adjust the level of detail based on that information. For example, if the user has a specific area of ​​interest, the analysis unit will prioritize analyzing information related to that area. For example, if the user's lifestyle changes, the generating AI will adjust the level of detail of the analysis to reflect that change. By adjusting the level of detail of the analysis based on the user's current lifestyle and areas of interest, the analysis unit can suggest more relevant advertisements. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's lifestyle and areas of interest into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0069] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is excited, the generating AI will prioritize displaying analysis results related to factors causing excitement. If the user is depressed, the generating AI will prioritize displaying analysis results related to the cause of depression. If the user is relaxed, the generating AI will prioritize displaying analysis results related to factors promoting relaxation. By prioritizing analysis results based on the user's emotions, more appropriate advertisements can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0070] The analysis unit can improve the relevance of its analysis by considering the user's geographical location information during the analysis process. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. For example, if the user is traveling, the analysis unit will prioritize analyzing information related to their travel destination. For example, if the user is at home, the analysis unit will prioritize analyzing information around their home. By considering the user's geographical location information, the analysis unit can improve the relevance of its analysis and suggest more appropriate advertisements. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information into the AI ​​and have the AI ​​perform processes to improve the relevance of the analysis.

[0071] The analysis unit can analyze a user's social media activity and obtain relevant information during the analysis process. For example, the analysis unit can analyze the content a user frequently posts on social media and obtain relevant information. For example, the analysis unit can analyze information about accounts a user follows on social media and obtain relevant information. For example, the analysis unit can analyze information about groups a user participates in on social media and obtain relevant information. By analyzing a user's social media activity, relevant information can be obtained, and more appropriate advertisements can be suggested. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity data into AI and have AI perform the acquisition of relevant information.

[0072] The suggestion unit can estimate the user's emotions and adjust the advertising presentation based on those emotions. For example, if the user is relaxed, the suggestion unit might suggest an advertisement in a calm tone. If the user is excited, the suggestion unit might suggest an advertisement in an energetic tone. If the user is depressed, the suggestion unit might suggest an advertisement containing an encouraging message. By adjusting the advertising presentation based on the user's emotions, more effective advertisements can be suggested. 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. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the advertising presentation.

[0073] The proposal unit can adjust the level of detail in its proposals based on the importance of the advertisements. For example, for highly important advertisements, the proposal unit will provide a detailed proposal. For less important advertisements, the proposal unit will provide a concise proposal. For medium-importance advertisements, the proposal unit will provide a proposal with an appropriate level of detail. By adjusting the level of detail in proposals based on the importance of the advertisements, more effective advertisements can be proposed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input advertisement importance data into AI and have the AI ​​adjust the level of detail in the proposals.

[0074] The proposal unit can apply different proposal algorithms depending on the advertisement category when making proposals. For example, in the case of product advertisements, the proposal unit will make proposals that emphasize the features and benefits of the product. For example, in the case of service advertisements, the proposal unit will make proposals that emphasize how to use the service and its benefits. For example, in the case of event advertisements, the proposal unit will make proposals that emphasize the details of the event and how to participate. By applying different proposal algorithms depending on the advertisement category, more effective advertisements can be proposed. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input advertisement category data into AI and have the AI ​​perform the application of the proposal algorithm.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of the ad based on those emotions. For example, if the user is in a hurry, the suggestion unit will suggest a short, to-the-point ad. If the user is relaxed, the suggestion unit will suggest a longer ad with detailed explanations. If the user is excited, the suggestion unit will suggest an ad with visually stimulating effects. By adjusting the ad length based on the user's emotions, more effective ads can be suggested. 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. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the ad length.

[0076] The proposal department can determine the priority of proposals based on the submission timing of the advertisements. For example, the proposal department will prioritize proposals for advertisements with an upcoming submission date. For example, the proposal department will postpone proposals for advertisements with a distant submission date. For example, the proposal department will give appropriate priority to advertisements with a medium submission date. By determining the priority of proposals based on the submission timing of the advertisements, the proposal department can propose more effective advertisements. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input advertisement submission date data into AI and have the AI ​​perform the determination of proposal priority.

[0077] The suggestion unit can adjust the order of suggestions based on the relevance of the advertisements. For example, the suggestion unit will suggest highly relevant advertisements first. For example, the suggestion unit will suggest less relevant advertisements later. For example, the suggestion unit will suggest moderately relevant advertisements in an appropriate order. By adjusting the order of suggestions based on the relevance of the advertisements, the suggestion unit can suggest more effective advertisements. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input advertisement relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0078] The adjustment unit can estimate the user's emotions and adjust the content and timing of advertisements based on the estimated emotions. For example, if the user is relaxed, the adjustment unit will display advertisements in a calm tone. For example, if the user is excited, the adjustment unit will display advertisements in an energetic tone. For example, if the user is depressed, the adjustment unit will display advertisements containing encouraging messages. This allows for the display of more effective advertisements by adjusting the content and timing of advertisements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform the adjustment of the content and timing of advertisements.

[0079] The adjustment unit can optimize its adjustment algorithm by referring to the user's past response history during the adjustment process. For example, the adjustment unit can analyze patterns of advertisements in which the user has previously shown a positive response and reflect them in the current advertisement. For example, the adjustment unit can analyze patterns of advertisements in which the user has previously shown a negative response and avoid reflecting them in the current advertisement. For example, the adjustment unit can adjust the optimal timing for displaying advertisements based on the user's past response history. In this way, by referring to the user's past response history, the adjustment algorithm can be optimized and more effective advertisements can be displayed. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past response history data into AI and have the AI ​​perform the optimization of the adjustment algorithm.

[0080] The adjustment unit can customize the means of adjustment based on the user's current lifestyle during the adjustment process. For example, if the user is busy, the adjustment unit may display short, concise advertisements. If the user is relaxed, the adjustment unit may display advertisements with detailed explanations. If the user is participating in a particular event, the adjustment unit may display advertisements related to that event. This allows for the display of more effective advertisements by customizing the means of adjustment based on the user's current lifestyle. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user lifestyle data into AI and have the AI ​​perform the customization of the means of adjustment.

[0081] The adjustment unit can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is stressed, the adjustment unit provides a simple and highly visible display method. If the user is relaxed, the adjustment unit provides a display method that includes detailed information. If the user is in a hurry, the adjustment unit provides a display method that gets straight to the point. By adjusting how ads are displayed based on the user's emotions, more effective ads can be displayed. 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. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into an AI and have the AI ​​perform the adjustment of how ads are displayed.

[0082] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, if the user is in a specific region, the adjustment unit will display advertisements related to that region. For example, if the user is traveling, the adjustment unit will display advertisements related to the travel destination. For example, if the user is at home, the adjustment unit will display advertisements around the user's home. By considering the user's geographical location information, the adjustment unit can select the optimal adjustment method and display more effective advertisements. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's geographical location data into the AI ​​and have the AI ​​select the optimal adjustment method.

[0083] The adjustment unit can analyze the user's social media activity during the adjustment process and propose adjustment methods. For example, the adjustment unit can analyze the content that the user frequently posts on social media and display relevant advertisements. For example, the adjustment unit can analyze information about accounts that the user follows on social media and display relevant advertisements. For example, the adjustment unit can analyze information about groups that the user participates in on social media and display relevant advertisements. In this way, by analyzing the user's social media activity, adjustment methods can be proposed and more effective advertisements can be displayed. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's social media activity data into AI and have the AI ​​execute the proposal of adjustment methods.

[0084] The transparency unit can estimate the user's emotions and improve the transparency of data usage based on the estimated user emotions. For example, if the user is feeling anxious, the transparency unit will clearly explain the details of data usage. For example, if the user is relaxed, the transparency unit will ensure data usage transparency with a concise explanation. For example, if the user is excited, the transparency unit will explain data usage transparency in a visually easy-to-understand format. This allows the user's trust to be gained by improving the transparency of data usage based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transparency unit may be performed using AI or not. For example, the transparency unit can input user emotion data into an AI and have the AI ​​perform the improvement of data usage transparency.

[0085] The transparency assurance unit can optimize the transparency assurance algorithm by referring to the user's past data usage history when ensuring transparency. For example, the transparency assurance unit can refer to the content of data usage that the user has previously consented to and reflect it in the current transparency assurance. For example, the transparency assurance unit can refer to the content of data usage that the user has previously refused and not reflect it in the current transparency assurance. For example, the transparency assurance unit can propose the optimal transparency assurance method based on the user's past data usage history. In this way, by referring to the user's past data usage history, the transparency assurance algorithm can be optimized, and more effective transparency assurance can be achieved. Some or all of the above processing in the transparency assurance unit may be performed using AI or not. For example, the transparency assurance unit can input the user's past data usage history data into AI and have the AI ​​perform the optimization of the transparency assurance algorithm.

[0086] The transparency assurance unit can estimate the user's emotions and determine the priority of transparency assurance based on the estimated user emotions. For example, if the user is feeling anxious, the transparency assurance unit will set the priority of transparency assurance to a high level. For example, if the user is relaxed, the transparency assurance unit will set the priority of transparency assurance to a medium level. For example, if the user is excited, the transparency assurance unit will set the priority of transparency assurance to a low level. This allows for more effective transparency assurance by determining the priority of transparency assurance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transparency assurance unit may be performed using AI or not. For example, the transparency assurance unit can input user emotion data into an AI and have the AI ​​determine the priority of transparency assurance.

[0087] The transparency assurance unit can select means of ensuring transparency by considering the user's geographical location information when ensuring transparency. For example, if the user is in a specific region, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of that region. For example, if the user is traveling, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of the travel destination. For example, if the user is at home, the transparency assurance unit will select means of ensuring transparency based on the data usage regulations of the user's home area. By considering the user's geographical location information, the transparency assurance unit can select means of ensuring transparency and achieve more effective transparency. Some or all of the above processing in the transparency assurance unit may be performed using AI or not. For example, the transparency assurance unit can input the user's geographical location information data into AI and have the AI ​​perform the selection of means of ensuring transparency.

[0088] The operational efficiency improvement unit can estimate the user's emotions and improve operational efficiency based on the estimated emotions. For example, if the user is relaxed, the operational efficiency improvement unit will improve operational efficiency in a calm tone. For example, if the user is excited, the operational efficiency improvement unit will improve operational efficiency in an energetic tone. For example, if the user is depressed, the operational efficiency improvement unit will improve operational efficiency in a tone that includes encouraging messages. This makes it possible to achieve more effective advertising operations by improving operational efficiency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operational efficiency improvement unit may be performed using AI or not using AI. For example, the operational efficiency improvement unit can input user emotion data into an AI and have the AI ​​perform the operational efficiency improvement.

[0089] The operational efficiency improvement unit can optimize the operational algorithm by referring to past operational data when improving operational efficiency. For example, the operational efficiency improvement unit proposes the optimal operational method based on past operational data. For example, the operational efficiency improvement unit identifies patterns for improving operational efficiency from past operational data. For example, the operational efficiency improvement unit analyzes past operational data and optimizes the operational algorithm. By doing so, by referring to past operational data, the operational algorithm can be optimized, enabling more effective advertising operations. Some or all of the above processes in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can input past operational data into AI and have AI perform the optimization of the operational algorithm.

[0090] The operational efficiency improvement unit can estimate the user's emotions and determine operational efficiency priorities based on the estimated user emotions. For example, if the user is feeling anxious, the operational efficiency improvement unit will set the operational efficiency priority high. For example, if the user is relaxed, the operational efficiency improvement unit will set the operational efficiency priority to a medium level. For example, if the user is excited, the operational efficiency improvement unit will set the operational efficiency priority to a low level. By determining operational efficiency priorities based on the user's emotions, more effective advertising operations can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can input user emotion data into an AI and have the AI ​​perform the determination of operational efficiency priorities.

[0091] The operational efficiency improvement unit can select operational efficiency measures by considering the user's geographical location information when improving operational efficiency. For example, if the user is in a specific region, the operational efficiency improvement unit will select operational efficiency improvement measures for that region. For example, if the user is traveling, the operational efficiency improvement unit will select operational efficiency improvement measures for the travel destination. For example, if the user is at home, the operational efficiency improvement unit will select operational efficiency improvement measures for the home area. By considering the user's geographical location information, the operational efficiency improvement unit can select operational efficiency measures and achieve more effective advertising operations. Some or all of the above processing in the operational efficiency improvement unit may be performed using AI or not. For example, the operational efficiency improvement unit can input the user's geographical location information data into AI and have the AI ​​perform the selection of operational efficiency measures.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The analytics unit can analyze the user's voice tone and speaking speed to estimate their emotions. For example, if a user speaks quickly, the analytics unit estimates that the user is excited and identifies factors that may be causing the excitement. If a user speaks slowly, the analytics unit estimates that the user is relaxed and identifies factors that promote relaxation. If a user's voice trembles, the analytics unit estimates that the user is anxious and identifies factors that alleviate anxiety. This allows the system to estimate emotions based on the user's voice tone and speaking speed and suggest more appropriate advertisements.

[0094] The suggestion department can refer to a user's past purchase history and suggest advertisements related to products and services the user has previously purchased. For example, if a user has previously purchased cosmetics from a specific brand, the suggestion department will suggest new products or related items from that brand. If a user has previously used travel-related services, the suggestion department will suggest new travel plans and offers. If a user has previously purchased health foods, the suggestion department will suggest new health foods and related health information. This allows for the suggestion of highly relevant advertisements based on the user's past purchase history, thereby improving the user experience.

[0095] The adjustment unit can analyze the user's device usage and optimize the timing of ad display. For example, if a user frequently uses a smartphone, the adjustment unit displays ads optimized for smartphones. If the user uses a computer, the adjustment unit displays ads optimized for computers. If the user uses a tablet, the adjustment unit displays ads optimized for tablets. This optimizes the timing of ad display based on the user's device usage, enabling the display of more effective ads.

[0096] The transparency unit can estimate the user's emotions and improve the transparency of data usage based on those emotions. For example, if the user is feeling anxious, the transparency unit will clearly explain the details of data usage. If the user is relaxed, the transparency unit will ensure data usage transparency with a concise explanation. If the user is excited, the transparency unit will explain data usage transparency in a visually easy-to-understand format. By improving data usage transparency based on the user's emotions, the system can gain the user's trust.

[0097] The operational efficiency improvement unit can monitor the effectiveness of advertisements in real time and automatically stop low-performing advertisements. For example, if an advertisement's click-through rate falls below a certain standard, the operational efficiency improvement unit will stop that advertisement. If an advertisement's conversion rate is low, the operational efficiency improvement unit will stop that advertisement. If an advertisement receives many impressions but few responses, the operational efficiency improvement unit will stop that advertisement. In this way, the efficiency of advertising operations can be improved by monitoring the effectiveness of advertisements in real time and automatically stopping low-performing advertisements.

[0098] The analytics unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is excited, the analytics unit will prioritize displaying analysis results related to factors causing the excitement. If the user is depressed, the analytics unit will prioritize displaying analysis results related to the cause of the depression. If the user is relaxed, the analytics unit will prioritize displaying analysis results related to factors promoting relaxation. By prioritizing analysis results based on the user's emotions, the system can suggest more appropriate advertisements.

[0099] The suggestion department can analyze a user's social media activity and suggest advertisements related to the influencers and brands the user follows. For example, if a user follows a specific influencer, the suggestion department will suggest advertisements for products and services promoted by that influencer. If a user follows a specific brand, the suggestion department will suggest advertisements for that brand's new products or campaigns. If a user is interested in a specific topic, the suggestion department will suggest advertisements related to that topic. This allows for the suggestion of highly relevant advertisements based on the user's social media activity, thereby improving the user experience.

[0100] The adjustment unit can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is stressed, the adjustment unit provides a simple and highly visible display. If the user is relaxed, the adjustment unit provides a display that includes detailed information. If the user is in a hurry, the adjustment unit provides a display that gets straight to the point. By adjusting how ads are displayed based on the user's emotions, more effective ads can be displayed.

[0101] The transparency assurance unit can select means to ensure data usage transparency by taking into account the user's geographical location information. For example, if the user is in a specific region, the transparency assurance means can be selected based on the data usage regulations of that region. If the user is traveling, the transparency assurance means can be selected based on the data usage regulations of the travel destination. If the user is at home, the transparency assurance means can be selected based on the data usage regulations of the user's home area. By considering the user's geographical location information, the transparency assurance means can be selected, resulting in more effective transparency assurance.

[0102] The operational efficiency improvement unit can estimate user emotions and determine operational efficiency priorities based on those emotions. For example, if a user is feeling anxious, the operational efficiency priority is set high. If a user is relaxed, the operational efficiency priority is set to medium. If a user is excited, the operational efficiency priority is set to low. By determining operational efficiency priorities based on user emotions, more effective advertising operations can be achieved.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The analysis unit analyzes the user's hobbies, preferences, behavior, and conversation context in real time. For example, it uses natural language processing technology to analyze the content of the user's conversation and identify the user's hobbies, preferences, and behavior. It also uses machine learning algorithms to analyze the user's behavior patterns and estimate the user's interests. Furthermore, it analyzes the user's website browsing history and purchase history to understand the user's behavior. Step 2: The proposal department proposes appropriate advertisements based on the information obtained by the analysis department. For example, it selects highly relevant advertisements based on the user's hobbies, preferences, and behavior. It also proposes appropriate advertisements based on the context of the user's conversation. Furthermore, it customizes the content of the advertisements based on the user's interests. Step 3: The adjustment unit monitors user responses to the advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements. For example, it analyzes user responses in real time and optimizes the content and timing of the advertisements to maximize their effectiveness. It also adjusts the display time of the advertisements based on user responses. Furthermore, it modifies the content of the advertisements based on user responses.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the analysis unit, proposal unit, adjustment unit, transparency assurance unit, and operational efficiency improvement unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the user's hobbies, preferences, behavior, and conversational context in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate advertisements based on the analysis results. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the content and timing of advertisements by monitoring the user's response. The transparency assurance unit is implemented by the specific processing unit 290 of the data processing device 12 and ensures transparency in data usage. The operational efficiency improvement unit is implemented by the processor 46 of the smart device 14 and improves the efficiency of advertisement operations by utilizing generated AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the analysis unit, proposal unit, adjustment unit, transparency assurance unit, and operational efficiency improvement unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the user's tastes, preferences, behavior, and conversational context in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate advertisements based on the analysis results. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the content and timing of advertisements by monitoring the user's reactions. The transparency assurance unit is implemented by the specific processing unit 290 of the data processing device 12 and ensures transparency in data usage. The operational efficiency improvement unit is implemented by the processor 46 of the smart glasses 214 and improves the efficiency of advertisement operations by utilizing generation AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the analysis unit, proposal unit, adjustment unit, transparency assurance unit, and operational efficiency improvement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the user's hobbies, preferences, behavior, and conversation context in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate advertisements based on the analysis results. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the content and timing of advertisements by monitoring the user's response. The transparency assurance unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures transparency in data usage. The operational efficiency improvement unit is implemented by the processor 46 of the headset terminal 314 and improves the efficiency of advertisement operations by utilizing generation AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the analysis unit, proposal unit, adjustment unit, transparency assurance unit, and operational efficiency improvement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the user's hobbies, preferences, behavior, and conversational context in real time. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate advertisements based on the analysis results. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the content and timing of advertisements by monitoring the user's reactions. The transparency assurance unit is implemented by the specific processing unit 290 of the data processing unit 12 and ensures transparency in data usage. The operational efficiency improvement unit is implemented by the processor 46 of the robot 414 and improves the efficiency of advertisement operations by utilizing generated AI. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) An analysis unit that analyzes the user's hobbies, preferences, behavior, and conversation context in real time, Based on the information obtained by the analysis unit, the proposal unit proposes an appropriate advertisement, The system includes an adjustment unit that monitors user reactions to advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements. A system characterized by the following features. (Note 2) It includes a transparency control unit to ensure transparency in data usage. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an operational efficiency improvement unit that utilizes generation AI to improve the efficiency of advertising operations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It estimates user emotions and improves the accuracy of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to improve the relevance of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and adjusts the ad's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the ad length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their ad submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, It estimates the user's emotions and adjusts the content and timing of ads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, During the adjustment process, the adjustment algorithm is optimized by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During the adjustment process, the adjustment methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, During the adjustment process, we analyze users' social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The transparency-enhancing section is, We estimate user sentiment and improve data usage transparency based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 23) The transparency-enhancing section is, When ensuring transparency, the transparency algorithm is optimized by referring to the user's past data usage history. The system described in Appendix 2, characterized by the features described herein. (Note 24) The transparency-enhancing section is, We estimate user sentiment and determine transparency priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 25) The transparency-enhancing section is, When ensuring transparency, the means of ensuring transparency will be selected while taking into account the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned operational efficiency improvement unit is, It estimates user emotions and improves operational efficiency based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned operational efficiency improvement unit is, When improving operational efficiency, the operational algorithm is optimized by referring to past operational data. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned operational efficiency improvement unit is, It estimates user sentiment and determines operational efficiency priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned operational efficiency improvement unit is, When improving operational efficiency, the means of improving operational efficiency will be selected while taking into account the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the user's hobbies, preferences, behavior, and conversation context in real time, Based on the information obtained by the analysis unit, the proposal unit proposes an appropriate advertisement, The system includes an adjustment unit that monitors user reactions to advertisements proposed by the proposal unit and adjusts the content and timing of the advertisements. A system characterized by the following features.

2. It includes a transparency control unit to ensure transparency in data usage. The system according to feature 1.

3. It features an operational efficiency improvement unit that utilizes generation AI to improve the efficiency of advertising operations. The system according to feature 1.

4. The aforementioned analysis unit, It estimates user emotions and improves the accuracy of analysis based on the estimated user emotions. The system according to feature 1.

5. The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past conversation history. The system according to feature 1.

6. The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the user's current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to improve the relevance of the analysis. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the user's social media activity is analyzed to obtain relevant information. The system according to feature 1.

10. The aforementioned proposal section is, It estimates the user's emotions and adjusts the ad's presentation based on those estimated emotions. The system according to feature 1.

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