System

Abstract

An object of a system according to an embodiment is to quickly and effectively propose an advertisement message that meets the interest or needs of a target audience.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, a production unit, and a provision unit. The collection unit collects behaviors, preferences, and trends of the target audience. The analysis unit analyzes the data collected by the collection unit and grasps the interest and needs of the target audience. The proposal unit proposes the advertisement message on the basis of the analysis result obtained by the analysis unit. The production unit produces an advertisement based on the advertisement message proposed by the proposal unit. The provision unit provides the advertisement phrase produced by the production unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to quickly and effectively suggest advertising copy that is in line with the interests and needs of the target audience.

[0005] The system according to the embodiment aims to quickly and effectively suggest advertising copy that is in line with the interests and needs of the target audience. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a production unit, and a provision unit. The collection unit collects the behavior, preferences, and trends of the target audience. The analysis unit analyzes the data collected by the collection unit to understand the interests and needs of the target audience. The proposal unit proposes advertising copy based on the analysis results obtained by the analysis unit. The production unit produces advertisements based on the advertising copy proposed by the proposal unit. The provision unit provides the advertising copy produced by the production unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and effectively suggest advertising copy that matches the interests and needs of the target audience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) An advertising proposal system according to an embodiment of the present invention is a system in which a generation AI analyzes the behavior, preferences, and trends of a target audience in real time and proposes optimal advertising copy. The advertising proposal system collects and analyzes the behavior, preferences, and trends of the target audience, proposes advertising copy, and creates and provides advertisements. For example, the advertising proposal system collects data such as social media posting content, search history, and purchase history. The advertising proposal system then analyzes the collected data to understand the interests and needs of the target audience. The advertising proposal system then uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. The advertising proposal system then creates advertisements based on the advertising copy proposed by the generation AI. The advertising proposal system then provides the created advertising copy. This allows the advertising proposal system to deeply understand the psychology and trends of the target audience and develop effective advertising strategies. The advertising proposal system thus analyzes the behavior, preferences, and trends of the target audience in real time and proposes optimal advertising copy, resulting in fast and effective advertising creation. For example, advertising suggestion systems can help companies develop effective advertising strategies by gaining a deep understanding of the psychology and trends of their target audience. AI analysis enables companies to quickly adapt to market changes and maintain or strengthen their competitive edge.

[0029] An advertisement proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a production unit, and a provision unit. The collection unit collects the behavior, preferences, and trends of a target audience. The collection unit can collect data such as social media postings, search histories, and purchase histories. The collection unit can also collect data such as users' browsing histories and geographical location information. The analysis unit analyzes the data collected by the collection unit to understand the interests and needs of the target audience. The analysis unit can, for example, identify the product categories of interest and purchasing intentions of the target audience based on the collected data. The proposal unit proposes advertising copy based on the analysis results obtained by the analysis unit. The proposal unit uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. For example, the generation AI can propose catchy advertising copy related to a particular product to a target audience who is highly interested in that product. The production unit creates advertisements based on the advertising copy proposed by the proposal unit. The production department can significantly reduce the time required for advertisement production by, for example, using the advertising copy proposed by the generation AI as is. The provision department provides the advertising copy created by the production department. The provision department can provide the created advertising copy to the target audience through media such as a website or social media, for example. As a result, the advertisement proposal system according to the embodiment analyzes the behavior, preferences, and trends of the target audience in real time and proposes optimal advertising copy, thereby enabling advertisement production to be carried out quickly and effectively.

[0030] The collection unit can collect data on social media posts, search history, and purchase history. The collection unit, for example, collects social media posts. The social media posts include, but are not limited to, text, images, and videos. The collection unit, for example, collects search history. The search history includes, but is not limited to, the type of search engine and the collection period. The collection unit, for example, collects purchase history. The purchase history includes, but is not limited to, online shopping history and in-store purchase history. By collecting data such as social media posts, search history, and purchase history, the behavior and preferences of the target audience can be understood in detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on social media posts, search history, and purchase history into a generation AI and cause the generation AI to collect the data.

[0031] The analysis unit can analyze the collected data to understand the interests and needs of the target audience. For example, the analysis unit can analyze the collected data to understand the interests and needs of the target audience. For example, the analysis unit can identify the product categories of interest and purchasing intentions of the target audience based on the collected data. The analysis unit can also identify the topics of interest and the strength of interest of the target audience based on the collected data. Furthermore, the analysis unit can identify the purchasing patterns and behavioral patterns of the target audience based on the collected data. In this way, the interests and needs of the target audience can be accurately understood by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI understand the interests and needs of the target audience.

[0032] The suggestion unit can generate advertising copy tailored to the interests and needs of the target audience. The suggestion unit, for example, uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. For example, the generation AI can suggest catchy advertising copy related to a particular product to a target audience who is highly interested in that product. The suggestion unit can also use the generation AI to customize the format and style of the advertising copy based on the interests and needs of the target audience. Furthermore, the suggestion unit can also use the generation AI to adjust the length and content of the advertising copy based on the interests and needs of the target audience. This increases the effectiveness of the advertisement by generating advertising copy tailored to the interests and needs of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the suggestion unit can input data related to the interests and needs of the target audience into the generation AI and cause the generation AI to generate advertising copy.

[0033] The production department can create advertisements based on the advertising text proposed by the generation AI. For example, the production department creates advertisements based on the advertising text proposed by the generation AI. For example, the production department can significantly reduce the time required for advertisement creation by using the advertising text proposed by the generation AI as is. The production department can also adjust the design and layout of the advertisement based on the advertising text proposed by the generation AI. Furthermore, the production department can select the advertisement distribution method and media based on the advertising text proposed by the generation AI. In this way, by creating advertisements based on the advertising text proposed by the generation AI, the time required for advertisement creation can be significantly reduced. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can have AI adjust the design and layout of the advertisement based on the advertising text proposed by the generation AI.

[0034] The provision unit can provide the created advertising copy. For example, the provision unit can provide the created advertising copy to the target audience through a medium such as a website or social media. For example, the provision unit can also send the created advertising copy directly to the target audience via email or a messaging app. The provision unit can also distribute the created advertising copy through an advertising network. Furthermore, the provision unit can also provide the created advertising copy in real time. As a result, by providing the created advertising copy, the advertisement can be effectively delivered to the target audience. Some or all of the above-mentioned processing in the provision unit may be performed using AI or may be performed without using AI. For example, the provision unit can cause AI to select the medium and timing for distributing the created advertising copy.

[0035] The collection unit can collect the user's browsing history in addition to the content of social media posts, search history, and purchase history. For example, the collection unit can record the URLs of websites visited by the user and the browsing time to analyze the user's preferences. For example, the collection unit can track the advertisements and links clicked by the user to identify topics of interest. For example, the collection unit can analyze the categories of websites frequently visited by the user to identify areas of interest. In this way, by collecting the user's browsing history, more detailed preferences and interests can be identified. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history into the generation AI and have the generation AI collect the data.

[0036] When collecting data, the collection unit can select the optimal collection means by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit can collect data through a mobile app. For example, if the user is using a desktop, the collection unit can collect data through a browser extension. For example, if the user is using a smartwatch, the collection unit can collect data from a wearable device. This allows the optimal collection means to be selected by taking into account the user's device information. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's device information into the generation AI and cause the generation AI to select the optimal collection means.

[0037] When collecting data, the collection unit can analyze the user's past behavioral patterns and determine the priority of the data to be collected. For example, the collection unit can prioritize collecting keywords that the user has frequently searched for in the past. For example, the collection unit can prioritize collecting categories of products that the user has purchased in the past. For example, the collection unit can prioritize collecting data on websites on which the user has spent a lot of time in the past. This allows the priority of the data to be collected to be determined by analyzing the user's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral patterns into a generation AI and have the generation AI determine the priority of the data.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can collect event information related to that area. For example, when the user is traveling, the collection unit can collect information about tourist spots and restaurants. For example, when the user is at home, the collection unit can collect information about nearby stores and services. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit can collect the content of posts that the user has "liked" on social media. For example, the collection unit can collect the content of posts from accounts the user follows. For example, the collection unit can collect the content of posts shared by the user. This makes it possible to collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0040] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit, for example, can preferentially use data collection methods for which the user has provided favorable feedback in the past. The collection unit, for example, can avoid data collection methods for which the user has provided negative feedback in the past. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0041] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by taking into account the user's past behavioral patterns. The analysis unit improves the accuracy of the analysis, for example, based on behaviors the user frequently performed in the past. The analysis unit can identify products of interest to the user, for example, by taking into account the user's past purchase history. The analysis unit can identify topics of interest to the user, for example, based on the user's past search history. This improves the accuracy of the analysis by taking into account the user's past behavioral patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral patterns into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can apply different analysis methods depending on the category of data. For example, the analysis unit can apply a purchase pattern analysis method to purchase history data. For example, the analysis unit can apply a search trend analysis method to search history data. For example, the analysis unit can apply a sentiment analysis method to SNS post data. In this way, by applying different analysis methods depending on the category of data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to apply the analysis method depending on the category of data.

[0043] During analysis, the analysis unit can customize the analysis results by taking into account the user's attribute information. The analysis unit can, for example, identify topics of interest according to the user's age. The analysis unit can, for example, identify products of interest according to the user's gender. The analysis unit can, for example, identify events of interest according to the user's place of residence. This allows the analysis results to be customized by taking into account the user's attribute information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute information into the generation AI and have the generation AI customize the analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data to grasp real-time trends. The analysis unit, for example, can analyze past data to grasp long-term trends. The analysis unit, for example, can prioritize analysis of data submitted during a specific period to grasp seasonal trends. In this way, real-time trends can be grasped by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can postpone analysis of data with low relevance. For example, the analysis unit can group and analyze highly relevant data. This allows the analysis order to be adjusted based on the relevance of the data, so that more relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, the analysis unit can provide detailed analysis results to a user with high expertise. For example, the analysis unit can provide concise analysis results to a user with low expertise. For example, the analysis unit can customize the display method of the analysis results according to the user's level of expertise. This allows for adjusting the level of detail of the analysis results according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the analysis results.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the interest level of the target audience. For example, the suggestion unit can make a detailed proposal to a target audience with a high interest level. For example, the suggestion unit can make a concise proposal to a target audience with a low interest level. For example, the suggestion unit can customize the content of the proposal according to the interest level. As a result, more effective proposals can be made by adjusting the level of detail of the proposal based on the interest level of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the interest level of the target audience to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the target audience category. For example, the suggestion unit can apply a trend-sensitive proposal algorithm to younger generations. For example, the suggestion unit can apply a stability-oriented proposal algorithm to older generations. For example, the suggestion unit can apply a specialized proposal algorithm to groups with specific interests. This allows for more appropriate proposals by applying different proposal algorithms depending on the target audience category. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the target audience category into the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit makes similar proposals based on proposals that the user has accepted in the past. For example, the suggestion unit can avoid proposals that the user has rejected in the past. For example, the suggestion unit can analyze the user's past proposal results and make optimal suggestions. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the behavioral history of the target audience. For example, the suggestion unit can prioritize suggestions based on behavior that the target audience frequently performed in the past. For example, the suggestion unit can prioritize suggestions that are of interest to the target audience, taking into account their past purchasing history. For example, the suggestion unit can prioritize suggestions that are of interest to the target audience, based on their past search history. This allows for more effective suggestions by determining the priority of suggestions based on the behavioral history of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the behavioral history of the target audience into the generation AI and have the generation AI determine the priority of the suggestions.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the target audience. For example, the suggestion unit can prioritize proposals for target audiences with high relevance. For example, the suggestion unit can postpone proposals for target audiences with low relevance. For example, the suggestion unit can group highly relevant target audiences and make proposals. This allows for more effective proposals to be made by adjusting the order of proposals based on the relevance of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the relevance of the target audience into the generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can make proposals that use a lot of technical terminology for a user with high level of expertise. For example, the suggestion unit can make proposals that are concise and easy to understand for a user with low level of expertise. The suggestion unit can customize the content of the proposal according to the user's level of expertise. This allows for more appropriate proposals to be made by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal.

[0053] When producing an advertisement, the production department can analyze the target audience's past responses and select the optimal production method. For example, the production department can reuse an advertisement style to which the target audience responded favorably in the past. For example, the production department can avoid an advertisement style to which the target audience responded negatively in the past. For example, the production department can analyze the target audience's past responses and select the optimal advertisement style. In this way, the optimal advertisement production method can be selected by analyzing the target audience's past responses. Some or all of the above-mentioned processing in the production department may be performed using AI or without AI. For example, the production department can input the target audience's past responses into a generation AI and have the generation AI select the optimal advertisement style.

[0054] When producing advertisements, the production department can customize the production content by taking into account current trends among the target audience. For example, the production department can produce advertisements that reflect current fashion trends. For example, the production department can produce advertisements that reflect current technology trends. For example, the production department can produce advertisements that reflect current entertainment trends. This allows for the production of more effective advertisements by taking into account current trends among the target audience. Some or all of the above-described processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input data regarding current trends into a generation AI and have the generation AI customize the production content.

[0055] The production department can improve the production method by reflecting user feedback when producing an advertisement. For example, the production department can improve the design of the advertisement based on the feedback provided by the user. For example, the production department can improve the message of the advertisement based on the feedback provided by the user. For example, the production department can improve the advertisement delivery method based on the feedback provided by the user. In this way, the advertisement production method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input user feedback into a generation AI and have the generation AI improve the production method.

[0056] When producing an advertisement, the production department can select the optimal production method by taking into account the geographic location information of the target audience. For example, if the target audience is in a specific area, the production department can produce an advertisement related to that area. For example, if the target audience is traveling, the production department can produce an advertisement related to the travel destination. For example, if the target audience is at home, the production department can produce an advertisement related to nearby stores and services. This allows for the production of more effective advertisements by taking into account the geographic location information of the target audience. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can input the geographic location information of the target audience into the generation AI and have the generation AI select the optimal production method.

[0057] When creating an advertisement, the production department can analyze the social media activity of the target audience and suggest production content. For example, the production department can create an advertisement that reflects the content of posts that the target audience has "liked" on social media. For example, the production department can create an advertisement that reflects the content of posts from accounts that the target audience follows. For example, the production department can create an advertisement that reflects the content of posts shared by the target audience. In this way, by analyzing the social media activity of the target audience, more effective advertisements can be created. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can input the social media activity of the target audience into a generation AI and have the generation AI execute production content suggestions.

[0058] When producing an advertisement, the production department can customize the production method by reflecting the user's past feedback. For example, the production department customizes the advertisement design based on feedback provided by the user in the past. For example, the production department can customize the advertisement message based on feedback provided by the user in the past. For example, the production department can customize the advertisement delivery method based on feedback provided by the user in the past. In this way, the advertisement production method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input the user's past feedback into a generation AI and have the generation AI customize the production method.

[0059] When providing an advertisement, the provision unit can select an optimal provision method by analyzing the target audience's past responses. For example, the provision unit reuses an advertisement style to which the target audience responded favorably in the past. For example, the provision unit can avoid an advertisement style to which the target audience responded negatively in the past. For example, the provision unit can analyze the target audience's past responses and select an optimal advertisement style. In this way, the optimal advertisement provision method can be selected by analyzing the target audience's past responses. Some or all of the above-described processing in the provision unit may be performed using AI or without AI. For example, the provision unit can input the target audience's past responses to a generation AI and cause the generation AI to select an optimal advertisement style.

[0060] The provision unit can customize the content of advertisements by taking into account current trends of the target audience when providing advertisements. The provision unit can, for example, provide advertisements that reflect current fashion trends. The provision unit can, for example, provide advertisements that reflect current technology trends. The provision unit can, for example, provide advertisements that reflect current entertainment trends. This makes it possible to provide more effective advertisements by taking into account current trends of the target audience. Some or all of the above-described processing in the provision unit may be performed using AI or may be performed without using AI. For example, the provision unit can input data regarding current trends into a generation AI and cause the generation AI to customize the content of advertisements.

[0061] The providing unit can improve the method of providing an advertisement by reflecting user feedback when providing the advertisement. The providing unit can, for example, improve the design of the advertisement based on the feedback provided by the user. The providing unit can, for example, improve the message of the advertisement based on the feedback provided by the user. The providing unit can, for example, improve the method of delivering the advertisement based on the feedback provided by the user. In this way, the method of providing the advertisement can be improved by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the method of providing the advertisement.

[0062] The provision unit can select the optimal provision method when providing advertisements by taking into account the geographical location information of the target audience. For example, if the target audience is in a specific area, the provision unit can provide advertisements related to that area. For example, if the target audience is traveling, the provision unit can provide advertisements related to the travel destination. For example, if the target audience is at home, the provision unit can provide advertisements related to nearby stores and services. This allows for more effective advertisements to be provided by taking into account the geographical location information of the target audience. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the geographical location information of the target audience into the generation AI and cause the generation AI to select the optimal provision method.

[0063] The provision unit can analyze the social media activity of the target audience and suggest content to be provided when providing an advertisement. For example, the provision unit can provide an advertisement that reflects the content of posts that the target audience has "liked" on social media. For example, the provision unit can provide an advertisement that reflects the content of posts of accounts that the target audience follows. For example, the provision unit can provide an advertisement that reflects the content of posts shared by the target audience. In this way, by analyzing the social media activity of the target audience, more effective advertisements can be provided. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the social media activity of the target audience into a generation AI and cause the generation AI to suggest content to be provided.

[0064] The providing unit can customize the advertisement providing method by reflecting the user's past feedback when providing an advertisement. The providing unit, for example, customizes the advertisement design based on feedback provided by the user in the past. The providing unit can customize the advertisement message based on feedback provided by the user in the past. The providing unit can customize the advertisement delivery method based on feedback provided by the user in the past. This makes it possible to customize the advertisement providing method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the advertisement providing method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can predict the effectiveness of an advertisement based on the behavioral data of the target audience. For example, the analysis unit can analyze data from past advertising campaigns to identify what kind of advertisement was most effective. The analysis unit can also analyze the behavioral patterns of the target audience to predict the most effective timing for delivering an advertisement. Furthermore, the analysis unit can analyze the preferences of the target audience to predict what kind of advertising content will attract the most interest. This allows the advertisement suggestion system to develop a more effective advertising strategy.

[0067] The collection unit can adjust the frequency of data collection taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the collection unit can reduce the frequency of data collection to reduce battery consumption. When the remaining battery level is sufficient, the collection unit can increase the frequency of data collection to collect more data. Furthermore, when the user is charging, the collection unit can maximize the frequency of data collection to efficiently collect data. This makes it possible to optimize the frequency of data collection according to the battery status of the user's device.

[0068] The providing unit may adjust the method of providing an advertisement by taking into consideration the user's internet connection status. For example, if the user has a high-speed internet connection, the providing unit may provide a high-quality video advertisement. If the user has a slow internet connection, the providing unit may provide a lightweight text advertisement. Furthermore, if the user is offline, the providing unit may provide a pre-downloaded advertisement. This allows the optimal advertisement providing method to be selected depending on the user's internet connection status.

[0069] The collection unit can adjust the timing of data collection taking into account the usage status of the user's device. For example, when the user is using the device frequently, the collection unit can adjust the timing of data collection so as not to interfere with the user's operations. Furthermore, when the user is not using the device, the collection unit can maximize the timing of data collection and collect data efficiently. Furthermore, when the user is charging the device, the collection unit can optimize the timing of data collection and reduce battery consumption. In this way, the timing of data collection can be optimized according to the usage status of the user's device.

[0070] The providing unit can adjust the method of providing an advertisement by taking into consideration the screen size of the user's device. For example, if the user is using a device with a large screen, the providing unit can provide a visually rich advertisement. Also, if the user is using a device with a small screen, the providing unit can provide a concise and highly visible advertisement. Furthermore, if the user is using multiple devices, the providing unit can provide an optimal advertisement for each device. This makes it possible to select the optimal advertisement providing method according to the screen size of the user's device.

[0071] The collection unit can adjust the data collection method taking into account the security settings of the user's device. For example, if the user has a high security setting, the collection unit can adjust the data collection method to protect the user's privacy. Alternatively, if the user has a low security setting, the collection unit can optimize the data collection method to collect more data. Furthermore, the collection unit can adjust the frequency and scope of data collection according to the user's security setting. This allows the data collection method to be optimized according to the security settings of the user's device.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects data on the behavior, preferences, and trends of the target audience, including social media posts, search history, purchase history, browsing history, and geographic location information. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the interests and needs of the target audience. Specifically, based on the collected data, it identifies the product categories that the target audience is interested in and their purchasing intentions. Step 3: The proposal unit proposes advertising copy based on the analysis results obtained by the analysis unit. Specifically, it uses generative AI to generate advertising copy that matches the interests and needs of the target audience. Step 4: The production department creates the ad based on the ad copy proposed by the proposal department. Specifically, by using the ad copy proposed by the generation AI as is, the time required for ad creation is significantly reduced. Step 5: The distribution department provides the advertising copy created by the production department. Specifically, the created advertising copy is provided to the target audience through media such as websites and social media.

[0074] (Example 2) An advertising proposal system according to an embodiment of the present invention is a system in which a generation AI analyzes the behavior, preferences, and trends of a target audience in real time and proposes optimal advertising copy. The advertising proposal system collects and analyzes the behavior, preferences, and trends of the target audience, proposes advertising copy, and creates and provides advertisements. For example, the advertising proposal system collects data such as social media posting content, search history, and purchase history. The advertising proposal system then analyzes the collected data to understand the interests and needs of the target audience. The advertising proposal system then uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. The advertising proposal system then creates advertisements based on the advertising copy proposed by the generation AI. The advertising proposal system then provides the created advertising copy. This allows the advertising proposal system to deeply understand the psychology and trends of the target audience and develop effective advertising strategies. The advertising proposal system thus analyzes the behavior, preferences, and trends of the target audience in real time and proposes optimal advertising copy, resulting in fast and effective advertising creation. For example, advertising suggestion systems can help companies develop effective advertising strategies by gaining a deep understanding of the psychology and trends of their target audience. AI analysis enables companies to quickly adapt to market changes and maintain or strengthen their competitive edge.

[0075] An advertisement proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a production unit, and a provision unit. The collection unit collects the behavior, preferences, and trends of a target audience. The collection unit can collect data such as social media postings, search histories, and purchase histories. The collection unit can also collect data such as users' browsing histories and geographical location information. The analysis unit analyzes the data collected by the collection unit to understand the interests and needs of the target audience. The analysis unit can, for example, identify the product categories of interest and purchasing intentions of the target audience based on the collected data. The proposal unit proposes advertising copy based on the analysis results obtained by the analysis unit. The proposal unit uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. For example, the generation AI can propose catchy advertising copy related to a particular product to a target audience who is highly interested in that product. The production unit creates advertisements based on the advertising copy proposed by the proposal unit. The production department can significantly reduce the time required for advertisement production by, for example, using the advertising copy proposed by the generation AI as is. The provision department provides the advertising copy created by the production department. The provision department can provide the created advertising copy to the target audience through media such as a website or social media, for example. As a result, the advertisement proposal system according to the embodiment analyzes the behavior, preferences, and trends of the target audience in real time and proposes optimal advertising copy, thereby enabling advertisement production to be carried out quickly and effectively.

[0076] The collection unit can collect data on social media posts, search history, and purchase history. The collection unit, for example, collects social media posts. The social media posts include, but are not limited to, text, images, and videos. The collection unit, for example, collects search history. The search history includes, but is not limited to, the type of search engine and the collection period. The collection unit, for example, collects purchase history. The purchase history includes, but is not limited to, online shopping history and in-store purchase history. By collecting data such as social media posts, search history, and purchase history, the behavior and preferences of the target audience can be understood in detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on social media posts, search history, and purchase history into a generation AI and cause the generation AI to collect the data.

[0077] The analysis unit can analyze the collected data to understand the interests and needs of the target audience. For example, the analysis unit can analyze the collected data to understand the interests and needs of the target audience. For example, the analysis unit can identify the product categories of interest and purchasing intentions of the target audience based on the collected data. The analysis unit can also identify the topics of interest and the strength of interest of the target audience based on the collected data. Furthermore, the analysis unit can identify the purchasing patterns and behavioral patterns of the target audience based on the collected data. In this way, the interests and needs of the target audience can be accurately understood by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI understand the interests and needs of the target audience.

[0078] The suggestion unit can generate advertising copy tailored to the interests and needs of the target audience. The suggestion unit, for example, uses a generation AI to generate advertising copy tailored to the interests and needs of the target audience. For example, the generation AI can suggest catchy advertising copy related to a particular product to a target audience who is highly interested in that product. The suggestion unit can also use the generation AI to customize the format and style of the advertising copy based on the interests and needs of the target audience. Furthermore, the suggestion unit can also use the generation AI to adjust the length and content of the advertising copy based on the interests and needs of the target audience. This increases the effectiveness of the advertisement by generating advertising copy tailored to the interests and needs of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the suggestion unit can input data related to the interests and needs of the target audience into the generation AI and cause the generation AI to generate advertising copy.

[0079] The production department can create advertisements based on the advertising text proposed by the generation AI. For example, the production department creates advertisements based on the advertising text proposed by the generation AI. For example, the production department can significantly reduce the time required for advertisement creation by using the advertising text proposed by the generation AI as is. The production department can also adjust the design and layout of the advertisement based on the advertising text proposed by the generation AI. Furthermore, the production department can select the advertisement distribution method and media based on the advertising text proposed by the generation AI. In this way, by creating advertisements based on the advertising text proposed by the generation AI, the time required for advertisement creation can be significantly reduced. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can have AI adjust the design and layout of the advertisement based on the advertising text proposed by the generation AI.

[0080] The provision unit can provide the created advertising copy. For example, the provision unit can provide the created advertising copy to the target audience through a medium such as a website or social media. For example, the provision unit can also send the created advertising copy directly to the target audience via email or a messaging app. The provision unit can also distribute the created advertising copy through an advertising network. Furthermore, the provision unit can also provide the created advertising copy in real time. As a result, by providing the created advertising copy, the advertisement can be effectively delivered to the target audience. Some or all of the above-mentioned processing in the provision unit may be performed using AI or may be performed without using AI. For example, the provision unit can cause AI to select the medium and timing for distributing the created advertising copy.

[0081] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. For example, if the user is excited, the collection unit can collect data in real time and immediately send it to analysis. For example, if the user is relaxed, the collection unit can periodically collect data and obtain stable data. This allows data collection at more appropriate times by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0082] The collection unit can collect the user's browsing history in addition to the content of social media posts, search history, and purchase history. For example, the collection unit can record the URLs of websites visited by the user and the browsing time to analyze the user's preferences. For example, the collection unit can track the advertisements and links clicked by the user to identify topics of interest. For example, the collection unit can analyze the categories of websites frequently visited by the user to identify areas of interest. In this way, by collecting the user's browsing history, more detailed preferences and interests can be identified. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's browsing history into the generation AI and have the generation AI collect the data.

[0083] When collecting data, the collection unit can select the optimal collection means by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit can collect data through a mobile app. For example, if the user is using a desktop, the collection unit can collect data through a browser extension. For example, if the user is using a smartwatch, the collection unit can collect data from a wearable device. This allows the optimal collection means to be selected by taking into account the user's device information. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's device information into the generation AI and cause the generation AI to select the optimal collection means.

[0084] When collecting data, the collection unit can analyze the user's past behavioral patterns and determine the priority of the data to be collected. For example, the collection unit can prioritize collecting keywords that the user has frequently searched for in the past. For example, the collection unit can prioritize collecting categories of products that the user has purchased in the past. For example, the collection unit can prioritize collecting data on websites on which the user has spent a lot of time in the past. This allows the priority of the data to be collected to be determined by analyzing the user's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral patterns into a generation AI and have the generation AI determine the priority of the data.

[0085] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation. For example, if the user is excited, the collection unit can prioritize collecting data related to entertainment. For example, if the user is relaxed, the collection unit can prioritize collecting data related to daily life. This allows for more appropriate data to be collected by adjusting the type of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of data to be collected.

[0086] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can collect event information related to that area. For example, when the user is traveling, the collection unit can collect information about tourist spots and restaurants. For example, when the user is at home, the collection unit can collect information about nearby stores and services. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0087] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit can collect the content of posts that the user has "liked" on social media. For example, the collection unit can collect the content of posts from accounts the user follows. For example, the collection unit can collect the content of posts shared by the user. This makes it possible to collect related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0088] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit, for example, can preferentially use data collection methods for which the user has provided favorable feedback in the past. The collection unit, for example, can avoid data collection methods for which the user has provided negative feedback in the past. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. For example, if the user is in a hurry, the analysis unit can perform a quick analysis and provide concise results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows for adjusting the analysis algorithm based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using AI or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0090] When analyzing collected data, the analysis unit can improve the accuracy of the analysis by taking into account the user's past behavioral patterns. The analysis unit improves the accuracy of the analysis, for example, based on behaviors the user frequently performed in the past. The analysis unit can identify products of interest to the user, for example, by taking into account the user's past purchase history. The analysis unit can identify topics of interest to the user, for example, based on the user's past search history. This improves the accuracy of the analysis by taking into account the user's past behavioral patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral patterns into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] During analysis, the analysis unit can apply different analysis methods depending on the category of data. For example, the analysis unit can apply a purchase pattern analysis method to purchase history data. For example, the analysis unit can apply a search trend analysis method to search history data. For example, the analysis unit can apply a sentiment analysis method to SNS post data. In this way, by applying different analysis methods depending on the category of data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to apply the analysis method depending on the category of data.

[0092] During analysis, the analysis unit can customize the analysis results by taking into account the user's attribute information. The analysis unit can, for example, identify topics of interest according to the user's age. The analysis unit can, for example, identify products of interest according to the user's gender. The analysis unit can, for example, identify events of interest according to the user's place of residence. This allows the analysis results to be customized by taking into account the user's attribute information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute information into the generation AI and have the generation AI customize the analysis results.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results based on the user's emotions to provide a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data to grasp real-time trends. The analysis unit, for example, can analyze past data to grasp long-term trends. The analysis unit, for example, can prioritize analysis of data submitted during a specific period to grasp seasonal trends. In this way, real-time trends can be grasped by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can postpone analysis of data with low relevance. For example, the analysis unit can group and analyze highly relevant data. This allows the analysis order to be adjusted based on the relevance of the data, so that more relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, the analysis unit can provide detailed analysis results to a user with high expertise. For example, the analysis unit can provide concise analysis results to a user with low expertise. For example, the analysis unit can customize the display method of the analysis results according to the user's level of expertise. This allows for adjusting the level of detail of the analysis results according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the analysis results.

[0097] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions using soft expressions. For example, if the user is in a hurry, the suggestion unit can make suggestions using concise and direct expressions. For example, if the user is excited, the suggestion unit can make suggestions using visually appealing expressions. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0098] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the interest level of the target audience. For example, the suggestion unit can make a detailed proposal to a target audience with a high interest level. For example, the suggestion unit can make a concise proposal to a target audience with a low interest level. For example, the suggestion unit can customize the content of the proposal according to the interest level. As a result, more effective proposals can be made by adjusting the level of detail of the proposal based on the interest level of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the interest level of the target audience to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0099] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the target audience category. For example, the suggestion unit can apply a trend-sensitive proposal algorithm to younger generations. For example, the suggestion unit can apply a stability-oriented proposal algorithm to older generations. For example, the suggestion unit can apply a specialized proposal algorithm to groups with specific interests. This allows for more appropriate proposals by applying different proposal algorithms depending on the target audience category. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the target audience category into the generation AI and cause the generation AI to apply the proposal algorithm.

[0100] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit makes similar proposals based on proposals that the user has accepted in the past. For example, the suggestion unit can avoid proposals that the user has rejected in the past. For example, the suggestion unit can analyze the user's past proposal results and make optimal suggestions. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions to be made by adjusting the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0102] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the behavioral history of the target audience. For example, the suggestion unit can prioritize suggestions based on behavior that the target audience frequently performed in the past. For example, the suggestion unit can prioritize suggestions that are of interest to the target audience, taking into account their past purchasing history. For example, the suggestion unit can prioritize suggestions that are of interest to the target audience, based on their past search history. This allows for more effective suggestions by determining the priority of suggestions based on the behavioral history of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the behavioral history of the target audience into the generation AI and have the generation AI determine the priority of the suggestions.

[0103] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the target audience. For example, the suggestion unit can prioritize proposals for target audiences with high relevance. For example, the suggestion unit can postpone proposals for target audiences with low relevance. For example, the suggestion unit can group highly relevant target audiences and make proposals. This allows for more effective proposals to be made by adjusting the order of proposals based on the relevance of the target audience. Some or all of the above-mentioned processing in the suggestion unit can be performed using a generation AI. For example, the suggestion unit can input the relevance of the target audience into the generation AI and cause the generation AI to adjust the order of proposals.

[0104] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can make proposals that use a lot of technical terminology for a user with high level of expertise. For example, the suggestion unit can make proposals that are concise and easy to understand for a user with low level of expertise. The suggestion unit can customize the content of the proposal according to the user's level of expertise. This allows for more appropriate proposals to be made by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal.

[0105] The production department can estimate the user's emotions and adjust the advertisement production method based on the estimated user emotions. For example, if the user is relaxed, the production department can produce an advertisement in a soft tone. For example, if the user is in a hurry, the production department can produce a concise and direct advertisement. For example, if the user is excited, the production department can produce a visually stimulating advertisement. This allows for the production of more effective advertisements by adjusting the advertisement production method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the production department can be performed using AI or without AI. For example, the production department can input user emotion data into the generation AI and have the generation AI adjust the advertisement production method.

[0106] When producing an advertisement, the production department can analyze the target audience's past responses and select the optimal production method. For example, the production department can reuse an advertisement style to which the target audience responded favorably in the past. For example, the production department can avoid an advertisement style to which the target audience responded negatively in the past. For example, the production department can analyze the target audience's past responses and select the optimal advertisement style. In this way, the optimal advertisement production method can be selected by analyzing the target audience's past responses. Some or all of the above-mentioned processing in the production department may be performed using AI or without AI. For example, the production department can input the target audience's past responses into a generation AI and have the generation AI select the optimal advertisement style.

[0107] When producing advertisements, the production department can customize the production content by taking into account current trends among the target audience. For example, the production department can produce advertisements that reflect current fashion trends. For example, the production department can produce advertisements that reflect current technology trends. For example, the production department can produce advertisements that reflect current entertainment trends. This allows for the production of more effective advertisements by taking into account current trends among the target audience. Some or all of the above-described processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input data regarding current trends into a generation AI and have the generation AI customize the production content.

[0108] The production department can improve the production method by reflecting user feedback when producing an advertisement. For example, the production department can improve the design of the advertisement based on the feedback provided by the user. For example, the production department can improve the message of the advertisement based on the feedback provided by the user. For example, the production department can improve the advertisement delivery method based on the feedback provided by the user. In this way, the advertisement production method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input user feedback into a generation AI and have the generation AI improve the production method.

[0109] The production department can estimate a user's emotions and prioritize advertisement production based on the estimated user emotions. For example, if the user is excited, the production department can immediately create and distribute an advertisement. For example, if the user is relaxed, the production department can create an advertisement on a regular schedule. For example, if the user is stressed, the production department can temporarily postpone advertisement production. This allows for more effective advertisements to be produced by prioritizing advertisement production based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the production department can be performed using AI or without AI. For example, the production department can input user emotion data into the generation AI and have the generation AI determine the priority of advertisement production.

[0110] When producing an advertisement, the production department can select the optimal production method by taking into account the geographic location information of the target audience. For example, if the target audience is in a specific area, the production department can produce an advertisement related to that area. For example, if the target audience is traveling, the production department can produce an advertisement related to the travel destination. For example, if the target audience is at home, the production department can produce an advertisement related to nearby stores and services. This allows for the production of more effective advertisements by taking into account the geographic location information of the target audience. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can input the geographic location information of the target audience into the generation AI and have the generation AI select the optimal production method.

[0111] When creating an advertisement, the production department can analyze the social media activity of the target audience and suggest production content. For example, the production department can create an advertisement that reflects the content of posts that the target audience has "liked" on social media. For example, the production department can create an advertisement that reflects the content of posts from accounts that the target audience follows. For example, the production department can create an advertisement that reflects the content of posts shared by the target audience. In this way, by analyzing the social media activity of the target audience, more effective advertisements can be created. Some or all of the above-mentioned processing in the production department may be performed using AI, or may be performed without using AI. For example, the production department can input the social media activity of the target audience into a generation AI and have the generation AI execute production content suggestions.

[0112] When producing an advertisement, the production department can customize the production method by reflecting the user's past feedback. For example, the production department customizes the advertisement design based on feedback provided by the user in the past. For example, the production department can customize the advertisement message based on feedback provided by the user in the past. For example, the production department can customize the advertisement delivery method based on feedback provided by the user in the past. In this way, the advertisement production method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the production department may be performed using AI or may be performed without using AI. For example, the production department can input the user's past feedback into a generation AI and have the generation AI customize the production method.

[0113] The providing unit can estimate the user's emotions and adjust the method of providing advertisements based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide an advertisement in a soft tone. For example, if the user is in a hurry, the providing unit can provide a concise and direct advertisement. For example, if the user is excited, the providing unit can provide a visually stimulating advertisement. This allows for more effective advertisements to be provided by adjusting the method of providing advertisements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input user emotion data into the generation AI and cause the generation AI to adjust the method of providing advertisements.

[0114] When providing an advertisement, the provision unit can select an optimal provision method by analyzing the target audience's past responses. For example, the provision unit reuses an advertisement style to which the target audience responded favorably in the past. For example, the provision unit can avoid an advertisement style to which the target audience responded negatively in the past. For example, the provision unit can analyze the target audience's past responses and select an optimal advertisement style. In this way, the optimal advertisement provision method can be selected by analyzing the target audience's past responses. Some or all of the above-described processing in the provision unit may be performed using AI or without AI. For example, the provision unit can input the target audience's past responses to a generation AI and cause the generation AI to select an optimal advertisement style.

[0115] The provision unit can customize the content of advertisements by taking into account current trends of the target audience when providing advertisements. The provision unit can, for example, provide advertisements that reflect current fashion trends. The provision unit can, for example, provide advertisements that reflect current technology trends. The provision unit can, for example, provide advertisements that reflect current entertainment trends. This makes it possible to provide more effective advertisements by taking into account current trends of the target audience. Some or all of the above-described processing in the provision unit may be performed using AI or may be performed without using AI. For example, the provision unit can input data regarding current trends into a generation AI and cause the generation AI to customize the content of advertisements.

[0116] The providing unit can improve the method of providing an advertisement by reflecting user feedback when providing the advertisement. The providing unit can, for example, improve the design of the advertisement based on the feedback provided by the user. The providing unit can, for example, improve the message of the advertisement based on the feedback provided by the user. The providing unit can, for example, improve the method of delivering the advertisement based on the feedback provided by the user. In this way, the method of providing the advertisement can be improved by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the method of providing the advertisement.

[0117] The providing unit can estimate the user's emotions and determine the priority of advertisement provision based on the estimated user's emotions. For example, if the user is excited, the providing unit can immediately provide advertisements. For example, if the user is relaxed, the providing unit can provide advertisements on a regular schedule. For example, if the user is stressed, the providing unit can temporarily postpone advertisement provision. This allows for more effective advertisement provision by determining the priority of advertisement provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user emotion data into the generation AI and cause the generation AI to determine the priority of advertisement provision.

[0118] The provision unit can select the optimal provision method when providing advertisements by taking into account the geographical location information of the target audience. For example, if the target audience is in a specific area, the provision unit can provide advertisements related to that area. For example, if the target audience is traveling, the provision unit can provide advertisements related to the travel destination. For example, if the target audience is at home, the provision unit can provide advertisements related to nearby stores and services. This allows for more effective advertisements to be provided by taking into account the geographical location information of the target audience. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the geographical location information of the target audience into the generation AI and cause the generation AI to select the optimal provision method.

[0119] The provision unit can analyze the social media activity of the target audience and suggest content to be provided when providing an advertisement. For example, the provision unit can provide an advertisement that reflects the content of posts that the target audience has "liked" on social media. For example, the provision unit can provide an advertisement that reflects the content of posts of accounts that the target audience follows. For example, the provision unit can provide an advertisement that reflects the content of posts shared by the target audience. In this way, by analyzing the social media activity of the target audience, more effective advertisements can be provided. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input the social media activity of the target audience into a generation AI and cause the generation AI to suggest content to be provided.

[0120] The providing unit can customize the advertisement providing method by reflecting the user's past feedback when providing an advertisement. The providing unit, for example, customizes the advertisement design based on feedback provided by the user in the past. The providing unit can customize the advertisement message based on feedback provided by the user in the past. The providing unit can customize the advertisement delivery method based on feedback provided by the user in the past. This makes it possible to customize the advertisement providing method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the advertisement providing method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, production unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the behaviors and preferences of the target audience using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the interests and needs of the target audience. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates advertising copy using a generation AI. The production unit is realized by the control unit 46A of the smart device 14 and creates advertisements based on the generated advertising copy. The provision unit provides the advertising copy through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, production unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the behaviors and preferences of the target audience using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the interests and needs of the target audience. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates advertising copy using a generation AI. The production unit is realized by the control unit 46A of the smart glasses 214 and creates advertisements based on the generated advertising copy. The provision unit provides the advertising copy through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, production unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect the behaviors and preferences of the target audience using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the interests and needs of the target audience. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates advertising copy using a generation AI. The production unit is realized by the control unit 46A of the headset type terminal 314 and creates advertisements based on the generated advertising copy. The provision unit provides the advertising copy through the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, production unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the behaviors and preferences of the target audience using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the interests and needs of the target audience. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates advertising copy using a generation AI. The production unit is realized by the control unit 46A of the robot 414 and creates advertisements based on the generated advertising copy. The provision unit provides the advertising copy through the speaker 240 of the robot 414.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can predict the effectiveness of an advertisement based on the behavioral data of the target audience. For example, the analysis unit can analyze data from past advertising campaigns to identify what kind of advertisement was most effective. The analysis unit can also analyze the behavioral patterns of the target audience to predict the most effective timing for delivering an advertisement. Furthermore, the analysis unit can analyze the preferences of the target audience to predict what kind of advertising content will attract the most interest. This allows the advertisement suggestion system to develop a more effective advertising strategy.

[0123] The collection unit can adjust the frequency of data collection taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the collection unit can reduce the frequency of data collection to reduce battery consumption. When the remaining battery level is sufficient, the collection unit can increase the frequency of data collection to collect more data. Furthermore, when the user is charging, the collection unit can maximize the frequency of data collection to efficiently collect data. This makes it possible to optimize the frequency of data collection according to the battery status of the user's device.

[0124] The suggestion unit can estimate the emotions of the target audience and adjust the tone of the advertising copy based on the estimated emotions. For example, if the target audience is relaxed, the suggestion unit can generate advertising copy with a soft tone. If the target audience is excited, the suggestion unit can generate advertising copy with an energetic tone. Furthermore, if the target audience is stressed, the suggestion unit can generate advertising copy with a tone that gives a sense of security. This makes it possible to increase the effectiveness of advertising by generating advertising copy that matches the emotions of the target audience.

[0125] The providing unit may adjust the method of providing an advertisement by taking into consideration the user's internet connection status. For example, if the user has a high-speed internet connection, the providing unit may provide a high-quality video advertisement. If the user has a slow internet connection, the providing unit may provide a lightweight text advertisement. Furthermore, if the user is offline, the providing unit may provide a pre-downloaded advertisement. This allows the optimal advertisement providing method to be selected depending on the user's internet connection status.

[0126] The analysis unit can estimate the emotions of the target audience and evaluate the effectiveness of the advertisement based on the estimated emotions. For example, the analysis unit can analyze the emotions felt by the target audience after viewing the advertisement and evaluate the effectiveness of the advertisement. The analysis unit can also analyze changes in the emotions of the target audience before and after viewing the advertisement and evaluate the impact of the advertisement. Furthermore, the analysis unit can identify areas for improvement in the advertisement based on the emotional data of the target audience. This allows for a more accurate evaluation of the effectiveness of the advertisement based on the emotions of the target audience.

[0127] The collection unit can adjust the timing of data collection taking into account the usage status of the user's device. For example, when the user is using the device frequently, the collection unit can adjust the timing of data collection so as not to interfere with the user's operations. Furthermore, when the user is not using the device, the collection unit can maximize the timing of data collection and collect data efficiently. Furthermore, when the user is charging the device, the collection unit can optimize the timing of data collection and reduce battery consumption. In this way, the timing of data collection can be optimized according to the usage status of the user's device.

[0128] The suggestion unit can estimate the emotions of the target audience and adjust the timing of advertisement delivery based on the estimated emotions. For example, if the target audience is relaxed, the suggestion unit can adjust the timing of advertisement delivery so that the target audience can receive the advertisement in a relaxed state. Furthermore, if the target audience is excited, the suggestion unit can adjust the timing of advertisement delivery so that the target audience can receive the advertisement in an excited state. Furthermore, if the target audience is feeling stressed, the suggestion unit can adjust the timing of advertisement delivery so that the target audience can receive an advertisement that reduces stress. In this way, the effectiveness of the advertisement can be increased by adjusting the timing of advertisement delivery to match the emotions of the target audience.

[0129] The providing unit can adjust the method of providing an advertisement by taking into consideration the screen size of the user's device. For example, if the user is using a device with a large screen, the providing unit can provide a visually rich advertisement. Also, if the user is using a device with a small screen, the providing unit can provide a concise and highly visible advertisement. Furthermore, if the user is using multiple devices, the providing unit can provide an optimal advertisement for each device. This makes it possible to select the optimal advertisement providing method according to the screen size of the user's device.

[0130] The analysis unit can estimate the emotions of the target audience and improve the accuracy of ad targeting based on the estimated emotions. For example, if the target audience has a particular emotion, the analysis unit can preferentially display ads related to that emotion. The analysis unit can also adjust the ad targeting algorithm based on the emotion data of the target audience. Furthermore, the analysis unit can track changes in the emotions of the target audience in real time and continuously improve the accuracy of ad targeting. This makes it possible to improve the accuracy of ad targeting based on the emotions of the target audience.

[0131] The collection unit can adjust the data collection method taking into account the security settings of the user's device. For example, if the user has a high security setting, the collection unit can adjust the data collection method to protect the user's privacy. Alternatively, if the user has a low security setting, the collection unit can optimize the data collection method to collect more data. Furthermore, the collection unit can adjust the frequency and scope of data collection according to the user's security setting. This allows the data collection method to be optimized according to the security settings of the user's device.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection department collects data on the behavior, preferences, and trends of the target audience, including social media posts, search history, purchase history, browsing history, and geographic location information. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the interests and needs of the target audience. Specifically, based on the collected data, it identifies the product categories that the target audience is interested in and their purchasing intentions. Step 3: The proposal unit proposes advertising copy based on the analysis results obtained by the analysis unit. Specifically, it uses generative AI to generate advertising copy that matches the interests and needs of the target audience. Step 4: The production department creates the ad based on the ad copy proposed by the proposal department. Specifically, by using the ad copy proposed by the generation AI as is, the time required for ad creation is significantly reduced. Step 5: The distribution department provides the advertising copy created by the production department. Specifically, the created advertising copy is provided to the target audience through media such as websites and social media.

[0134] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects the behavior, preferences, and trends of a target audience; an analysis unit that analyzes the data collected by the collection unit and understands the interests and needs of the target audience; a proposal unit that proposes advertising copy based on the analysis results obtained by the analysis unit; a production unit that creates advertisements based on the advertising copy proposed by the proposal unit; and a provision unit that provides the advertising copy created by the production unit.

2. The system according to claim 1 , wherein the collection unit collects data on social networking site posts, search history, and purchase history.

3. The analysis unit Analyze the collected data to understand the interests and needs of your target audience 2. The system of claim 1.

4. The proposal unit Generate ad copy tailored to the interests and needs of your target audience 2. The system of claim 1.

5. The production department: Advertisements are created based on the advertising wording suggested by the generative AI.

2. The system of claim 1.

6. The providing unit Provide the created advertising copy 2. The system of claim 1.

7. The system according to claim 1 , wherein the collection unit estimates a user's emotion and adjusts the timing of data collection based on the estimated user's emotion.

8. The collecting unit Collects user browsing history in addition to social media posts, search history, and purchase history.

2. The system of claim 1.

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