system

Abstract

The system according to this embodiment aims to acquire advertising patterns and effectiveness measurement data of competing companies in real time and to improve the company's advertising strategy based on the analysis results. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit acquires advertising patterns and effectiveness measurement data of competing companies in real time. The analysis unit analyzes the data collected by the data collection unit and identifies the success and failure factors of competitors. The proposal unit proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to obtain and analyze the advertising submission patterns and effect measurement data of competing companies in real time, and there is a problem that it is difficult to formulate a rapid strategy.

[0005] The system according to the embodiment aims to obtain the advertising submission patterns and effect measurement data of competing companies in real time and improve the company's own advertising strategy based on the analysis results.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit acquires advertising patterns and effectiveness measurement data of competing companies in real time. The analysis unit analyzes the data collected by the data collection unit to identify the success and failure factors of competitors. The proposal unit proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can acquire advertising patterns and effectiveness measurement data of competing companies in real time, and improve its own advertising strategy based on the analysis results. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agent system according to an embodiment of the present invention is a system that utilizes generative AI to monitor and analyze the digital advertising activities of competitors and to reveal the actions and trends of competitors. This agent system acquires advertising patterns and effectiveness measurement data of competitors in real time, and the generative AI analyzes this data to identify the success and failure factors of competitors. Furthermore, based on the analysis results, the generative AI proposes specific improvement plans for the company's advertising strategy. This mechanism allows marketing teams to quickly formulate appropriate strategies and maximize the effectiveness of their advertising. For example, the agent system acquires advertising patterns and effectiveness measurement data of competitors in real time. At this time, it grasps what kind of creatives competitors are using on which platforms. For example, it collects data such as what kind of ads competitors are running on social media and which ads are getting high click-through rates. Next, the agent system analyzes the collected data using generative AI. The generative AI analyzes the advertising patterns and effectiveness measurement data of competitors and identifies success and failure factors. For example, if a particular creative is getting a high click-through rate, it analyzes the elements of that creative and identifies the success factors. Furthermore, the agent system, based on the analysis results, has the generative AI propose specific improvement plans for the company's advertising strategy. For example, the system proposes new advertising creatives that incorporate the success factors of competitors. It also proposes strategies to avoid the failure factors of competitors. This mechanism allows marketing teams to quickly develop appropriate strategies and maximize the effectiveness of their advertising. For instance, it enables the rapid creation of advertising creatives that incorporate the success factors of competitors, allowing for effective targeting of the target audience. Furthermore, by avoiding the failure factors of competitors, advertising effectiveness can be maximized. As a result, the agent system monitors and analyzes the advertising activities of competitors, revealing competitor trends and strategies, which enables differentiation of marketing strategies and effective targeting.

[0029] The agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit acquires advertising patterns and effectiveness measurement data of competitor companies in real time. For example, the data collection unit can understand what kind of creatives competitor companies are using on which platforms. For example, the data collection unit can collect advertising data on social media and understand which ads are getting high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks. For example, the data collection unit can analyze advertising patterns and understand the frequency and timing of ad placements. Furthermore, the data collection unit can collect effectiveness measurement data and acquire data such as click-through rates, conversion rates, and impressions. The analysis unit analyzes the data collected by the data collection unit and identifies the success and failure factors of competitors. For example, if a particular creative is getting a high click-through rate, the analysis unit analyzes the elements of that creative and identifies the success factors. For example, the analysis unit can use generative AI to analyze the collected data and identify success and failure factors. The generation AI can analyze elements of advertising creatives using, for example, text generation AI (e.g., LLM). The analysis unit can also analyze elements of advertising creatives using multimodal generation AI. The analysis unit can analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. The proposal unit proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis unit. The proposal unit can propose new advertising creatives that incorporate the success factors of competitors. The proposal unit can generate new advertising creatives that incorporate the success factors of competitors using, for example, generation AI. The proposal unit can also propose strategies to avoid the failure factors of competitors. The proposal unit can generate strategies to avoid the failure factors of competitors using, for example, generation AI.As a result, the agent system according to this embodiment can monitor and analyze the advertising activities of competing companies, clarify the actions and trends of competitors, and thereby enable differentiation of marketing strategies and effective targeting.

[0030] The data collection department acquires real-time data on competitors' advertising patterns and performance measurement data. Specifically, the department collects data from multiple platforms, including social media, search engines, and display advertising networks, to understand what kind of creative content competitors are using on which platforms. For example, on social media, it can collect advertising data to understand which ads are achieving high click-through rates. This allows for a detailed understanding of what kind of creative content competitors are using and what kind of ads they are running on which platforms. The data collection department can also analyze advertising patterns to understand the frequency and timing of ad placements. For example, if ads placed during specific time slots are achieving high click-through rates, focusing on those time slots can help develop effective advertising strategies. Furthermore, the data collection department can collect performance measurement data, obtaining data such as click-through rates, conversion rates, and impressions. This allows for a detailed analysis of the effectiveness of competitors' ads and the ability to reflect this in one's own advertising strategy. The data collection department centrally manages this data and stores it in a database that is updated in real time. This allows the analysis and proposal departments to quickly access the necessary data. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify the success and failure factors of competitors. Specifically, if a particular creative is achieving a high click-through rate, the analysis unit analyzes the elements of that creative to identify the success factors. For example, a generative AI can be used to analyze the collected data and identify the success and failure factors. The generative AI can use text generation AI (e.g., LLM) to analyze the elements of an advertising creative. This allows for detailed identification of success factors such as the ad's wording, design, and targeting elements. The analysis unit can also use a multimodal generative AI to analyze the elements of an advertising creative. Because the multimodal generative AI can analyze multiple data formats such as images and videos as well as text, it can analyze the overall effect of the advertising creative in detail. Furthermore, the analysis unit can analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. For example, if a particular ad is achieving a high conversion rate, the analysis unit can analyze the elements of that ad in detail to identify the success factors. This allows the analysis unit to quickly and accurately analyze the collected data and identify the success and failure factors of competitors. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the effectiveness of specific creatives and targeting based on past advertising data and formulate future advertising strategies. This allows the analytics department to not only grasp the situation in real time but also to formulate long-term advertising strategies, improving the reliability and effectiveness of the entire system.

[0032] The proposal department will propose specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis department. Specifically, they will propose new advertising creatives that incorporate the success factors of competitors. For example, they can use generative AI to generate new advertising creatives that incorporate the success factors of competitors. The generative AI can use text generation AI (e.g., LLM) to generate advertising copy that reflects the success factors of competitors. They can also use image generation AI to generate advertising designs that reflect the success factors of competitors. This allows the proposal department to quickly propose effective advertising creatives that incorporate the success factors of competitors. Furthermore, the proposal department can also propose strategies to avoid the failure factors of competitors. For example, they can use generative AI to generate strategies to avoid the failure factors of competitors. Based on the collected data, the generative AI can identify the failure factors of competitors and propose specific strategies to avoid those factors. This allows the proposal department to quickly propose effective strategies to avoid the failure factors of competitors. In addition, based on the analysis results, the proposal department can propose specific improvement plans such as reviewing the timing, frequency, and targeting of advertising. For example, if a company is achieving a high click-through rate during a specific time period, a strategy can be proposed to concentrate advertising during that time. Furthermore, by suggesting effective ad creatives for specific target audiences, the accuracy of targeting can be improved. This allows the proposal department to quickly propose concrete improvement plans based on analysis results, maximizing the effectiveness of their advertising strategy.

[0033] The data collection unit can understand what kind of creative content competitors are using on which platforms. For example, the data collection unit can collect advertising data from social media to understand which ads are getting high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks. For example, the data collection unit can analyze advertising patterns to understand the frequency and timing of ad placements. This allows for a more detailed understanding of competitors' advertising activities, enabling the development of more effective marketing strategies. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input competitors' advertising data into a generating AI and have the generating AI perform the data analysis.

[0034] The analysis unit analyzes the collected data and, if a particular creative is achieving a high click-through rate, it can analyze the elements of that creative and identify the factors contributing to its success. For example, the analysis unit can use a generative AI to analyze the collected data and identify factors contributing to success and failure. The generative AI can, for example, use a text generation AI (e.g., LLM) to analyze the elements of an advertising creative. The analysis unit can also use a multimodal generation AI to analyze the elements of an advertising creative. The analysis unit can, for example, analyze performance measurement data such as click-through rates and conversion rates to identify factors contributing to success and failure. By identifying factors contributing to success, it is possible to obtain reference information for creating effective advertising creatives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI identify factors contributing to success and failure.

[0035] The proposal department can propose new advertising creatives that incorporate the success factors of competitors. For example, the proposal department can use a generative AI to generate new advertising creatives that incorporate the success factors of competitors. The generative AI can use a text generation AI (e.g., LLM) to generate new advertising creatives. The proposal department can also use a multimodal generative AI to generate new advertising creatives. This allows companies to improve the effectiveness of their own advertising by incorporating the success factors of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the success factors of competitors into a generative AI and have the generative AI generate new advertising creatives.

[0036] The proposal department can propose strategies to avoid the failure factors of competitors. The proposal department can generate strategies to avoid the failure factors of competitors using, for example, generative AI. The generative AI can generate strategies to avoid failure factors using, for example, text generation AI (e.g., LLM). The proposal department can also generate strategies to avoid failure factors using multimodal generative AI. This reduces the risk of one's own advertising strategy by avoiding the failure factors of competitors. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the failure factors of competitors into the generative AI and have the generative AI generate strategies to avoid those failure factors.

[0037] The data collection unit can analyze the advertising history of competitors and select the optimal data collection method. For example, the data collection unit can analyze the past advertising history of competitors and select an effective data collection method. For example, the data collection unit can optimize the data collection method on a specific platform based on the advertising history of competitors. The data collection unit can also determine the priority of data to collect based on the advertising history of competitors. This allows for the selection of an effective data collection method by analyzing the advertising history of competitors. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the advertising history of competitors into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter data based on the current marketing campaigns and areas of interest of competitors during data collection. For example, the data collection unit can analyze the current marketing campaigns of competitors and prioritize the collection of relevant data. For example, the data collection unit can filter the data to be collected based on the areas of interest of competitors. The data collection unit can also select the data to be collected considering the progress of competitors' marketing campaigns. This allows for the priority collection of highly relevant data by filtering the data based on the current marketing campaigns and areas of interest of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input competitor marketing campaign data into a generating AI and have the generating AI perform data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of competitors during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of competitors. For example, the data collection unit can adjust the range of data to be collected by considering the geographical location information of competitors. The data collection unit can also determine the priority of data to be collected based on the geographical location information of competitors. This allows for the efficient collection of highly relevant data by considering the geographical location information of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of competitors into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze the social media activities of competitors and collect relevant data during data collection. For example, the data collection unit can analyze the social media activities of competitors and prioritize the collection of relevant data. For example, the data collection unit can grasp the trends in the social media activities of competitors and select the data to collect. The data collection unit can also determine the priority of the data to collect based on the social media activities of competitors. This allows for the efficient collection of relevant data by analyzing the social media activities of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the social media activities of competitors into a generating AI and have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the advertisements during the analysis. For example, the analysis unit can perform a detailed analysis for important advertisements, and a simplified analysis for less important advertisements. The analysis unit can also determine the priority of the analysis according to the importance of the advertisements. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the advertisements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the ad category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the ad category. For example, the analysis unit can improve accuracy by applying different analysis algorithms for each ad category. The analysis unit can also adjust the way the analysis results are presented based on the ad category. This improves analysis accuracy by applying the optimal analysis algorithm according to the ad category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ad category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the timing of advertisement placement during the analysis. For example, the analysis unit prioritizes the analysis of the most recent advertisement. The analysis unit can determine the priority of analysis based on the timing of advertisement placement. The analysis unit can also adjust the level of detail of the analysis according to the timing of advertisement placement. This enables rapid analysis of the most recent advertisement by determining the priority of analysis based on the timing of advertisement placement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement placement timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the advertisements during the analysis. For example, the analysis unit determines the order of analysis based on the relevance of the advertisements. For example, the analysis unit can prioritize the analysis of advertisements with high relevance. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the advertisements. This allows for the prioritization of highly relevant advertisements by adjusting the order of analysis based on the relevance of the advertisements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The proposal unit can adjust the level of detail of its proposals based on the success factors of the advertisement. For example, the proposal unit can provide detailed proposals based on the success factors of the advertisement. For example, the proposal unit can provide concise proposals that incorporate the success factors of the advertisement. The proposal unit can also determine the priority of proposals according to the success factors of the advertisement. This allows for more effective proposals by adjusting the level of detail based on the success factors of the advertisement. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input advertising success factor data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0046] The proposal unit can apply different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the ad category. For example, the proposal unit can improve accuracy by applying different proposal algorithms for each ad category. The proposal unit can also adjust how the proposal results are presented based on the ad category. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the ad category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input ad category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0047] The proposal department can determine the priority of proposals based on the timing of advertisement placement. For example, the proposal department will prioritize proposals for the most recent advertisements. The proposal department can also adjust the level of detail of proposals according to the timing of advertisement placement. This allows for quick proposals for the most recent advertisements by determining the priority of proposals based on the timing of advertisement placement. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input advertisement placement timing data into a generating AI and have the generating AI determine the priority of proposals.

[0048] The proposal unit can adjust the order of proposals based on the relevance of the advertisements when making a proposal. For example, the proposal unit determines the order of proposals based on the relevance of the advertisements. For example, the proposal unit can prioritize proposing advertisements that are highly relevant. The proposal unit can also adjust the level of detail of the proposals according to the relevance of the advertisements. This allows for prioritizing the proposal of highly relevant advertisements by adjusting the order of proposals based on the relevance of the advertisements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input advertisement relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the visual elements of advertisements when collecting advertising data from competing companies, thereby identifying visually appealing advertisements. For example, the unit can analyze the color scheme, layout, and image quality of advertisements to identify visually superior ads. Furthermore, the unit can analyze the correlation between visual elements and advertising effectiveness to determine whether visually appealing advertisements achieve high click-through rates. In addition, the unit can grasp trends in competing companies' advertising strategies based on visual elements. This enables data collection that takes visual elements into account, which can be used to develop more effective advertising strategies.

[0051] The proposal department can consider the audio elements of advertisements when analyzing the advertising strategies of competing companies. For example, the proposal department can analyze the tone and volume of music and narration used in advertisements to identify effective audio elements. Furthermore, the proposal department can analyze the correlation between audio elements and advertising effectiveness to clarify how audio elements contribute to the success of advertisements. In addition, the proposal department can propose specific improvements to their own advertising strategy based on audio elements. This enables the proposal of advertising strategies that take audio elements into consideration, thereby maximizing the effectiveness of advertisements.

[0052] The data collection unit can filter data while considering the language of competitors' advertising creatives. For example, the unit can prioritize collecting advertising creatives created in a specific language. Furthermore, the unit can analyze advertising creatives created in multiple languages ​​and compare their effectiveness across languages. In addition, the unit can determine the priority of data to collect based on language. This enables language-aware data collection, which can help in developing more effective advertising strategies.

[0053] The analytics department can utilize color psychology when analyzing the visual elements of advertisements. For example, it can analyze the psychological impact that color combinations used in an advertisement have on viewers. It can also investigate how specific colors affect the click-through rate and conversion rate of an advertisement. Furthermore, the analytics department can identify elements of visually appealing advertising creatives based on color psychology. This enables analysis utilizing color psychology, which can be used to create visually effective advertising creatives.

[0054] The proposal department can make suggestions that take the visual elements of an advertisement into consideration. For example, the proposal department can analyze the colors, layout, and image quality used in an advertisement and propose visually appealing advertising creatives. Furthermore, the proposal department can analyze the correlation between visual elements and advertising effectiveness to determine whether visually superior advertisements are achieving high click-through rates. In addition, the proposal department can propose specific improvements to the company's advertising strategy based on visual elements. This enables the proposal of advertising strategies that consider visual elements, thereby maximizing the effectiveness of advertising.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit acquires real-time data on competitors' advertising patterns and performance measurement data. For example, the data collection unit can understand which platforms competitors are using and what kind of creative content they are using, collect advertising data on social media, and identify which ads are achieving high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks, analyze advertising patterns, and understand the frequency and timing of ad placements. Furthermore, the data collection unit can collect performance measurement data, obtaining data such as click-through rates, conversion rates, and impressions. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the success and failure factors of competitors. For example, if a particular creative is achieving a high click-through rate, the analysis unit analyzes the elements of that creative to identify the success factors. The analysis unit can use generative AI to analyze the collected data and identify success and failure factors. Generative AI can analyze the elements of advertising creatives using text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can also analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. Step 3: Based on the analysis results obtained by the analysis department, the proposal department proposes specific improvement plans for the company's advertising strategy. For example, it proposes new advertising creatives that incorporate the success factors of competitors. The proposal department can use generative AI to generate new advertising creatives that incorporate the success factors of competitors. The proposal department can also propose strategies to avoid the failure factors of competitors. The proposal department can use generative AI to generate strategies to avoid the failure factors of competitors.

[0057] (Example of form 2) An agent system according to an embodiment of the present invention is a system that utilizes generative AI to monitor and analyze the digital advertising activities of competitors and to reveal the actions and trends of competitors. This agent system acquires advertising patterns and effectiveness measurement data of competitors in real time, and the generative AI analyzes this data to identify the success and failure factors of competitors. Furthermore, based on the analysis results, the generative AI proposes specific improvement plans for the company's advertising strategy. This mechanism allows marketing teams to quickly formulate appropriate strategies and maximize the effectiveness of their advertising. For example, the agent system acquires advertising patterns and effectiveness measurement data of competitors in real time. At this time, it grasps what kind of creatives competitors are using on which platforms. For example, it collects data such as what kind of ads competitors are running on social media and which ads are getting high click-through rates. Next, the agent system analyzes the collected data using generative AI. The generative AI analyzes the advertising patterns and effectiveness measurement data of competitors and identifies success and failure factors. For example, if a particular creative is getting a high click-through rate, it analyzes the elements of that creative and identifies the success factors. Furthermore, the agent system, based on the analysis results, has the generative AI propose specific improvement plans for the company's advertising strategy. For example, the system proposes new advertising creatives that incorporate the success factors of competitors. It also proposes strategies to avoid the failure factors of competitors. This mechanism allows marketing teams to quickly develop appropriate strategies and maximize the effectiveness of their advertising. For instance, it enables the rapid creation of advertising creatives that incorporate the success factors of competitors, allowing for effective targeting of the target audience. Furthermore, by avoiding the failure factors of competitors, advertising effectiveness can be maximized. As a result, the agent system monitors and analyzes the advertising activities of competitors, revealing competitor trends and strategies, which enables differentiation of marketing strategies and effective targeting.

[0058] The agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit acquires advertising patterns and effectiveness measurement data of competitor companies in real time. For example, the data collection unit can understand what kind of creatives competitor companies are using on which platforms. For example, the data collection unit can collect advertising data on social media and understand which ads are getting high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks. For example, the data collection unit can analyze advertising patterns and understand the frequency and timing of ad placements. Furthermore, the data collection unit can collect effectiveness measurement data and acquire data such as click-through rates, conversion rates, and impressions. The analysis unit analyzes the data collected by the data collection unit and identifies the success and failure factors of competitors. For example, if a particular creative is getting a high click-through rate, the analysis unit analyzes the elements of that creative and identifies the success factors. For example, the analysis unit can use generative AI to analyze the collected data and identify success and failure factors. The generation AI can analyze elements of advertising creatives using, for example, text generation AI (e.g., LLM). The analysis unit can also analyze elements of advertising creatives using multimodal generation AI. The analysis unit can analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. The proposal unit proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis unit. The proposal unit can propose new advertising creatives that incorporate the success factors of competitors. The proposal unit can generate new advertising creatives that incorporate the success factors of competitors using, for example, generation AI. The proposal unit can also propose strategies to avoid the failure factors of competitors. The proposal unit can generate strategies to avoid the failure factors of competitors using, for example, generation AI.As a result, the agent system according to this embodiment can monitor and analyze the advertising activities of competing companies, clarify the actions and trends of competitors, and thereby enable differentiation of marketing strategies and effective targeting.

[0059] The data collection department acquires real-time data on competitors' advertising patterns and performance measurement data. Specifically, the department collects data from multiple platforms, including social media, search engines, and display advertising networks, to understand what kind of creative content competitors are using on which platforms. For example, on social media, it can collect advertising data to understand which ads are achieving high click-through rates. This allows for a detailed understanding of what kind of creative content competitors are using and what kind of ads they are running on which platforms. The data collection department can also analyze advertising patterns to understand the frequency and timing of ad placements. For example, if ads placed during specific time slots are achieving high click-through rates, focusing on those time slots can help develop effective advertising strategies. Furthermore, the data collection department can collect performance measurement data, obtaining data such as click-through rates, conversion rates, and impressions. This allows for a detailed analysis of the effectiveness of competitors' ads and the ability to reflect this in one's own advertising strategy. The data collection department centrally manages this data and stores it in a database that is updated in real time. This allows the analysis and proposal departments to quickly access the necessary data. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The analysis unit analyzes the data collected by the data collection unit to identify the success and failure factors of competitors. Specifically, if a particular creative is achieving a high click-through rate, the analysis unit analyzes the elements of that creative to identify the success factors. For example, a generative AI can be used to analyze the collected data and identify the success and failure factors. The generative AI can use text generation AI (e.g., LLM) to analyze the elements of an advertising creative. This allows for detailed identification of success factors such as the ad's wording, design, and targeting elements. The analysis unit can also use a multimodal generative AI to analyze the elements of an advertising creative. Because the multimodal generative AI can analyze multiple data formats such as images and videos as well as text, it can analyze the overall effect of the advertising creative in detail. Furthermore, the analysis unit can analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. For example, if a particular ad is achieving a high conversion rate, the analysis unit can analyze the elements of that ad in detail to identify the success factors. This allows the analysis unit to quickly and accurately analyze the collected data and identify the success and failure factors of competitors. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the effectiveness of specific creatives and targeting based on past advertising data and formulate future advertising strategies. This allows the analytics department to not only grasp the situation in real time but also to formulate long-term advertising strategies, improving the reliability and effectiveness of the entire system.

[0061] The proposal department will propose specific improvement plans for the company's advertising strategy based on the analysis results obtained by the analysis department. Specifically, they will propose new advertising creatives that incorporate the success factors of competitors. For example, they can use generative AI to generate new advertising creatives that incorporate the success factors of competitors. The generative AI can use text generation AI (e.g., LLM) to generate advertising copy that reflects the success factors of competitors. They can also use image generation AI to generate advertising designs that reflect the success factors of competitors. This allows the proposal department to quickly propose effective advertising creatives that incorporate the success factors of competitors. Furthermore, the proposal department can also propose strategies to avoid the failure factors of competitors. For example, they can use generative AI to generate strategies to avoid the failure factors of competitors. Based on the collected data, the generative AI can identify the failure factors of competitors and propose specific strategies to avoid those factors. This allows the proposal department to quickly propose effective strategies to avoid the failure factors of competitors. In addition, based on the analysis results, the proposal department can propose specific improvement plans such as reviewing the timing, frequency, and targeting of advertising. For example, if a company is achieving a high click-through rate during a specific time period, a strategy can be proposed to concentrate advertising during that time. Furthermore, by suggesting effective ad creatives for specific target audiences, the accuracy of targeting can be improved. This allows the proposal department to quickly propose concrete improvement plans based on analysis results, maximizing the effectiveness of their advertising strategy.

[0062] The data collection unit can understand what kind of creative content competitors are using on which platforms. For example, the data collection unit can collect advertising data from social media to understand which ads are getting high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks. For example, the data collection unit can analyze advertising patterns to understand the frequency and timing of ad placements. This allows for a more detailed understanding of competitors' advertising activities, enabling the development of more effective marketing strategies. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input competitors' advertising data into a generating AI and have the generating AI perform the data analysis.

[0063] The analysis unit analyzes the collected data and, if a particular creative is achieving a high click-through rate, it can analyze the elements of that creative and identify the factors contributing to its success. For example, the analysis unit can use a generative AI to analyze the collected data and identify factors contributing to success and failure. The generative AI can, for example, use a text generation AI (e.g., LLM) to analyze the elements of an advertising creative. The analysis unit can also use a multimodal generation AI to analyze the elements of an advertising creative. The analysis unit can, for example, analyze performance measurement data such as click-through rates and conversion rates to identify factors contributing to success and failure. By identifying factors contributing to success, it is possible to obtain reference information for creating effective advertising creatives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI identify factors contributing to success and failure.

[0064] The proposal department can propose new advertising creatives that incorporate the success factors of competitors. For example, the proposal department can use a generative AI to generate new advertising creatives that incorporate the success factors of competitors. The generative AI can use a text generation AI (e.g., LLM) to generate new advertising creatives. The proposal department can also use a multimodal generative AI to generate new advertising creatives. This allows companies to improve the effectiveness of their own advertising by incorporating the success factors of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the success factors of competitors into a generative AI and have the generative AI generate new advertising creatives.

[0065] The proposal department can propose strategies to avoid the failure factors of competitors. The proposal department can generate strategies to avoid the failure factors of competitors using, for example, generative AI. The generative AI can generate strategies to avoid failure factors using, for example, text generation AI (e.g., LLM). The proposal department can also generate strategies to avoid failure factors using multimodal generative AI. This reduces the risk of one's own advertising strategy by avoiding the failure factors of competitors. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the failure factors of competitors into the generative AI and have the generative AI generate strategies to avoid those failure factors.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0067] The data collection unit can analyze the advertising history of competitors and select the optimal data collection method. For example, the data collection unit can analyze the past advertising history of competitors and select an effective data collection method. For example, the data collection unit can optimize the data collection method on a specific platform based on the advertising history of competitors. The data collection unit can also determine the priority of data to collect based on the advertising history of competitors. This allows for the selection of an effective data collection method by analyzing the advertising history of competitors. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the advertising history of competitors into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter data based on the current marketing campaigns and areas of interest of competitors during data collection. For example, the data collection unit can analyze the current marketing campaigns of competitors and prioritize the collection of relevant data. For example, the data collection unit can filter the data to be collected based on the areas of interest of competitors. The data collection unit can also select the data to be collected considering the progress of competitors' marketing campaigns. This allows for the priority collection of highly relevant data by filtering the data based on the current marketing campaigns and areas of interest of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input competitor marketing campaign data into a generating AI and have the generating AI perform data filtering.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of competitors during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of competitors. For example, the data collection unit can adjust the range of data to be collected by considering the geographical location information of competitors. The data collection unit can also determine the priority of data to be collected based on the geographical location information of competitors. This allows for the efficient collection of highly relevant data by considering the geographical location information of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of competitors into a generating AI and have the generating AI perform the collection of highly relevant data.

[0071] The data collection unit can analyze the social media activities of competitors and collect relevant data during data collection. For example, the data collection unit can analyze the social media activities of competitors and prioritize the collection of relevant data. For example, the data collection unit can grasp the trends in the social media activities of competitors and select the data to collect. The data collection unit can also determine the priority of the data to collect based on the social media activities of competitors. This allows for the efficient collection of relevant data by analyzing the social media activities of competitors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the social media activities of competitors into a generating AI and have the generating AI collect the relevant data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the advertisements during the analysis. For example, the analysis unit can perform a detailed analysis for important advertisements, and a simplified analysis for less important advertisements. The analysis unit can also determine the priority of the analysis according to the importance of the advertisements. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the advertisements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the ad category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the ad category. For example, the analysis unit can improve accuracy by applying different analysis algorithms for each ad category. The analysis unit can also adjust the way the analysis results are presented based on the ad category. This improves analysis accuracy by applying the optimal analysis algorithm according to the ad category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ad category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the timing of advertisement placement during the analysis. For example, the analysis unit prioritizes the analysis of the most recent advertisement. The analysis unit can determine the priority of analysis based on the timing of advertisement placement. The analysis unit can also adjust the level of detail of the analysis according to the timing of advertisement placement. This enables rapid analysis of the most recent advertisement by determining the priority of analysis based on the timing of advertisement placement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement placement timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the advertisements during the analysis. For example, the analysis unit determines the order of analysis based on the relevance of the advertisements. For example, the analysis unit can prioritize the analysis of advertisements with high relevance. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the advertisements. This allows for the prioritization of highly relevant advertisements by adjusting the order of analysis based on the relevance of the advertisements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input advertisement relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the presentation of suggestions according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggestions.

[0079] The proposal unit can adjust the level of detail of its proposals based on the success factors of the advertisement. For example, the proposal unit can provide detailed proposals based on the success factors of the advertisement. For example, the proposal unit can provide concise proposals that incorporate the success factors of the advertisement. The proposal unit can also determine the priority of proposals according to the success factors of the advertisement. This allows for more effective proposals by adjusting the level of detail based on the success factors of the advertisement. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input advertising success factor data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0080] The proposal unit can apply different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the ad category. For example, the proposal unit can improve accuracy by applying different proposal algorithms for each ad category. The proposal unit can also adjust how the proposal results are presented based on the ad category. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the ad category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input ad category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. In this way, by adjusting the length of suggestions according to the user's emotions, the optimal suggestions can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0082] The proposal department can determine the priority of proposals based on the timing of advertisement placement. For example, the proposal department will prioritize proposals for the most recent advertisements. The proposal department can also adjust the level of detail of proposals according to the timing of advertisement placement. This allows for quick proposals for the most recent advertisements by determining the priority of proposals based on the timing of advertisement placement. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input advertisement placement timing data into a generating AI and have the generating AI determine the priority of proposals.

[0083] The proposal unit can adjust the order of proposals based on the relevance of the advertisements when making a proposal. For example, the proposal unit determines the order of proposals based on the relevance of the advertisements. For example, the proposal unit can prioritize proposing advertisements that are highly relevant. The proposal unit can also adjust the level of detail of the proposals according to the relevance of the advertisements. This allows for prioritizing the proposal of highly relevant advertisements by adjusting the order of proposals based on the relevance of the advertisements. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input advertisement relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The agent system can further estimate the user's emotions and customize advertising strategy suggestions based on those emotions. For example, if the user is stressed, the suggestion system can provide concise and to-the-point suggestions. If the user is relaxed, it can provide comprehensive suggestions along with detailed analysis results. Furthermore, if the user is in a hurry, the suggestion system can prioritize suggesting actionable items that can be implemented quickly. This enables flexible suggestions tailored to the user's emotions, thereby improving user satisfaction.

[0086] The data collection unit can analyze the visual elements of advertisements when collecting advertising data from competing companies, thereby identifying visually appealing advertisements. For example, the unit can analyze the color scheme, layout, and image quality of advertisements to identify visually superior ads. Furthermore, the unit can analyze the correlation between visual elements and advertising effectiveness to determine whether visually appealing advertisements achieve high click-through rates. In addition, the unit can grasp trends in competing companies' advertising strategies based on visual elements. This enables data collection that takes visual elements into account, which can be used to develop more effective advertising strategies.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can present the results using visually easy-to-understand graphs and charts. If the user is relaxed, it can provide a detailed text report. Furthermore, if the user is in a hurry, it can provide a concise summary of the key points. This makes it possible to present analysis results in a way that suits the user's emotions, thereby promoting user understanding.

[0088] The proposal department can consider the audio elements of advertisements when analyzing the advertising strategies of competing companies. For example, the proposal department can analyze the tone and volume of music and narration used in advertisements to identify effective audio elements. Furthermore, the proposal department can analyze the correlation between audio elements and advertising effectiveness to clarify how audio elements contribute to the success of advertisements. In addition, the proposal department can propose specific improvements to their own advertising strategy based on audio elements. This enables the proposal of advertising strategies that take audio elements into consideration, thereby maximizing the effectiveness of advertisements.

[0089] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize important suggestions to reduce the user's burden. If the user is relaxed, it can present detailed suggestions sequentially. Furthermore, if the user is in a hurry, it can prioritize suggestions that can be quickly implemented. By prioritizing suggestions according to the user's emotions, efficient suggestions can be made, leading to improved user satisfaction.

[0090] The data collection unit can filter data while considering the language of competitors' advertising creatives. For example, the unit can prioritize collecting advertising creatives created in a specific language. Furthermore, the unit can analyze advertising creatives created in multiple languages ​​and compare their effectiveness across languages. In addition, the unit can determine the priority of data to collect based on language. This enables language-aware data collection, which can help in developing more effective advertising strategies.

[0091] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those estimates. For example, if the user is stressed, the unit prioritizes collecting only essential data. If the user is relaxed, it can collect detailed data. Furthermore, if the user is in a hurry, it can prioritize collecting data that can be retrieved quickly. By adjusting the types of data collected according to the user's emotions, efficient data collection becomes possible, reducing the burden on the user.

[0092] The analytics department can utilize color psychology when analyzing the visual elements of advertisements. For example, it can analyze the psychological impact that color combinations used in an advertisement have on viewers. It can also investigate how specific colors affect the click-through rate and conversion rate of an advertisement. Furthermore, the analytics department can identify elements of visually appealing advertising creatives based on color psychology. This enables analysis utilizing color psychology, which can be used to create visually effective advertising creatives.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is stressed, it can also provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0094] The proposal department can make suggestions that take the visual elements of an advertisement into consideration. For example, the proposal department can analyze the colors, layout, and image quality used in an advertisement and propose visually appealing advertising creatives. Furthermore, the proposal department can analyze the correlation between visual elements and advertising effectiveness to determine whether visually superior advertisements are achieving high click-through rates. In addition, the proposal department can propose specific improvements to the company's advertising strategy based on visual elements. This enables the proposal of advertising strategies that consider visual elements, thereby maximizing the effectiveness of advertising.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit acquires real-time data on competitors' advertising patterns and performance measurement data. For example, the data collection unit can understand which platforms competitors are using and what kind of creative content they are using, collect advertising data on social media, and identify which ads are achieving high click-through rates. The data collection unit can also collect data from multiple platforms, such as search engines and display advertising networks, analyze advertising patterns, and understand the frequency and timing of ad placements. Furthermore, the data collection unit can collect performance measurement data, obtaining data such as click-through rates, conversion rates, and impressions. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the success and failure factors of competitors. For example, if a particular creative is achieving a high click-through rate, the analysis unit analyzes the elements of that creative to identify the success factors. The analysis unit can use generative AI to analyze the collected data and identify success and failure factors. Generative AI can analyze the elements of advertising creatives using text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can also analyze performance measurement data such as click-through rates and conversion rates to identify success and failure factors. Step 3: Based on the analysis results obtained by the analysis department, the proposal department proposes specific improvement plans for the company's advertising strategy. For example, it proposes new advertising creatives that incorporate the success factors of competitors. The proposal department can use generative AI to generate new advertising creatives that incorporate the success factors of competitors. The proposal department can also propose strategies to avoid the failure factors of competitors. The proposal department can use generative AI to generate strategies to avoid the failure factors of competitors.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to acquire advertising patterns and effectiveness measurement data of competitors in real time. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the success and failure factors of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes specific improvement plans for the company's advertising strategy based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to acquire advertising patterns and effectiveness measurement data of competitors in real time. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the success and failure factors of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes specific improvement plans for the company's advertising strategy based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to acquire advertising patterns and effectiveness measurement data of competing companies in real time. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the success and failure factors of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes specific improvement plans for the company's advertising strategy based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the robot 414 to acquire advertising patterns and effectiveness measurement data of competing companies in real time. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the success and failure factors of competitors. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes specific improvement plans for the company's advertising strategy based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A data collection unit that acquires advertising patterns and effectiveness measurement data of competing companies in real time, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the success and failure factors of competitors, The system includes a proposal unit that proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Understand what kind of creative content your competitors are using and on which platforms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, if a particular creative is achieving a high click-through rate, we analyze the elements of that creative and identify the factors contributing to its success. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose new advertising creatives that incorporate the success factors of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose strategies to avoid the factors that led to the failures of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the advertising history of competitors and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the current marketing campaigns and areas of interest of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical location information of competing companies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze the social media activities of competitors and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of the advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the success factors of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their advertising campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that acquires advertising patterns and effectiveness measurement data of competing companies in real time, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the success and failure factors of competitors, The system includes a proposal unit that proposes specific improvement plans for the company's advertising strategy based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Understand what kind of creative content your competitors are using and on which platforms. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, if a particular creative is achieving a high click-through rate, we analyze the elements of that creative and identify the factors contributing to its success. The system according to feature 1.

4. The aforementioned proposal section is, We propose new advertising creatives that incorporate the success factors of our competitors. The system according to feature 1.

5. The aforementioned proposal section is, We propose strategies to avoid the factors that led to the failures of our competitors. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the advertising history of competitors and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the current marketing campaigns and areas of interest of competitors. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical location information of competing companies. The system according to feature 1.

Images