system

Abstract

The system according to this embodiment aims to quickly propose customized advertising plans that meet the client's needs. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects industry data, competitor information, and client requests. The analysis unit analyzes the data collected by the data collection unit and extracts patterns. The proposal unit generates a customized advertising plan based on the analysis results obtained by the analysis unit. The feedback unit evaluates the proposal results of the advertising plan generated by the proposal unit, and the AI ​​continues to learn.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for advertising agencies to quickly and accurately make proposals that met the needs of clients.

[0005] The system according to the embodiment aims to quickly propose a customized advertising plan that meets the needs of clients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects industry data, competitor information, and client requests. The analysis unit analyzes the data collected by the data collection unit and extracts patterns. The proposal unit generates a customized advertising plan based on the analysis results obtained by the analysis unit. The feedback unit evaluates the proposal results of the advertising plan generated by the proposal unit, and the AI ​​continues to learn. [Effects of the Invention]

[0007] The system according to this embodiment can quickly propose customized advertising plans that meet the client's needs. [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, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that enables advertising agencies to quickly and efficiently make proposals that meet the needs of their clients. This system analyzes the client's industry, goals, and target audience in real time and proposes a customized advertising plan. For example, the AI ​​agent system collects data such as industry data, competitor information, and client requests. Next, it analyzes the collected data using machine learning and extracts patterns. This allows it to learn the advertising strategy best suited to the client. Furthermore, the AI ​​agent system automatically generates individually optimized advertising plans and makes proposals quickly. The proposal results are evaluated through a feedback loop, and the accuracy of the proposals improves as the AI ​​continues to learn. This mechanism allows advertising agencies to reduce proposal creation time and research costs. In addition, by incorporating feedback into the learning process, the accuracy of proposals improves, and client satisfaction increases. For example, after an advertising agency introduced the AI ​​agent system, proposal creation time was reduced by 50%, and client satisfaction increased by 30%. The present invention contributes to improving the operational efficiency, reducing costs, improving accuracy, and securing a competitive advantage for advertising agencies, thereby achieving strong differentiation within the industry. This allows the AI ​​agent system to enable advertising agencies to quickly and efficiently provide proposals that meet their clients' needs.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects industry data, competitor information, and client requests. For example, the data collection unit collects market research data and industry reports. The data collection unit can also collect product information and marketing strategies of competing companies. Furthermore, the data collection unit can collect client requests through surveys and interviews. For example, the data collection unit conducts online surveys to collect client requests. The data collection unit can also understand detailed client requests through interviews. The analysis unit analyzes the data collected by the data collection unit and extracts patterns. For example, the analysis unit analyzes the data using data mining techniques. The analysis unit can also extract data patterns using statistical analysis. Furthermore, the analysis unit can analyze the data and extract patterns using machine learning algorithms. For example, the analysis unit groups the data using clustering algorithms and extracts patterns. The analysis unit can also clarify the relationships between data using regression analysis. The Proposal Department generates customized advertising plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department identifies target audiences and adjusts advertising messages. The Proposal Department can also optimize the timing of ad delivery. Furthermore, the Proposal Department can adjust the creative elements of the ads. For example, the Proposal Department customizes advertising messages based on the attributes of the target audience. The Proposal Department can also analyze historical advertising performance data to optimize the timing of ad delivery. The Feedback Department evaluates the results of the advertising plans generated by the Proposal Department, and the AI ​​continues to learn. For example, the Feedback Department evaluates click-through rates and conversion rates. The Feedback Department can also collect advertising performance data and use it as data for the AI ​​to learn. Furthermore, the Feedback Department can collect feedback from clients and use it as data for the AI ​​to learn.For example, the feedback unit evaluates the click-through rate of advertisements and provides feedback to the proposal unit. The feedback unit can also collect feedback from clients and provide it to the proposal unit. As a result, the AI ​​agent system according to this embodiment enables advertising agencies to quickly and efficiently make proposals that meet the needs of their clients.

[0030] The data collection department collects industry data, competitive information, and client requirements. For example, it collects market research data and industry reports. Specifically, it obtains the latest market trends and industry benchmark data from online databases and industry-specific publications. The data collection department can also collect product information and marketing strategies of competitors. This includes gathering information from competitor websites, press releases, and social media posts. Furthermore, the data collection department can collect client requirements through surveys and interviews. For example, it conducts online surveys to collect client requirements. These online surveys include questions designed to understand client needs and expectations in detail, and the response data is automatically stored in a database. The data collection department can also understand detailed client requirements through interviews. Interviews provide deeper insights through direct interaction with client representatives. This allows the data collection department to collect comprehensive data from diverse sources and build a foundational dataset for the entire system. Furthermore, the data collection department can centrally manage the collected data and collaborate with other departments as needed. For example, collected data is stored in cloud-based data storage, making it accessible to the analysis and proposal departments. Furthermore, the data collection department can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department and extracts patterns. For example, the analysis department can analyze data using data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, revealing client behavior patterns and market trends. The analysis department can also extract data patterns using statistical analysis. Statistical analysis reveals data distribution and correlations, forming the basis for predicting client needs and market trends. Furthermore, the analysis department can analyze data and extract patterns using machine learning algorithms. For example, the analysis department can group data using clustering algorithms and extract patterns. Clustering algorithms can identify client segments and market segments by grouping similar data points. The analysis department can also reveal data relationships using regression analysis. Regression analysis is a method for modeling relationships between variables and predicting future trends. This allows the analysis department to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past advertising performance data, the system can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The proposal department generates customized advertising plans based on the analysis results obtained by the analysis department. For example, the proposal department identifies target audiences and tailors advertising messages. Specifically, based on the data provided by the analysis department, the proposal department analyzes the attributes and behavioral patterns of target audiences in detail to create optimal advertising messages. The proposal department can also optimize the timing of ad delivery. This includes analyzing past advertising performance data to identify the most effective delivery timing. Furthermore, the proposal department can adjust the creative elements of the ads. For example, the proposal department customizes advertising messages based on the attributes of the target audience. This includes considering attributes such as age, gender, and interests to create the most effective message. The proposal department can also analyze past advertising performance data to optimize the timing of ad delivery. This allows the proposal department to deliver ads at the optimal time for the target audience and maximize their effectiveness. In addition, by adjusting the creative elements of the ads, the proposal department can enhance visual appeal and attract the attention of the target audience. For example, the proposal team can refine the design and copy of advertisements to create messages that resonate most with the target audience. They can also optimize advertising channels and identify the most effective ways to reach the target audience. This allows the proposal team to generate customized advertising plans and provide the best possible advertising strategy for the client's needs.

[0033] The Feedback Department evaluates the results of the advertising plans generated by the Proposal Department, allowing the AI ​​to continuously learn. For example, the Feedback Department evaluates click-through rates (CTR) and conversion rates. Specifically, it collects advertising performance data and analyzes metrics such as CTR and conversion rates. The Feedback Department can also collect advertising performance data and use it as training data for the AI. This includes data such as ad impressions, clicks, and conversions. Furthermore, the Feedback Department can collect client feedback and use it as training data for the AI. For example, the Feedback Department evaluates ad click-through rates and provides feedback to the Proposal Department. This allows the Proposal Department to evaluate the effectiveness of the advertising plan and make adjustments as needed. The Feedback Department can also collect client feedback and provide it to the Proposal Department. Client feedback is valuable information that helps improve advertising plans, and the Proposal Department can use it to revise its advertising strategy. Additionally, the Feedback Department can continuously provide data for the AI ​​to learn from, improving the overall accuracy and effectiveness of the system. For example, the Feedback Department regularly collects advertising performance data and uses it as training data for the AI ​​model. This allows the AI ​​to continuously learn from the latest data, improving the accuracy and effectiveness of advertising plans. This enables the feedback department to collaborate with the proposal department to maximize the effectiveness of advertising plans and meet client needs.

[0034] The data collection unit can analyze the client's past advertising campaign history and select the optimal data collection method. For example, the data collection unit can collect data using similar methods based on data from the client's past successful campaigns. The data collection unit can also analyze data from the client's past unsuccessful campaigns and select a data collection method that reflects areas for improvement. Furthermore, the data collection unit can compare the performance of the client's past campaigns and select the most effective data collection method. For example, the data collection unit can analyze the click-through rate and conversion rate of past campaigns to select the optimal data collection method. The data collection unit can also analyze the target audience's response to past campaigns and adjust the data collection method accordingly. This allows the optimal data collection method to be selected by analyzing the client's past advertising campaign history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input past campaign data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter data based on the client's current market conditions and competitive trends during data collection. For example, the data collection unit can prioritize collecting data from competitors, taking into account the client's current market share. The data collection unit can also analyze the client's industry trends and filter and collect relevant data. Furthermore, the data collection unit can analyze the advertising strategies of the client's competitors and select the optimal data collection method. For example, the data collection unit can analyze competitors' advertising performance data and set filtering criteria. The data collection unit can also adjust the priority of data collection, taking into account fluctuations in the client's market share. This allows for the collection of highly relevant data by filtering data based on the client's current market conditions and competitive trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input competitor data into a generating AI and have the generating AI set the filtering criteria.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the client's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the client's location. It can also prioritize the collection of relevant data by considering the geographical location information of the client's target market. Furthermore, the data collection unit can prioritize the collection of data related to the client's competitors by considering their locations. For example, the data collection unit can collect regional consumer behavior data based on the client's location. It can also collect data for formulating region-specific advertising strategies based on the geographical location information of the target market. This allows for the priority collection of highly relevant data by considering the client's geographical location information. 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 geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0037] The data collection unit can analyze the client's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze the content of the client's social media posts and collect relevant data. The data collection unit can also analyze the reactions of the client's social media followers and collect relevant data. Furthermore, the data collection unit can analyze the engagement rate of the client's social media and collect relevant data. For example, the data collection unit can analyze the content of social media posts using text analysis technology and extract relevant keywords. The data collection unit can also analyze follower reactions using sentiment analysis technology and identify positive and negative reactions. In this way, relevant data can be collected by analyzing the client's social media activities. 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 social media post data into a generating AI and have the generating AI perform the collection of relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on highly important data. Conversely, it can perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. For example, the analysis unit will perform a detailed analysis on data with a high business impact. It can also perform a detailed analysis if the data is highly reliable. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a topic modeling algorithm to text data to extract the data's topics. It can also apply a deep learning algorithm to image data to extract image features. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0040] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data and use past data supplementarily. The analysis unit can also dynamically adjust the priority of analysis based on the data collection timing. This allows for analysis that emphasizes the most recent data by determining the priority of analysis based on the data collection timing. 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 the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis stepwise according to the relevance of the data. For example, the analysis unit can use correlation analysis to evaluate the relevance of the data and determine the order of analysis. The analysis unit can also evaluate the relevance of the data to clarify causal relationships. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0042] The proposal department can adjust the level of detail in its proposals based on the importance of the advertising plan. For example, it can provide detailed proposals for high-importance advertising plans, and simplified proposals for low-importance plans. Furthermore, it can adjust the level of detail in stages according to the importance of the advertising plan. For example, it can provide detailed proposals for advertising plans with large budgets, and also provide detailed proposals when the target market is large. This allows for efficient proposals by adjusting the level of detail based on the importance of the advertising plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the advertising plan into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0043] The proposal unit can apply different proposal algorithms depending on the category of the advertising plan when making a proposal. For example, the proposal unit can apply a digital marketing algorithm to a digital advertising plan. It can also apply an algorithm based on television viewing data to a television advertising plan. Furthermore, it can apply an algorithm that analyzes the effectiveness of printed materials to a print advertising plan. For example, the proposal unit can apply an algorithm based on click-through rates and conversion rates to a digital advertising plan. It can also apply an algorithm based on viewership data to a television advertising plan. By applying different proposal algorithms depending on the category of the advertising plan, more accurate proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the category of the advertising plan into a generating AI and have the generating AI select the proposal algorithm to apply.

[0044] The proposal department can determine the priority of proposals based on the submission timing of the advertising plans. For example, the proposal department can prioritize advertising plans with approaching deadlines. It can also postpone advertising plans with later submission deadlines. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing of the advertising plans. For example, the proposal department can prioritize advertising plans with approaching campaign start dates. It can also adjust the priority of proposals based on the client's deadlines. This allows for efficient proposals by determining the priority of proposals based on the submission timing of the advertising plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the advertising plans into a generating AI and have the generating AI determine the priority of proposals.

[0045] The proposal department can adjust the order of proposals based on the relevance of the advertising plans during the proposal process. For example, the proposal department can prioritize proposing highly relevant advertising plans. It can also postpone less relevant advertising plans. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the advertising plans. For example, the proposal department can evaluate the relevance of advertising plans based on their degree of match with the target audience. It can also evaluate the relevance of advertising plans by considering market trends. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the advertising plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of the advertising plans into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0046] The feedback unit can select the optimal feedback method by referring to past feedback data during the feedback process. For example, the feedback unit can analyze past feedback data and select the most effective feedback method. The feedback unit can also provide a customized feedback method by referring to the client's past feedback history. Furthermore, the feedback unit can adjust the content and format of the feedback based on past feedback data. For example, the feedback unit can analyze past evaluation results and select the optimal feedback method. The feedback unit can also adjust the content of the feedback based on the client's feedback history. This allows the optimal feedback method to be selected by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI select the optimal feedback method.

[0047] The feedback unit can customize the means of feedback based on the client's current market situation. For example, the feedback unit can adjust the content of the feedback considering the client's current market share. The feedback unit can also analyze the client's industry trends and provide relevant feedback. Furthermore, the feedback unit can customize the means of feedback by referring to the trends of the client's competitors. For example, the feedback unit can adjust the content of the feedback considering fluctuations in the client's market share. The feedback unit can also customize the content of the feedback based on industry trends. This allows for more effective feedback by customizing the means of feedback based on the client's current market situation. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input market situation data into a generating AI and have the generating AI perform the customization of the means of feedback.

[0048] The feedback unit can select the optimal feedback method by considering the client's geographical location information during the feedback process. For example, the feedback unit can provide region-specific feedback based on the client's location. It can also consider the geographical location information of the client's target market and provide relevant feedback. Furthermore, the feedback unit can consider the locations of the client's competitors and provide feedback relevant to those competitors. For example, the feedback unit can provide feedback based on regional consumer behavior data based on the client's location. It can also provide region-specific feedback based on the geographical location information of the target market. This allows for the selection of the optimal feedback method by considering the client's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0049] The feedback unit can analyze the client's social media activity and propose methods for providing feedback during the feedback process. For example, the feedback unit can analyze the content of the client's social media posts and provide relevant feedback. It can also analyze the reactions of the client's social media followers and provide relevant feedback. Furthermore, the feedback unit can analyze the engagement rate of the client's social media and provide relevant feedback. For example, the feedback unit can analyze the content of social media posts using text analysis technology and extract relevant keywords. It can also analyze follower reactions using sentiment analysis technology and identify positive and negative reactions. In this way, by analyzing the client's social media activity, it can provide relevant feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input social media post data into a generating AI and have the generating AI propose methods for providing feedback.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection department can analyze the success factors of a client's past advertising campaigns and prioritize the collection of new data with similar success factors. For example, it can identify keywords and media channels that were particularly effective in past campaigns and collect new data based on them. It can also prioritize the collection of data from target audiences that showed high engagement in past campaigns. Furthermore, it can analyze feedback from past campaigns and select data collection methods that reflect areas for improvement. This enables more effective data collection by leveraging past success factors.

[0052] The data collection unit can analyze a client's past advertising campaign history and select the optimal data collection method. For example, it can collect data using similar methods based on data from a client's past successful campaigns. It can also analyze data from a client's past unsuccessful campaigns and select a data collection method that reflects areas for improvement. Furthermore, it can compare the performance of a client's past campaigns and select the most effective data collection method. In this way, by analyzing a client's past advertising campaign history, the optimal data collection method can be selected.

[0053] The data collection unit can filter data based on the client's current market conditions and competitive trends. For example, it can prioritize collecting data on competitors, taking into account the client's current market share. It can also analyze the client's industry trends and filter and collect relevant data. Furthermore, it can analyze the advertising strategies of the client's competitors and select the optimal data collection method. This allows for the collection of highly relevant data by filtering data based on the client's current market conditions and competitive trends.

[0054] The data collection unit can prioritize the collection of highly relevant data by considering the client's geographical location during data collection. For example, it can prioritize the collection of region-specific data based on the client's location. It can also prioritize the collection of relevant data by considering the geographical location of the client's target market. Furthermore, it can prioritize the collection of data related to the client's competitors by considering their locations. In this way, by considering the client's geographical location, it is possible to prioritize the collection of highly relevant data.

[0055] The data collection unit can analyze the client's social media activity and collect relevant data during the data collection process. For example, it can analyze the content of the client's social media posts and collect relevant data. It can also analyze the reactions of the client's social media followers and collect relevant data. Furthermore, it can analyze the engagement rate of the client's social media and collect relevant data. In this way, relevant data can be collected by analyzing the client's social media activity.

[0056] The analysis department can adjust the level of detail in the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can adjust the level of detail in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0057] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, natural language processing algorithms can be applied to text data. Image recognition algorithms can be applied to image data. Furthermore, statistical analysis algorithms can be applied to numerical data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection department gathers industry data, competitive information, and client requirements. For example, they collect market research data and industry reports, as well as product information and marketing strategies of competing companies. They can also gather client requirements through online surveys and interviews. Step 2: The analysis unit analyzes the data collected by the data collection unit and extracts patterns. For example, it analyzes the data using data mining techniques, statistical analysis, and machine learning algorithms, and extracts patterns using clustering algorithms and regression analysis. Step 3: The proposal team generates a customized advertising plan based on the analysis results obtained by the analysis team. For example, they identify the target audience and adjust the advertising message, delivery timing, and creative elements. Step 4: The Feedback Department evaluates the results of the advertising plan proposed by the Proposal Department, and the AI ​​continues to learn. For example, it evaluates click-through rates and conversion rates, and collects advertising performance data and client feedback to use as data for the AI ​​to learn.

[0060] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that enables advertising agencies to quickly and efficiently make proposals that meet the needs of their clients. This system analyzes the client's industry, goals, and target audience in real time and proposes a customized advertising plan. For example, the AI ​​agent system collects data such as industry data, competitor information, and client requests. Next, it analyzes the collected data using machine learning and extracts patterns. This allows it to learn the advertising strategy best suited to the client. Furthermore, the AI ​​agent system automatically generates individually optimized advertising plans and makes proposals quickly. The proposal results are evaluated through a feedback loop, and the accuracy of the proposals improves as the AI ​​continues to learn. This mechanism allows advertising agencies to reduce proposal creation time and research costs. In addition, by incorporating feedback into the learning process, the accuracy of proposals improves, and client satisfaction increases. For example, after an advertising agency introduced the AI ​​agent system, proposal creation time was reduced by 50%, and client satisfaction increased by 30%. The present invention contributes to improving the operational efficiency, reducing costs, improving accuracy, and securing a competitive advantage for advertising agencies, thereby achieving strong differentiation within the industry. This allows the AI ​​agent system to enable advertising agencies to quickly and efficiently provide proposals that meet their clients' needs.

[0061] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects industry data, competitor information, and client requests. For example, the data collection unit collects market research data and industry reports. The data collection unit can also collect product information and marketing strategies of competing companies. Furthermore, the data collection unit can collect client requests through surveys and interviews. For example, the data collection unit conducts online surveys to collect client requests. The data collection unit can also understand detailed client requests through interviews. The analysis unit analyzes the data collected by the data collection unit and extracts patterns. For example, the analysis unit analyzes the data using data mining techniques. The analysis unit can also extract data patterns using statistical analysis. Furthermore, the analysis unit can analyze the data and extract patterns using machine learning algorithms. For example, the analysis unit groups the data using clustering algorithms and extracts patterns. The analysis unit can also clarify the relationships between data using regression analysis. The Proposal Department generates customized advertising plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department identifies target audiences and adjusts advertising messages. The Proposal Department can also optimize the timing of ad delivery. Furthermore, the Proposal Department can adjust the creative elements of the ads. For example, the Proposal Department customizes advertising messages based on the attributes of the target audience. The Proposal Department can also analyze historical advertising performance data to optimize the timing of ad delivery. The Feedback Department evaluates the results of the advertising plans generated by the Proposal Department, and the AI ​​continues to learn. For example, the Feedback Department evaluates click-through rates and conversion rates. The Feedback Department can also collect advertising performance data and use it as data for the AI ​​to learn. Furthermore, the Feedback Department can collect feedback from clients and use it as data for the AI ​​to learn.For example, the feedback unit evaluates the click-through rate of advertisements and provides feedback to the proposal unit. The feedback unit can also collect feedback from clients and provide it to the proposal unit. As a result, the AI ​​agent system according to this embodiment enables advertising agencies to quickly and efficiently make proposals that meet the needs of their clients.

[0062] The data collection department collects industry data, competitive information, and client requirements. For example, it collects market research data and industry reports. Specifically, it obtains the latest market trends and industry benchmark data from online databases and industry-specific publications. The data collection department can also collect product information and marketing strategies of competitors. This includes gathering information from competitor websites, press releases, and social media posts. Furthermore, the data collection department can collect client requirements through surveys and interviews. For example, it conducts online surveys to collect client requirements. These online surveys include questions designed to understand client needs and expectations in detail, and the response data is automatically stored in a database. The data collection department can also understand detailed client requirements through interviews. Interviews provide deeper insights through direct interaction with client representatives. This allows the data collection department to collect comprehensive data from diverse sources and build a foundational dataset for the entire system. Furthermore, the data collection department can centrally manage the collected data and collaborate with other departments as needed. For example, collected data is stored in cloud-based data storage, making it accessible to the analysis and proposal departments. Furthermore, the data collection department can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis department analyzes data collected by the data collection department and extracts patterns. For example, the analysis department can analyze data using data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, revealing client behavior patterns and market trends. The analysis department can also extract data patterns using statistical analysis. Statistical analysis reveals data distribution and correlations, forming the basis for predicting client needs and market trends. Furthermore, the analysis department can analyze data and extract patterns using machine learning algorithms. For example, the analysis department can group data using clustering algorithms and extract patterns. Clustering algorithms can identify client segments and market segments by grouping similar data points. The analysis department can also reveal data relationships using regression analysis. Regression analysis is a method for modeling relationships between variables and predicting future trends. This allows the analysis department to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past advertising performance data, the system can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0064] The proposal department generates customized advertising plans based on the analysis results obtained by the analysis department. For example, the proposal department identifies target audiences and tailors advertising messages. Specifically, based on the data provided by the analysis department, the proposal department analyzes the attributes and behavioral patterns of target audiences in detail to create optimal advertising messages. The proposal department can also optimize the timing of ad delivery. This includes analyzing past advertising performance data to identify the most effective delivery timing. Furthermore, the proposal department can adjust the creative elements of the ads. For example, the proposal department customizes advertising messages based on the attributes of the target audience. This includes considering attributes such as age, gender, and interests to create the most effective message. The proposal department can also analyze past advertising performance data to optimize the timing of ad delivery. This allows the proposal department to deliver ads at the optimal time for the target audience and maximize their effectiveness. In addition, by adjusting the creative elements of the ads, the proposal department can enhance visual appeal and attract the attention of the target audience. For example, the proposal team can refine the design and copy of advertisements to create messages that resonate most with the target audience. They can also optimize advertising channels and identify the most effective ways to reach the target audience. This allows the proposal team to generate customized advertising plans and provide the best possible advertising strategy for the client's needs.

[0065] The Feedback Department evaluates the results of the advertising plans generated by the Proposal Department, allowing the AI ​​to continuously learn. For example, the Feedback Department evaluates click-through rates (CTR) and conversion rates. Specifically, it collects advertising performance data and analyzes metrics such as CTR and conversion rates. The Feedback Department can also collect advertising performance data and use it as training data for the AI. This includes data such as ad impressions, clicks, and conversions. Furthermore, the Feedback Department can collect client feedback and use it as training data for the AI. For example, the Feedback Department evaluates ad click-through rates and provides feedback to the Proposal Department. This allows the Proposal Department to evaluate the effectiveness of the advertising plan and make adjustments as needed. The Feedback Department can also collect client feedback and provide it to the Proposal Department. Client feedback is valuable information that helps improve advertising plans, and the Proposal Department can use it to revise its advertising strategy. Additionally, the Feedback Department can continuously provide data for the AI ​​to learn from, improving the overall accuracy and effectiveness of the system. For example, the Feedback Department regularly collects advertising performance data and uses it as training data for the AI ​​model. This allows the AI ​​to continuously learn from the latest data, improving the accuracy and effectiveness of advertising plans. This enables the feedback department to collaborate with the proposal department to maximize the effectiveness of advertising plans and meet client needs.

[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 lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can speed up the timing of data collection to collect the necessary data in a short time. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the timing of data collection to be adjusted according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0067] The data collection unit can analyze the client's past advertising campaign history and select the optimal data collection method. For example, the data collection unit can collect data using similar methods based on data from the client's past successful campaigns. The data collection unit can also analyze data from the client's past unsuccessful campaigns and select a data collection method that reflects areas for improvement. Furthermore, the data collection unit can compare the performance of the client's past campaigns and select the most effective data collection method. For example, the data collection unit can analyze the click-through rate and conversion rate of past campaigns to select the optimal data collection method. The data collection unit can also analyze the target audience's response to past campaigns and adjust the data collection method accordingly. This allows the optimal data collection method to be selected by analyzing the client's past advertising campaign history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input past campaign data 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 client's current market conditions and competitive trends during data collection. For example, the data collection unit can prioritize collecting data from competitors, taking into account the client's current market share. The data collection unit can also analyze the client's industry trends and filter and collect relevant data. Furthermore, the data collection unit can analyze the advertising strategies of the client's competitors and select the optimal data collection method. For example, the data collection unit can analyze competitors' advertising performance data and set filtering criteria. The data collection unit can also adjust the priority of data collection, taking into account fluctuations in the client's market share. This allows for the collection of highly relevant data by filtering data based on the client's current market conditions and competitive trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input competitor data into a generating AI and have the generating AI set the filtering criteria.

[0069] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the priority of data collection based on the user's emotions, thereby prioritizing the collection of important data. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the client's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the client's location. It can also prioritize the collection of relevant data by considering the geographical location information of the client's target market. Furthermore, the data collection unit can prioritize the collection of data related to the client's competitors by considering their locations. For example, the data collection unit can collect regional consumer behavior data based on the client's location. It can also collect data for formulating region-specific advertising strategies based on the geographical location information of the target market. This allows for the priority collection of highly relevant data by considering the client's geographical location information. 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 geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0071] The data collection unit can analyze the client's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze the content of the client's social media posts and collect relevant data. The data collection unit can also analyze the reactions of the client's social media followers and collect relevant data. Furthermore, the data collection unit can analyze the engagement rate of the client's social media and collect relevant data. For example, the data collection unit can analyze the content of social media posts using text analysis technology and extract relevant keywords. The data collection unit can also analyze follower reactions using sentiment analysis technology and identify positive and negative reactions. In this way, relevant data can be collected by analyzing the client's social media activities. 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 social media post data into a generating AI and have the generating AI perform the collection of 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 tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide results that are easy for the user to understand by adjusting the presentation of the analysis 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on highly important data. Conversely, it can perform a simplified analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. For example, the analysis unit will perform a detailed analysis on data with a high business impact. It can also perform a detailed analysis if the data is highly reliable. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a topic modeling algorithm to text data to extract the data's topics. It can also apply a deep learning algorithm to image data to extract image features. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[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 will provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide the user with the most optimal analysis results by adjusting the length of the analysis according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0076] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit may prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data and use past data supplementarily. The analysis unit can also dynamically adjust the priority of analysis based on the data collection timing. This allows for analysis that emphasizes the most recent data by determining the priority of analysis based on the data collection timing. 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 the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis stepwise according to the relevance of the data. For example, the analysis unit can use correlation analysis to evaluate the relevance of the data and determine the order of analysis. The analysis unit can also evaluate the relevance of the data to clarify causal relationships. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions 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 includes, 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 proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0079] The proposal department can adjust the level of detail in its proposals based on the importance of the advertising plan. For example, it can provide detailed proposals for high-importance advertising plans, and simplified proposals for low-importance plans. Furthermore, it can adjust the level of detail in stages according to the importance of the advertising plan. For example, it can provide detailed proposals for advertising plans with large budgets, and also provide detailed proposals when the target market is large. This allows for efficient proposals by adjusting the level of detail based on the importance of the advertising plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the advertising plan into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0080] The proposal unit can apply different proposal algorithms depending on the category of the advertising plan when making a proposal. For example, the proposal unit can apply a digital marketing algorithm to a digital advertising plan. It can also apply an algorithm based on television viewing data to a television advertising plan. Furthermore, it can apply an algorithm that analyzes the effectiveness of printed materials to a print advertising plan. For example, the proposal unit can apply an algorithm based on click-through rates and conversion rates to a digital advertising plan. It can also apply an algorithm based on viewership data to a television advertising plan. By applying different proposal algorithms depending on the category of the advertising plan, more accurate proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the category of the advertising plan into a generating AI and have the generating AI select the proposal algorithm to apply.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to provide the most suitable suggestions for the user by adjusting the length of the suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0082] The proposal department can determine the priority of proposals based on the submission timing of the advertising plans. For example, the proposal department can prioritize advertising plans with approaching deadlines. It can also postpone advertising plans with later submission deadlines. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing of the advertising plans. For example, the proposal department can prioritize advertising plans with approaching campaign start dates. It can also adjust the priority of proposals based on the client's deadlines. This allows for efficient proposals by determining the priority of proposals based on the submission timing of the advertising plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the advertising plans into a generating AI and have the generating AI determine the priority of proposals.

[0083] The proposal department can adjust the order of proposals based on the relevance of the advertising plans during the proposal process. For example, the proposal department can prioritize proposing highly relevant advertising plans. It can also postpone less relevant advertising plans. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the advertising plans. For example, the proposal department can evaluate the relevance of advertising plans based on their degree of match with the target audience. It can also evaluate the relevance of advertising plans by considering market trends. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the advertising plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of the advertising plans into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0084] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide simple and easily understandable feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the feedback method to be adjusted according to the user's emotions, providing feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0085] The feedback unit can select the optimal feedback method by referring to past feedback data during the feedback process. For example, the feedback unit can analyze past feedback data and select the most effective feedback method. The feedback unit can also provide a customized feedback method by referring to the client's past feedback history. Furthermore, the feedback unit can adjust the content and format of the feedback based on past feedback data. For example, the feedback unit can analyze past evaluation results and select the optimal feedback method. The feedback unit can also adjust the content of the feedback based on the client's feedback history. This allows the optimal feedback method to be selected by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI select the optimal feedback method.

[0086] The feedback unit can customize the means of feedback based on the client's current market situation. For example, the feedback unit can adjust the content of the feedback considering the client's current market share. The feedback unit can also analyze the client's industry trends and provide relevant feedback. Furthermore, the feedback unit can customize the means of feedback by referring to the trends of the client's competitors. For example, the feedback unit can adjust the content of the feedback considering fluctuations in the client's market share. The feedback unit can also customize the content of the feedback based on industry trends. This allows for more effective feedback by customizing the means of feedback based on the client's current market situation. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input market situation data into a generating AI and have the generating AI perform the customization of the means of feedback.

[0087] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize providing high-priority feedback. If the user is relaxed, the feedback unit can also prioritize providing detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize providing feedback that can be delivered quickly. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the feedback unit to prioritize important feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0088] The feedback unit can select the optimal feedback method by considering the client's geographical location information during the feedback process. For example, the feedback unit can provide region-specific feedback based on the client's location. It can also consider the geographical location information of the client's target market and provide relevant feedback. Furthermore, the feedback unit can consider the locations of the client's competitors and provide feedback relevant to those competitors. For example, the feedback unit can provide feedback based on regional consumer behavior data based on the client's location. It can also provide region-specific feedback based on the geographical location information of the target market. This allows for the selection of the optimal feedback method by considering the client's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0089] The feedback unit can analyze the client's social media activity and propose methods for providing feedback during the feedback process. For example, the feedback unit can analyze the content of the client's social media posts and provide relevant feedback. It can also analyze the reactions of the client's social media followers and provide relevant feedback. Furthermore, the feedback unit can analyze the engagement rate of the client's social media and provide relevant feedback. For example, the feedback unit can analyze the content of social media posts using text analysis technology and extract relevant keywords. It can also analyze follower reactions using sentiment analysis technology and identify positive and negative reactions. In this way, by analyzing the client's social media activity, it can provide relevant feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input social media post data into a generating AI and have the generating AI propose methods for providing feedback.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection department can analyze the success factors of a client's past advertising campaigns and prioritize the collection of new data with similar success factors. For example, it can identify keywords and media channels that were particularly effective in past campaigns and collect new data based on them. It can also prioritize the collection of data from target audiences that showed high engagement in past campaigns. Furthermore, it can analyze feedback from past campaigns and select data collection methods that reflect areas for improvement. This enables more effective data collection by leveraging past success factors.

[0092] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be accelerated to collect the necessary data in a short amount of time. In this way, by adjusting the timing of data collection according to the user's emotions, the burden on the user can be reduced.

[0093] The data collection unit can analyze a client's past advertising campaign history and select the optimal data collection method. For example, it can collect data using similar methods based on data from a client's past successful campaigns. It can also analyze data from a client's past unsuccessful campaigns and select a data collection method that reflects areas for improvement. Furthermore, it can compare the performance of a client's past campaigns and select the most effective data collection method. In this way, by analyzing a client's past advertising campaign history, the optimal data collection method can be selected.

[0094] The data collection unit can filter data based on the client's current market conditions and competitive trends. For example, it can prioritize collecting data on competitors, taking into account the client's current market share. It can also analyze the client's industry trends and filter and collect relevant data. Furthermore, it can analyze the advertising strategies of the client's competitors and select the optimal data collection method. This allows for the collection of highly relevant data by filtering data based on the client's current market conditions and competitive trends.

[0095] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting high-priority data. If the user is relaxed, it can prioritize collecting detailed data. Furthermore, if the user is in a hurry, it can prioritize collecting data that can be retrieved quickly. In this way, by prioritizing the data to be collected according to the user's emotions, it is possible to prioritize the collection of important data.

[0096] The data collection unit can prioritize the collection of highly relevant data by considering the client's geographical location during data collection. For example, it can prioritize the collection of region-specific data based on the client's location. It can also prioritize the collection of relevant data by considering the geographical location of the client's target market. Furthermore, it can prioritize the collection of data related to the client's competitors by considering their locations. In this way, by considering the client's geographical location, it is possible to prioritize the collection of highly relevant data.

[0097] The data collection unit can analyze the client's social media activity and collect relevant data during the data collection process. For example, it can analyze the content of the client's social media posts and collect relevant data. It can also analyze the reactions of the client's social media followers and collect relevant data. Furthermore, it can analyze the engagement rate of the client's social media and collect relevant data. In this way, relevant data can be collected by analyzing the client's social media activity.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0099] The analysis department can adjust the level of detail in the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can adjust the level of detail in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0100] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, natural language processing algorithms can be applied to text data. Image recognition algorithms can be applied to image data. Furthermore, statistical analysis algorithms can be applied to numerical data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection department gathers industry data, competitive information, and client requirements. For example, they collect market research data and industry reports, as well as product information and marketing strategies of competing companies. They can also gather client requirements through online surveys and interviews. Step 2: The analysis unit analyzes the data collected by the data collection unit and extracts patterns. For example, it analyzes the data using data mining techniques, statistical analysis, and machine learning algorithms, and extracts patterns using clustering algorithms and regression analysis. Step 3: The proposal team generates a customized advertising plan based on the analysis results obtained by the analysis team. For example, they identify the target audience and adjust the advertising message, delivery timing, and creative elements. Step 4: The Feedback Department evaluates the results of the advertising plan proposed by the Proposal Department, and the AI ​​continues to learn. For example, it evaluates click-through rates and conversion rates, and collects advertising performance data and client feedback to use as data for the AI ​​to learn.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects industry data and client requests using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized advertising plan based on the analysis results. The feedback unit is implemented in the control unit 46A of the smart device 14 and evaluates the proposal results and collects data for the AI ​​to learn from. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects industry data and client requests using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates a customized advertising plan based on the analysis results. The feedback unit is implemented, for example, in the control unit 46A of the smart glasses 214 and evaluates the proposal results and collects data for the AI ​​to learn from. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects industry data and client requests using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized advertising plan based on the analysis results. The feedback unit is implemented in the control unit 46A of the headset terminal 314 and evaluates the proposal results and collects data for the AI ​​to learn from. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects industry data and client requests using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a customized advertising plan based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 and evaluates the proposal results and collects data for the AI ​​to learn from. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) The data collection department collects industry data, competitor information, and client requests. An analysis unit analyzes the data collected by the aforementioned data collection unit and extracts patterns, A proposal unit that generates a customized advertising plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that evaluates the proposed advertising plan results generated by the aforementioned proposal unit and allows the AI ​​to continuously learn. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit, 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 3) The aforementioned data acquisition unit, We analyze the client's past advertising campaign history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data acquisition unit, When collecting data, filtering is performed based on the client's current market conditions and competitive trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data acquisition unit, 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 6) The aforementioned data acquisition unit, When collecting data, the system prioritizes the collection of highly relevant data, taking into account the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit, During data collection, we analyze the client's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is 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 12) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the advertising plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making proposals, different proposal algorithms are applied depending on the category of the advertising plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, we prioritize them based on when the advertising plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the advertising plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, refer to past feedback data to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the client's current market situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we select the most appropriate feedback method, taking into account the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is During the feedback process, we analyze the client's social media activity and propose feedback methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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. The data collection department collects industry data, competitor information, and client requests. An analysis unit analyzes the data collected by the aforementioned data collection unit and extracts patterns, A proposal unit that generates a customized advertising plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that evaluates the proposed advertising plan results generated by the proposal unit and allows the AI ​​to continuously learn. A system characterized by the following features.

2. The aforementioned data acquisition 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.

3. The aforementioned data acquisition unit is We analyze the client's past advertising campaign history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned data acquisition unit is When collecting data, filtering is performed based on the client's current market conditions and competitive trends. The system according to feature 1.

5. The aforementioned data acquisition 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.

6. The aforementioned data acquisition unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the client's geographical location. The system according to feature 1.

7. The aforementioned data acquisition unit is During data collection, we analyze the client's social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system according to feature 1.

Images