system

Abstract

The system according to this embodiment aims to analyze consumer behavior and formulate highly accurate marketing strategies. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a strategy formulation unit, and an evaluation unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The strategy formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the effectiveness of the marketing strategy formulated by the strategy formulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to achieve highly accurate consumer targeting.

[0005] The system according to the embodiment aims to analyze consumer behavior and formulate a highly accurate marketing strategy.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a formulation unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The formulation unit formulates a marketing strategy based on the analysis result obtained by the analysis unit. The evaluation unit evaluates the effect of the marketing strategy formulated by the formulation unit. ​​​​​​​​​​​​[Effects of the Invention]

[0007] The system according to this embodiment can analyze consumer behavior and formulate highly accurate marketing strategies. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) A marketing system according to an embodiment of the present invention is a system that analyzes consumer behavior using AI and provides individualized marketing strategies. This marketing system collects online and offline data and performs integrated analysis using AI. Next, it uses generative AI to analyze consumer behavior data in real time and formulate individually optimized marketing strategies. This improves the accuracy of targeting and is expected to increase sales. For example, the marketing system collects data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. This data is input into the AI ​​and performed integrated analysis. Next, the collected data is analyzed in real time using generative AI. The generative AI analyzes consumer behavior patterns and formulates individually optimized marketing strategies. For example, it can provide consumers who have shown interest in a particular product with promotional information related to that product. Furthermore, the generative AI also provides data for evaluating and improving advertising effectiveness. For example, it can analyze how effective a particular advertisement is and, if it is ineffective, improve the content and delivery method of the advertisement. This mechanism allows for a rapid response to consumer needs and improves the accuracy of targeting. It also improves advertising effectiveness and reduces costs. This is expected to increase sales and improve customer satisfaction and loyalty. For example, by implementing this system for users of online shopping platforms and e-commerce stores, consumer behavior can be predicted and individually optimized marketing strategies can be provided. This enables more precise targeting and a better understanding of consumer behavior. This system is crucial for responding to the rapid growth of e-commerce and strengthening the integration of digital and physical stores. There is a need to provide an integrated customer experience through digital channels such as apps, websites, and social media, as well as physical stores. Thus, a system that analyzes consumer behavior with AI and provides individualized marketing strategies is expected to contribute to improved targeting accuracy, increased sales, and enhanced customer satisfaction and loyalty.This allows the marketing system to analyze consumer behavior and provide personalized marketing strategies, leading to improved targeting accuracy and increased sales.

[0029] The marketing system according to the embodiment comprises a data collection unit, an analysis unit, a strategy formulation unit, and an evaluation unit. The data collection unit collects data. The data collection unit collects data such as a consumer's website browsing history, purchase history, social media activity, and physical store purchase history. The data collection unit can collect cookie information, for example. The data collection unit can collect transaction data, for example. The data collection unit can collect social media posts, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit analyzes the data using, for example, statistical analysis techniques. The analysis unit analyzes the data using, for example, machine learning techniques. The strategy formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The strategy formulation unit formulates, for example, a targeting strategy. The strategy formulation unit formulates, for example, a promotion strategy. The strategy formulation unit formulates, for example, an advertising strategy. The evaluation unit evaluates the effectiveness of the marketing strategy formulated by the strategy formulation unit. The evaluation unit evaluates, for example, sales growth. The evaluation unit evaluates, for example, customer satisfaction. The evaluation unit also evaluates, for example, reach. As a result, the marketing system according to the embodiment can analyze consumer behavior and provide individualized marketing strategies, thereby improving targeting accuracy and increasing sales.

[0030] The data collection unit collects data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. Specifically, website browsing history includes information such as which pages a user visited, which links they clicked, and how long they spent on each page. This data is collected through cookie information, allowing for detailed tracking of users' online behavior. Purchase history includes information on products and services that users have purchased in the past, enabling an understanding of users' purchasing trends and preferences. Social media activity collects information such as what users post, what content they react to, and what communities they participate in, allowing for a deeper understanding of users' interests and concerns. Physical store purchase history includes information on products that users have purchased at physical stores, enabling an integrated analysis of online and offline purchasing behavior. The data collection unit centrally manages and updates this data in real time, enabling an understanding of the latest consumer behavior. Furthermore, the data collection unit also collects transaction data and social media posts. Transaction data includes details of transactions made by users, enabling an analysis of consumers' purchasing patterns and spending trends. Social media posts collect information that users share on social media, allowing for a real-time understanding of consumer opinions and sentiments. This enables the data collection unit to gather a wide range of data from diverse data sources and provide a foundation for detailed analysis of consumer behavior.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques to analyze the data. Data mining techniques are used to extract useful patterns and relationships from large amounts of data, thereby revealing consumer behavior patterns and purchasing trends. For example, it can analyze combinations of frequently purchased products or purchasing behavior trends at specific times of day. The analysis unit also uses statistical analysis techniques to analyze the data. Statistical analysis techniques analyze the distribution and correlation of data, thereby quantitatively understanding consumer attributes and behavioral characteristics. For example, it can analyze purchasing trends by attributes such as age, gender, and region to identify target segments. Furthermore, the analysis unit uses machine learning techniques to analyze the data. Machine learning techniques learn from data and build predictive models, thereby predicting future consumer behavior. For example, it can predict which products consumers are most likely to purchase next based on their past purchase history, or predict their response to specific campaigns. In addition, the analysis unit uses AI to process data in real time, enabling immediate understanding of consumer behavior. AI can analyze social media posts using natural language processing technology to extract consumer emotions and opinions. This allows the analysis unit to analyze the collected data from multiple perspectives and uncover the deeper reasons behind consumer behavior.

[0032] The Strategy Planning Department formulates marketing strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Planning Department formulates targeting strategies. Targeting strategies are strategies that provide optimal marketing messages to specific consumer segments, thereby maximizing the effectiveness of marketing. For example, strategies are formulated that are tailored to consumer attributes and behavior, such as using social media for promotions for products aimed at young people and television advertising for products aimed at the elderly. The Strategy Planning Department also formulates promotion strategies. Promotion strategies are strategies that convey the appeal of products and services to consumers, thereby stimulating their desire to purchase. For example, promotions that attract consumer interest, such as limited-time discount campaigns and the provision of purchase benefits, are planned. The Strategy Planning Department also formulates advertising strategies. Advertising strategies are strategies that effectively convey information about products and services to consumers, thereby improving brand awareness. For example, online and offline advertising are combined to approach consumers from multiple angles. Furthermore, based on the data from the Analysis Department, the Strategy Planning Department can predict consumer behavior and reactions and deliver marketing messages at the optimal timing. This allows the planning department to develop flexible marketing strategies that meet consumer needs and to implement effective marketing activities.

[0033] The evaluation department assesses the effectiveness of the marketing strategy formulated by the planning department. For example, the evaluation department evaluates sales growth. Sales growth is a direct indicator of the effectiveness of the marketing strategy, allowing for a quantitative assessment of its success. For example, they might analyze sales data during a specific campaign period to evaluate the campaign's effectiveness. The evaluation department also evaluates customer satisfaction. Customer satisfaction is an indicator of consumer satisfaction, allowing for an assessment of the quality of the marketing strategy. For example, they might collect surveys and customer reviews and analyze consumer opinions and feedback. The evaluation department also evaluates reach. Reach is an indicator of how many consumers the marketing message reached, allowing for an assessment of the strategy's reach. For example, they might analyze online ad impressions and clicks to evaluate the effectiveness of the ads. Furthermore, based on these evaluation results, the evaluation department can identify areas for improvement in the marketing strategy and reflect them in the next strategy formulation. For example, if sales growth is not as expected, they can analyze the cause and revise the promotion content and targeting. If customer satisfaction is low, they can identify the reasons and clarify areas for improvement in the product or service. This allows the evaluation department to continuously assess the effectiveness of marketing strategies and make improvements, thereby achieving more effective marketing activities.

[0034] The data collection unit can collect data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. For example, the data collection unit can collect consumers' website browsing history. For example, the data collection unit can collect consumers' purchase history. For example, the data collection unit can collect consumers' social media activity. For example, the data collection unit can collect consumers' physical store purchase history. By collecting diverse consumer behavior data, more accurate analysis becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input consumers' website browsing history into AI, and the AI ​​can collect the data.

[0035] The analysis unit can analyze collected data in real time and analyze consumer behavior patterns. For example, the analysis unit analyzes collected data in real time. For example, the analysis unit analyzes consumer behavior patterns. For example, the analysis unit performs frequency analysis. For example, the analysis unit performs sequence analysis. This enables the rapid formulation of marketing strategies through real-time data analysis. 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 collected data into AI, and the AI ​​can analyze the data in real time.

[0036] The strategy development department can develop individually optimized marketing strategies based on consumer behavior patterns. For example, the strategy development department develops marketing strategies based on consumer behavior patterns. For example, the strategy development department conducts personalized marketing. For example, the strategy development department conducts customized promotions. For example, the strategy development department develops targeting strategies. This improves the accuracy of targeting by providing marketing strategies optimized for each consumer. Some or all of the above processes in the strategy development department may be performed using AI, for example, or not using AI. For example, the strategy development department can input consumer behavior patterns into AI, and the AI ​​can develop individually optimized marketing strategies.

[0037] The evaluation unit can assess the effectiveness of advertisements and, if the effectiveness is low, can improve the content and delivery methods of the advertisements. For example, the evaluation unit can evaluate the effectiveness of advertisements. For example, the evaluation unit can evaluate the click-through rate. For example, the evaluation unit can evaluate the conversion rate. For example, the evaluation unit can evaluate the reach. For example, the evaluation unit can improve the content of advertisements. For example, the evaluation unit can improve the delivery methods of advertisements. In this way, the effectiveness of advertisements can be maximized by evaluating and improving their effectiveness. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input advertising effectiveness data into AI, which can evaluate the advertising effectiveness and suggest areas for improvement.

[0038] The data collection unit can analyze a user's past behavioral history and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user has frequently visited in the past. For example, the data collection unit can collect data based on a user's past purchase history. For example, the data collection unit can analyze a user's social media activity history and collect relevant data. This allows for more effective data collection by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of store information in the area where the user is currently located. For example, the data collection unit can collect nearby event information based on the user's location information. For example, the data collection unit can collect advertising data related to the user's geographical location. This allows for the collection of more relevant data by considering the user'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 the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze the activity of a user's followers and friends and collect relevant data. For example, the data collection unit can collect data related to groups and communities in which the user participates. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for more effective analysis by adjusting the level of detail of the analysis according to 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 the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a sentiment analysis algorithm to social media data. For example, the analysis unit applies a behavioral analysis algorithm to website browsing history data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This makes it possible to perform more effective analysis 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 the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more effective 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 relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The strategy formulation department can adjust the level of detail of a marketing strategy based on the importance of consumer behavior patterns when formulating the strategy. For example, the strategy formulation department can formulate a detailed marketing strategy for high-importance behavior patterns. For example, the strategy formulation department can formulate a simplified marketing strategy for low-importance behavior patterns. For example, the strategy formulation department can determine the priority of the marketing strategy according to the importance of the behavior patterns. This allows for more effective marketing by adjusting the level of detail of the marketing strategy according to the importance of consumer behavior patterns. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not using AI. For example, the strategy formulation department can input the importance of consumer behavior patterns into the AI, and the AI ​​can adjust the level of detail of the strategy.

[0047] The strategy development department can apply different strategic algorithms depending on the consumer category when formulating marketing strategies. For example, the department might develop a marketing strategy utilizing social media for young people. For example, the department might develop a marketing strategy utilizing television advertising for seniors. For example, the department might develop a marketing strategy utilizing business-related media for business professionals. By applying the appropriate strategic algorithm according to the consumer category, the accuracy of marketing is improved. Some or all of the above processes in the strategy development department may be performed using AI, for example, or not using AI. For example, the strategy development department can input consumer categories into AI, and the AI ​​can apply an appropriate strategic algorithm.

[0048] The strategy planning department can prioritize strategies based on the timing of consumer behavior pattern data collection when formulating marketing strategies. For example, the planning department might formulate marketing strategies by prioritizing the latest behavior patterns. For example, the planning department might consider the latest behavior patterns while referring to past behavior patterns. For example, the planning department might adjust the priority of marketing strategies according to the timing of behavior pattern data collection. This allows for more effective marketing by prioritizing strategies based on the timing of consumer behavior pattern data collection. Some or all of the above processes in the strategy planning department may be performed using AI, or not. For example, the strategy planning department could input the timing of consumer behavior pattern data collection into an AI, which could then determine the priority of strategies.

[0049] The strategy formulation department can adjust the order of strategies based on the relevance of consumer behavior patterns when formulating marketing strategies. For example, the strategy formulation department can formulate marketing strategies by prioritizing highly relevant behavior patterns. For example, the strategy formulation department can postpone less relevant behavior patterns. For example, the strategy formulation department can adjust the order of marketing strategies according to the relevance of behavior patterns. By adjusting the order of strategies based on the relevance of consumer behavior patterns, more effective marketing becomes possible. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not using AI. For example, the strategy formulation department can input the relevance of consumer behavior patterns into AI, and the AI ​​can adjust the order of strategies.

[0050] The evaluation unit can optimize its evaluation algorithm by referring to past advertising effectiveness data when evaluating advertising effectiveness. For example, the evaluation unit adjusts the current advertising effectiveness evaluation algorithm based on past advertising effectiveness data. For example, the evaluation unit analyzes past advertising effectiveness data and selects the optimal evaluation algorithm. For example, the evaluation unit improves the accuracy of the evaluation algorithm by referring to past advertising effectiveness data. As a result, the accuracy of the evaluation algorithm is improved by referring to past advertising effectiveness data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past advertising effectiveness data into AI, and the AI ​​can optimize the evaluation algorithm.

[0051] The evaluation unit can take consumer attribute information into consideration when evaluating advertising effectiveness. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's age and gender. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's purchase history. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's regional information. By taking consumer attribute information into consideration, it becomes possible to evaluate advertising effectiveness with greater accuracy. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input consumer attribute information into AI, and the AI ​​can evaluate advertising effectiveness.

[0052] The evaluation unit can consider the geographical distribution of advertisements when evaluating their effectiveness. For example, the evaluation unit can evaluate the effectiveness of advertisements in each region where they were delivered. For example, the evaluation unit can analyze differences in advertising effectiveness based on geographical distribution. For example, the evaluation unit can compare advertising effectiveness in each region and formulate the optimal advertising delivery strategy. This makes it possible to evaluate advertising effectiveness with greater accuracy by considering the geographical distribution of advertisements. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of advertisements into AI, and the AI ​​can evaluate the effectiveness of the advertisements.

[0053] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature on advertising when evaluating advertising effectiveness. For example, the evaluation unit can improve its evaluation algorithm by referring to the latest research on advertising effectiveness. For example, the evaluation unit can introduce new metrics for evaluating advertising effectiveness based on relevant literature. For example, the evaluation unit can improve the accuracy of its evaluations by referring to literature on advertising effectiveness. As a result, the accuracy of the evaluation is improved by referring to relevant literature on advertising. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature on advertising into AI, and the AI ​​can improve the accuracy of the evaluation.

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

[0055] The data collection unit can analyze a user's past behavioral history and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user has frequently visited in the past. For example, the data collection unit can collect data based on a user's past purchase history. For example, the data collection unit can analyze a user's social media activity history and collect relevant data. This allows for more effective data collection by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal data collection method.

[0056] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of store information in the area where the user is currently located. For example, the data collection unit can collect nearby event information based on the user's location information. For example, the data collection unit can collect advertising data related to the user's geographical location. This allows for the collection of more relevant data by considering the user'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 the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0058] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze the activity of a user's followers and friends and collect relevant data. For example, the data collection unit can collect data related to groups and communities in which the user participates. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0059] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects data. The collection unit collects data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. The collection unit can collect cookie information, for example. The collection unit can collect transaction data, for example. The collection unit can collect social media posts, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit analyzes the data using, for example, statistical analysis techniques. The analysis unit analyzes the data using, for example, machine learning techniques. Step 3: The Planning Department formulates a marketing strategy based on the analysis results obtained by the Analysis Department. For example, the Planning Department formulates a targeting strategy. For example, the Planning Department formulates a promotion strategy. For example, the Planning Department formulates an advertising strategy. Step 4: The evaluation department evaluates the effectiveness of the marketing strategy formulated by the planning department. The evaluation department evaluates, for example, sales growth. The evaluation department evaluates, for example, customer satisfaction. The evaluation department evaluates, for example, reach.

[0062] (Example of form 2) A marketing system according to an embodiment of the present invention is a system that analyzes consumer behavior using AI and provides individualized marketing strategies. This marketing system collects online and offline data and performs integrated analysis using AI. Next, it uses generative AI to analyze consumer behavior data in real time and formulate individually optimized marketing strategies. This improves the accuracy of targeting and is expected to increase sales. For example, the marketing system collects data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. This data is input into the AI ​​and performed integrated analysis. Next, the collected data is analyzed in real time using generative AI. The generative AI analyzes consumer behavior patterns and formulates individually optimized marketing strategies. For example, it can provide consumers who have shown interest in a particular product with promotional information related to that product. Furthermore, the generative AI also provides data for evaluating and improving advertising effectiveness. For example, it can analyze how effective a particular advertisement is and, if it is ineffective, improve the content and delivery method of the advertisement. This mechanism allows for a rapid response to consumer needs and improves the accuracy of targeting. It also improves advertising effectiveness and reduces costs. This is expected to increase sales and improve customer satisfaction and loyalty. For example, by implementing this system for users of online shopping platforms and e-commerce stores, consumer behavior can be predicted and individually optimized marketing strategies can be provided. This enables more precise targeting and a better understanding of consumer behavior. This system is crucial for responding to the rapid growth of e-commerce and strengthening the integration of digital and physical stores. There is a need to provide an integrated customer experience through digital channels such as apps, websites, and social media, as well as physical stores. Thus, a system that analyzes consumer behavior with AI and provides individualized marketing strategies is expected to contribute to improved targeting accuracy, increased sales, and enhanced customer satisfaction and loyalty.This allows the marketing system to analyze consumer behavior and provide personalized marketing strategies, leading to improved targeting accuracy and increased sales.

[0063] The marketing system according to the embodiment comprises a data collection unit, an analysis unit, a strategy formulation unit, and an evaluation unit. The data collection unit collects data. The data collection unit collects data such as a consumer's website browsing history, purchase history, social media activity, and physical store purchase history. The data collection unit can collect cookie information, for example. The data collection unit can collect transaction data, for example. The data collection unit can collect social media posts, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit analyzes the data using, for example, statistical analysis techniques. The analysis unit analyzes the data using, for example, machine learning techniques. The strategy formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The strategy formulation unit formulates, for example, a targeting strategy. The strategy formulation unit formulates, for example, a promotion strategy. The strategy formulation unit formulates, for example, an advertising strategy. The evaluation unit evaluates the effectiveness of the marketing strategy formulated by the strategy formulation unit. The evaluation unit evaluates, for example, sales growth. The evaluation unit evaluates, for example, customer satisfaction. The evaluation unit also evaluates, for example, reach. As a result, the marketing system according to the embodiment can analyze consumer behavior and provide individualized marketing strategies, thereby improving targeting accuracy and increasing sales.

[0064] The data collection unit collects data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. Specifically, website browsing history includes information such as which pages a user visited, which links they clicked, and how long they spent on each page. This data is collected through cookie information, allowing for detailed tracking of users' online behavior. Purchase history includes information on products and services that users have purchased in the past, enabling an understanding of users' purchasing trends and preferences. Social media activity collects information such as what users post, what content they react to, and what communities they participate in, allowing for a deeper understanding of users' interests and concerns. Physical store purchase history includes information on products that users have purchased at physical stores, enabling an integrated analysis of online and offline purchasing behavior. The data collection unit centrally manages and updates this data in real time, enabling an understanding of the latest consumer behavior. Furthermore, the data collection unit also collects transaction data and social media posts. Transaction data includes details of transactions made by users, enabling an analysis of consumers' purchasing patterns and spending trends. Social media posts collect information that users share on social media, allowing for a real-time understanding of consumer opinions and sentiments. This enables the data collection unit to gather a wide range of data from diverse data sources and provide a foundation for detailed analysis of consumer behavior.

[0065] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques to analyze the data. Data mining techniques are used to extract useful patterns and relationships from large amounts of data, thereby revealing consumer behavior patterns and purchasing trends. For example, it can analyze combinations of frequently purchased products or purchasing behavior trends at specific times of day. The analysis unit also uses statistical analysis techniques to analyze the data. Statistical analysis techniques analyze the distribution and correlation of data, thereby quantitatively understanding consumer attributes and behavioral characteristics. For example, it can analyze purchasing trends by attributes such as age, gender, and region to identify target segments. Furthermore, the analysis unit uses machine learning techniques to analyze the data. Machine learning techniques learn from data and build predictive models, thereby predicting future consumer behavior. For example, it can predict which products consumers are most likely to purchase next based on their past purchase history, or predict their response to specific campaigns. In addition, the analysis unit uses AI to process data in real time, enabling immediate understanding of consumer behavior. AI can analyze social media posts using natural language processing technology to extract consumer emotions and opinions. This allows the analysis unit to analyze the collected data from multiple perspectives and uncover the deeper reasons behind consumer behavior.

[0066] The Strategy Planning Department formulates marketing strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Planning Department formulates targeting strategies. Targeting strategies are strategies that provide optimal marketing messages to specific consumer segments, thereby maximizing the effectiveness of marketing. For example, strategies are formulated that are tailored to consumer attributes and behavior, such as using social media for promotions for products aimed at young people and television advertising for products aimed at the elderly. The Strategy Planning Department also formulates promotion strategies. Promotion strategies are strategies that convey the appeal of products and services to consumers, thereby stimulating their desire to purchase. For example, promotions that attract consumer interest, such as limited-time discount campaigns and the provision of purchase benefits, are planned. The Strategy Planning Department also formulates advertising strategies. Advertising strategies are strategies that effectively convey information about products and services to consumers, thereby improving brand awareness. For example, online and offline advertising are combined to approach consumers from multiple angles. Furthermore, based on the data from the Analysis Department, the Strategy Planning Department can predict consumer behavior and reactions and deliver marketing messages at the optimal timing. This allows the planning department to develop flexible marketing strategies that meet consumer needs and to implement effective marketing activities.

[0067] The evaluation department assesses the effectiveness of the marketing strategy formulated by the planning department. For example, the evaluation department evaluates sales growth. Sales growth is a direct indicator of the effectiveness of the marketing strategy, allowing for a quantitative assessment of its success. For example, they might analyze sales data during a specific campaign period to evaluate the campaign's effectiveness. The evaluation department also evaluates customer satisfaction. Customer satisfaction is an indicator of consumer satisfaction, allowing for an assessment of the quality of the marketing strategy. For example, they might collect surveys and customer reviews and analyze consumer opinions and feedback. The evaluation department also evaluates reach. Reach is an indicator of how many consumers the marketing message reached, allowing for an assessment of the strategy's reach. For example, they might analyze online ad impressions and clicks to evaluate the effectiveness of the ads. Furthermore, based on these evaluation results, the evaluation department can identify areas for improvement in the marketing strategy and reflect them in the next strategy formulation. For example, if sales growth is not as expected, they can analyze the cause and revise the promotion content and targeting. If customer satisfaction is low, they can identify the reasons and clarify areas for improvement in the product or service. This allows the evaluation department to continuously assess the effectiveness of marketing strategies and make improvements, thereby achieving more effective marketing activities.

[0068] The data collection unit can collect data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. For example, the data collection unit can collect consumers' website browsing history. For example, the data collection unit can collect consumers' purchase history. For example, the data collection unit can collect consumers' social media activity. For example, the data collection unit can collect consumers' physical store purchase history. By collecting diverse consumer behavior data, more accurate analysis becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input consumers' website browsing history into AI, and the AI ​​can collect the data.

[0069] The analysis unit can analyze collected data in real time and analyze consumer behavior patterns. For example, the analysis unit analyzes collected data in real time. For example, the analysis unit analyzes consumer behavior patterns. For example, the analysis unit performs frequency analysis. For example, the analysis unit performs sequence analysis. This enables the rapid formulation of marketing strategies through real-time data analysis. 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 collected data into AI, and the AI ​​can analyze the data in real time.

[0070] The strategy development department can develop individually optimized marketing strategies based on consumer behavior patterns. For example, the strategy development department develops marketing strategies based on consumer behavior patterns. For example, the strategy development department conducts personalized marketing. For example, the strategy development department conducts customized promotions. For example, the strategy development department develops targeting strategies. This improves the accuracy of targeting by providing marketing strategies optimized for each consumer. Some or all of the above processes in the strategy development department may be performed using AI, for example, or not using AI. For example, the strategy development department can input consumer behavior patterns into AI, and the AI ​​can develop individually optimized marketing strategies.

[0071] The evaluation unit can assess the effectiveness of advertisements and, if the effectiveness is low, can improve the content and delivery methods of the advertisements. For example, the evaluation unit can evaluate the effectiveness of advertisements. For example, the evaluation unit can evaluate the click-through rate. For example, the evaluation unit can evaluate the conversion rate. For example, the evaluation unit can evaluate the reach. For example, the evaluation unit can improve the content of advertisements. For example, the evaluation unit can improve the delivery methods of advertisements. In this way, the effectiveness of advertisements can be maximized by evaluating and improving their effectiveness. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input advertising effectiveness data into AI, which can evaluate the advertising effectiveness and suggest areas for improvement.

[0072] 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 may temporarily delay data collection. For example, if the user is relaxed, the data collection unit may actively collect data. For example, if the user is in a hurry, the data collection unit may quickly collect data. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze a user's past behavioral history and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user has frequently visited in the past. For example, the data collection unit can collect data based on a user's past purchase history. For example, the data collection unit can analyze a user's social media activity history and collect relevant data. This allows for more effective data collection by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting relevant entertainment data. For example, if the user is calm, the data collection unit will prioritize collecting education-related data. For example, if the user is stressed, the data collection unit will prioritize collecting relaxation-related data. This allows for more effective data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of store information in the area where the user is currently located. For example, the data collection unit can collect nearby event information based on the user's location information. For example, the data collection unit can collect advertising data related to the user's geographical location. This allows for the collection of more relevant data by considering the user'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 the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze the activity of a user's followers and friends and collect relevant data. For example, the data collection unit can collect data related to groups and communities in which the user participates. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for more effective analysis by adjusting the level of detail of the analysis according to 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 the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a sentiment analysis algorithm to social media data. For example, the analysis unit applies a behavioral analysis algorithm to website browsing history data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0081] 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 provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This makes it possible to perform more effective analysis 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 the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more effective 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 relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0084] The strategy development unit can estimate the user's emotions and adjust the way the marketing strategy is presented based on those emotions. For example, if the user is relaxed, the strategy development unit can provide a detailed marketing strategy. If the user is in a hurry, the strategy development unit can provide a concise marketing strategy that gets straight to the point. If the user is excited, the strategy development unit can provide a visually appealing marketing strategy. By adjusting the way the marketing strategy is presented according to the user's emotions, more effective marketing becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy development unit may be performed using AI or not. For example, the strategy development unit can input user emotion data into an AI, which can then adjust the way the marketing strategy is presented.

[0085] The strategy formulation department can adjust the level of detail of a marketing strategy based on the importance of consumer behavior patterns when formulating the strategy. For example, the strategy formulation department can formulate a detailed marketing strategy for high-importance behavior patterns. For example, the strategy formulation department can formulate a simplified marketing strategy for low-importance behavior patterns. For example, the strategy formulation department can determine the priority of the marketing strategy according to the importance of the behavior patterns. This allows for more effective marketing by adjusting the level of detail of the marketing strategy according to the importance of consumer behavior patterns. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not using AI. For example, the strategy formulation department can input the importance of consumer behavior patterns into the AI, and the AI ​​can adjust the level of detail of the strategy.

[0086] The strategy development department can apply different strategic algorithms depending on the consumer category when formulating marketing strategies. For example, the department might develop a marketing strategy utilizing social media for young people. For example, the department might develop a marketing strategy utilizing television advertising for seniors. For example, the department might develop a marketing strategy utilizing business-related media for business professionals. By applying the appropriate strategic algorithm according to the consumer category, the accuracy of marketing is improved. Some or all of the above processes in the strategy development department may be performed using AI, for example, or not using AI. For example, the strategy development department can input consumer categories into AI, and the AI ​​can apply an appropriate strategic algorithm.

[0087] The strategy development unit can estimate the user's emotions and adjust the length of the marketing strategy based on the estimated emotions. For example, if the user is in a hurry, the strategy development unit will provide a short, to-the-point marketing strategy. For example, if the user is relaxed, the strategy development unit will provide a detailed marketing strategy. For example, if the user is excited, the strategy development unit will provide a visually appealing marketing strategy. By adjusting the length of the marketing strategy according to the user's emotions, a more appropriate marketing strategy can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy development unit may be performed using AI or not using AI. For example, the strategy development unit can input user emotion data into AI, and the AI ​​can adjust the length of the marketing strategy.

[0088] The strategy planning department can prioritize strategies based on the timing of consumer behavior pattern data collection when formulating marketing strategies. For example, the planning department might formulate marketing strategies by prioritizing the latest behavior patterns. For example, the planning department might consider the latest behavior patterns while referring to past behavior patterns. For example, the planning department might adjust the priority of marketing strategies according to the timing of behavior pattern data collection. This allows for more effective marketing by prioritizing strategies based on the timing of consumer behavior pattern data collection. Some or all of the above processes in the strategy planning department may be performed using AI, or not. For example, the strategy planning department could input the timing of consumer behavior pattern data collection into an AI, which could then determine the priority of strategies.

[0089] The strategy formulation department can adjust the order of strategies based on the relevance of consumer behavior patterns when formulating marketing strategies. For example, the strategy formulation department can formulate marketing strategies by prioritizing highly relevant behavior patterns. For example, the strategy formulation department can postpone less relevant behavior patterns. For example, the strategy formulation department can adjust the order of marketing strategies according to the relevance of behavior patterns. By adjusting the order of strategies based on the relevance of consumer behavior patterns, more effective marketing becomes possible. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not using AI. For example, the strategy formulation department can input the relevance of consumer behavior patterns into AI, and the AI ​​can adjust the order of strategies.

[0090] The evaluation unit can estimate the user's emotions and adjust the advertising effectiveness evaluation method based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit will perform a detailed advertising effectiveness evaluation. For example, if the user is in a hurry, the evaluation unit will perform a concise advertising effectiveness evaluation. For example, if the user is excited, the evaluation unit will perform a visually appealing advertising effectiveness evaluation. By adjusting the advertising effectiveness evaluation method according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can adjust the advertising effectiveness evaluation method.

[0091] The evaluation unit can optimize its evaluation algorithm by referring to past advertising effectiveness data when evaluating advertising effectiveness. For example, the evaluation unit adjusts the current advertising effectiveness evaluation algorithm based on past advertising effectiveness data. For example, the evaluation unit analyzes past advertising effectiveness data and selects the optimal evaluation algorithm. For example, the evaluation unit improves the accuracy of the evaluation algorithm by referring to past advertising effectiveness data. As a result, the accuracy of the evaluation algorithm is improved by referring to past advertising effectiveness data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past advertising effectiveness data into AI, and the AI ​​can optimize the evaluation algorithm.

[0092] The evaluation unit can take consumer attribute information into consideration when evaluating advertising effectiveness. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's age and gender. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's purchase history. For example, the evaluation unit can evaluate advertising effectiveness based on the consumer's regional information. By taking consumer attribute information into consideration, it becomes possible to evaluate advertising effectiveness with greater accuracy. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input consumer attribute information into AI, and the AI ​​can evaluate advertising effectiveness.

[0093] The evaluation unit can estimate the user's emotions and adjust how the advertising effectiveness evaluation results are displayed based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit displays detailed evaluation results. For example, if the user is in a hurry, the evaluation unit displays concise evaluation results. For example, if the user is excited, the evaluation unit displays visually appealing evaluation results. This allows for more appropriate evaluation results to be provided by adjusting how the advertising effectiveness evaluation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can adjust how the advertising effectiveness evaluation results are displayed.

[0094] The evaluation unit can consider the geographical distribution of advertisements when evaluating their effectiveness. For example, the evaluation unit can evaluate the effectiveness of advertisements in each region where they were delivered. For example, the evaluation unit can analyze differences in advertising effectiveness based on geographical distribution. For example, the evaluation unit can compare advertising effectiveness in each region and formulate the optimal advertising delivery strategy. This makes it possible to evaluate advertising effectiveness with greater accuracy by considering the geographical distribution of advertisements. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of advertisements into AI, and the AI ​​can evaluate the effectiveness of the advertisements.

[0095] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature on advertising when evaluating advertising effectiveness. For example, the evaluation unit can improve its evaluation algorithm by referring to the latest research on advertising effectiveness. For example, the evaluation unit can introduce new metrics for evaluating advertising effectiveness based on relevant literature. For example, the evaluation unit can improve the accuracy of its evaluations by referring to literature on advertising effectiveness. As a result, the accuracy of the evaluation is improved by referring to relevant literature on advertising. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature on advertising into AI, and the AI ​​can improve the accuracy of the evaluation.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] 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 may temporarily delay data collection. For example, if the user is relaxed, the data collection unit may actively collect data. For example, if the user is in a hurry, the data collection unit may quickly collect data. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0099] The strategy development unit can estimate the user's emotions and adjust the way the marketing strategy is presented based on those emotions. For example, if the user is relaxed, the strategy development unit can provide a detailed marketing strategy. If the user is in a hurry, the strategy development unit can provide a concise marketing strategy that gets straight to the point. If the user is excited, the strategy development unit can provide a visually appealing marketing strategy. By adjusting the way the marketing strategy is presented according to the user's emotions, more effective marketing becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy development unit may be performed using AI or not. For example, the strategy development unit can input user emotion data into an AI, which can then adjust the way the marketing strategy is presented.

[0100] The evaluation unit can estimate the user's emotions and adjust the advertising effectiveness evaluation method based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit will perform a detailed advertising effectiveness evaluation. For example, if the user is in a hurry, the evaluation unit will perform a concise advertising effectiveness evaluation. For example, if the user is excited, the evaluation unit will perform a visually appealing advertising effectiveness evaluation. By adjusting the advertising effectiveness evaluation method according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can adjust the advertising effectiveness evaluation method.

[0101] The evaluation unit can estimate the user's emotions and adjust how the advertising effectiveness evaluation results are displayed based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit displays detailed evaluation results. For example, if the user is in a hurry, the evaluation unit displays concise evaluation results. For example, if the user is excited, the evaluation unit displays visually appealing evaluation results. This allows for more appropriate evaluation results to be provided by adjusting how the advertising effectiveness evaluation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into AI, and the AI ​​can adjust how the advertising effectiveness evaluation results are displayed.

[0102] The data collection unit can analyze a user's past behavioral history and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user has frequently visited in the past. For example, the data collection unit can collect data based on a user's past purchase history. For example, the data collection unit can analyze a user's social media activity history and collect relevant data. This allows for more effective data collection by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal data collection method.

[0103] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of store information in the area where the user is currently located. For example, the data collection unit can collect nearby event information based on the user's location information. For example, the data collection unit can collect advertising data related to the user's geographical location. This allows for the collection of more relevant data by considering the user'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 the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0105] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze the activity of a user's followers and friends and collect relevant data. For example, the data collection unit can collect data related to groups and communities in which the user participates. This allows for the collection of more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0106] The data collection unit can filter data based on the user's current areas of interest and purchase intent during data collection. For example, the data collection unit prioritizes collecting data related to products the user is currently interested in. For example, if the user has high purchase intent, the data collection unit collects data corresponding to that intent. For example, the data collection unit collects relevant advertising data based on the user's areas of interest. This allows for the collection of more relevant data by filtering data based on the user's areas of interest and purchase intent. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and purchase intent into the AI, which can then filter the data.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects data. The collection unit collects data such as consumers' website browsing history, purchase history, social media activity, and physical store purchase history. The collection unit can collect cookie information, for example. The collection unit can collect transaction data, for example. The collection unit can collect social media posts, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit analyzes the data using, for example, statistical analysis techniques. The analysis unit analyzes the data using, for example, machine learning techniques. Step 3: The Planning Department formulates a marketing strategy based on the analysis results obtained by the Analysis Department. For example, the Planning Department formulates a targeting strategy. For example, the Planning Department formulates a promotion strategy. For example, the Planning Department formulates an advertising strategy. Step 4: The evaluation department evaluates the effectiveness of the marketing strategy formulated by the planning department. The evaluation department evaluates, for example, sales growth. The evaluation department evaluates, for example, customer satisfaction. The evaluation department evaluates, for example, reach.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, formulation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to collect data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using data mining techniques, statistical analysis techniques, and machine learning techniques. The formulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and formulates targeting strategies, promotion strategies, and advertising strategies based on the analysis results. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of the formulated marketing strategies from the perspectives of increased sales, customer satisfaction, and reach. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, formulation unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to collect data such as the consumer's website browsing history, purchase history, social media activity, and purchase history at physical stores. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using data mining techniques, statistical analysis techniques, and machine learning techniques. The formulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and formulates targeting strategies, promotion strategies, and advertising strategies based on the analysis results. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of the formulated marketing strategies from the perspectives of increased sales, customer satisfaction, and reach. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, formulation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to collect data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using data mining techniques, statistical analysis techniques, and machine learning techniques. The formulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and formulates targeting strategies, promotion strategies, and advertising strategies based on the analysis results. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of the formulated marketing strategies from the perspectives of increased sales, customer satisfaction, and reach. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, formulation unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. 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 data mining techniques, statistical analysis techniques, and machine learning techniques. The formulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and formulates targeting strategies, promotion strategies, and advertising strategies based on the analysis results. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the effectiveness of the formulated marketing strategies from the perspectives of increased sales, customer satisfaction, and reach. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A formulation unit formulates a marketing strategy based on the analysis results obtained by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the effectiveness of the marketing strategy formulated by the formulation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in real time to analyze consumer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Develop individually optimized marketing strategies based on consumer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, We evaluate the effectiveness of our ads, and if they are ineffective, we improve the content and delivery methods of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, 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 18) The aforementioned planning department, We estimate user emotions and adjust the way marketing strategies are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, When developing a marketing strategy, adjust the level of detail based on the importance of consumer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, When formulating a marketing strategy, apply different strategic algorithms depending on the consumer category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, Estimate user sentiment and adjust the length of the marketing strategy based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, When developing a marketing strategy, prioritize strategies based on when consumer behavior patterns are collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When developing a marketing strategy, adjust the order of strategies based on the relationships between consumer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, We estimate user sentiment and adjust the advertising effectiveness evaluation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, When evaluating advertising effectiveness, the evaluation algorithm is optimized by referring to past advertising effectiveness data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating the effectiveness of advertising, consumer attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, We estimate the user's emotions and adjust how the advertising effectiveness evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, When evaluating the effectiveness of an advertisement, the geographical distribution of the advertisement should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, When evaluating the effectiveness of advertising, referencing relevant literature on advertising can improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A formulation unit formulates a marketing strategy based on the analysis results obtained by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the effectiveness of the marketing strategy formulated by the formulation unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as consumers' website browsing history, purchase history, social media activity, and purchase history at physical stores. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in real time to analyze consumer behavior patterns. The system according to feature 1.

4. The aforementioned planning department, Develop individually optimized marketing strategies based on consumer behavior patterns. The system according to feature 1.

5. The evaluation unit, We evaluate the effectiveness of our ads, and if they are ineffective, we improve the content and delivery methods of the ads. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and purchasing intent. The system according to feature 1.

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

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

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