system

Abstract

The system according to this embodiment aims to propose and implement optimal promotional tools and campaigns by utilizing sales data and customer data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a monitoring unit, and a generation unit. The collection unit collects sales data and customer data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes promotional tools and campaigns based on the data analyzed by the analysis unit. The monitoring unit monitors the effectiveness of the promotional tools and campaigns proposed by the proposal unit. The generation unit automatically generates the design and content of the promotional tools based on the effectiveness monitored by the monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 conventional technology, there is a problem that sales data and customer data are not centrally managed, and the optimization of sales promotion tools and campaigns is not sufficiently carried out.

[0005] The system according to the embodiment aims to propose and implement an optimal sales promotion tool and campaign by utilizing sales data and customer data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a monitoring unit, and a generation unit. The collection unit collects sales data and customer data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes promotional tools and campaigns based on the data analyzed by the analysis unit. The monitoring unit monitors the effectiveness of the promotional tools and campaigns proposed by the proposal unit. The generation unit automatically generates the design and content of the promotional tools based on the effectiveness monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and implement optimal promotional tools and campaigns by utilizing sales data and customer data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personalized sales promotion optimization agent "PromoSmart" according to an embodiment of the present invention is a system that prevents unnecessary ordering of promotional items and centrally manages and utilizes necessary data. This system uses AI that has learned sales data and customer data to propose optimal sales promotion tools and campaigns tailored to the characteristics of each store. Furthermore, it monitors the effectiveness of sales promotions in real time and provides suggestions for continuous improvement. In addition, the design and content of sales promotion tools are also streamlined with an automatic generation function. This mechanism prevents unnecessary ordering of promotional items and is expected to reduce costs by 10-20%. For example, sales data and customer data are used to train the AI. At this time, data such as sales history, customer attributes, and regional characteristics of each store are collected and analyzed by the AI. For example, if a store has many students, it can propose a sales promotion tool that emphasizes the benefits of student discount plans. This allows for the proposal of optimal sales promotion tools and campaigns for each store. Next, the effectiveness of sales promotions is monitored in real time. The AI ​​monitors the effectiveness of sales promotion campaigns implemented at each store in real time and analyzes the data. For example, it can determine how much sales a particular campaign generated and which customer segments were affected. This allows for the visualization of the effectiveness of promotional activities and the provision of continuous improvement suggestions. Furthermore, the design and content of promotional tools are streamlined through an automated generation function. Based on sales data, customer attributes, and regional characteristics, the AI ​​automatically generates the optimal design and content of promotional tools. For example, it can automatically create flyers and posters targeted at specific customer segments. This reduces the time and cost involved in creating promotional tools. This prevents unnecessary orders of promotional items and is expected to reduce costs by 10-20%. In addition, by visualizing the effectiveness of promotional activities and providing continuous improvement suggestions, customer satisfaction and profitability can be improved simultaneously. For example, by automatically generating the design and content of promotional tools, promotional activities can be made more efficient, and the optimal promotional tools and campaigns can be proposed for each store. This enables efficient and effective marketing across the mobile carrier industry. As a result, the PromoSmart system can prevent unnecessary orders of promotional items and achieve a 10-20% cost reduction.

[0029] The PromoSmart system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a monitoring unit, and a generation unit. The collection unit collects sales data and customer data. The collection unit can collect data such as sales history, customer attributes, and regional characteristics for each store. For example, the collection unit can collect past sales data and sales trends as sales history. For example, it can collect data such as age, gender, occupation, and purchase history as customer attributes. For example, it can collect data such as population density, climate, and cultural background as regional characteristics. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can propose optimal promotional tools and campaigns for each store based on the collected data. For example, the analysis unit can use AI to analyze sales data and customer data and propose optimal promotional tools and campaigns for each store. The proposal unit proposes promotional tools and campaigns based on the data analyzed by the analysis unit. For example, the proposal unit can use AI to propose optimal promotional tools and campaigns for each store. For example, the proposal unit can propose designs for flyers and posters targeted at specific customer segments. The Monitoring Unit monitors the effectiveness of promotional tools and campaigns proposed by the Proposal Unit. For example, the Monitoring Unit can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. For example, the Monitoring Unit can understand how much sales a particular campaign generated and which customer segments were affected. The Generation Unit automatically generates the design and content of promotional tools based on the effectiveness monitored by the Monitoring Unit. For example, the Generation Unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. For example, the Generation Unit can automatically create designs for flyers and posters targeted at specific customer segments. As a result, the PromoSmart system according to this embodiment can prevent unnecessary orders of promotional items and achieve a 10-20% cost reduction.

[0030] The data collection department collects sales data and customer data. For example, it can collect data such as sales history, customer attributes, and regional characteristics for each store. Specifically, as sales history, it can collect past sales data and sales trends. This includes detailed data such as sales quantity, sales amount, and sales date and time for each product. This data is important for understanding the different sales patterns and trends for each store. As customer attributes, it can collect data such as age, gender, occupation, and purchase history. For example, since purchasing trends differ depending on the age group and gender of customers, collecting this data can clarify the target customer segment. Furthermore, by analyzing occupation and purchase history, it is possible to understand customers' lifestyles and purchasing motivations and conduct more effective promotional activities. As regional characteristics, it can collect data such as population density, climate, and cultural background. For example, certain products may tend to sell more in areas with high population density. Also, since purchasing behavior is influenced by climate and cultural background, collecting this data allows for promotional activities tailored to the characteristics of each region. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, based on the collected data, the analysis department can propose the most suitable promotional tools and campaigns for each store. Specifically, it uses AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. The AI ​​uses machine learning algorithms to analyze past sales data and customer data and extract patterns and trends. For example, if sales of a particular product tend to increase in relation to a particular season or event, the AI ​​can recognize that pattern and propose a promotional campaign at the appropriate time. In addition, by analyzing customer attribute data, it can propose promotional tools that are effective for specific customer segments. For example, based on analysis results such as SNS advertising being effective for younger customers, while newspaper advertising and direct mail are effective for older customers, the optimal promotional tools can be selected. Furthermore, the analysis department can utilize regional characteristic data to propose promotional campaigns tailored to the characteristics of each region. For example, based on analysis results such as the effectiveness of promotional campaigns for heating appliances and winter clothing in cold climates, and for air conditioning appliances and summer clothing in warm climates, it is possible to propose the most suitable promotional campaign for each region. This allows the analysis department to quickly and accurately analyze the collected data and propose the most suitable promotional tools and campaigns for each store.

[0032] The Proposal Department proposes promotional tools and campaigns based on data analyzed by the Analysis Department. For example, the Proposal Department can use AI to propose the most suitable promotional tools and campaigns for each store. Specifically, it can propose designs for flyers and posters targeted at specific customer segments. Based on customer attribute data, the AI ​​generates the most effective designs and messages for the target customer segment. For example, it can propose colorful and visually appealing designs for younger customers and easy-to-read and simple designs for older customers. The Proposal Department can also propose the content and timing of promotional campaigns. For example, it can propose campaigns tailored to specific seasons or events to increase customer purchasing intent. Furthermore, the Proposal Department can propose promotional tools and campaigns tailored to the characteristics of each region. For example, in cold climates, it can propose promotional campaigns for heating appliances and winter clothing, while in warm climates, it can propose promotional campaigns for air conditioning appliances and summer clothing. In this way, the Proposal Department can propose effective promotional tools and campaigns for each store and support increased sales.

[0033] The Monitoring Department monitors the effectiveness of promotional tools and campaigns proposed by the Proposal Department. For example, the Monitoring Department can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. Specifically, it can understand how much sales a particular campaign generated and which customer segments were most affected. Based on sales and customer data, the Monitoring Department quantitatively evaluates the effectiveness of campaigns and identifies factors for success and areas for improvement. For example, it can analyze how many customers a particular flyer or poster attracted and what messages were most effective. Furthermore, the Monitoring Department can collect data in real time and monitor the progress of campaigns. This allows for a rapid evaluation of campaign effectiveness and adjustments as needed. In addition, the Monitoring Department can accumulate past campaign data and utilize it for planning future campaigns. This allows the Monitoring Department to continuously evaluate and improve the effectiveness of promotional campaigns, maximizing the overall effectiveness of the system.

[0034] The generation unit automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. For example, the generation unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. Specifically, it can automatically create designs for flyers and posters targeted at specific customer segments. Based on past data, the AI ​​generates effective designs and messages, creating promotional tools that are best suited to the target customer segment. For example, it can generate colorful and visually appealing designs for younger customers and easy-to-read and simple designs for older customers. Furthermore, the generation unit can automatically generate promotional tools tailored to the characteristics of each region. For example, in cold climates, it can generate promotional tools for heating appliances and winter clothing, and in warm climates, it can generate promotional tools for air conditioning appliances and summer clothing. In addition, the generation unit can continuously improve the effectiveness of promotional tools based on the data collected by the monitoring unit. This allows the generation unit to automatically generate effective promotional tools based on the latest data, maximizing the efficiency and effectiveness of promotional activities.

[0035] The data collection unit can collect data such as sales history, customer attributes, and regional characteristics for each store. For example, the data collection unit can collect sales history for each store. For example, the data collection unit can collect past sales data and sales trends. The data collection unit can also collect customer attributes. For example, the data collection unit can collect data such as age, gender, occupation, and purchase history. Furthermore, the data collection unit can also collect regional characteristics. For example, the data collection unit can collect data such as population density, climate, and cultural background. By collecting data such as sales history, customer attributes, and regional characteristics for each store, it becomes possible to propose more accurate promotional tools and campaigns. 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 data such as sales history, customer attributes, and regional characteristics for each store into AI and have the AI ​​perform the data collection.

[0036] The analysis unit can analyze the collected data and propose the most suitable promotional tools and campaigns for each store. For example, the analysis unit can propose the most suitable promotional tools and campaigns for each store based on the collected data. For example, the analysis unit can use AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. For example, the analysis unit can optimize for increased sales based on sales data. The analysis unit can also improve customer satisfaction based on customer data. Furthermore, the analysis unit can propose region-specific promotional tools and campaigns based on regional characteristics. In this way, by analyzing the collected data, the analysis unit can propose the most suitable promotional tools and campaigns for each store. 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 collected data into AI and have AI perform the data analysis.

[0037] The monitoring unit can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. For example, the monitoring unit can monitor the effectiveness of promotional campaigns implemented at each store in real time. The monitoring unit can, for example, understand how much sales a particular campaign generated and which customer segments were affected. The monitoring unit can, for example, use AI to monitor the effectiveness of promotional campaigns in real time and analyze the data. The monitoring unit can, for example, measure effects such as the sales increase rate and the customer response rate. By monitoring the effectiveness of promotional campaigns implemented at each store in real time and analyzing the data, the effectiveness of promotional activities can be visualized and suggestions for continuous improvement can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the effectiveness of promotional campaigns into AI and have AI perform the monitoring and analysis of the effects.

[0038] The generation unit can automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. For example, the generation unit can automatically generate the optimal design and content of promotional tools based on sales data. For example, the generation unit can automatically generate the design and content of promotional tools for a specific customer segment based on customer attributes. For example, the generation unit can automatically generate the design and content of promotional tools specific to a region based on regional characteristics. For example, the generation unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. This reduces the time and cost required to create promotional tools by automatically generating the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input sales data, customer attributes, and regional characteristics into AI and have the AI ​​perform the automatic generation of promotional tool designs and content.

[0039] The data collection unit can analyze the past sales history of each store and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on past sales history. For example, the data collection unit can focus data collection on specific product categories based on sales history. For example, the data collection unit can analyze sales history and collect data according to seasonal sales trends. By analyzing the past sales history of each store, the optimal data collection method can be selected, improving the efficiency of data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sales history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the store's current inventory status and seasonal factors during data collection. For example, the data collection unit can prioritize collecting data on products with low inventory levels, taking current inventory status into consideration. For example, the data collection unit can focus on collecting data on seasonal products, taking seasonal factors into consideration. For example, the data collection unit can combine inventory status and seasonal factors to perform optimal data collection. This allows for the collection of more relevant data by filtering data collection based on the store's current inventory status and seasonal factors. 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 inventory status and seasonal factor data into a generating AI and have the generating AI perform the data collection filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of stores during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the geographical location information of stores. For example, the data collection unit can compare and collect data from nearby stores by considering geographical location information. For example, the data collection unit can collect data related to local events and seasonal factors based on geographical location information. In this way, region-specific data can be effectively collected by prioritizing the collection of highly relevant data by considering the geographical location information of stores. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of stores into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the store's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the store's social media activity to understand customer reactions and trends. For example, the data collection unit can collect relevant data based on the content of social media posts. For example, the data collection unit can collect social media engagement data and use it for promotional activities. In this way, by analyzing the store's social media activity, it is possible to understand customer reactions and trends and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the sales data during the analysis. For example, the analysis unit can perform a detailed analysis on sales data with high importance. For example, the analysis unit can perform a simplified analysis on sales data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the sales data. By adjusting the level of detail of the analysis based on the importance of the sales data, it is possible to perform a detailed analysis on important data and improve accuracy. 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 importance of the sales data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data. For example, the analysis unit can apply a customer segmentation algorithm to customer data. For example, the analysis unit can apply a regional characteristics analysis algorithm to regional data. By applying different analysis algorithms depending on the data category, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time information. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can optimize the analysis schedule based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent information and provide real-time information by determining the priority of analysis based on the data submission date. 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 submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data and improving accuracy by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal department can adjust the level of detail in a proposal based on the importance of the promotional tools. For example, the proposal department can provide detailed proposals for high-importance promotional tools. For example, it can provide simplified proposals for low-importance promotional tools. The proposal department can also optimally allocate resources to a proposal according to the importance of the promotional tools. By adjusting the level of detail in a proposal based on the importance of the promotional tools, it can provide detailed proposals for important promotional tools and improve accuracy. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the promotional tools into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0048] The proposal department can apply different proposal algorithms depending on the category of the promotional tool when making a proposal. For example, the proposal department can apply a design proposal algorithm to a flyer. For example, the proposal department can apply a layout proposal algorithm to a poster. For example, the proposal department can apply a content proposal algorithm to a digital advertisement. By applying different proposal algorithms depending on the category of the promotional tool, the proposal department can provide the most suitable proposal for each category. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the category of the promotional tool into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the submission dates of the promotional tools. For example, the proposal department can prioritize proposals for promotional tools with upcoming submission dates. For example, the proposal department can postpone proposals for promotional tools with later submission dates. For example, the proposal department can optimize the proposal schedule based on the submission dates. By prioritizing proposals based on the submission dates of the promotional tools, the proposal department can prioritize proposals for promotional tools with upcoming submission dates, thereby improving efficiency. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the submission dates of the promotional tools into a generating AI and have the generating AI determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the promotional tools. For example, the proposal unit can prioritize proposals for highly relevant promotional tools. For example, the proposal unit can postpone proposals for less relevant promotional tools. For example, the proposal unit can optimally allocate resources for proposals based on the relevance of the promotional tools. By adjusting the order of proposals based on the relevance of the promotional tools, it can prioritize proposals for highly relevant promotional tools and improve accuracy. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the promotional tools into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0051] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of promotional campaigns during monitoring. For example, the monitoring unit can compare the effects of multiple promotional campaigns and analyze their interrelationships. For example, the monitoring unit can monitor the effects of different combinations of campaigns and propose the optimal combination. For example, the monitoring unit can monitor the effects of campaigns in real time by considering their interrelationships. This maximizes the effectiveness of campaigns by improving the accuracy of monitoring by considering the interrelationships of promotional campaigns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input interrelationship data of promotional campaigns into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0052] The monitoring unit can perform monitoring while considering the attribute information of the person implementing the promotional campaign. For example, the monitoring unit can analyze the effectiveness of the campaign based on the attribute information of the person implementing it. For example, the monitoring unit can improve the accuracy of monitoring by considering the past performance of the person implementing it. For example, the monitoring unit can select the optimal monitoring method based on the attribute information of the person implementing it. In this way, the accuracy of monitoring is improved by performing monitoring while considering the attribute information of the person implementing the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the attribute information of the person implementing it into a generating AI and have the generating AI perform the monitoring.

[0053] The monitoring unit can perform monitoring while considering the geographical distribution of the promotional campaign. For example, the monitoring unit can monitor the campaign effectiveness for each region based on the geographical distribution. For example, the monitoring unit can apply region-specific monitoring methods while considering the geographical distribution. For example, the monitoring unit can optimize campaigns for each region based on the geographical distribution. This allows for the optimization of campaign effectiveness for each region by monitoring while considering the geographical distribution of the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data into a generating AI and have the generating AI perform the monitoring.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the promotional campaign during monitoring. For example, the monitoring unit can analyze the effectiveness of the campaign based on the relevant literature. For example, the monitoring unit can improve the monitoring method by referring to relevant literature. For example, the monitoring unit can optimize the campaign based on relevant literature. This maximizes the effectiveness of the campaign by improving the accuracy of monitoring by referring to relevant literature on the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform the monitoring.

[0055] The generation unit can improve the accuracy of generation by considering the interrelationships of promotional tools during the generation process. For example, the generation unit can compare the effects of multiple promotional tools and analyze their interrelationships. For example, the generation unit can generate the optimal design and content by considering the effects of combinations of tools. For example, the generation unit can generate the design and content that maximizes the effectiveness of the tools by considering their interrelationships. In this way, the effectiveness of the tools can be maximized by improving the accuracy of generation by considering the interrelationships of promotional tools. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input interrelationship data of promotional tools into a generation AI and have the generation AI perform the generation.

[0056] The generation unit can perform generation while considering the attribute information of the submitter of the promotional tool. For example, the generation unit can generate the optimal design and content based on the submitter's attribute information. For example, the generation unit can improve the accuracy of generation by considering the submitter's past performance. For example, the generation unit can select the optimal generation method based on the submitter's attribute information. This improves the accuracy of generation by considering the attribute information of the submitter of the promotional tool. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submitter's attribute information into a generation AI and have the generation AI perform the generation.

[0057] The generation unit can perform generation while considering the geographical distribution of promotional tools. For example, the generation unit can generate optimal designs and content for each region based on geographical distribution. For example, the generation unit can apply region-specific generation methods while considering geographical distribution. For example, the generation unit can optimize promotional tools for each region based on geographical distribution. As a result, by performing generation while considering the geographical distribution of promotional tools, it is possible to provide optimal designs and content for each region. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input geographical distribution data into a generation AI and have the generation AI perform the generation.

[0058] The generation unit can improve the accuracy of its generation by referring to relevant literature on promotional tools during the generation process. For example, the generation unit can generate the optimal design and content based on the relevant literature. For example, the generation unit can improve its generation method by referring to relevant literature. For example, the generation unit can optimize promotional tools based on relevant literature. This maximizes the effectiveness of the tools by improving the accuracy of generation by referring to relevant literature on promotional tools. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the generation.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The PromoSmart system can also be equipped with a forecasting unit. This unit can predict future sales trends based on collected data. For example, it can analyze past sales data to predict which products will sell in the next season. It can also predict which products a particular customer is likely to purchase next, based on their purchase history. Furthermore, it can predict sales peaks during specific periods, taking into account local events and seasonal factors. This allows for a better understanding of future sales trends and more effective promotional activities.

[0061] The data collection unit can further collect customer social media activity. For example, it can collect information on what products customers mention on social media. It can also collect information on what trends customers are interested in on social media. Furthermore, it can collect information on the feedback customers provide on social media. By collecting customer social media activity, it becomes possible to understand customer interests and trends and propose more effective promotional tools and campaigns.

[0062] The monitoring unit can further perform monitoring by considering the attribute information of those implementing the promotional campaign. For example, the monitoring unit can analyze the effectiveness of the campaign based on the attribute information of the implementers. Furthermore, the monitoring unit can improve the accuracy of monitoring by considering the implementers' past performance. In addition, the monitoring unit can select the optimal monitoring method based on the attribute information of the implementers. This allows for improved monitoring accuracy by considering the attribute information of those implementing the promotional campaign.

[0063] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of stores. For example, the data collection unit can prioritize the collection of region-specific data based on the geographical location of stores. Furthermore, the data collection unit can compare and collect data from nearby stores, taking geographical location into consideration. Additionally, the data collection unit can collect data related to local events and seasonal factors based on geographical location. This allows for the effective collection of region-specific data by prioritizing the collection of highly relevant data while considering the geographical location of stores.

[0064] The analysis unit can apply different analysis algorithms depending on the data category. For example, it can apply a sales forecasting algorithm to sales data. It can also apply a customer segmentation algorithm to customer data. Furthermore, it can apply a regional characteristics analysis algorithm to regional data. By applying different analysis algorithms depending on the data category, it is possible to provide analysis results that are optimal for each category.

[0065] The monitoring unit can also perform monitoring while considering the geographical distribution of the promotional campaign. For example, the monitoring unit can monitor the effectiveness of the campaign in each region based on the geographical distribution. Furthermore, the monitoring unit can apply region-specific monitoring methods while considering the geographical distribution. In addition, the monitoring unit can optimize the campaign in each region based on the geographical distribution. This allows for the optimization of the campaign's effectiveness in each region by monitoring while considering the geographical distribution of the promotional campaign.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects sales data and customer data. For example, it can collect data such as sales history for each store, customer attributes, and regional characteristics. Sales history includes past sales data and sales trends, while customer attributes include data such as age, gender, occupation, and purchase history. Regional characteristics include data such as population density, climate, and cultural background. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. Step 3: The proposal department proposes promotional tools and campaigns based on the data analyzed by the analysis department. For example, they can use AI to propose the most suitable promotional tools and campaigns for each store, and suggest designs for flyers and posters targeted at specific customer segments. Step 4: The Monitoring Department monitors the effectiveness of the promotional tools and campaigns proposed by the Proposal Department. For example, they can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. They can understand how much sales a particular campaign generated and which customer segments were affected. Step 5: The generation unit automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. For example, using AI, it can automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics, and automatically create designs for flyers and posters targeted at specific customer segments.

[0068] (Example of form 2) The personalized sales promotion optimization agent "PromoSmart" according to an embodiment of the present invention is a system that prevents unnecessary ordering of promotional items and centrally manages and utilizes necessary data. This system uses AI that has learned sales data and customer data to propose optimal sales promotion tools and campaigns tailored to the characteristics of each store. Furthermore, it monitors the effectiveness of sales promotions in real time and provides suggestions for continuous improvement. In addition, the design and content of sales promotion tools are also streamlined with an automatic generation function. This mechanism prevents unnecessary ordering of promotional items and is expected to reduce costs by 10-20%. For example, sales data and customer data are used to train the AI. At this time, data such as sales history, customer attributes, and regional characteristics of each store are collected and analyzed by the AI. For example, if a store has many students, it can propose a sales promotion tool that emphasizes the benefits of student discount plans. This allows for the proposal of optimal sales promotion tools and campaigns for each store. Next, the effectiveness of sales promotions is monitored in real time. The AI ​​monitors the effectiveness of sales promotion campaigns implemented at each store in real time and analyzes the data. For example, it can determine how much sales a particular campaign generated and which customer segments were affected. This allows for the visualization of the effectiveness of promotional activities and the provision of continuous improvement suggestions. Furthermore, the design and content of promotional tools are streamlined through an automated generation function. Based on sales data, customer attributes, and regional characteristics, the AI ​​automatically generates the optimal design and content of promotional tools. For example, it can automatically create flyers and posters targeted at specific customer segments. This reduces the time and cost involved in creating promotional tools. This prevents unnecessary orders of promotional items and is expected to reduce costs by 10-20%. In addition, by visualizing the effectiveness of promotional activities and providing continuous improvement suggestions, customer satisfaction and profitability can be improved simultaneously. For example, by automatically generating the design and content of promotional tools, promotional activities can be made more efficient, and the optimal promotional tools and campaigns can be proposed for each store. This enables efficient and effective marketing across the mobile carrier industry. As a result, the PromoSmart system can prevent unnecessary orders of promotional items and achieve a 10-20% cost reduction.

[0069] The PromoSmart system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a monitoring unit, and a generation unit. The collection unit collects sales data and customer data. The collection unit can collect data such as sales history, customer attributes, and regional characteristics for each store. For example, the collection unit can collect past sales data and sales trends as sales history. For example, it can collect data such as age, gender, occupation, and purchase history as customer attributes. For example, it can collect data such as population density, climate, and cultural background as regional characteristics. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can propose optimal promotional tools and campaigns for each store based on the collected data. For example, the analysis unit can use AI to analyze sales data and customer data and propose optimal promotional tools and campaigns for each store. The proposal unit proposes promotional tools and campaigns based on the data analyzed by the analysis unit. For example, the proposal unit can use AI to propose optimal promotional tools and campaigns for each store. For example, the proposal unit can propose designs for flyers and posters targeted at specific customer segments. The Monitoring Unit monitors the effectiveness of promotional tools and campaigns proposed by the Proposal Unit. For example, the Monitoring Unit can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. For example, the Monitoring Unit can understand how much sales a particular campaign generated and which customer segments were affected. The Generation Unit automatically generates the design and content of promotional tools based on the effectiveness monitored by the Monitoring Unit. For example, the Generation Unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. For example, the Generation Unit can automatically create designs for flyers and posters targeted at specific customer segments. As a result, the PromoSmart system according to this embodiment can prevent unnecessary orders of promotional items and achieve a 10-20% cost reduction.

[0070] The data collection department collects sales data and customer data. For example, it can collect data such as sales history, customer attributes, and regional characteristics for each store. Specifically, as sales history, it can collect past sales data and sales trends. This includes detailed data such as sales quantity, sales amount, and sales date and time for each product. This data is important for understanding the different sales patterns and trends for each store. As customer attributes, it can collect data such as age, gender, occupation, and purchase history. For example, since purchasing trends differ depending on the age group and gender of customers, collecting this data can clarify the target customer segment. Furthermore, by analyzing occupation and purchase history, it is possible to understand customers' lifestyles and purchasing motivations and conduct more effective promotional activities. As regional characteristics, it can collect data such as population density, climate, and cultural background. For example, certain products may tend to sell more in areas with high population density. Also, since purchasing behavior is influenced by climate and cultural background, collecting this data allows for promotional activities tailored to the characteristics of each region. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis department analyzes the data collected by the data collection department. For example, based on the collected data, the analysis department can propose the most suitable promotional tools and campaigns for each store. Specifically, it uses AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. The AI ​​uses machine learning algorithms to analyze past sales data and customer data and extract patterns and trends. For example, if sales of a particular product tend to increase in relation to a particular season or event, the AI ​​can recognize that pattern and propose a promotional campaign at the appropriate time. In addition, by analyzing customer attribute data, it can propose promotional tools that are effective for specific customer segments. For example, based on analysis results such as SNS advertising being effective for younger customers, while newspaper advertising and direct mail are effective for older customers, the optimal promotional tools can be selected. Furthermore, the analysis department can utilize regional characteristic data to propose promotional campaigns tailored to the characteristics of each region. For example, based on analysis results such as the effectiveness of promotional campaigns for heating appliances and winter clothing in cold climates, and for air conditioning appliances and summer clothing in warm climates, it is possible to propose the most suitable promotional campaign for each region. This allows the analysis department to quickly and accurately analyze the collected data and propose the most suitable promotional tools and campaigns for each store.

[0072] The Proposal Department proposes promotional tools and campaigns based on data analyzed by the Analysis Department. For example, the Proposal Department can use AI to propose the most suitable promotional tools and campaigns for each store. Specifically, it can propose designs for flyers and posters targeted at specific customer segments. Based on customer attribute data, the AI ​​generates the most effective designs and messages for the target customer segment. For example, it can propose colorful and visually appealing designs for younger customers and easy-to-read and simple designs for older customers. The Proposal Department can also propose the content and timing of promotional campaigns. For example, it can propose campaigns tailored to specific seasons or events to increase customer purchasing intent. Furthermore, the Proposal Department can propose promotional tools and campaigns tailored to the characteristics of each region. For example, in cold climates, it can propose promotional campaigns for heating appliances and winter clothing, while in warm climates, it can propose promotional campaigns for air conditioning appliances and summer clothing. In this way, the Proposal Department can propose effective promotional tools and campaigns for each store and support increased sales.

[0073] The Monitoring Department monitors the effectiveness of promotional tools and campaigns proposed by the Proposal Department. For example, the Monitoring Department can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. Specifically, it can understand how much sales a particular campaign generated and which customer segments were most affected. Based on sales and customer data, the Monitoring Department quantitatively evaluates the effectiveness of campaigns and identifies factors for success and areas for improvement. For example, it can analyze how many customers a particular flyer or poster attracted and what messages were most effective. Furthermore, the Monitoring Department can collect data in real time and monitor the progress of campaigns. This allows for a rapid evaluation of campaign effectiveness and adjustments as needed. In addition, the Monitoring Department can accumulate past campaign data and utilize it for planning future campaigns. This allows the Monitoring Department to continuously evaluate and improve the effectiveness of promotional campaigns, maximizing the overall effectiveness of the system.

[0074] The generation unit automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. For example, the generation unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. Specifically, it can automatically create designs for flyers and posters targeted at specific customer segments. Based on past data, the AI ​​generates effective designs and messages, creating promotional tools that are best suited to the target customer segment. For example, it can generate colorful and visually appealing designs for younger customers and easy-to-read and simple designs for older customers. Furthermore, the generation unit can automatically generate promotional tools tailored to the characteristics of each region. For example, in cold climates, it can generate promotional tools for heating appliances and winter clothing, and in warm climates, it can generate promotional tools for air conditioning appliances and summer clothing. In addition, the generation unit can continuously improve the effectiveness of promotional tools based on the data collected by the monitoring unit. This allows the generation unit to automatically generate effective promotional tools based on the latest data, maximizing the efficiency and effectiveness of promotional activities.

[0075] The data collection unit can collect data such as sales history, customer attributes, and regional characteristics for each store. For example, the data collection unit can collect sales history for each store. For example, the data collection unit can collect past sales data and sales trends. The data collection unit can also collect customer attributes. For example, the data collection unit can collect data such as age, gender, occupation, and purchase history. Furthermore, the data collection unit can also collect regional characteristics. For example, the data collection unit can collect data such as population density, climate, and cultural background. By collecting data such as sales history, customer attributes, and regional characteristics for each store, it becomes possible to propose more accurate promotional tools and campaigns. 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 data such as sales history, customer attributes, and regional characteristics for each store into AI and have the AI ​​perform the data collection.

[0076] The analysis unit can analyze the collected data and propose the most suitable promotional tools and campaigns for each store. For example, the analysis unit can propose the most suitable promotional tools and campaigns for each store based on the collected data. For example, the analysis unit can use AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. For example, the analysis unit can optimize for increased sales based on sales data. The analysis unit can also improve customer satisfaction based on customer data. Furthermore, the analysis unit can propose region-specific promotional tools and campaigns based on regional characteristics. In this way, by analyzing the collected data, the analysis unit can propose the most suitable promotional tools and campaigns for each store. 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 collected data into AI and have AI perform the data analysis.

[0077] The monitoring unit can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. For example, the monitoring unit can monitor the effectiveness of promotional campaigns implemented at each store in real time. The monitoring unit can, for example, understand how much sales a particular campaign generated and which customer segments were affected. The monitoring unit can, for example, use AI to monitor the effectiveness of promotional campaigns in real time and analyze the data. The monitoring unit can, for example, measure effects such as the sales increase rate and the customer response rate. By monitoring the effectiveness of promotional campaigns implemented at each store in real time and analyzing the data, the effectiveness of promotional activities can be visualized and suggestions for continuous improvement can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the effectiveness of promotional campaigns into AI and have AI perform the monitoring and analysis of the effects.

[0078] The generation unit can automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. For example, the generation unit can automatically generate the optimal design and content of promotional tools based on sales data. For example, the generation unit can automatically generate the design and content of promotional tools for a specific customer segment based on customer attributes. For example, the generation unit can automatically generate the design and content of promotional tools specific to a region based on regional characteristics. For example, the generation unit can use AI to automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. This reduces the time and cost required to create promotional tools by automatically generating the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input sales data, customer attributes, and regional characteristics into AI and have the AI ​​perform the automatic generation of promotional tool designs and content.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the burden on the user and improves the accuracy of data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can analyze the past sales history of each store and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on past sales history. For example, the data collection unit can focus data collection on specific product categories based on sales history. For example, the data collection unit can analyze sales history and collect data according to seasonal sales trends. By analyzing the past sales history of each store, the optimal data collection method can be selected, improving the efficiency of data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sales history data into a generating AI and have the generating AI select the optimal data collection method.

[0081] The data collection unit can filter data based on the store's current inventory status and seasonal factors during data collection. For example, the data collection unit can prioritize collecting data on products with low inventory levels, taking current inventory status into consideration. For example, the data collection unit can focus on collecting data on seasonal products, taking seasonal factors into consideration. For example, the data collection unit can combine inventory status and seasonal factors to perform optimal data collection. This allows for the collection of more relevant data by filtering data collection based on the store's current inventory status and seasonal factors. 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 inventory status and seasonal factor data into a generating AI and have the generating AI perform the data collection filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can collect detailed data to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of stores during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the geographical location information of stores. For example, the data collection unit can compare and collect data from nearby stores by considering geographical location information. For example, the data collection unit can collect data related to local events and seasonal factors based on geographical location information. In this way, region-specific data can be effectively collected by prioritizing the collection of highly relevant data by considering the geographical location information of stores. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of stores into a generating AI and have the generating AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze the store's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the store's social media activity to understand customer reactions and trends. For example, the data collection unit can collect relevant data based on the content of social media posts. For example, the data collection unit can collect social media engagement data and use it for promotional activities. In this way, by analyzing the store's social media activity, it is possible to understand customer reactions and trends and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis to improve accuracy. For example, if the user is in a hurry, the analysis unit can perform a simplified data analysis to obtain results quickly. For example, if the user is stressed, the analysis unit can focus on important data. By adjusting the data analysis method based on the user's emotions, the analysis unit can provide optimal analysis results tailored to the user's situation. 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-described processes 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 a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the sales data during the analysis. For example, the analysis unit can perform a detailed analysis on sales data with high importance. For example, the analysis unit can perform a simplified analysis on sales data with low importance. For example, the analysis unit can optimally allocate analysis resources according to the importance of the sales data. By adjusting the level of detail of the analysis based on the importance of the sales data, it is possible to perform a detailed analysis on important data and improve accuracy. 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 importance of the sales data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data. For example, the analysis unit can apply a customer segmentation algorithm to customer data. For example, the analysis unit can apply a regional characteristics analysis algorithm to regional data. By applying different analysis algorithms depending on the data category, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time information. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can optimize the analysis schedule based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent information and provide real-time information by determining the priority of analysis based on the data submission date. 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 submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data and improving accuracy by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0091] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the way it presents suggestions based on the user's emotions, it can provide the user with the most suitable suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The proposal department can adjust the level of detail in a proposal based on the importance of the promotional tools. For example, the proposal department can provide detailed proposals for high-importance promotional tools. For example, it can provide simplified proposals for low-importance promotional tools. The proposal department can also optimally allocate resources to a proposal according to the importance of the promotional tools. By adjusting the level of detail in a proposal based on the importance of the promotional tools, it can provide detailed proposals for important promotional tools and improve accuracy. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the promotional tools into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0093] The proposal department can apply different proposal algorithms depending on the category of the promotional tool when making a proposal. For example, the proposal department can apply a design proposal algorithm to a flyer. For example, the proposal department can apply a layout proposal algorithm to a poster. For example, the proposal department can apply a content proposal algorithm to a digital advertisement. By applying different proposal algorithms depending on the category of the promotional tool, the proposal department can provide the most suitable proposal for each category. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the category of the promotional tool into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide the most suitable suggestions for the user. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The proposal department can determine the priority of proposals based on the submission dates of the promotional tools. For example, the proposal department can prioritize proposals for promotional tools with upcoming submission dates. For example, the proposal department can postpone proposals for promotional tools with later submission dates. For example, the proposal department can optimize the proposal schedule based on the submission dates. By prioritizing proposals based on the submission dates of the promotional tools, the proposal department can prioritize proposals for promotional tools with upcoming submission dates, thereby improving efficiency. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the submission dates of the promotional tools into a generating AI and have the generating AI determine the priority of proposals.

[0096] The proposal unit can adjust the order of proposals based on the relevance of the promotional tools. For example, the proposal unit can prioritize proposals for highly relevant promotional tools. For example, the proposal unit can postpone proposals for less relevant promotional tools. For example, the proposal unit can optimally allocate resources for proposals based on the relevance of the promotional tools. By adjusting the order of proposals based on the relevance of the promotional tools, it can prioritize proposals for highly relevant promotional tools and improve accuracy. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the promotional tools into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0097] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can perform detailed monitoring. For example, if the user is in a hurry, the monitoring unit can perform simplified monitoring. For example, if the user is stressed, the monitoring unit can perform monitoring that focuses on important data. This allows for optimal monitoring for the user by adjusting the monitoring criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of promotional campaigns during monitoring. For example, the monitoring unit can compare the effects of multiple promotional campaigns and analyze their interrelationships. For example, the monitoring unit can monitor the effects of different combinations of campaigns and propose the optimal combination. For example, the monitoring unit can monitor the effects of campaigns in real time by considering their interrelationships. This maximizes the effectiveness of campaigns by improving the accuracy of monitoring by considering the interrelationships of promotional campaigns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input interrelationship data of promotional campaigns into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0099] The monitoring unit can perform monitoring while considering the attribute information of the person implementing the promotional campaign. For example, the monitoring unit can analyze the effectiveness of the campaign based on the attribute information of the person implementing it. For example, the monitoring unit can improve the accuracy of monitoring by considering the past performance of the person implementing it. For example, the monitoring unit can select the optimal monitoring method based on the attribute information of the person implementing it. In this way, the accuracy of monitoring is improved by performing monitoring while considering the attribute information of the person implementing the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the attribute information of the person implementing it into a generating AI and have the generating AI perform the monitoring.

[0100] The monitoring unit can estimate the user's emotions and adjust the display order of monitoring results based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit can display detailed monitoring results. For example, if the user is in a hurry, the monitoring unit can prioritize displaying important monitoring results. For example, if the user is stressed, the monitoring unit can display monitoring results that are easy to understand visually. In this way, by adjusting the display order of monitoring results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The monitoring unit can perform monitoring while considering the geographical distribution of the promotional campaign. For example, the monitoring unit can monitor the campaign effectiveness for each region based on the geographical distribution. For example, the monitoring unit can apply region-specific monitoring methods while considering the geographical distribution. For example, the monitoring unit can optimize campaigns for each region based on the geographical distribution. This allows for the optimization of campaign effectiveness for each region by monitoring while considering the geographical distribution of the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data into a generating AI and have the generating AI perform the monitoring.

[0102] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the promotional campaign during monitoring. For example, the monitoring unit can analyze the effectiveness of the campaign based on the relevant literature. For example, the monitoring unit can improve the monitoring method by referring to relevant literature. For example, the monitoring unit can optimize the campaign based on relevant literature. This maximizes the effectiveness of the campaign by improving the accuracy of monitoring by referring to relevant literature on the promotional campaign. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform the monitoring.

[0103] The generation unit can estimate the user's emotions and adjust the design and content of the promotional tools it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed designs and content. If the user is in a hurry, the generation unit can provide concise designs and content. If the user is stressed, the generation unit can provide visually easy-to-understand designs and content. By adjusting the design and content of the promotional tools generated based on the user's emotions, the system can provide the optimal design and content for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0104] The generation unit can improve the accuracy of generation by considering the interrelationships of promotional tools during the generation process. For example, the generation unit can compare the effects of multiple promotional tools and analyze their interrelationships. For example, the generation unit can generate the optimal design and content by considering the effects of combinations of tools. For example, the generation unit can generate the design and content that maximizes the effectiveness of the tools by considering their interrelationships. In this way, the effectiveness of the tools can be maximized by improving the accuracy of generation by considering the interrelationships of promotional tools. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input interrelationship data of promotional tools into a generation AI and have the generation AI perform the generation.

[0105] The generation unit can perform generation while considering the attribute information of the submitter of the promotional tool. For example, the generation unit can generate the optimal design and content based on the submitter's attribute information. For example, the generation unit can improve the accuracy of generation by considering the submitter's past performance. For example, the generation unit can select the optimal generation method based on the submitter's attribute information. This improves the accuracy of generation by considering the attribute information of the submitter of the promotional tool. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submitter's attribute information into a generation AI and have the generation AI perform the generation.

[0106] The generation unit can estimate the user's emotions and adjust the display method of the promotional tools it generates based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide a detailed display method. For example, if the user is in a hurry, the generation unit can provide a concise display method. For example, if the user is stressed, the generation unit can provide a visually easy-to-understand display method. By adjusting the display method of the promotional tools generated based on the user's emotions, the optimal display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0107] The generation unit can perform generation while considering the geographical distribution of promotional tools. For example, the generation unit can generate optimal designs and content for each region based on geographical distribution. For example, the generation unit can apply region-specific generation methods while considering geographical distribution. For example, the generation unit can optimize promotional tools for each region based on geographical distribution. As a result, by performing generation while considering the geographical distribution of promotional tools, it is possible to provide optimal designs and content for each region. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input geographical distribution data into a generation AI and have the generation AI perform the generation.

[0108] The generation unit can improve the accuracy of its generation by referring to relevant literature on promotional tools during the generation process. For example, the generation unit can generate the optimal design and content based on the relevant literature. For example, the generation unit can improve its generation method by referring to relevant literature. For example, the generation unit can optimize promotional tools based on relevant literature. This maximizes the effectiveness of the tools by improving the accuracy of generation by referring to relevant literature on promotional tools. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the generation.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The PromoSmart system can also be equipped with a forecasting unit. This unit can predict future sales trends based on collected data. For example, it can analyze past sales data to predict which products will sell in the next season. It can also predict which products a particular customer is likely to purchase next, based on their purchase history. Furthermore, it can predict sales peaks during specific periods, taking into account local events and seasonal factors. This allows for a better understanding of future sales trends and more effective promotional activities.

[0111] The data collection unit can further collect customer social media activity. For example, it can collect information on what products customers mention on social media. It can also collect information on what trends customers are interested in on social media. Furthermore, it can collect information on the feedback customers provide on social media. By collecting customer social media activity, it becomes possible to understand customer interests and trends and propose more effective promotional tools and campaigns.

[0112] The analysis unit can further estimate customer emotions and adjust the data analysis method based on the estimated emotions. For example, if the customer has positive emotions, the analysis unit can perform a detailed data analysis to improve accuracy. If the customer has negative emotions, the analysis unit can perform a simplified data analysis to obtain results quickly. Furthermore, if the customer is stressed, the analysis unit can focus on important data. By adjusting the data analysis method based on customer emotions, it is possible to provide optimal analysis results tailored to the customer's situation.

[0113] The monitoring unit can further perform monitoring by considering the attribute information of those implementing the promotional campaign. For example, the monitoring unit can analyze the effectiveness of the campaign based on the attribute information of the implementers. Furthermore, the monitoring unit can improve the accuracy of monitoring by considering the implementers' past performance. In addition, the monitoring unit can select the optimal monitoring method based on the attribute information of the implementers. This allows for improved monitoring accuracy by considering the attribute information of those implementing the promotional campaign.

[0114] The generation unit can further estimate the user's emotions and adjust the design and content of the promotional tools it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed designs and content. If the user is in a hurry, it can provide concise designs and content. Furthermore, if the user is stressed, it can provide visually easy-to-understand designs and content. By adjusting the design and content of the promotional tools generated based on the user's emotions, it is possible to provide the most suitable designs and content for the user.

[0115] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of stores. For example, the data collection unit can prioritize the collection of region-specific data based on the geographical location of stores. Furthermore, the data collection unit can compare and collect data from nearby stores, taking geographical location into consideration. Additionally, the data collection unit can collect data related to local events and seasonal factors based on geographical location. This allows for the effective collection of region-specific data by prioritizing the collection of highly relevant data while considering the geographical location of stores.

[0116] The data collection unit can further estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting high-priority data. If the user is relaxed, the unit can collect more detailed data, improving accuracy. Furthermore, if the user is in a hurry, the unit can quickly collect only the essential data. This allows for the priority collection of important data by prioritizing data collection based on the user's emotions.

[0117] The analysis unit can apply different analysis algorithms depending on the data category. For example, it can apply a sales forecasting algorithm to sales data. It can also apply a customer segmentation algorithm to customer data. Furthermore, it can apply a regional characteristics analysis algorithm to regional data. By applying different analysis algorithms depending on the data category, it is possible to provide analysis results that are optimal for each category.

[0118] The suggestion function can further estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented based on the user's emotions, the system can provide the most suitable suggestions for the user.

[0119] The monitoring unit can also perform monitoring while considering the geographical distribution of the promotional campaign. For example, the monitoring unit can monitor the effectiveness of the campaign in each region based on the geographical distribution. Furthermore, the monitoring unit can apply region-specific monitoring methods while considering the geographical distribution. In addition, the monitoring unit can optimize the campaign in each region based on the geographical distribution. This allows for the optimization of the campaign's effectiveness in each region by monitoring while considering the geographical distribution of the promotional campaign.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects sales data and customer data. For example, it can collect data such as sales history for each store, customer attributes, and regional characteristics. Sales history includes past sales data and sales trends, while customer attributes include data such as age, gender, occupation, and purchase history. Regional characteristics include data such as population density, climate, and cultural background. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze sales data and customer data and propose the most suitable promotional tools and campaigns for each store. Step 3: The proposal department proposes promotional tools and campaigns based on the data analyzed by the analysis department. For example, they can use AI to propose the most suitable promotional tools and campaigns for each store, and suggest designs for flyers and posters targeted at specific customer segments. Step 4: The Monitoring Department monitors the effectiveness of the promotional tools and campaigns proposed by the Proposal Department. For example, they can monitor the effectiveness of promotional campaigns implemented at each store in real time and analyze the data. They can understand how much sales a particular campaign generated and which customer segments were affected. Step 5: The generation unit automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. For example, using AI, it can automatically generate the optimal design and content of promotional tools based on sales data, customer attributes, and regional characteristics, and automatically create designs for flyers and posters targeted at specific customer segments.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, monitoring unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects sales data and customer data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the most suitable promotional tools and campaigns for each store. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes promotional tools and campaigns based on the analysis results. The monitoring unit is implemented in the specific processing unit 46A of the smart device 14 and monitors the effectiveness of the proposed promotional tools and campaigns in real time. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically generates the design and content of the promotional tools based on the monitored effects. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, monitoring unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects sales data and customer data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and proposes the most suitable promotional tools and campaigns for each store. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes promotional tools and campaigns based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, which monitors the effectiveness of the proposed promotional tools and campaigns in real time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically generates the design and content of the promotional tools based on the monitored effects. 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.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In 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.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, monitoring unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects sales data and customer data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose the most suitable promotional tools and campaigns for each store. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes promotional tools and campaigns based on the analysis results. The monitoring unit is implemented in the specific processing unit 46A of the headset terminal 314 and monitors the effectiveness of the proposed promotional tools and campaigns in real time. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically generates the design and content of the promotional tools based on the monitored effects. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, monitoring unit, and generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects sales data and customer data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to propose the most suitable promotional tools and campaigns for each store. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes promotional tools and campaigns based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, and monitors the effectiveness of the proposed promotional tools and campaigns in real time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates the design and content of the promotional tools based on the monitored effects. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A data collection unit that collects sales data and customer data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes promotional tools and campaigns. A monitoring unit monitors the effectiveness of the promotional tools and campaigns proposed by the aforementioned proposal unit, The system includes a generation unit that automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as sales history, customer demographics, and regional characteristics for each store. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and propose the most suitable promotional tools and campaigns for each store. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, The effectiveness of promotional campaigns implemented at each store is monitored in real time, and the data is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on sales data, customer attributes, and regional characteristics, the system automatically generates the optimal design and content for promotional tools. 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 We will analyze the past sales history of each store 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 store's current inventory status and seasonal factors. 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 geographical location of the stores. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze the store's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the sales 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. 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 proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the promotional tools. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the promotional tool. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the promotional tools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, we improve the accuracy of monitoring by considering the interrelationships of promotional campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the attribute information of the person implementing the promotional campaign should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, It estimates the user's emotions and adjusts the display order of monitoring results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, the geographical distribution of the promotional campaign should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, refer to relevant literature on promotional campaigns to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is We estimate user emotions and adjust the design and content of promotional tools generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, the accuracy of the generation is improved by considering the interrelationships of promotional tools. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, the system takes into account the attribute information of the person submitting the promotional tool. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is It estimates user emotions and adjusts how promotional tools are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is During generation, the geographical distribution of promotional tools is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is During generation, we improve the accuracy of the generation by referring to relevant literature on promotional tools. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 sales data and customer data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes promotional tools and campaigns. A monitoring unit monitors the effectiveness of the promotional tools and campaigns proposed by the aforementioned proposal unit, The system includes a generation unit that automatically generates the design and content of promotional tools based on the effects monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as sales history, customer demographics, and regional characteristics for each store. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and propose the most suitable promotional tools and campaigns for each store. The system according to feature 1.

4. The monitoring unit, The effectiveness of promotional campaigns implemented at each store is monitored in real time, and the data is analyzed. The system according to feature 1.

5. The generating unit is Based on sales data, customer attributes, and regional characteristics, the system automatically generates the optimal design and content for promotional tools. 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 We will analyze the past sales history of each store 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 store's current inventory status and seasonal factors. The system according to feature 1.

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