system

Abstract

The system according to this embodiment aims to efficiently achieve loss reduction and sales improvement in the retail industry. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a sales promotion unit, an advertising unit, and a coupon unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The forecasting unit forecasts demand based on the analysis results obtained by the analysis unit. The sales promotion unit conducts sales promotions based on the forecast results obtained by the forecasting unit. The advertising unit delivers advertisements to consumers. The coupon unit distributes coupons.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 it is difficult to efficiently achieve loss reduction and sales improvement in the retail industry.

[0005] The system according to the embodiment aims to efficiently achieve loss reduction and sales improvement in the retail industry.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a sales promotion unit, an advertising unit, and a coupon unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The forecasting unit forecasts demand based on the analysis results obtained by the analysis unit. The sales promotion unit conducts sales promotions based on the forecast results obtained by the forecasting unit. The advertising unit delivers advertisements to consumers. The coupon unit distributes coupons. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently achieve loss reduction and sales improvement in the retail industry. [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, etc. The communication I / F controls 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 AI ​​agent system for the retail industry according to an embodiment of the present invention is a system that contributes to reducing losses and increasing sales in the retail industry by leveraging the powerful data infrastructure and user touchpoints of a specific corporate group. This system uses customer-specific data and data from a specific corporate group to forecast demand, predict product losses in addition to inventory management, and implement promotions to prevent losses. As a result, losses are reduced and sales increase. Furthermore, for consumers, advertisements are delivered and coupons are distributed via electronic payment systems using the user touchpoints of a specific corporate group, providing advantageous consumption opportunities. As a result, consumers can receive discounts. In addition, the generating AI agent automatically integrates multiple data sources and performs real-time analysis. Demand forecasting models are trained using time-series data analysis to optimize inventory. Timely promotional notifications are automated through messaging apps, and attractive coupons are issued using electronic payment systems. This simultaneously reduces food waste and maximizes sales, realizing a sustainable and efficient retail business. For example, demand forecasting is performed by collecting and analyzing customer-specific data and data from a specific corporate group. Next, in addition to inventory management, product losses are predicted, and promotions are implemented to prevent losses. As a result, losses are reduced and sales increase. Furthermore, the system leverages user touchpoints within specific corporate groups to deliver advertisements and distribute coupons through electronic payment systems, offering consumers attractive shopping opportunities. This allows consumers to receive discounts. In addition, the generating AI agent automatically integrates multiple data sources and performs real-time analysis. Time-series data analysis is used to train demand forecasting models and optimize inventory. Timely promotional notifications are automated through messaging apps, and attractive coupons are issued using electronic payment systems. This simultaneously reduces food waste and maximizes sales, resulting in a sustainable and efficient retail business. Thus, the AI ​​agent system for the retail industry can achieve both waste reduction and sales growth at the same time.

[0029] The AI ​​agent system for the retail industry according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a sales promotion unit, an advertising unit, and a coupon unit. The data collection unit collects data. For example, the data collection unit collects customer-specific data and data for a specific corporate group. For example, the data collection unit can collect customer purchase history, behavioral patterns, sales data for a specific corporate group, marketing data, etc. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts product losses based on the collected data. For example, the analysis unit analyzes the collected data and uses an algorithm to predict product losses. The forecasting unit forecasts demand based on the analysis results obtained by the analysis unit. For example, the forecasting unit trains a demand forecasting model using time series data analysis. For example, the forecasting unit forecasts demand using time series data analysis algorithms such as the ARIMA model or LSTM. The sales promotion unit conducts sales promotions based on the forecast results obtained by the forecasting unit. For example, the sales promotion unit conducts sales promotions to prevent losses. The sales promotion department conducts sales promotions based on inventory management and demand forecasting, for example. The advertising department delivers advertisements to consumers. The advertising department delivers advertisements using user touchpoints, for example. The advertising department delivers advertisements using user touchpoints such as websites, mobile apps, and stores, for example. The coupon department distributes coupons. The coupon department distributes coupons using electronic payment systems, for example. The coupon department distributes coupons using electronic payment systems such as credit card payments and mobile payments, for example. As a result, the AI ​​agent system for the retail industry according to this embodiment can efficiently collect and analyze data, forecast demand, conduct sales promotions, deliver advertisements, and distribute coupons.

[0030] The data collection unit collects data. For example, it collects customer-specific data and data for specific corporate groups. Specifically, it can collect customer purchase history, behavioral patterns, sales data for specific corporate groups, and marketing data. Customer purchase history is obtained from POS systems and online shopping platforms, allowing for a detailed understanding of which products are purchased and how often. Behavioral patterns are collected by tracking customer website browsing history, mobile app usage, and in-store movement routes. This allows for analysis of what products customers are interested in and when they consider purchasing them. Sales data for specific corporate groups aggregates sales information from various stores and online channels, allowing for an understanding of overall sales trends and sales trends for specific product categories. Marketing data includes measuring the effectiveness of advertising campaigns and promotional response rates, and is used to evaluate which marketing measures are most effective. This data is aggregated in a cloud-based data warehouse and updated in real time. The data collection unit centrally manages this data, making it easily accessible to the analysis and forecasting units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts product losses based on the collected data. Specifically, it uses algorithms to analyze the collected data and predict product losses. For example, it uses machine learning algorithms to analyze past sales and inventory data to predict which products are likely to remain unsold and at what point in time. This includes analyzing sales patterns and inventory trends using regression analysis and clustering methods. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual sales patterns and inventory anomalies, enabling early countermeasures. For example, if a particular product suddenly stops selling, it can identify the cause and take appropriate measures. The analysis unit can also analyze customer purchasing behavior and marketing data to evaluate which promotions are most effective. As a result, the analysis unit can quickly and accurately analyze the collected data and provide information to maximize the effectiveness of product loss predictions and marketing measures. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and marketing strategy planning, thereby improving the reliability and effectiveness of the entire system.

[0032] The forecasting unit performs demand forecasting based on the analysis results obtained by the analysis unit. For example, the forecasting unit trains a demand forecasting model using time series data analysis. Specifically, it uses time series data analysis algorithms such as the ARIMA model and LSTM to perform demand forecasting. The ARIMA model is a statistical method for predicting future sales based on past sales data, and it can make forecasts that take seasonality and trends into account. On the other hand, the LSTM (Long Short-Term Memory) model is a time series data analysis method using a neural network, and it can make highly accurate forecasts that take long-term dependencies into account. Using these algorithms, the forecasting unit can predict product demand with high accuracy, which can be used for inventory management and production planning. Furthermore, the forecasting unit can continuously revise its forecast results based on data updated in real time, allowing it to respond to the latest situation. For example, if a particular product suddenly starts selling rapidly, the forecasting unit immediately incorporates new data and updates the forecast results. The forecasting unit can also perform more accurate demand forecasts by considering regional characteristics and past sales history. As a result, the forecasting unit can always provide highly accurate demand forecasts based on the latest information, supporting inventory optimization and the efficiency of production planning.

[0033] The marketing department conducts promotions based on forecasts obtained by the forecasting department. For example, the marketing department implements promotions to prevent losses. Specifically, it conducts promotions based on inventory management and demand forecasts. For example, if a particular product is likely to remain unsold, it will implement a discount campaign for that product to quickly clear the inventory. It will also strengthen promotions for products where demand is predicted to increase to maximize sales. The marketing department can also analyze customer purchase history and behavior patterns to provide personalized promotions to individual customers. For example, it can provide discount coupons for related products based on products that a particular customer has purchased in the past. Furthermore, the marketing department can continuously evaluate the effectiveness of promotional measures based on real-time updated data and modify measures as needed. This allows the marketing department to conduct promotions efficiently and effectively, maximizing sales and optimizing inventory. In addition, the marketing department can collect customer feedback and continuously improve the accuracy and effectiveness of promotional measures. This allows the marketing department to improve customer satisfaction and maximize sales.

[0034] The advertising department delivers advertisements to consumers. For example, the advertising department utilizes user touchpoints to deliver advertisements. Specifically, it uses user touchpoints such as websites, mobile apps, and stores to deliver advertisements. On websites, it displays advertisements for relevant products based on customers' browsing and search history. On mobile apps, it delivers timely advertisements to customers through push notifications and in-app ads. In stores, it provides promotional information to customers through digital signage and in-store broadcasts. The advertising department can also analyze customers' purchase history and behavior patterns to deliver personalized advertisements. For example, it can display advertisements for relevant products based on products a particular customer has purchased in the past. Furthermore, the advertising department can continuously evaluate the effectiveness of advertisements based on real-time updated data and modify the advertisement content as needed. This allows the advertising department to deliver advertisements efficiently and effectively, attracting customer interest. In addition, the advertising department can manage multiple advertising channels in an integrated manner to maximize the effectiveness of advertising campaigns. This allows the advertising department to deliver consistent messages to customers and improve brand awareness.

[0035] The coupon department distributes coupons. For example, it distributes coupons using electronic payment systems. Specifically, it distributes coupons using electronic payment systems such as credit card payments and mobile payments. The coupon department can analyze customer purchase history and behavioral patterns to provide personalized coupons. For example, it can provide discount coupons for related products based on products a particular customer has purchased in the past. Furthermore, the coupon department can continuously evaluate the effectiveness of coupons based on real-time updated data and modify coupon content as needed. This allows the coupon department to distribute coupons efficiently and effectively, increasing customer purchase intent. In addition, the coupon department can manage multiple distribution channels in an integrated manner to maximize the effectiveness of coupon campaigns. For example, it can distribute coupons to customers through email, SMS, and mobile app notifications. This allows the coupon department to provide consistent messaging to customers, increasing purchase intent.

[0036] The data collection unit can collect customer-specific data and data for specific corporate groups. For example, the data collection unit can collect customer purchase history. For example, the data collection unit can collect customer behavior patterns. For example, the data collection unit can collect sales data for specific corporate groups. For example, the data collection unit can collect marketing data for specific corporate groups. By collecting customer-specific data and data for specific corporate groups, more accurate analysis becomes possible. 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 customer purchase history into AI, and the AI ​​can analyze the purchase history and collect data.

[0037] The analysis unit can analyze the collected data and predict product losses. For example, the analysis unit predicts product losses based on the collected data. For example, the analysis unit uses an algorithm to analyze the collected data and predict product losses. For example, the analysis unit constructs a model to analyze the collected data and predict product losses. This makes it possible to reduce losses by predicting product losses. 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 collected data into AI, and the AI ​​can analyze the data and predict product losses.

[0038] The forecasting unit can train a demand forecasting model using time series data analysis. For example, the forecasting unit trains a demand forecasting model based on time series data analysis. For example, the forecasting unit trains a demand forecasting model using the ARIMA model. For example, the forecasting unit trains a demand forecasting model using LSTM. This improves the accuracy of demand forecasting by utilizing time series data analysis. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input time series data into AI, and the AI ​​can analyze the data to train a demand forecasting model.

[0039] The sales promotion department can implement sales promotions to prevent losses. For example, the sales promotion department can implement sales promotions based on inventory management. For example, the sales promotion department can implement sales promotions based on demand forecasts. For example, the sales promotion department can use algorithms to implement sales promotions to prevent losses. By implementing sales promotions to prevent losses, losses can be reduced. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or not using AI. For example, the sales promotion department can input inventory data into AI, and the AI ​​can analyze the data to implement sales promotions.

[0040] The advertising department can deliver advertisements by utilizing user touchpoints. For example, the advertising department can deliver advertisements using websites. For example, the advertising department can deliver advertisements using mobile apps. For example, the advertising department can deliver advertisements using stores. This makes it possible to deliver effective advertisements by utilizing user touchpoints. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input user touchpoint data into AI, and the AI ​​can analyze the data and deliver advertisements.

[0041] The coupon unit can distribute coupons using an electronic payment system. For example, the coupon unit can distribute coupons using credit card payments. For example, the coupon unit can distribute coupons using mobile payments. For example, the coupon unit uses an algorithm for distributing coupons using an electronic payment system. This enables efficient coupon distribution by utilizing an electronic payment system. Some or all of the above-described processes in the coupon unit may be performed using AI, or not. For example, the coupon unit can input data from the electronic payment system into an AI, which can then analyze the data and distribute coupons.

[0042] The data collection unit can collect customer-specific data and data from specific corporate groups in real time, preparing for immediate analysis. For example, the data collection unit can collect customer purchase history in real time, preparing for immediate analysis. For example, the data collection unit can collect inventory data from specific corporate groups in real time, preparing for immediate analysis. For example, the data collection unit can collect customer behavior patterns in real time, preparing for immediate analysis. This enables immediate analysis by collecting data in real time. 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 customer purchase history into AI, which can analyze the data and collect it in real time.

[0043] The data collection unit can select the types of data to collect by considering the customer's purchase history and behavioral patterns during data collection. For example, the data collection unit can select the types of data to collect based on the customer's past purchase history. For example, the data collection unit can select the types of data to collect by analyzing the customer's behavioral patterns. For example, the data collection unit can select the types of data to collect by combining the customer's purchase history and behavioral patterns. This allows for the optimization of the types of data collected by considering the customer's purchase history and behavioral patterns. 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 customer's purchase history into AI, and the AI ​​can analyze the data and select the types of data to collect.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the customer's current location. For example, the data collection unit can prioritize the collection of highly relevant data by analyzing the customer's past location information. For example, the data collection unit can prioritize the collection of highly relevant data by combining the customer's geographical location information with their purchase history. This allows for the priority collection of highly relevant data by considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0045] The data collection unit can analyze the customer's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the customer's social media posts and collect relevant data. For example, the data collection unit can analyze the customer's social media following relationships and collect relevant data. For example, the data collection unit can analyze the customer's social media activity history and collect relevant data. In this way, relevant data can be collected by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's social media data into AI, which can then analyze the data and collect relevant data.

[0046] The analysis unit can simultaneously predict product losses and sales when analyzing collected data. For example, the analysis unit can simultaneously predict product losses and sales based on collected data. For example, the analysis unit can simultaneously predict product losses and sales based on customer purchase history. For example, the analysis unit can simultaneously predict product losses and sales based on customer behavior patterns. By simultaneously predicting product losses and sales, a more comprehensive analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, and the AI ​​can analyze the data to simultaneously predict product losses and sales.

[0047] The analysis unit can optimize the analysis algorithm by considering the customer's purchase history and behavioral patterns during analysis. For example, the analysis unit optimizes the analysis algorithm based on the customer's purchase history. For example, the analysis unit optimizes the analysis algorithm based on the customer's behavioral patterns. For example, the analysis unit optimizes the analysis algorithm by combining the customer's purchase history and behavioral patterns. In this way, the analysis algorithm can be optimized by considering the customer's purchase history and behavioral patterns. 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 customer's purchase history into AI, and the AI ​​can analyze the data to optimize the analysis algorithm.

[0048] The analysis unit can classify analysis results by region, taking into account the customer's geographical location information during analysis. For example, the analysis unit classifies analysis results by region based on the customer's current location. For example, the analysis unit classifies analysis results by region based on the customer's past location information. For example, the analysis unit classifies analysis results by region by combining the customer's geographical location information and purchase history. In this way, by considering the customer's geographical location information, analysis results can be classified by region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify the analysis results by region.

[0049] The analysis unit can analyze the customer's social media activity during analysis and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media posts and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media following relationships and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media activity history and reflect the relevant data in the analysis. In this way, by analyzing the customer's social media activity, relevant data can be reflected in the analysis. 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 customer's social media data into AI, and the AI ​​can analyze the data and reflect the relevant data in the analysis.

[0050] The forecasting unit can consider seasonal variations when training a demand forecasting model using time series data analysis. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on time series data analysis. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on historical data. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on customer purchase history. This improves the accuracy of demand forecasting by considering seasonal variations. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input time series data into AI, and the AI ​​can analyze the data to train a demand forecasting model that considers seasonal variations.

[0051] The prediction unit can optimize its prediction algorithm by considering the customer's purchase history and behavioral patterns during prediction. For example, the prediction unit optimizes the prediction algorithm based on the customer's purchase history. For example, the prediction unit optimizes the prediction algorithm based on the customer's behavioral patterns. For example, the prediction unit optimizes the prediction algorithm by combining the customer's purchase history and behavioral patterns. In this way, the prediction algorithm can be optimized by considering the customer's purchase history and behavioral patterns. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the customer's purchase history into AI, and the AI ​​can analyze the data to optimize the prediction algorithm.

[0052] The forecasting unit can classify demand forecasts by region, taking into account the customer's geographical location information during forecasting. For example, the forecasting unit classifies demand forecasts by region based on the customer's current location. For example, the forecasting unit classifies demand forecasts by region based on the customer's past location information. For example, the forecasting unit classifies demand forecasts by region by combining the customer's geographical location information and purchase history. In this way, by taking into account the customer's geographical location information, demand forecasts can be classified by region. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify demand forecasts by region.

[0053] The prediction unit can analyze the customer's social media activity and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media posts and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media following relationships and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media activity history and reflect relevant data in the prediction. In this way, by analyzing the customer's social media activity, relevant data can be reflected in the prediction. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the customer's social media data into AI, which can analyze the data and reflect relevant data in the prediction.

[0054] The sales promotion department can consider customer purchase history and behavioral patterns when implementing sales promotions to prevent losses. For example, the sales promotion department can implement sales promotions to prevent losses based on customer purchase history. For example, the sales promotion department can implement sales promotions to prevent losses based on customer behavioral patterns. For example, the sales promotion department can implement sales promotions to prevent losses by combining customer purchase history and behavioral patterns. In this way, sales promotions that prevent losses become possible by considering customer purchase history and behavioral patterns. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or not using AI. For example, the sales promotion department can input customer purchase history into AI, and the AI ​​can analyze the data to implement sales promotions that prevent losses.

[0055] The sales promotion department can optimize its sales promotion algorithms by considering customer purchase history and behavioral patterns during sales promotions. For example, the sales promotion department can optimize the sales promotion algorithm based on customer purchase history. For example, the sales promotion department can optimize the sales promotion algorithm based on customer behavioral patterns. For example, the sales promotion department can optimize the sales promotion algorithm by combining customer purchase history and behavioral patterns. In this way, the sales promotion algorithm can be optimized by considering customer purchase history and behavioral patterns. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or without AI. For example, the sales promotion department can input customer purchase history into AI, and the AI ​​can analyze the data to optimize the sales promotion algorithm.

[0056] The sales promotion department can categorize promotional content by region, taking into account the customer's geographical location. For example, the sales promotion department can categorize promotional content by region based on the customer's current location. For example, the sales promotion department can categorize promotional content by region based on the customer's past location information. For example, the sales promotion department can categorize promotional content by region by combining the customer's geographical location information with their purchase history. In this way, promotional content can be categorized by region by considering the customer's geographical location information. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or without AI. For example, the sales promotion department can input the customer's geographical location information into AI, and the AI ​​can analyze the data and categorize promotional content by region.

[0057] The marketing department can analyze customers' social media activity during promotions and incorporate relevant data into those promotions. For example, the marketing department can analyze customers' social media posts and incorporate relevant data into promotions. For example, the marketing department can analyze customers' social media following relationships and incorporate relevant data into promotions. For example, the marketing department can analyze customers' social media activity history and incorporate relevant data into promotions. In this way, by analyzing customers' social media activity, relevant data can be incorporated into promotions. Some or all of the above processes in the marketing department may be performed using AI, for example, or not using AI. For example, the marketing department can input customer social media data into AI, which can analyze the data and incorporate relevant data into promotions.

[0058] The advertising department can consider customer purchase history and behavioral patterns when delivering advertisements using user touchpoints. For example, the advertising department can deliver advertisements using user touchpoints based on customer purchase history. For example, the advertising department can deliver advertisements using user touchpoints based on customer behavioral patterns. For example, the advertising department can deliver advertisements using user touchpoints by combining customer purchase history and behavioral patterns. This makes it possible to deliver effective advertisements by considering customer purchase history and behavioral patterns. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer purchase history into AI, and the AI ​​can analyze the data and deliver advertisements using user touchpoints.

[0059] The advertising department can optimize its advertising algorithms when delivering ads, taking into account customer purchase history and behavioral patterns. For example, the advertising department can optimize the advertising algorithm based on customer purchase history. For example, the advertising department can optimize the advertising algorithm based on customer behavioral patterns. For example, the advertising department can optimize the advertising algorithm by combining customer purchase history and behavioral patterns. In this way, the advertising algorithm can be optimized by taking into account customer purchase history and behavioral patterns. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer purchase history into AI, and the AI ​​can analyze the data to optimize the advertising algorithm.

[0060] The advertising department can classify ad content by region when delivering ads, taking into account the customer's geographical location. For example, the advertising department can classify ad content by region based on the customer's current location. For example, the advertising department can classify ad content by region based on the customer's past location information. For example, the advertising department can classify ad content by region by combining the customer's geographical location information and purchase history. In this way, ad content can be classified by region by taking into account the customer's geographical location information. Some or all of the above processing in the advertising department may be performed using AI, for example, or without using AI. For example, the advertising department can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify ad content by region.

[0061] The advertising department can analyze customers' social media activity and reflect relevant data in advertisements when delivering ads. For example, the advertising department can analyze customers' social media posts and reflect relevant data in advertisements. For example, the advertising department can analyze customers' social media following relationships and reflect relevant data in advertisements. For example, the advertising department can analyze customers' social media activity history and reflect relevant data in advertisements. In this way, by analyzing customers' social media activity, relevant data can be reflected in advertisements. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer social media data into AI, which can analyze the data and reflect relevant data in advertisements.

[0062] The coupon unit can consider customer purchase history and behavioral patterns when distributing coupons using an electronic payment system. For example, the coupon unit can distribute coupons using an electronic payment system based on customer purchase history. For example, the coupon unit can distribute coupons using an electronic payment system based on customer behavioral patterns. For example, the coupon unit can combine customer purchase history and behavioral patterns to distribute coupons using an electronic payment system. This makes it possible to distribute coupons more effectively by considering customer purchase history and behavioral patterns. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without AI. For example, the coupon unit can input customer purchase history into AI, and the AI ​​can analyze the data and distribute coupons using an electronic payment system.

[0063] The coupon unit can optimize the coupon algorithm when distributing coupons, taking into account the customer's purchase history and behavioral patterns. For example, the coupon unit optimizes the coupon algorithm based on the customer's purchase history. For example, the coupon unit optimizes the coupon algorithm based on the customer's behavioral patterns. For example, the coupon unit optimizes the coupon algorithm by combining the customer's purchase history and behavioral patterns. In this way, the coupon algorithm can be optimized by taking into account the customer's purchase history and behavioral patterns. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without using AI. For example, the coupon unit can input the customer's purchase history into AI, and the AI ​​can analyze the data to optimize the coupon algorithm.

[0064] The coupon unit can classify coupon content by region when distributing coupons, taking into account the customer's geographical location information. For example, the coupon unit can classify coupon content by region based on the customer's current location. For example, the coupon unit can classify coupon content by region based on the customer's past location information. For example, the coupon unit can classify coupon content by region by combining the customer's geographical location information and purchase history. In this way, coupon content can be classified by region by taking into account the customer's geographical location information. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without using AI. For example, the coupon unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify coupon content by region.

[0065] The coupon department can analyze customers' social media activity when distributing coupons and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media posts and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media following relationships and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media activity history and reflect the relevant data in the coupons. In this way, by analyzing customers' social media activity, relevant data can be reflected in the coupons. Some or all of the above processing in the coupon department may be performed using AI, for example, or without AI. For example, the coupon department can input customer social media data into AI, which can analyze the data and reflect the relevant data in the coupons.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can collect not only customer purchase history but also customer social media activity and online behavior data. For example, if a customer mentions a specific product on social media, that information can be collected and used for analysis by the analysis unit. It can also collect data on products viewed online and advertisements clicked, allowing for a more accurate understanding of customer interests and preferences. This enables the data collection unit to collect more comprehensive data by gathering not only customer purchase history but also social media activity and online behavior data.

[0068] The forecasting unit can consider not only customer purchase history and behavioral patterns, but also external economic indicators and weather data when forecasting demand. For example, it can improve the accuracy of demand forecasts by considering economic indicators such as the consumer confidence index and unemployment rate. It can also use weather data to forecast demand in accordance with seasonal and weather changes. Furthermore, the forecasting unit can acquire this external data in real time and reflect it in the demand forecasting model. As a result, the forecasting unit can provide highly accurate forecasts by considering a wider range of factors.

[0069] The advertising department can consider not only customers' purchase history and behavioral patterns when delivering ads, but also their geographical location. For example, if a customer is in a specific region, delivering ads relevant to that region can lead to more effective ad delivery. Furthermore, by analyzing a customer's past location data, ads related to places they frequently visit can be delivered. It's also possible to combine a customer's geographical location with their purchase history to deliver even more personalized ads. This allows the advertising department to maximize the effectiveness of their ads by considering customers' geographical location.

[0070] The data collection unit can collect not only customer purchase history and behavioral patterns, but also customer health data during data collection. For example, if a customer uses a health management app, that data can be collected, and products tailored to the customer's health condition can be recommended. It can also analyze customer health data and suggest health-conscious products. Furthermore, it is possible to combine customer health data with purchase history to provide more personalized product recommendations. In this way, the data collection unit can collect more comprehensive data by taking customer health data into consideration during data collection.

[0071] The forecasting unit can consider not only customer purchase history and behavioral patterns but also customer life event data when making demand forecasts. For example, if a customer is experiencing a life event such as marriage or childbirth, considering this data in the demand forecast will enable more accurate predictions. It can also make product suggestions tailored to the customer's life events. Furthermore, it is possible to combine customer life event data with purchase history to make more personalized demand forecasts. As a result, the forecasting unit can provide highly accurate forecasts by considering customer life event data in its demand forecasting.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects data. The collection unit collects, for example, customer-specific data and data for specific corporate groups. The collection unit can collect, for example, customer purchase history, behavioral patterns, sales data for specific corporate groups, marketing data, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, an algorithm to predict product losses based on the collected data. Step 3: The forecasting unit performs demand forecasting based on the analysis results obtained by the analysis unit. The forecasting unit trains a demand forecasting model using, for example, time series data analysis. The forecasting unit performs demand forecasting using, for example, time series data analysis algorithms such as the ARIMA model or LSTM. Step 4: The sales promotion department conducts sales promotions based on the forecast results obtained by the forecasting department. For example, the sales promotion department implements sales promotions to prevent losses. For example, the sales promotion department conducts sales promotions based on inventory management and demand forecasts. Step 5: The advertising department delivers advertisements to consumers. The advertising department delivers advertisements using user touchpoints, for example. The advertising department delivers advertisements using user touchpoints such as websites, mobile apps, and stores. Step 6: The coupon department distributes the coupons. The coupon department distributes the coupons using, for example, an electronic payment system. The coupon department distributes the coupons using, for example, an electronic payment system such as credit card payment or mobile payment.

[0074] (Example of form 2) The AI ​​agent system for the retail industry according to an embodiment of the present invention is a system that contributes to reducing losses and increasing sales in the retail industry by leveraging the powerful data infrastructure and user touchpoints of a specific corporate group. This system uses customer-specific data and data from a specific corporate group to forecast demand, predict product losses in addition to inventory management, and implement promotions to prevent losses. As a result, losses are reduced and sales increase. Furthermore, for consumers, advertisements are delivered and coupons are distributed via electronic payment systems using the user touchpoints of a specific corporate group, providing advantageous consumption opportunities. As a result, consumers can receive discounts. In addition, the generating AI agent automatically integrates multiple data sources and performs real-time analysis. Demand forecasting models are trained using time-series data analysis to optimize inventory. Timely promotional notifications are automated through messaging apps, and attractive coupons are issued using electronic payment systems. This simultaneously reduces food waste and maximizes sales, realizing a sustainable and efficient retail business. For example, demand forecasting is performed by collecting and analyzing customer-specific data and data from a specific corporate group. Next, in addition to inventory management, product losses are predicted, and promotions are implemented to prevent losses. As a result, losses are reduced and sales increase. Furthermore, the system leverages user touchpoints within specific corporate groups to deliver advertisements and distribute coupons through electronic payment systems, offering consumers attractive shopping opportunities. This allows consumers to receive discounts. In addition, the generating AI agent automatically integrates multiple data sources and performs real-time analysis. Time-series data analysis is used to train demand forecasting models and optimize inventory. Timely promotional notifications are automated through messaging apps, and attractive coupons are issued using electronic payment systems. This simultaneously reduces food waste and maximizes sales, resulting in a sustainable and efficient retail business. Thus, the AI ​​agent system for the retail industry can achieve both waste reduction and sales growth at the same time.

[0075] The AI ​​agent system for the retail industry according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, a sales promotion unit, an advertising unit, and a coupon unit. The data collection unit collects data. For example, the data collection unit collects customer-specific data and data for a specific corporate group. For example, the data collection unit can collect customer purchase history, behavioral patterns, sales data for a specific corporate group, marketing data, etc. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts product losses based on the collected data. For example, the analysis unit analyzes the collected data and uses an algorithm to predict product losses. The forecasting unit forecasts demand based on the analysis results obtained by the analysis unit. For example, the forecasting unit trains a demand forecasting model using time series data analysis. For example, the forecasting unit forecasts demand using time series data analysis algorithms such as the ARIMA model or LSTM. The sales promotion unit conducts sales promotions based on the forecast results obtained by the forecasting unit. For example, the sales promotion unit conducts sales promotions to prevent losses. The sales promotion department conducts sales promotions based on inventory management and demand forecasting, for example. The advertising department delivers advertisements to consumers. The advertising department delivers advertisements using user touchpoints, for example. The advertising department delivers advertisements using user touchpoints such as websites, mobile apps, and stores, for example. The coupon department distributes coupons. The coupon department distributes coupons using electronic payment systems, for example. The coupon department distributes coupons using electronic payment systems such as credit card payments and mobile payments, for example. As a result, the AI ​​agent system for the retail industry according to this embodiment can efficiently collect and analyze data, forecast demand, conduct sales promotions, deliver advertisements, and distribute coupons.

[0076] The data collection unit collects data. For example, it collects customer-specific data and data for specific corporate groups. Specifically, it can collect customer purchase history, behavioral patterns, sales data for specific corporate groups, and marketing data. Customer purchase history is obtained from POS systems and online shopping platforms, allowing for a detailed understanding of which products are purchased and how often. Behavioral patterns are collected by tracking customer website browsing history, mobile app usage, and in-store movement routes. This allows for analysis of what products customers are interested in and when they consider purchasing them. Sales data for specific corporate groups aggregates sales information from various stores and online channels, allowing for an understanding of overall sales trends and sales trends for specific product categories. Marketing data includes measuring the effectiveness of advertising campaigns and promotional response rates, and is used to evaluate which marketing measures are most effective. This data is aggregated in a cloud-based data warehouse and updated in real time. The data collection unit centrally manages this data, making it easily accessible to the analysis and forecasting units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0077] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts product losses based on the collected data. Specifically, it uses algorithms to analyze the collected data and predict product losses. For example, it uses machine learning algorithms to analyze past sales and inventory data to predict which products are likely to remain unsold and at what point in time. This includes analyzing sales patterns and inventory trends using regression analysis and clustering methods. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual sales patterns and inventory anomalies, enabling early countermeasures. For example, if a particular product suddenly stops selling, it can identify the cause and take appropriate measures. The analysis unit can also analyze customer purchasing behavior and marketing data to evaluate which promotions are most effective. As a result, the analysis unit can quickly and accurately analyze the collected data and provide information to maximize the effectiveness of product loss predictions and marketing measures. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and marketing strategy planning, thereby improving the reliability and effectiveness of the entire system.

[0078] The forecasting unit performs demand forecasting based on the analysis results obtained by the analysis unit. For example, the forecasting unit trains a demand forecasting model using time series data analysis. Specifically, it uses time series data analysis algorithms such as the ARIMA model and LSTM to perform demand forecasting. The ARIMA model is a statistical method for predicting future sales based on past sales data, and it can make forecasts that take seasonality and trends into account. On the other hand, the LSTM (Long Short-Term Memory) model is a time series data analysis method using a neural network, and it can make highly accurate forecasts that take long-term dependencies into account. Using these algorithms, the forecasting unit can predict product demand with high accuracy, which can be used for inventory management and production planning. Furthermore, the forecasting unit can continuously revise its forecast results based on data updated in real time, allowing it to respond to the latest situation. For example, if a particular product suddenly starts selling rapidly, the forecasting unit immediately incorporates new data and updates the forecast results. The forecasting unit can also perform more accurate demand forecasts by considering regional characteristics and past sales history. As a result, the forecasting unit can always provide highly accurate demand forecasts based on the latest information, supporting inventory optimization and the efficiency of production planning.

[0079] The marketing department conducts promotions based on forecasts obtained by the forecasting department. For example, the marketing department implements promotions to prevent losses. Specifically, it conducts promotions based on inventory management and demand forecasts. For example, if a particular product is likely to remain unsold, it will implement a discount campaign for that product to quickly clear the inventory. It will also strengthen promotions for products where demand is predicted to increase to maximize sales. The marketing department can also analyze customer purchase history and behavior patterns to provide personalized promotions to individual customers. For example, it can provide discount coupons for related products based on products that a particular customer has purchased in the past. Furthermore, the marketing department can continuously evaluate the effectiveness of promotional measures based on real-time updated data and modify measures as needed. This allows the marketing department to conduct promotions efficiently and effectively, maximizing sales and optimizing inventory. In addition, the marketing department can collect customer feedback and continuously improve the accuracy and effectiveness of promotional measures. This allows the marketing department to improve customer satisfaction and maximize sales.

[0080] The advertising department delivers advertisements to consumers. For example, the advertising department utilizes user touchpoints to deliver advertisements. Specifically, it uses user touchpoints such as websites, mobile apps, and stores to deliver advertisements. On websites, it displays advertisements for relevant products based on customers' browsing and search history. On mobile apps, it delivers timely advertisements to customers through push notifications and in-app ads. In stores, it provides promotional information to customers through digital signage and in-store broadcasts. The advertising department can also analyze customers' purchase history and behavior patterns to deliver personalized advertisements. For example, it can display advertisements for relevant products based on products a particular customer has purchased in the past. Furthermore, the advertising department can continuously evaluate the effectiveness of advertisements based on real-time updated data and modify the advertisement content as needed. This allows the advertising department to deliver advertisements efficiently and effectively, attracting customer interest. In addition, the advertising department can manage multiple advertising channels in an integrated manner to maximize the effectiveness of advertising campaigns. This allows the advertising department to deliver consistent messages to customers and improve brand awareness.

[0081] The coupon department distributes coupons. For example, it distributes coupons using electronic payment systems. Specifically, it distributes coupons using electronic payment systems such as credit card payments and mobile payments. The coupon department can analyze customer purchase history and behavioral patterns to provide personalized coupons. For example, it can provide discount coupons for related products based on products a particular customer has purchased in the past. Furthermore, the coupon department can continuously evaluate the effectiveness of coupons based on real-time updated data and modify coupon content as needed. This allows the coupon department to distribute coupons efficiently and effectively, increasing customer purchase intent. In addition, the coupon department can manage multiple distribution channels in an integrated manner to maximize the effectiveness of coupon campaigns. For example, it can distribute coupons to customers through email, SMS, and mobile app notifications. This allows the coupon department to provide consistent messaging to customers, increasing purchase intent.

[0082] The data collection unit can collect customer-specific data and data for specific corporate groups. For example, the data collection unit can collect customer purchase history. For example, the data collection unit can collect customer behavior patterns. For example, the data collection unit can collect sales data for specific corporate groups. For example, the data collection unit can collect marketing data for specific corporate groups. By collecting customer-specific data and data for specific corporate groups, more accurate analysis becomes possible. 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 customer purchase history into AI, and the AI ​​can analyze the purchase history and collect data.

[0083] The analysis unit can analyze the collected data and predict product losses. For example, the analysis unit predicts product losses based on the collected data. For example, the analysis unit uses an algorithm to analyze the collected data and predict product losses. For example, the analysis unit constructs a model to analyze the collected data and predict product losses. This makes it possible to reduce losses by predicting product losses. 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 collected data into AI, and the AI ​​can analyze the data and predict product losses.

[0084] The forecasting unit can train a demand forecasting model using time series data analysis. For example, the forecasting unit trains a demand forecasting model based on time series data analysis. For example, the forecasting unit trains a demand forecasting model using the ARIMA model. For example, the forecasting unit trains a demand forecasting model using LSTM. This improves the accuracy of demand forecasting by utilizing time series data analysis. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input time series data into AI, and the AI ​​can analyze the data to train a demand forecasting model.

[0085] The sales promotion department can implement sales promotions to prevent losses. For example, the sales promotion department can implement sales promotions based on inventory management. For example, the sales promotion department can implement sales promotions based on demand forecasts. For example, the sales promotion department can use algorithms to implement sales promotions to prevent losses. By implementing sales promotions to prevent losses, losses can be reduced. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or not using AI. For example, the sales promotion department can input inventory data into AI, and the AI ​​can analyze the data to implement sales promotions.

[0086] The advertising department can deliver advertisements by utilizing user touchpoints. For example, the advertising department can deliver advertisements using websites. For example, the advertising department can deliver advertisements using mobile apps. For example, the advertising department can deliver advertisements using stores. This makes it possible to deliver effective advertisements by utilizing user touchpoints. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input user touchpoint data into AI, and the AI ​​can analyze the data and deliver advertisements.

[0087] The coupon unit can distribute coupons using an electronic payment system. For example, the coupon unit can distribute coupons using credit card payments. For example, the coupon unit can distribute coupons using mobile payments. For example, the coupon unit uses an algorithm for distributing coupons using an electronic payment system. This enables efficient coupon distribution by utilizing an electronic payment system. Some or all of the above-described processes in the coupon unit may be performed using AI, or not. For example, the coupon unit can input data from the electronic payment system into an AI, which can then analyze the data and distribute coupons.

[0088] 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 user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can analyze the data and adjust the timing of data collection.

[0089] The data collection unit can collect customer-specific data and data from specific corporate groups in real time, preparing for immediate analysis. For example, the data collection unit can collect customer purchase history in real time, preparing for immediate analysis. For example, the data collection unit can collect inventory data from specific corporate groups in real time, preparing for immediate analysis. For example, the data collection unit can collect customer behavior patterns in real time, preparing for immediate analysis. This enables immediate analysis by collecting data in real time. 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 customer purchase history into AI, which can analyze the data and collect it in real time.

[0090] The data collection unit can select the types of data to collect by considering the customer's purchase history and behavioral patterns during data collection. For example, the data collection unit can select the types of data to collect based on the customer's past purchase history. For example, the data collection unit can select the types of data to collect by analyzing the customer's behavioral patterns. For example, the data collection unit can select the types of data to collect by combining the customer's purchase history and behavioral patterns. This allows for the optimization of the types of data collected by considering the customer's purchase history and behavioral patterns. 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 customer's purchase history into AI, and the AI ​​can analyze the data and select the types of data to collect.

[0091] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can analyze the data and determine the priority of data to collect.

[0092] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the customer's current location. For example, the data collection unit can prioritize the collection of highly relevant data by analyzing the customer's past location information. For example, the data collection unit can prioritize the collection of highly relevant data by combining the customer's geographical location information with their purchase history. This allows for the priority collection of highly relevant data by considering the customer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0093] The data collection unit can analyze the customer's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the customer's social media posts and collect relevant data. For example, the data collection unit can analyze the customer's social media following relationships and collect relevant data. For example, the data collection unit can analyze the customer's social media activity history and collect relevant data. In this way, relevant data can be collected by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's social media data into AI, which can then analyze the data and collect relevant data.

[0094] 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 provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to 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 an AI, and the AI ​​can analyze the data and adjust the display method.

[0095] The analysis unit can simultaneously predict product losses and sales when analyzing collected data. For example, the analysis unit can simultaneously predict product losses and sales based on collected data. For example, the analysis unit can simultaneously predict product losses and sales based on customer purchase history. For example, the analysis unit can simultaneously predict product losses and sales based on customer behavior patterns. By simultaneously predicting product losses and sales, a more comprehensive analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, and the AI ​​can analyze the data to simultaneously predict product losses and sales.

[0096] The analysis unit can optimize the analysis algorithm by considering the customer's purchase history and behavioral patterns during analysis. For example, the analysis unit optimizes the analysis algorithm based on the customer's purchase history. For example, the analysis unit optimizes the analysis algorithm based on the customer's behavioral patterns. For example, the analysis unit optimizes the analysis algorithm by combining the customer's purchase history and behavioral patterns. In this way, the analysis algorithm can be optimized by considering the customer's purchase history and behavioral patterns. 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 customer's purchase history into AI, and the AI ​​can analyze the data to optimize the analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize displaying analysis results that can be displayed quickly. In this way, by determining the priority of analysis results according to the user's emotions, important analysis results can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can analyze the data and determine the priority of analysis results.

[0098] The analysis unit can classify analysis results by region, taking into account the customer's geographical location information during analysis. For example, the analysis unit classifies analysis results by region based on the customer's current location. For example, the analysis unit classifies analysis results by region based on the customer's past location information. For example, the analysis unit classifies analysis results by region by combining the customer's geographical location information and purchase history. In this way, by considering the customer's geographical location information, analysis results can be classified by region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify the analysis results by region.

[0099] The analysis unit can analyze the customer's social media activity during analysis and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media posts and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media following relationships and reflect the relevant data in the analysis. For example, the analysis unit can analyze the customer's social media activity history and reflect the relevant data in the analysis. In this way, by analyzing the customer's social media activity, relevant data can be reflected in the analysis. 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 customer's social media data into AI, and the AI ​​can analyze the data and reflect the relevant data in the analysis.

[0100] The forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated emotions. For example, if the user is stressed, the forecasting unit provides a highly accurate demand forecast. For example, if the user is relaxed, the forecasting unit provides a detailed demand forecast. For example, if the user is in a hurry, the forecasting unit provides a demand forecast that can be delivered quickly. By adjusting the accuracy of the demand forecast according to the user's emotions, more accurate demand forecasting becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not using AI. For example, the forecasting unit can input user emotion data into an AI, and the AI ​​can analyze the data to adjust the accuracy of the demand forecast.

[0101] The forecasting unit can consider seasonal variations when training a demand forecasting model using time series data analysis. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on time series data analysis. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on historical data. For example, the forecasting unit trains a demand forecasting model that considers seasonal variations based on customer purchase history. This improves the accuracy of demand forecasting by considering seasonal variations. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input time series data into AI, and the AI ​​can analyze the data to train a demand forecasting model that considers seasonal variations.

[0102] The prediction unit can optimize its prediction algorithm by considering the customer's purchase history and behavioral patterns during prediction. For example, the prediction unit optimizes the prediction algorithm based on the customer's purchase history. For example, the prediction unit optimizes the prediction algorithm based on the customer's behavioral patterns. For example, the prediction unit optimizes the prediction algorithm by combining the customer's purchase history and behavioral patterns. In this way, the prediction algorithm can be optimized by considering the customer's purchase history and behavioral patterns. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the customer's purchase history into AI, and the AI ​​can analyze the data to optimize the prediction algorithm.

[0103] The forecasting unit can estimate the user's emotions and determine the priority of demand forecasts based on the estimated emotions. For example, if the user is stressed, the forecasting unit will prioritize providing important demand forecasts. For example, if the user is relaxed, the forecasting unit will prioritize providing detailed demand forecasts. For example, if the user is in a hurry, the forecasting unit will prioritize providing demand forecasts that can be delivered quickly. In this way, important demand forecasts can be prioritized by determining the priority of demand forecasts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the forecasting unit may be performed using AI or not using AI. For example, the forecasting unit can input user emotion data into an AI, and the AI ​​can analyze the data to determine the priority of demand forecasts.

[0104] The forecasting unit can classify demand forecasts by region, taking into account the customer's geographical location information during forecasting. For example, the forecasting unit classifies demand forecasts by region based on the customer's current location. For example, the forecasting unit classifies demand forecasts by region based on the customer's past location information. For example, the forecasting unit classifies demand forecasts by region by combining the customer's geographical location information and purchase history. In this way, by taking into account the customer's geographical location information, demand forecasts can be classified by region. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify demand forecasts by region.

[0105] The prediction unit can analyze the customer's social media activity and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media posts and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media following relationships and reflect relevant data in the prediction. For example, the prediction unit can analyze the customer's social media activity history and reflect relevant data in the prediction. In this way, by analyzing the customer's social media activity, relevant data can be reflected in the prediction. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the customer's social media data into AI, which can analyze the data and reflect relevant data in the prediction.

[0106] The marketing department can estimate the user's emotions and adjust the content of the promotions based on those emotions. For example, if the user is relaxed, the marketing department can provide detailed promotional information. If the user is in a hurry, the marketing department can provide concise promotional information that gets straight to the point. If the user is excited, the marketing department can provide visually stimulating promotional information. By adjusting the content of the promotions according to the user's emotions, more effective promotions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the marketing department may be performed using AI, for example, or not using AI. For example, the marketing department can input user emotion data into an AI, and the AI ​​can analyze the data and adjust the content of the promotions.

[0107] The sales promotion department can consider customer purchase history and behavioral patterns when implementing sales promotions to prevent losses. For example, the sales promotion department can implement sales promotions to prevent losses based on customer purchase history. For example, the sales promotion department can implement sales promotions to prevent losses based on customer behavioral patterns. For example, the sales promotion department can implement sales promotions to prevent losses by combining customer purchase history and behavioral patterns. In this way, sales promotions that prevent losses become possible by considering customer purchase history and behavioral patterns. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or not using AI. For example, the sales promotion department can input customer purchase history into AI, and the AI ​​can analyze the data to implement sales promotions that prevent losses.

[0108] The sales promotion department can optimize its sales promotion algorithms by considering customer purchase history and behavioral patterns during sales promotions. For example, the sales promotion department can optimize the sales promotion algorithm based on customer purchase history. For example, the sales promotion department can optimize the sales promotion algorithm based on customer behavioral patterns. For example, the sales promotion department can optimize the sales promotion algorithm by combining customer purchase history and behavioral patterns. In this way, the sales promotion algorithm can be optimized by considering customer purchase history and behavioral patterns. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or without AI. For example, the sales promotion department can input customer purchase history into AI, and the AI ​​can analyze the data to optimize the sales promotion algorithm.

[0109] The marketing department can estimate the user's emotions and determine the priority of promotions based on those emotions. For example, if the user is stressed, the marketing department will prioritize providing important promotional information. For example, if the user is relaxed, the marketing department will prioritize providing detailed promotional information. For example, if the user is in a hurry, the marketing department will prioritize providing promotional information that can be delivered quickly. This allows for the priority delivery of important promotional information by determining the priority of promotions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 marketing department may be performed using AI or not. For example, the marketing department can input user emotion data into an AI, which can analyze the data to determine the priority of promotions.

[0110] The sales promotion department can categorize promotional content by region, taking into account the customer's geographical location. For example, the sales promotion department can categorize promotional content by region based on the customer's current location. For example, the sales promotion department can categorize promotional content by region based on the customer's past location information. For example, the sales promotion department can categorize promotional content by region by combining the customer's geographical location information with their purchase history. In this way, promotional content can be categorized by region by considering the customer's geographical location information. Some or all of the above processes in the sales promotion department may be performed using AI, for example, or without AI. For example, the sales promotion department can input the customer's geographical location information into AI, and the AI ​​can analyze the data and categorize promotional content by region.

[0111] The marketing department can analyze customers' social media activity during promotions and incorporate relevant data into those promotions. For example, the marketing department can analyze customers' social media posts and incorporate relevant data into promotions. For example, the marketing department can analyze customers' social media following relationships and incorporate relevant data into promotions. For example, the marketing department can analyze customers' social media activity history and incorporate relevant data into promotions. In this way, by analyzing customers' social media activity, relevant data can be incorporated into promotions. Some or all of the above processes in the marketing department may be performed using AI, for example, or not using AI. For example, the marketing department can input customer social media data into AI, which can analyze the data and incorporate relevant data into promotions.

[0112] The advertising department can estimate the user's emotions and adjust the content of the advertisement based on those emotions. For example, if the user is relaxed, the advertising department can provide detailed advertising information. If the user is in a hurry, the advertising department can provide concise advertising information that gets straight to the point. If the user is excited, the advertising department can provide visually stimulating advertising information. By adjusting the content of the advertisement according to the user's emotions, more effective advertising becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI, or not using AI. For example, the advertising department can input user emotion data into AI, and the AI ​​can analyze the data and adjust the content of the advertisement.

[0113] The advertising department can consider customer purchase history and behavioral patterns when delivering advertisements using user touchpoints. For example, the advertising department can deliver advertisements using user touchpoints based on customer purchase history. For example, the advertising department can deliver advertisements using user touchpoints based on customer behavioral patterns. For example, the advertising department can deliver advertisements using user touchpoints by combining customer purchase history and behavioral patterns. This makes it possible to deliver effective advertisements by considering customer purchase history and behavioral patterns. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer purchase history into AI, and the AI ​​can analyze the data and deliver advertisements using user touchpoints.

[0114] The advertising department can optimize its advertising algorithms when delivering ads, taking into account customer purchase history and behavioral patterns. For example, the advertising department can optimize the advertising algorithm based on customer purchase history. For example, the advertising department can optimize the advertising algorithm based on customer behavioral patterns. For example, the advertising department can optimize the advertising algorithm by combining customer purchase history and behavioral patterns. In this way, the advertising algorithm can be optimized by taking into account customer purchase history and behavioral patterns. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer purchase history into AI, and the AI ​​can analyze the data to optimize the advertising algorithm.

[0115] The advertising department can estimate the user's emotions and prioritize ads based on those emotions. For example, if the user is stressed, the advertising department will prioritize providing important advertising information. For example, if the user is relaxed, the advertising department will prioritize providing detailed advertising information. For example, if the user is in a hurry, the advertising department will prioritize providing advertising information that can be delivered quickly. This allows for the priority delivery of important advertising information by prioritizing ads according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user emotion data into an AI, which can analyze the data to determine the priority of ads.

[0116] The advertising department can classify ad content by region when delivering ads, taking into account the customer's geographical location. For example, the advertising department can classify ad content by region based on the customer's current location. For example, the advertising department can classify ad content by region based on the customer's past location information. For example, the advertising department can classify ad content by region by combining the customer's geographical location information and purchase history. In this way, ad content can be classified by region by taking into account the customer's geographical location information. Some or all of the above processing in the advertising department may be performed using AI, for example, or without using AI. For example, the advertising department can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify ad content by region.

[0117] The advertising department can analyze customers' social media activity and reflect relevant data in advertisements when delivering ads. For example, the advertising department can analyze customers' social media posts and reflect relevant data in advertisements. For example, the advertising department can analyze customers' social media following relationships and reflect relevant data in advertisements. For example, the advertising department can analyze customers' social media activity history and reflect relevant data in advertisements. In this way, by analyzing customers' social media activity, relevant data can be reflected in advertisements. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input customer social media data into AI, which can analyze the data and reflect relevant data in advertisements.

[0118] The coupon unit can estimate the user's emotions and adjust the coupon content based on the estimated emotions. For example, if the user is relaxed, the coupon unit provides detailed coupon information. If the user is in a hurry, the coupon unit provides concise coupon information that gets straight to the point. If the user is excited, the coupon unit provides visually stimulating coupon information. By adjusting the coupon content according to the user's emotions, more effective coupon distribution becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon unit may be performed using AI, or not using AI. For example, the coupon unit can input user emotion data into an AI, which can analyze the data and adjust the coupon content.

[0119] The coupon unit can consider customer purchase history and behavioral patterns when distributing coupons using an electronic payment system. For example, the coupon unit can distribute coupons using an electronic payment system based on customer purchase history. For example, the coupon unit can distribute coupons using an electronic payment system based on customer behavioral patterns. For example, the coupon unit can combine customer purchase history and behavioral patterns to distribute coupons using an electronic payment system. This makes it possible to distribute coupons more effectively by considering customer purchase history and behavioral patterns. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without AI. For example, the coupon unit can input customer purchase history into AI, and the AI ​​can analyze the data and distribute coupons using an electronic payment system.

[0120] The coupon unit can optimize the coupon algorithm when distributing coupons, taking into account the customer's purchase history and behavioral patterns. For example, the coupon unit optimizes the coupon algorithm based on the customer's purchase history. For example, the coupon unit optimizes the coupon algorithm based on the customer's behavioral patterns. For example, the coupon unit optimizes the coupon algorithm by combining the customer's purchase history and behavioral patterns. In this way, the coupon algorithm can be optimized by taking into account the customer's purchase history and behavioral patterns. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without using AI. For example, the coupon unit can input the customer's purchase history into AI, and the AI ​​can analyze the data to optimize the coupon algorithm.

[0121] The coupon unit can estimate the user's emotions and determine the priority of coupons based on the estimated emotions. For example, if the user is stressed, the coupon unit will prioritize providing important coupon information. For example, if the user is relaxed, the coupon unit will prioritize providing detailed coupon information. For example, if the user is in a hurry, the coupon unit will prioritize providing coupon information that can be delivered quickly. In this way, important coupon information can be prioritized by determining the priority of coupons according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon unit may be performed using AI or not using AI. For example, the coupon unit can input user emotion data into an AI, and the AI ​​can analyze the data to determine the priority of coupons.

[0122] The coupon unit can classify coupon content by region when distributing coupons, taking into account the customer's geographical location information. For example, the coupon unit can classify coupon content by region based on the customer's current location. For example, the coupon unit can classify coupon content by region based on the customer's past location information. For example, the coupon unit can classify coupon content by region by combining the customer's geographical location information and purchase history. In this way, coupon content can be classified by region by taking into account the customer's geographical location information. Some or all of the above processing in the coupon unit may be performed using AI, for example, or without using AI. For example, the coupon unit can input the customer's geographical location information into AI, and the AI ​​can analyze the data and classify coupon content by region.

[0123] The coupon department can analyze customers' social media activity when distributing coupons and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media posts and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media following relationships and reflect the relevant data in the coupons. For example, the coupon department can analyze customers' social media activity history and reflect the relevant data in the coupons. In this way, by analyzing customers' social media activity, relevant data can be reflected in the coupons. Some or all of the above processing in the coupon department may be performed using AI, for example, or without AI. For example, the coupon department can input customer social media data into AI, which can analyze the data and reflect the relevant data in the coupons.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The data collection unit can collect not only customer purchase history but also customer social media activity and online behavior data. For example, if a customer mentions a specific product on social media, that information can be collected and used for analysis by the analysis unit. It can also collect data on products viewed online and advertisements clicked, allowing for a more accurate understanding of customer interests and preferences. This enables the data collection unit to collect more comprehensive data by gathering not only customer purchase history but also social media activity and online behavior data.

[0126] The analysis unit can estimate customer emotions when analyzing collected data and adjust the analysis results based on those estimated emotions. For example, if a customer shows positive emotions, the list of recommended products can be adjusted based on those emotions. Conversely, if a customer shows negative emotions, a different approach can be taken based on those emotions. Furthermore, it is possible to track changes in customer emotions in real time and dynamically adjust the analysis results. This allows the analysis unit to provide more personalized services by offering analysis results that are tailored to the customer's emotions.

[0127] The forecasting unit can consider not only customer purchase history and behavioral patterns, but also external economic indicators and weather data when forecasting demand. For example, it can improve the accuracy of demand forecasts by considering economic indicators such as the consumer confidence index and unemployment rate. It can also use weather data to forecast demand in accordance with seasonal and weather changes. Furthermore, the forecasting unit can acquire this external data in real time and reflect it in the demand forecasting model. As a result, the forecasting unit can provide highly accurate forecasts by considering a wider range of factors.

[0128] The marketing department can estimate customer emotions and adjust the timing of promotions based on those estimates. For example, if a customer is relaxed, sending a promotional message at that time can yield a more effective response. Conversely, if a customer is stressed, avoiding sending promotional messages at that time can reduce the customer's burden. Furthermore, it is possible to track changes in customer emotions in real time and dynamically adjust the timing of promotions. This allows the marketing department to conduct more effective promotional activities by timing promotions according to customer emotions.

[0129] The advertising department can consider not only customers' purchase history and behavioral patterns when delivering ads, but also their geographical location. For example, if a customer is in a specific region, delivering ads relevant to that region can lead to more effective ad delivery. Furthermore, by analyzing a customer's past location data, ads related to places they frequently visit can be delivered. It's also possible to combine a customer's geographical location with their purchase history to deliver even more personalized ads. This allows the advertising department to maximize the effectiveness of their ads by considering customers' geographical location.

[0130] The coupon department can estimate customer emotions when distributing coupons and adjust the distribution method based on those emotions. For example, if a customer shows positive emotions, providing a special coupon tailored to those emotions can improve customer satisfaction. Conversely, if a customer shows negative emotions, a coupon can be provided along with an encouraging message tailored to those emotions. Furthermore, it is possible to track changes in customer emotions in real time and dynamically adjust the coupon distribution method. This allows the coupon department to achieve more effective coupon distribution by distributing coupons in accordance with customer emotions.

[0131] The data collection unit can collect not only customer purchase history and behavioral patterns, but also customer health data during data collection. For example, if a customer uses a health management app, that data can be collected, and products tailored to the customer's health condition can be recommended. It can also analyze customer health data and suggest health-conscious products. Furthermore, it is possible to combine customer health data with purchase history to provide more personalized product recommendations. In this way, the data collection unit can collect more comprehensive data by taking customer health data into consideration during data collection.

[0132] The analysis unit can estimate customer emotions when analyzing collected data and adjust the display method of the analysis results based on the estimated emotions. For example, if a customer is relaxed, detailed analysis results can be provided to offer useful information to the customer. Conversely, if a customer is in a hurry, concise analysis results can be provided to save the customer time. Furthermore, it is possible to track changes in customer emotions in real time and dynamically adjust the display method of the analysis results. This allows the analysis unit to provide an easy-to-understand display for customers by providing analysis results in a way that suits their emotions.

[0133] The forecasting unit can consider not only customer purchase history and behavioral patterns but also customer life event data when making demand forecasts. For example, if a customer is experiencing a life event such as marriage or childbirth, considering this data in the demand forecast will enable more accurate predictions. It can also make product suggestions tailored to the customer's life events. Furthermore, it is possible to combine customer life event data with purchase history to make more personalized demand forecasts. As a result, the forecasting unit can provide highly accurate forecasts by considering customer life event data in its demand forecasting.

[0134] The sales promotion department can estimate customer emotions when conducting promotional activities and adjust the content of the promotions based on those estimated emotions. For example, if a customer is showing positive emotions, they can increase their purchasing intent by offering special offers tailored to those emotions. Conversely, if a customer is showing negative emotions, they can provide promotional information along with encouraging messages tailored to those emotions. Furthermore, it is possible to track changes in customer emotions in real time and dynamically adjust the content of promotions. As a result, the sales promotion department can achieve more effective sales promotions by conducting promotional activities that are tailored to customer emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects data. The collection unit collects, for example, customer-specific data and data for specific corporate groups. The collection unit can collect, for example, customer purchase history, behavioral patterns, sales data for specific corporate groups, marketing data, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, an algorithm to predict product losses based on the collected data. Step 3: The forecasting unit performs demand forecasting based on the analysis results obtained by the analysis unit. The forecasting unit trains a demand forecasting model using, for example, time series data analysis. The forecasting unit performs demand forecasting using, for example, time series data analysis algorithms such as the ARIMA model or LSTM. Step 4: The sales promotion department conducts sales promotions based on the forecast results obtained by the forecasting department. For example, the sales promotion department implements sales promotions to prevent losses. For example, the sales promotion department conducts sales promotions based on inventory management and demand forecasts. Step 5: The advertising department delivers advertisements to consumers. The advertising department delivers advertisements using user touchpoints, for example. The advertising department delivers advertisements using user touchpoints such as websites, mobile apps, and stores. Step 6: The coupon department distributes the coupons. The coupon department distributes the coupons using, for example, an electronic payment system. The coupon department distributes the coupons using, for example, an electronic payment system such as credit card payment or mobile payment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, forecasting unit, sales promotion unit, advertising unit, and coupon unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects customer-specific data or data of a specific corporate group by the control unit 46A of the smart device 14. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12 to predict product losses. The forecasting unit uses time-series data analysis by, for example, the specific processing unit 290 of the data processing unit 12 to forecast demand. The sales promotion unit implements sales promotions to prevent losses by, for example, the control unit 46A of the smart device 14. The advertising unit delivers advertisements by utilizing user touchpoints by, for example, the control unit 46A of the smart device 14. The coupon unit distributes coupons by utilizing an electronic payment system by, for example, the control unit 46A of the smart device 14. 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.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, forecasting unit, sales promotion unit, advertising unit, and coupon unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer-specific data or data of a specific corporate group by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to predict product losses. The forecasting unit uses time-series data analysis by the specific processing unit 290 of the data processing unit 12 to forecast demand. The sales promotion unit implements sales promotions to prevent losses by the control unit 46A of the smart glasses 214. The advertising unit delivers advertisements by utilizing user touchpoints by the control unit 46A of the smart glasses 214. The coupon unit distributes coupons by utilizing an electronic payment system by the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the data collection unit, analysis unit, forecasting unit, sales promotion unit, advertising unit, and coupon unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects customer-specific data or data of a specific corporate group by the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12 to predict product losses. The forecasting unit uses time-series data analysis by, for example, the specific processing unit 290 of the data processing unit 12 to forecast demand. The sales promotion unit implements sales promotions to prevent losses by, for example, the control unit 46A of the headset terminal 314. The advertising unit delivers advertisements by utilizing user touchpoints by, for example, the control unit 46A of the headset terminal 314. The coupon unit distributes coupons by utilizing an electronic payment system by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the data collection unit, analysis unit, forecasting unit, sales promotion unit, advertising unit, and coupon unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects customer-specific data or data of a specific corporate group by the control unit 46A of the robot 414. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12 to predict product losses. The forecasting unit uses time-series data analysis by, for example, the specific processing unit 290 of the data processing unit 12 to forecast demand. The sales promotion unit implements sales promotions to prevent losses by, for example, the control unit 46A of the robot 414. The advertising unit delivers advertisements by utilizing user touchpoints by, for example, the control unit 46A of the robot 414. The coupon unit distributes coupons by utilizing an electronic payment system by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that performs demand forecasting based on the analysis results obtained by the aforementioned analysis unit, A sales promotion unit that carries out sales promotion based on the prediction results obtained by the prediction unit, The advertising department delivers advertisements to consumers, It includes a coupon department that distributes coupons, A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect customer-specific data and data from specific corporate groups. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to predict product losses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Train demand forecasting models using time series data analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned sales promotion department, We will implement sales promotions to prevent losses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advertising department, Deliver ads by leveraging user touchpoints. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned coupon section is, We will distribute coupons using an electronic payment system. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned collection unit is We collect customer-specific data and data from specific corporate groups in real time, preparing for immediate analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the types of data to be collected are selected considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, When analyzing the collected data, we not only predict product losses but also simultaneously forecast sales. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis results are classified by region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, we analyze customers' social media activity and incorporate relevant data into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When training a demand forecasting model using time series data analysis, seasonal variations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, the prediction algorithm is optimized by considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates user sentiment and determines demand forecast priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making forecasts, the demand forecast is categorized by region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, During the forecasting process, we analyze customers' social media activity and incorporate relevant data into the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sales promotion department, We estimate the user's emotions and adjust the promotional content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned sales promotion department, When implementing sales promotions to prevent losses, consider the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned sales promotion department, During promotional activities, the promotional algorithm is optimized by considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned sales promotion department, It estimates user emotions and determines promotional priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned sales promotion department, During sales promotions, the promotional content is categorized by region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned sales promotion department, During promotional activities, analyze customers' social media activity and incorporate relevant data into the promotional efforts. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advertising department, It estimates the user's emotions and adjusts the ad content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advertising department, When delivering ads using user touchpoints, consider the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advertising department, When delivering ads, the ad algorithm is optimized by considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advertising department, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advertising department, When delivering ads, the ad content is categorized by region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advertising department, When delivering ads, analyze customers' social media activity and incorporate relevant data into the ads. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned coupon section is, The system estimates the user's emotions and adjusts the coupon content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned coupon section is, When distributing coupons using an electronic payment system, consider the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned coupon section is, When distributing coupons, the coupon algorithm is optimized by considering the customer's purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned coupon section is, The system estimates user sentiment and prioritizes coupons based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned coupon section is, When distributing coupons, the coupon content will be categorized by region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned coupon section is, When distributing coupons, analyze customers' social media activity and incorporate relevant data into the coupons. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that performs demand forecasting based on the analysis results obtained by the aforementioned analysis unit, A sales promotion unit that carries out sales promotion based on the prediction results obtained by the prediction unit, The advertising department delivers advertisements to consumers, It includes a coupon department that distributes coupons, A system characterized by the following features.

2. The aforementioned collection unit is Collect customer-specific data and data from specific corporate groups. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to predict product losses. The system according to feature 1.

4. The prediction unit, Train demand forecasting models using time series data analysis. The system according to feature 1.

5. The aforementioned sales promotion department, We will implement sales promotions to prevent losses. The system according to feature 1.

6. The aforementioned advertising department, Deliver ads by leveraging user touchpoints. The system according to feature 1.

7. The aforementioned coupon section is, We will distribute coupons using an electronic payment system. The system according to feature 1.

8. 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.

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