system

Abstract

The system according to this embodiment aims to dynamically optimize prices based on information such as the expiration date of food products, weather conditions, and the estimated number and timing of customer visits. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an optimization unit, and a supply unit. The collection unit collects statistical information such as the expiration date of food products, weather, and the estimated number and time of customer visits. The analysis unit analyzes the information collected by the collection unit. The optimization unit dynamically optimizes the price based on the information analyzed by the analysis unit. The supply unit provides the price optimized by the optimization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation about 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, prices have not been fully dynamically optimized based on information such as the expiration date of food, the weather, the expected number of customers and time of store visits, and there is room for improvement.

[0005] The system according to the embodiment aims to dynamically optimize prices based on information such as the expiration date of food, the weather, the expected number of customers and time of store visits.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a supply unit. The data collection unit collects statistical information such as the expiration date of food products, weather conditions, and the estimated number and time of customer visits. The analysis unit analyzes the information collected by the data collection unit. The optimization unit dynamically optimizes the price based on the information analyzed by the analysis unit. The supply unit provides the price optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can dynamically optimize prices based on information such as the expiration date of food products, weather conditions, and the estimated number and timing of customer visits. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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-driven price optimization system according to an embodiment of the present invention is a system that dynamically optimizes prices based on statistical information such as the expiration date of food products, weather, and the expected number and time of customer visits. The AI-driven price optimization system collects statistical information such as the expiration date of food products, weather, and the expected number and time of customer visits, and the AI ​​analyzes the collected information to dynamically optimize prices. Furthermore, the AI ​​applies discounts to products that are nearing their expiration date, discouraging customers from purchasing products with longer expiration dates. This prevents products nearing their expiration date from remaining unsold and reduces food waste. In addition, automating price change responses with AI significantly reduces the burden on store staff. Furthermore, by automatically applying reward programs for loyal customers, it promotes customer loyalty and sustainable consumption. This system targets medium to large-scale retailers, supermarket chains, and specialty food stores. As a result, it is expected to reduce food waste due to expired products, reduce the effort and cost of price changes, improve the accuracy of predicting customer purchasing behavior, and enhance customer loyalty. Furthermore, by promoting sustainable consumption and reducing food waste, we aim to foster socially responsible corporate behavior and build a sustainable food supply chain. This allows our AI-driven price optimization system to dynamically optimize prices based on statistical information such as food expiration dates, weather, and estimated customer visit times.

[0029] The AI-driven price optimization system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a provision unit. The data collection unit collects statistical information such as the expiration date of food products, weather, and the estimated number and time of customer visits. For example, the data collection unit collects the expiration date of food products based on the number of days elapsed since the manufacturing date and storage conditions. The data collection unit can also collect weather data based on elements such as temperature, precipitation, and wind speed. Furthermore, the data collection unit can also collect the estimated number and time of customer visits based on past data and event information. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining or machine learning algorithms. Based on the collected information, the analysis unit generates data for dynamic price optimization. The optimization unit dynamically optimizes prices based on the information analyzed by the analysis unit. For example, the optimization unit executes an algorithm that optimizes prices using real-time data. The optimization unit applies discounts to products with approaching expiration dates to discourage customers from purchasing products with longer expiration dates. The supply unit provides prices optimized by the optimization unit. The supply unit provides price information, for example, through online platforms or in-store displays. The supply unit can also automatically apply reward programs to customers and notify them of campaign information. As a result, the AI-driven price optimization system according to the embodiment can dynamically optimize prices based on statistical information such as food expiration dates, weather, and the expected number and time of customer visits.

[0030] The data collection unit collects statistical information such as food expiration dates, weather, and estimated customer visit numbers and times. Specifically, it collects food expiration dates based on the number of days elapsed since the manufacturing date and storage conditions. For example, it automatically acquires information such as the manufacturing date, storage temperature, and humidity using sensors and barcode readers and stores it in a database. Weather data can be collected based on elements such as temperature, precipitation, and wind speed. This includes obtaining real-time weather data provided by the Japan Meteorological Agency and private weather services via APIs. Furthermore, it can collect estimated customer visit numbers and times based on historical data and event information. For example, it analyzes past sales data, visit history, and local event calendars to predict the number of visitors on specific days of the week and time slots. This allows the data collection unit to efficiently collect necessary information from diverse data sources and improve the overall accuracy of the system. In addition, the data collection unit can dynamically adjust the frequency and accuracy of data collection by linking with IoT devices and cloud services. For example, by increasing the frequency of data collection under specific conditions, more detailed information can be obtained and provided to the analysis and optimization units. This allows the data collection unit to maximize the overall system performance and provide a foundation for improving the accuracy of price optimization.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it analyzes the information using data mining and machine learning algorithms. For example, it uses data mining techniques to extract patterns and trends from past sales data and customer visit history to predict future demand. It also uses machine learning algorithms to analyze collected weather data and estimated customer visit numbers and times to generate data for dynamic price optimization. Specifically, it uses techniques such as regression analysis, clustering, and deep learning to model the impact of multiple factors on price. Furthermore, the analysis unit can dynamically update analysis results based on real-time updated data, enabling it to respond to the latest situations. For example, in the event of a sudden change in weather or an unexpected event, the analysis unit immediately incorporates new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only analyze collected data quickly and accurately, providing a foundation for price optimization, but also respond to abnormal situations, improving the reliability and security of the entire system.

[0032] The optimization unit dynamically optimizes prices based on information analyzed by the analysis unit. Specifically, it executes algorithms that optimize prices using real-time data. For example, it might apply discounts to products nearing their expiration date and set prices to discourage customers from purchasing products with longer expiration dates. The optimization unit calculates the optimal price by considering demand forecasts, inventory levels, and competitor pricing information. This includes methods that use dynamic pricing algorithms and optimization models to consider multiple factors simultaneously and derive the optimal price. Furthermore, the optimization unit can continuously adjust pricing based on real-time updated data to respond to the latest situations. For example, if the expected number of customers visiting the store increases sharply, the optimization unit immediately incorporates the new data and updates the pricing. The optimization unit can also perform simulations and consider multiple scenarios to select the most effective pricing. This allows the optimization unit to always provide the optimal price, providing a foundation for maximizing sales and profits. In addition, the optimization unit can collect customer purchasing behavior and feedback to continuously improve the accuracy and effectiveness of pricing. This enables the optimization unit to achieve dynamic and flexible pricing and improve the overall system performance.

[0033] The supply unit provides prices optimized by the optimization unit. Specifically, it provides price information through online platforms and in-store displays. For example, on online platforms, it provides customers with the latest price information through websites and mobile apps. In-store displays use digital signage and electronic shelf labels to display price information that is updated in real time. The supply unit can also automatically apply reward programs to customers and notify them of campaign information. For example, it can award points to customers who purchase specific products and apply discounts to their next purchase. In addition, the supply unit can provide personalized offers and promotions based on customers' purchase history and behavioral data. This allows the supply unit to provide customers with optimal price information quickly and effectively, increasing their purchase intent. Furthermore, the supply unit can reliably transmit information using multiple communication methods. For example, it can use a combination of email, SMS, and push notifications to ensure that important information is delivered reliably. The supply unit can also collect customer feedback and continuously improve the accuracy and effectiveness of the information it provides. This allows the supply unit to not only provide customers with price information quickly and reliably, increasing their purchase intent, but also to improve the overall system performance.

[0034] The optimization unit can apply discounts to products nearing their expiration date. For example, the optimization unit can apply a discount to products with an expiration date of one week or less. It can also apply a discount to products with an expiration date of three days or less. Furthermore, it can apply a discount to products with an expiration date of the same day. By applying discounts to products nearing their expiration date, it is possible to prevent unsold inventory and reduce food waste. The method and criteria for applying discounts are determined, for example, based on the discount rate and application conditions. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on products nearing their expiration date into a generating AI and have the generating AI apply the discount rate.

[0035] The optimization unit can adjust prices based on weather and the expected number of customers. For example, the optimization unit can adjust prices based on the expected number of customers on a day with bad weather. It can also adjust prices based on the expected number of customers on a day with good weather. Furthermore, the optimization unit can adjust prices based on the expected number of customers under specific weather conditions. This improves the accuracy of price optimization by adjusting prices based on weather and the expected number of customers. The method and criteria for price adjustment are determined, for example, based on upper and lower price limits and adjustment frequency. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and expected number of customers data into a generating AI and have the generating AI perform the price adjustment.

[0036] The service provider can automatically apply the rewards program. For example, the service provider can apply the rewards program based on the customer's purchase history. It can also apply the rewards program based on the customer's loyalty program status. Furthermore, it can apply the rewards program based on the customer's purchase frequency. This allows for the promotion of customer loyalty and sustainable consumption by automatically applying the rewards program. The content and application method of the rewards program are determined, for example, based on the points system and the rewards offered. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer purchase data into a generating AI and have the generating AI execute the application of the rewards program.

[0037] The service provider can notify customers of campaign information. For example, the service provider can notify customers of campaign information based on their purchase history. The service provider can also notify customers of campaign information based on their loyalty program status. Furthermore, the service provider can notify customers of campaign information based on their purchase frequency. This allows for effective promotion to be conducted by notifying customers of campaign information. The content and method of notification of campaign information are determined based on, for example, discount information or new product information. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input customer purchase data into a generating AI and have the generating AI execute the notification of campaign information.

[0038] The data collection unit can collect customer purchase data. For example, the data collection unit can collect customer purchase history. The data collection unit can also collect customer purchase frequency. Furthermore, the data collection unit can collect customer purchase patterns. By collecting customer purchase data, it is possible to analyze customer purchasing behavior and perform more effective price optimization. The type of purchase data and the method of collection are determined, for example, based on databases or sensor data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer purchase data into a generating AI and have the generating AI perform the data collection.

[0039] The data collection unit can monitor the inventory status of products nearing their expiration date in real time and collect data as needed. For example, the data collection unit can prioritize monitoring products with an expiration date of one week or less and collect data when inventory decreases. It can also pay particular attention to monitoring products with an expiration date of three days or less and collect data when inventory falls below a certain level. Furthermore, the data collection unit can monitor products with an expiration date of the same day in real time and collect data before inventory runs out. This allows for real-time monitoring of the inventory status of products nearing their expiration date, enabling data collection at the appropriate time. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input inventory data of products nearing their expiration date into a generating AI and have the generating AI perform inventory status monitoring and data collection.

[0040] The data collection unit can improve prediction accuracy by referring to past weather patterns when collecting weather data. For example, the data collection unit can predict the weather for the next week based on weather data from the past year and collect the data. The data collection unit can also analyze weather patterns from the past 10 years and predict weather variations in a specific season and collect the data. Furthermore, the data collection unit can combine past weather data with current weather information to make short-term weather forecasts and collect the data. This improves the accuracy of weather data predictions by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past weather data into a generating AI and have the generating AI perform improvements to the accuracy of weather forecasts.

[0041] The data collection unit can analyze customers' social media activity and collect relevant data when collecting customer purchase data. For example, the data collection unit can prioritize collecting products mentioned by customers on social media. The data collection unit can also analyze the time periods when customers are active on social media and collect data related to those times. Furthermore, the data collection unit can collect data related to brands and stores that customers follow on social media. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The data collection unit can improve the accuracy of weather data by considering geographical characteristics when collecting it. For example, the data collection unit can collect different weather data in urban and rural areas to improve accuracy. It can also collect data considering different weather patterns in coastal and inland areas. Furthermore, the data collection unit can collect different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data can be improved by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data that considers geographical characteristics into a generating AI and have the generating AI perform data accuracy improvement.

[0043] The analysis unit can analyze the sales performance of products nearing their expiration date and calculate the optimal discount rate. For example, the analysis unit can calculate the optimal discount rate for products with an expiration date of one week or less based on past sales performance. The analysis unit can also adjust the discount rate for products with an expiration date of three days or less based on inventory status and sales performance. Furthermore, the analysis unit can calculate the discount rate necessary to sell out products with an expiration date of the same day immediately. In this way, the optimal discount rate can be calculated by analyzing the sales performance of products nearing their expiration date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input sales data of products nearing their expiration date into a generating AI and have the generating AI calculate the optimal discount rate.

[0044] The analysis unit can improve the accuracy of customer visit forecasts by combining weather data with estimated customer numbers. For example, the analysis unit can analyze estimated customer numbers on bad weather days based on past data. It can also analyze estimated customer numbers on good weather days based on past data. Furthermore, the analysis unit can analyze estimated customer numbers under specific weather conditions to improve prediction accuracy. In this way, the accuracy of customer visit forecasts can be improved by combining weather data with estimated customer numbers. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform the task of improving the accuracy of customer visit forecasts.

[0045] The analysis unit can improve the accuracy of its analysis of sales of products nearing their expiration date by referring to past sales data. For example, the analysis unit can analyze sales of products nearing their expiration date based on sales data from the past year. The analysis unit can also improve the accuracy of its analysis by combining past sales data with current inventory status. Furthermore, the analysis unit can analyze past sales data to predict sales in specific seasons or events. In this way, the accuracy of the analysis of sales of products nearing their expiration date can be improved by referring to past sales data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of the sales analysis.

[0046] The analysis unit can improve the accuracy of its weather analysis by considering regional characteristics when analyzing weather data. For example, the analysis unit can analyze different weather data in urban and rural areas to improve accuracy. It can also analyze different weather patterns in coastal and inland areas. Furthermore, the analysis unit can analyze different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data analysis can be improved by considering regional characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather data that considers regional characteristics into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0047] The optimization unit can dynamically adjust the discount rate of products nearing their expiration date, thereby preventing unsold inventory. For example, the optimization unit can dynamically adjust the discount rate of products with an expiration date of less than one week based on past sales performance. It can also dynamically adjust the discount rate of products with an expiration date of less than three days based on inventory status and sales performance. Furthermore, the optimization unit can dynamically adjust the discount rate of products with an expiration date of the same day to ensure immediate sale. By dynamically adjusting the discount rate of products nearing their expiration date, it is possible to prevent unsold inventory and reduce food waste. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on products nearing their expiration date into a generating AI and have the generating AI perform the dynamic adjustment of the discount rate.

[0048] The optimization unit can optimize prices in real time based on weather and estimated customer numbers. For example, the optimization unit can optimize prices in real time based on estimated customer numbers on bad weather days. It can also optimize prices in real time based on estimated customer numbers on good weather days. Furthermore, the optimization unit can optimize prices in real time based on estimated customer numbers under specific weather conditions. This improves the accuracy of price optimization by optimizing prices in real time based on weather and estimated customer numbers. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform real-time price optimization.

[0049] The optimization unit can calculate the optimal discount rate for products nearing their expiration date by referring to the customer's purchase history. For example, the optimization unit can calculate the optimal discount rate based on the expiration dates of products the customer has purchased in the past. The optimization unit can also adjust the discount rate for specific products based on the customer's purchase history. Furthermore, the optimization unit can analyze the customer's purchase history and calculate the most effective discount rate. This allows the optimization unit to calculate the optimal discount rate for products nearing their expiration date by referring to the customer's purchase history. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input customer purchase history data into a generating AI and have the generating AI perform the calculation of the discount rate.

[0050] The optimization unit can improve the accuracy of price optimization by considering regional characteristics when optimizing prices based on weather and the expected number of customers. For example, the optimization unit may use different price optimization algorithms for urban and rural areas. It may also use different price optimization algorithms for coastal and inland areas. Furthermore, it may use different price optimization algorithms for highland and lowland areas to improve accuracy. In this way, the accuracy of price optimization can be improved by considering regional characteristics. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data that considers regional characteristics into a generating AI and have the generating AI perform the task of improving the accuracy of price optimization.

[0051] The service provider can refer to the customer's purchase history to notify them of campaign information at the optimal time. For example, the service provider can notify customers of campaign information related to products they have purchased in the past during specific time periods. Furthermore, if the service provider's purchase history indicates a tendency for customers to purchase on certain days of the week, it can also notify them of campaign information on those days. Additionally, the service provider can notify customers of relevant campaign information when the expiration date of products they have purchased in the past is approaching. This allows the service provider to notify customers of campaign information at the optimal time by referring to their purchase history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer purchase history data into a generating AI and have the generating AI execute the campaign information notification.

[0052] The service provider can dynamically adjust benefits to enhance customer loyalty when applying a rewards program. For example, the service provider can offer discounts on specific products based on a customer's purchase history. Furthermore, the service provider can dynamically adjust the content of benefits according to the customer's loyalty program status. In addition, the service provider can customize the content of benefits according to the customer's purchase frequency. This allows for improved customer satisfaction by dynamically adjusting benefits to enhance customer loyalty. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input customer purchase data into a generating AI and have the generating AI perform the dynamic adjustment of benefits.

[0053] The service provider can notify customers of campaign information at the optimal time, taking into account their geographical location. For example, if a customer is near a store, the service provider can notify them of campaign information in real time. Furthermore, if a customer is in a specific region, the service provider can notify them of campaign information relevant to that region. In addition, if a customer is on the move, the service provider can notify them of campaign information before they reach their destination. This allows for the notification of campaign information at the optimal time by considering the customer's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer geographical location data into a generating AI and have the generating AI execute the campaign information notification.

[0054] The service provider can analyze a customer's social media activity and provide relevant rewards when applying a rewards program. For example, the service provider can provide rewards related to products mentioned by the customer on social media. The service provider can also analyze the time periods of customer activity on social media and provide rewards during those times. Furthermore, the service provider can provide rewards related to brands and stores that the customer follows on social media. This allows for the provision of more relevant rewards by analyzing the customer's social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input customer social media data into a generating AI and have the generating AI execute the provision of rewards.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze customers' social media activity and collect relevant data when collecting customer purchase data. For example, the data collection unit can prioritize collecting products mentioned by customers on social media. The data collection unit can also analyze the time periods when customers are active on social media and collect data related to those times. Furthermore, the data collection unit can collect data related to brands and stores that customers follow on social media. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0057] The data collection unit can improve prediction accuracy by referring to past weather patterns when collecting weather data. For example, the data collection unit can predict the weather for the next week based on weather data from the past year and collect the data. The data collection unit can also analyze weather patterns from the past 10 years and predict weather variations in a specific season and collect the data. Furthermore, the data collection unit can combine past weather data with current weather information to make short-term weather forecasts and collect the data. This improves the accuracy of weather data predictions by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past weather data into a generating AI and have the generating AI perform improvements to the accuracy of weather forecasts.

[0058] The data collection unit can improve the accuracy of weather data by considering geographical characteristics when collecting it. For example, the data collection unit can collect different weather data in urban and rural areas to improve accuracy. It can also collect data considering different weather patterns in coastal and inland areas. Furthermore, the data collection unit can collect different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data can be improved by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data that considers geographical characteristics into a generating AI and have the generating AI perform data accuracy improvement.

[0059] The analysis unit can improve the accuracy of its analysis of sales of products nearing their expiration date by referring to past sales data. For example, the analysis unit can analyze sales of products nearing their expiration date based on sales data from the past year. The analysis unit can also improve the accuracy of its analysis by combining past sales data with current inventory status. Furthermore, the analysis unit can analyze past sales data to predict sales in specific seasons or events. In this way, the accuracy of the analysis of sales of products nearing their expiration date can be improved by referring to past sales data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of the sales analysis.

[0060] The optimization unit can optimize prices in real time based on weather and estimated customer numbers. For example, the optimization unit can optimize prices in real time based on estimated customer numbers on bad weather days. It can also optimize prices in real time based on estimated customer numbers on good weather days. Furthermore, the optimization unit can optimize prices in real time based on estimated customer numbers under specific weather conditions. This improves the accuracy of price optimization by optimizing prices in real time based on weather and estimated customer numbers. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform real-time price optimization.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects statistical information such as food expiration dates, weather, and estimated customer numbers and visit times. For example, food expiration dates are collected based on the number of days elapsed since the manufacturing date and storage conditions, and weather data is collected based on factors such as temperature, precipitation, and wind speed. In addition, estimated customer numbers and visit times are collected based on past data and event information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the information using data mining and machine learning algorithms to generate data for dynamic price optimization. Step 3: The optimization unit dynamically optimizes prices based on the information analyzed by the analysis unit. For example, it runs an algorithm that optimizes prices using real-time data and applies discounts to products nearing their expiration date. Step 4: The delivery unit provides prices optimized by the optimization unit. For example, it provides price information through online platforms and in-store displays, automatically applies reward programs to customers, and notifies them of campaign information.

[0063] (Example of form 2) The AI-driven price optimization system according to an embodiment of the present invention is a system that dynamically optimizes prices based on statistical information such as the expiration date of food products, weather, and the expected number and time of customer visits. The AI-driven price optimization system collects statistical information such as the expiration date of food products, weather, and the expected number and time of customer visits, and the AI ​​analyzes the collected information to dynamically optimize prices. Furthermore, the AI ​​applies discounts to products that are nearing their expiration date, discouraging customers from purchasing products with longer expiration dates. This prevents products nearing their expiration date from remaining unsold and reduces food waste. In addition, automating price change responses with AI significantly reduces the burden on store staff. Furthermore, by automatically applying reward programs for loyal customers, it promotes customer loyalty and sustainable consumption. This system targets medium to large-scale retailers, supermarket chains, and specialty food stores. As a result, it is expected to reduce food waste due to expired products, reduce the effort and cost of price changes, improve the accuracy of predicting customer purchasing behavior, and enhance customer loyalty. Furthermore, by promoting sustainable consumption and reducing food waste, we aim to foster socially responsible corporate behavior and build a sustainable food supply chain. This allows our AI-driven price optimization system to dynamically optimize prices based on statistical information such as food expiration dates, weather, and estimated customer visit times.

[0064] The AI-driven price optimization system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a provision unit. The data collection unit collects statistical information such as the expiration date of food products, weather, and the estimated number and time of customer visits. For example, the data collection unit collects the expiration date of food products based on the number of days elapsed since the manufacturing date and storage conditions. The data collection unit can also collect weather data based on elements such as temperature, precipitation, and wind speed. Furthermore, the data collection unit can also collect the estimated number and time of customer visits based on past data and event information. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining or machine learning algorithms. Based on the collected information, the analysis unit generates data for dynamic price optimization. The optimization unit dynamically optimizes prices based on the information analyzed by the analysis unit. For example, the optimization unit executes an algorithm that optimizes prices using real-time data. The optimization unit applies discounts to products with approaching expiration dates to discourage customers from purchasing products with longer expiration dates. The supply unit provides prices optimized by the optimization unit. The supply unit provides price information, for example, through online platforms or in-store displays. The supply unit can also automatically apply reward programs to customers and notify them of campaign information. As a result, the AI-driven price optimization system according to the embodiment can dynamically optimize prices based on statistical information such as food expiration dates, weather, and the expected number and time of customer visits.

[0065] The data collection unit collects statistical information such as food expiration dates, weather, and estimated customer visit numbers and times. Specifically, it collects food expiration dates based on the number of days elapsed since the manufacturing date and storage conditions. For example, it automatically acquires information such as the manufacturing date, storage temperature, and humidity using sensors and barcode readers and stores it in a database. Weather data can be collected based on elements such as temperature, precipitation, and wind speed. This includes obtaining real-time weather data provided by the Japan Meteorological Agency and private weather services via APIs. Furthermore, it can collect estimated customer visit numbers and times based on historical data and event information. For example, it analyzes past sales data, visit history, and local event calendars to predict the number of visitors on specific days of the week and time slots. This allows the data collection unit to efficiently collect necessary information from diverse data sources and improve the overall accuracy of the system. In addition, the data collection unit can dynamically adjust the frequency and accuracy of data collection by linking with IoT devices and cloud services. For example, by increasing the frequency of data collection under specific conditions, more detailed information can be obtained and provided to the analysis and optimization units. This allows the data collection unit to maximize the overall system performance and provide a foundation for improving the accuracy of price optimization.

[0066] The analysis unit analyzes the information collected by the data collection unit. Specifically, it analyzes the information using data mining and machine learning algorithms. For example, it uses data mining techniques to extract patterns and trends from past sales data and customer visit history to predict future demand. It also uses machine learning algorithms to analyze collected weather data and estimated customer visit numbers and times to generate data for dynamic price optimization. Specifically, it uses techniques such as regression analysis, clustering, and deep learning to model the impact of multiple factors on price. Furthermore, the analysis unit can dynamically update analysis results based on real-time updated data, enabling it to respond to the latest situations. For example, in the event of a sudden change in weather or an unexpected event, the analysis unit immediately incorporates new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only analyze collected data quickly and accurately, providing a foundation for price optimization, but also respond to abnormal situations, improving the reliability and security of the entire system.

[0067] The optimization unit dynamically optimizes prices based on information analyzed by the analysis unit. Specifically, it executes algorithms that optimize prices using real-time data. For example, it might apply discounts to products nearing their expiration date and set prices to discourage customers from purchasing products with longer expiration dates. The optimization unit calculates the optimal price by considering demand forecasts, inventory levels, and competitor pricing information. This includes methods that use dynamic pricing algorithms and optimization models to consider multiple factors simultaneously and derive the optimal price. Furthermore, the optimization unit can continuously adjust pricing based on real-time updated data to respond to the latest situations. For example, if the expected number of customers visiting the store increases sharply, the optimization unit immediately incorporates the new data and updates the pricing. The optimization unit can also perform simulations and consider multiple scenarios to select the most effective pricing. This allows the optimization unit to always provide the optimal price, providing a foundation for maximizing sales and profits. In addition, the optimization unit can collect customer purchasing behavior and feedback to continuously improve the accuracy and effectiveness of pricing. This enables the optimization unit to achieve dynamic and flexible pricing and improve the overall system performance.

[0068] The supply unit provides prices optimized by the optimization unit. Specifically, it provides price information through online platforms and in-store displays. For example, on online platforms, it provides customers with the latest price information through websites and mobile apps. In-store displays use digital signage and electronic shelf labels to display price information that is updated in real time. The supply unit can also automatically apply reward programs to customers and notify them of campaign information. For example, it can award points to customers who purchase specific products and apply discounts to their next purchase. In addition, the supply unit can provide personalized offers and promotions based on customers' purchase history and behavioral data. This allows the supply unit to provide customers with optimal price information quickly and effectively, increasing their purchase intent. Furthermore, the supply unit can reliably transmit information using multiple communication methods. For example, it can use a combination of email, SMS, and push notifications to ensure that important information is delivered reliably. The supply unit can also collect customer feedback and continuously improve the accuracy and effectiveness of the information it provides. This allows the supply unit to not only provide customers with price information quickly and reliably, increasing their purchase intent, but also to improve the overall system performance.

[0069] The optimization unit can apply discounts to products nearing their expiration date. For example, the optimization unit can apply a discount to products with an expiration date of one week or less. It can also apply a discount to products with an expiration date of three days or less. Furthermore, it can apply a discount to products with an expiration date of the same day. By applying discounts to products nearing their expiration date, it is possible to prevent unsold inventory and reduce food waste. The method and criteria for applying discounts are determined, for example, based on the discount rate and application conditions. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on products nearing their expiration date into a generating AI and have the generating AI apply the discount rate.

[0070] The optimization unit can adjust prices based on weather and the expected number of customers. For example, the optimization unit can adjust prices based on the expected number of customers on a day with bad weather. It can also adjust prices based on the expected number of customers on a day with good weather. Furthermore, the optimization unit can adjust prices based on the expected number of customers under specific weather conditions. This improves the accuracy of price optimization by adjusting prices based on weather and the expected number of customers. The method and criteria for price adjustment are determined, for example, based on upper and lower price limits and adjustment frequency. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and expected number of customers data into a generating AI and have the generating AI perform the price adjustment.

[0071] The service provider can automatically apply the rewards program. For example, the service provider can apply the rewards program based on the customer's purchase history. It can also apply the rewards program based on the customer's loyalty program status. Furthermore, it can apply the rewards program based on the customer's purchase frequency. This allows for the promotion of customer loyalty and sustainable consumption by automatically applying the rewards program. The content and application method of the rewards program are determined, for example, based on the points system and the rewards offered. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer purchase data into a generating AI and have the generating AI execute the application of the rewards program.

[0072] The service provider can notify customers of campaign information. For example, the service provider can notify customers of campaign information based on their purchase history. The service provider can also notify customers of campaign information based on their loyalty program status. Furthermore, the service provider can notify customers of campaign information based on their purchase frequency. This allows for effective promotion to be conducted by notifying customers of campaign information. The content and method of notification of campaign information are determined based on, for example, discount information or new product information. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input customer purchase data into a generating AI and have the generating AI execute the notification of campaign information.

[0073] The data collection unit can collect customer purchase data. For example, the data collection unit can collect customer purchase history. The data collection unit can also collect customer purchase frequency. Furthermore, the data collection unit can collect customer purchase patterns. By collecting customer purchase data, it is possible to analyze customer purchasing behavior and perform more effective price optimization. The type of purchase data and the method of collection are determined, for example, based on databases or sensor data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer purchase data into a generating AI and have the generating AI perform the data collection.

[0074] 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 may prioritize collecting data on products nearing their expiration date. It may also prioritize collecting weather data if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize collecting data on the expected number of customers. This allows for more appropriate data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0075] The data collection unit can monitor the inventory status of products nearing their expiration date in real time and collect data as needed. For example, the data collection unit can prioritize monitoring products with an expiration date of one week or less and collect data when inventory decreases. It can also pay particular attention to monitoring products with an expiration date of three days or less and collect data when inventory falls below a certain level. Furthermore, the data collection unit can monitor products with an expiration date of the same day in real time and collect data before inventory runs out. This allows for real-time monitoring of the inventory status of products nearing their expiration date, enabling data collection at the appropriate time. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input inventory data of products nearing their expiration date into a generating AI and have the generating AI perform inventory status monitoring and data collection.

[0076] The data collection unit can improve prediction accuracy by referring to past weather patterns when collecting weather data. For example, the data collection unit can predict the weather for the next week based on weather data from the past year and collect the data. The data collection unit can also analyze weather patterns from the past 10 years and predict weather variations in a specific season and collect the data. Furthermore, the data collection unit can combine past weather data with current weather information to make short-term weather forecasts and collect the data. This improves the accuracy of weather data predictions by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past weather data into a generating AI and have the generating AI perform improvements to the accuracy of weather forecasts.

[0077] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data on products nearing their expiration date. It may also prioritize collecting weather data if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize collecting data on the expected number of customers. This allows for more appropriate data collection by adjusting the types of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of data.

[0078] The data collection unit can analyze customers' social media activity and collect relevant data when collecting customer purchase data. For example, the data collection unit can prioritize collecting products mentioned by customers on social media. The data collection unit can also analyze the time periods when customers are active on social media and collect data related to those times. Furthermore, the data collection unit can collect data related to brands and stores that customers follow on social media. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0079] The data collection unit can improve the accuracy of weather data by considering geographical characteristics when collecting it. For example, the data collection unit can collect different weather data in urban and rural areas to improve accuracy. It can also collect data considering different weather patterns in coastal and inland areas. Furthermore, the data collection unit can collect different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data can be improved by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data that considers geographical characteristics into a generating AI and have the generating AI perform data accuracy improvement.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis algorithm. If the user is relaxed, the analysis unit can also use a more detailed analysis algorithm. Furthermore, if the user is in a hurry, the analysis unit can use an analysis algorithm designed to produce results quickly. By adjusting the analysis algorithm based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0081] The analysis unit can analyze the sales performance of products nearing their expiration date and calculate the optimal discount rate. For example, the analysis unit can calculate the optimal discount rate for products with an expiration date of one week or less based on past sales performance. The analysis unit can also adjust the discount rate for products with an expiration date of three days or less based on inventory status and sales performance. Furthermore, the analysis unit can calculate the discount rate necessary to sell out products with an expiration date of the same day immediately. In this way, the optimal discount rate can be calculated by analyzing the sales performance of products nearing their expiration date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input sales data of products nearing their expiration date into a generating AI and have the generating AI calculate the optimal discount rate.

[0082] The analysis unit can improve the accuracy of customer visit forecasts by combining weather data with estimated customer numbers. For example, the analysis unit can analyze estimated customer numbers on bad weather days based on past data. It can also analyze estimated customer numbers on good weather days based on past data. Furthermore, the analysis unit can analyze estimated customer numbers under specific weather conditions to improve prediction accuracy. In this way, the accuracy of customer visit forecasts can be improved by combining weather data with estimated customer numbers. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform the task of improving the accuracy of customer visit forecasts.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0084] The analysis unit can improve the accuracy of its analysis of sales of products nearing their expiration date by referring to past sales data. For example, the analysis unit can analyze sales of products nearing their expiration date based on sales data from the past year. The analysis unit can also improve the accuracy of its analysis by combining past sales data with current inventory status. Furthermore, the analysis unit can analyze past sales data to predict sales in specific seasons or events. In this way, the accuracy of the analysis of sales of products nearing their expiration date can be improved by referring to past sales data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of the sales analysis.

[0085] The analysis unit can improve the accuracy of its weather analysis by considering regional characteristics when analyzing weather data. For example, the analysis unit can analyze different weather data in urban and rural areas to improve accuracy. It can also analyze different weather patterns in coastal and inland areas. Furthermore, the analysis unit can analyze different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data analysis can be improved by considering regional characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input weather data that considers regional characteristics into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0086] The optimization unit can estimate the user's emotions and adjust the price optimization algorithm based on the estimated emotions. For example, if the user is stressed, the optimization unit can use a simple price optimization algorithm. If the user is relaxed, the optimization unit can also use a more detailed price optimization algorithm. Furthermore, if the user is in a hurry, the optimization unit can use a price optimization algorithm designed to produce results quickly. This allows for more appropriate price optimization by adjusting the price optimization algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the price optimization algorithm.

[0087] The optimization unit can dynamically adjust the discount rate of products nearing their expiration date, thereby preventing unsold inventory. For example, the optimization unit can dynamically adjust the discount rate of products with an expiration date of less than one week based on past sales performance. It can also dynamically adjust the discount rate of products with an expiration date of less than three days based on inventory status and sales performance. Furthermore, the optimization unit can dynamically adjust the discount rate of products with an expiration date of the same day to ensure immediate sale. By dynamically adjusting the discount rate of products nearing their expiration date, it is possible to prevent unsold inventory and reduce food waste. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data on products nearing their expiration date into a generating AI and have the generating AI perform the dynamic adjustment of the discount rate.

[0088] The optimization unit can optimize prices in real time based on weather and estimated customer numbers. For example, the optimization unit can optimize prices in real time based on estimated customer numbers on bad weather days. It can also optimize prices in real time based on estimated customer numbers on good weather days. Furthermore, the optimization unit can optimize prices in real time based on estimated customer numbers under specific weather conditions. This improves the accuracy of price optimization by optimizing prices in real time based on weather and estimated customer numbers. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform real-time price optimization.

[0089] The optimization unit can estimate the user's emotions and adjust the price display method based on the estimated emotions. For example, if the user is stressed, the optimization unit can provide a simple and highly visible display method. If the user is relaxed, the optimization unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the optimization unit can provide a concise display method. This allows for more appropriate display by adjusting the price display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the price display method.

[0090] The optimization unit can calculate the optimal discount rate for products nearing their expiration date by referring to the customer's purchase history. For example, the optimization unit can calculate the optimal discount rate based on the expiration dates of products the customer has purchased in the past. The optimization unit can also adjust the discount rate for specific products based on the customer's purchase history. Furthermore, the optimization unit can analyze the customer's purchase history and calculate the most effective discount rate. This allows the optimization unit to calculate the optimal discount rate for products nearing their expiration date by referring to the customer's purchase history. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input customer purchase history data into a generating AI and have the generating AI perform the calculation of the discount rate.

[0091] The optimization unit can improve the accuracy of price optimization by considering regional characteristics when optimizing prices based on weather and the expected number of customers. For example, the optimization unit may use different price optimization algorithms for urban and rural areas. It may also use different price optimization algorithms for coastal and inland areas. Furthermore, it may use different price optimization algorithms for highland and lowland areas to improve accuracy. In this way, the accuracy of price optimization can be improved by considering regional characteristics. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input data that considers regional characteristics into a generating AI and have the generating AI perform the task of improving the accuracy of price optimization.

[0092] The service provider can estimate the user's emotions and adjust how the reward program is applied based on the estimated emotions. For example, if the user is stressed, the service provider can offer a simple reward program. If the user is relaxed, the service provider can offer a more detailed reward program. Furthermore, if the user is in a hurry, the service provider can offer a reward program that can be applied quickly. This allows for the provision of more appropriate reward programs by adjusting how the reward program is applied based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the reward program is applied.

[0093] The service provider can refer to the customer's purchase history to notify them of campaign information at the optimal time. For example, the service provider can notify customers of campaign information related to products they have purchased in the past during specific time periods. Furthermore, if the service provider's purchase history indicates a tendency for customers to purchase on certain days of the week, it can also notify them of campaign information on those days. Additionally, the service provider can notify customers of relevant campaign information when the expiration date of products they have purchased in the past is approaching. This allows the service provider to notify customers of campaign information at the optimal time by referring to their purchase history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer purchase history data into a generating AI and have the generating AI execute the campaign information notification.

[0094] The service provider can dynamically adjust benefits to enhance customer loyalty when applying a rewards program. For example, the service provider can offer discounts on specific products based on a customer's purchase history. Furthermore, the service provider can dynamically adjust the content of benefits according to the customer's loyalty program status. In addition, the service provider can customize the content of benefits according to the customer's purchase frequency. This allows for improved customer satisfaction by dynamically adjusting benefits to enhance customer loyalty. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input customer purchase data into a generating AI and have the generating AI perform the dynamic adjustment of benefits.

[0095] The service provider can estimate the user's emotions and adjust the reward program content based on the estimated emotions. For example, if the user is stressed, the service provider can offer a simple, immediately available reward. If the user is relaxed, the service provider can offer a reward with a detailed explanation. Furthermore, if the user is in a hurry, the service provider can offer a quickly available reward. This allows for the provision of a more appropriate reward program by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the content of the reward program.

[0096] The service provider can notify customers of campaign information at the optimal time, taking into account their geographical location. For example, if a customer is near a store, the service provider can notify them of campaign information in real time. Furthermore, if a customer is in a specific region, the service provider can notify them of campaign information relevant to that region. In addition, if a customer is on the move, the service provider can notify them of campaign information before they reach their destination. This allows for the notification of campaign information at the optimal time by considering the customer's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input customer geographical location data into a generating AI and have the generating AI execute the campaign information notification.

[0097] The service provider can analyze a customer's social media activity and provide relevant rewards when applying a rewards program. For example, the service provider can provide rewards related to products mentioned by the customer on social media. The service provider can also analyze the time periods of customer activity on social media and provide rewards during those times. Furthermore, the service provider can provide rewards related to brands and stores that the customer follows on social media. This allows for the provision of more relevant rewards by analyzing the customer's social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input customer social media data into a generating AI and have the generating AI execute the provision of rewards.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis algorithm. If the user is relaxed, the analysis unit can also use a more detailed analysis algorithm. Furthermore, if the user is in a hurry, the analysis unit can use an analysis algorithm designed to produce results quickly. By adjusting the analysis algorithm based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0100] The service provider can estimate the user's emotions and adjust how the reward program is applied based on the estimated emotions. For example, if the user is stressed, the service provider can offer a simple reward program. If the user is relaxed, the service provider can offer a more detailed reward program. Furthermore, if the user is in a hurry, the service provider can offer a reward program that can be applied quickly. This allows for the provision of more appropriate reward programs by adjusting how the reward program is applied based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the reward program is applied.

[0101] The optimization unit can estimate the user's emotions and adjust the price optimization algorithm based on the estimated emotions. For example, if the user is stressed, the optimization unit can use a simple price optimization algorithm. If the user is relaxed, the optimization unit can also use a more detailed price optimization algorithm. Furthermore, if the user is in a hurry, the optimization unit can use a price optimization algorithm designed to produce results quickly. This allows for more appropriate price optimization by adjusting the price optimization algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the price optimization algorithm.

[0102] The service provider can estimate the user's emotions and adjust the reward program content based on the estimated emotions. For example, if the user is stressed, the service provider can offer a simple, immediately available reward. If the user is relaxed, the service provider can offer a reward with a detailed explanation. Furthermore, if the user is in a hurry, the service provider can offer a quickly available reward. This allows for the provision of a more appropriate reward program by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the content of the reward program.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0104] The data collection unit can analyze customers' social media activity and collect relevant data when collecting customer purchase data. For example, the data collection unit can prioritize collecting products mentioned by customers on social media. The data collection unit can also analyze the time periods when customers are active on social media and collect data related to those times. Furthermore, the data collection unit can collect data related to brands and stores that customers follow on social media. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0105] The data collection unit can improve prediction accuracy by referring to past weather patterns when collecting weather data. For example, the data collection unit can predict the weather for the next week based on weather data from the past year and collect the data. The data collection unit can also analyze weather patterns from the past 10 years and predict weather variations in a specific season and collect the data. Furthermore, the data collection unit can combine past weather data with current weather information to make short-term weather forecasts and collect the data. This improves the accuracy of weather data predictions by referring to past weather patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past weather data into a generating AI and have the generating AI perform improvements to the accuracy of weather forecasts.

[0106] The data collection unit can improve the accuracy of weather data by considering geographical characteristics when collecting it. For example, the data collection unit can collect different weather data in urban and rural areas to improve accuracy. It can also collect data considering different weather patterns in coastal and inland areas. Furthermore, the data collection unit can collect different weather data in high-altitude and low-altitude areas to improve prediction accuracy. In this way, the accuracy of weather data can be improved by considering geographical characteristics. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input weather data that considers geographical characteristics into a generating AI and have the generating AI perform data accuracy improvement.

[0107] The analysis unit can improve the accuracy of its analysis of sales of products nearing their expiration date by referring to past sales data. For example, the analysis unit can analyze sales of products nearing their expiration date based on sales data from the past year. The analysis unit can also improve the accuracy of its analysis by combining past sales data with current inventory status. Furthermore, the analysis unit can analyze past sales data to predict sales in specific seasons or events. In this way, the accuracy of the analysis of sales of products nearing their expiration date can be improved by referring to past sales data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of the sales analysis.

[0108] The optimization unit can optimize prices in real time based on weather and estimated customer numbers. For example, the optimization unit can optimize prices in real time based on estimated customer numbers on bad weather days. It can also optimize prices in real time based on estimated customer numbers on good weather days. Furthermore, the optimization unit can optimize prices in real time based on estimated customer numbers under specific weather conditions. This improves the accuracy of price optimization by optimizing prices in real time based on weather and estimated customer numbers. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather data and estimated customer number data into a generating AI and have the generating AI perform real-time price optimization.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects statistical information such as food expiration dates, weather, and estimated customer numbers and visit times. For example, food expiration dates are collected based on the number of days elapsed since the manufacturing date and storage conditions, and weather data is collected based on factors such as temperature, precipitation, and wind speed. In addition, estimated customer numbers and visit times are collected based on past data and event information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the information using data mining and machine learning algorithms to generate data for dynamic price optimization. Step 3: The optimization unit dynamically optimizes prices based on the information analyzed by the analysis unit. For example, it runs an algorithm that optimizes prices using real-time data and applies discounts to products nearing their expiration date. Step 4: The delivery unit provides prices optimized by the optimization unit. For example, it provides price information through online platforms and in-store displays, automatically applies reward programs to customers, and notifies them of campaign information.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart device 14 to collect food expiration dates and weather data, and the control unit 46A collects the estimated number of customers and their arrival times. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and dynamically optimizes prices based on the analyzed information. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the optimized price information through an online platform or in-store display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart glasses 214 to collect food expiration dates and weather data, and the control unit 46A collects the estimated number of customers and their expected arrival times. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information. The optimization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and dynamically optimizes prices based on the analyzed information. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the optimized price information through an online platform or in-store display. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the headset terminal 314 to collect food expiration dates and weather data, and the control unit 46A collects the estimated number of customers and their expected arrival times. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and dynamically optimizes prices based on the analyzed information. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the optimized price information through an online platform or in-store display. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the robot 414 to collect food expiration dates and weather data, and the control unit 46A collects the estimated number of customers and their expected arrival times. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to analyze the collected information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to dynamically optimize prices based on the analyzed information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 to provide optimized price information through an online platform or in-store display. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects statistical information such as food expiration dates, weather, and estimated number and time of customer visits, An analysis unit analyzes the information collected by the aforementioned collection unit, An optimization unit that dynamically optimizes the price based on the information analyzed by the aforementioned analysis unit, The system includes a supply unit that provides a price optimized by the optimization unit. A system characterized by the following features. (Note 2) The optimization unit, Discounts are applied to products nearing their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Prices are adjusted based on weather and the expected number of customers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Automatically apply the rewards program. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Notify us of campaign information The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect customer purchase data The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is The system monitors the inventory status of products nearing their expiration date in real time and collects data as needed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting weather data, we improve prediction accuracy by referring to past weather patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting customer purchase data, we analyze customers' social media activity to collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting weather data, we improve data accuracy by taking geographical characteristics into account. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We analyze the sales trends of products nearing their expiration date and calculate the optimal discount rate. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Combining weather data with estimated customer numbers improves the accuracy of customer visit forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, When analyzing sales of products nearing their expiration date, we improve the accuracy of the analysis by referring to past sales data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing weather data, we improve the accuracy of the analysis by taking into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, It estimates user sentiment and adjusts the price optimization algorithm based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, Dynamically adjust the discount rate on products nearing their expiration date to prevent unsold inventory. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, We optimize prices in real time based on weather and estimated customer numbers. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates user sentiment and adjusts how prices are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, When adjusting the discount rate for products nearing their expiration date, we refer to the customer's purchase history to calculate the optimal discount rate. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, When optimizing prices based on weather and estimated customer numbers, we improve optimization accuracy by taking into account regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the reward program is applied based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When notifying customers of campaign information, the system refers to their purchase history to send notifications at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When applying a rewards program, dynamically adjust the benefits to enhance customer loyalty. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates user sentiment and adjusts the reward program content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When notifying customers of campaign information, the system takes their geographical location into consideration and sends notifications at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When applying a rewards program, we analyze customers' social media activity and provide relevant benefits. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 department that collects statistical information such as food expiration dates, weather, and estimated number and time of customer visits, An analysis unit analyzes the information collected by the aforementioned collection unit, An optimization unit that dynamically optimizes the price based on the information analyzed by the aforementioned analysis unit, The system includes a supply unit that provides a price optimized by the optimization unit. A system characterized by the following features.

2. The optimization unit, Discounts are applied to products nearing their expiration date. The system according to feature 1.

3. The optimization unit, Prices are adjusted based on weather and the expected number of customers. The system according to feature 1.

4. The aforementioned supply unit is, Automatically apply the rewards program. The system according to feature 1.

5. The aforementioned supply unit is, Notify us of campaign information The system according to feature 1.

6. The aforementioned collection unit is Collect customer purchase data The system according to feature 1.

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

8. The aforementioned collection unit is The system monitors the inventory status of products nearing their expiration date in real time and collects data as needed. The system according to feature 1.

9. The aforementioned collection unit is When collecting weather data, we improve prediction accuracy by referring to past weather patterns. The system according to feature 1.

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