system

Abstract

The system according to this embodiment aims to automate and efficiently perform demand forecasting, procurement ordering, and inventory management. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an ordering unit, and an inventory management unit. The data collection unit collects data such as past sales data, seasonal events, weather information, regional events, and competitor information. The analysis unit integrates and analyzes the data collected by the data collection unit and predicts demand. The ordering unit automatically places purchase orders based on the demand predicted by the analysis unit. The inventory management unit manages the inventory ordered by the ordering unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it is difficult to efficiently perform demand forecasting, purchase ordering, and inventory management, and there is a risk of stock shortages or overstocking.

[0005] The system according to the embodiment aims to automate and efficiently perform demand forecasting, purchase ordering, and inventory management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an ordering unit, and an inventory management unit. The data collection unit collects data such as past sales data, seasonal events, weather information, regional events, and competitor information. The analysis unit integrates and analyzes the data collected by the data collection unit to predict demand. The ordering unit automatically places purchase orders based on the demand predicted by the analysis unit. The inventory management unit manages the inventory ordered by the ordering unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently perform demand forecasting, procurement ordering, and inventory management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 system according to an embodiment of the present invention is a system that optimizes procurement ordering and inventory management for retail stores and restaurants using SmartStock AI. This system collects data such as historical sales data, seasonal events, weather information, local events, and competitor information, and the AI ​​integrates and analyzes this data to predict demand in real time and automatically place orders. This reduces human labor, effectively prevents inventory shortages and excesses, and supports cost reduction and sales maximization. Furthermore, it helps monitor the activities of competitors and adjust the company's pricing strategy and promotional measures in a timely manner. This makes it a powerful tool for retail store and restaurant owners and managers to make quick and informed decisions, supporting smarter business operations and sustainable growth. For example, by streamlining inventory management, unnecessary inventory can be reduced and costs can be lowered. Also, by improving the accuracy of demand forecasting, sales can be maximized. In this way, the system can prevent inventory shortages and excesses, and support cost reduction and sales maximization by integrating and analyzing data such as historical sales data, seasonal events, weather information, local events, and competitor information, predicting demand in real time, and automatically placing orders.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, an ordering unit, and an inventory management unit. The data collection unit collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. For example, the data collection unit can collect sales data for the past year. The data collection unit can also collect data on seasonal events such as Christmas and Halloween. Furthermore, the data collection unit can collect weather information such as temperature and precipitation. As for local events, data on local festivals and sporting events can be collected. As for competitor information, price and promotional information of competitors can be collected. The analysis unit integrates and analyzes the collected data to forecast demand. For example, the analysis unit can build a demand forecasting model based on the collected data and forecast demand. Furthermore, the analysis unit can analyze demand fluctuation patterns based on the collected data. Furthermore, the analysis unit can predict the peak period of demand based on the collected data. The ordering unit automatically places purchase orders based on the forecasted demand. For example, the ordering unit can place purchase orders at the appropriate time based on the demand forecast. Furthermore, the ordering department can determine appropriate order quantities based on demand forecasts. Additionally, the ordering department can select suppliers based on demand forecasts. The inventory management department manages ordered inventory. For example, the inventory management department can manage the receipt of ordered inventory. It can also manage the dispatch of ordered inventory. Furthermore, the inventory management department can manage the inventory levels of ordered inventory. As a result, the system according to this embodiment can integrate and analyze data such as historical sales data, seasonal events, weather information, regional events, and competitor information, forecast demand in real time, and automatically place orders, thereby preventing inventory shortages and excesses, and supporting cost reduction and sales maximization.

[0030] The data collection department collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. Specifically, when collecting sales data for the past year, it obtains detailed data such as sales volume, sales amount, and sales date for each product. This allows for an understanding of best-selling products and sales trends. When collecting data on seasonal events, it collects sales data during specific event periods such as Christmas and Halloween and analyzes fluctuations in sales for each event. When collecting weather information, it obtains meteorological data such as temperature, precipitation, and wind speed and analyzes the impact of weather on sales. When collecting data on local events, it collects information such as the dates, scale, and number of participants of local festivals and sporting events and evaluates the impact of these events on sales. When collecting competitor information, it collects prices, promotional information, and sales strategies of competitors and incorporates them into the company's own sales strategy. The data collection department centrally manages this diverse data and updates it in real time, ensuring that the latest information is always available. Furthermore, the data collection department can also collect data from external sources such as the internet and social media, obtaining a wider range of information. As a result, the data collection department can collect comprehensive and detailed data and provide a foundation for data analysis of the entire system.

[0031] The analysis department integrates and analyzes collected data to forecast demand. Specifically, it builds a demand forecasting model based on collected historical sales data, seasonal events, weather information, regional events, and competitor information. The demand forecasting model is built using machine learning algorithms and learns demand patterns from historical data. For example, it combines historical sales data and weather information to predict fluctuations in sales under specific weather conditions. It also analyzes data on seasonal and regional events to evaluate the impact of these events on sales. Furthermore, it analyzes competitor information to predict the impact of competitors' prices and promotions on its own sales. The analysis department integrates this data to predict demand fluctuation patterns and peak periods. For example, it predicts that certain products will sell well during the Christmas season and secures inventory to meet that demand. Also, if a forecast of worsening weather is issued, it predicts a decrease in demand and adjusts procurement accordingly. The analysis department updates these forecast results in real time, always providing the latest demand forecast. Furthermore, the analysis department uses anomaly detection algorithms to detect unusual demand fluctuations early and respond quickly. This allows the analysis unit to perform accurate and rapid demand forecasting, improving the overall efficiency of the system.

[0032] The purchasing department automatically places purchase orders based on predicted demand. Specifically, it places purchase orders at the appropriate time based on the results of a demand forecasting model. For example, when demand is expected to be high, it places orders early to secure the necessary inventory in advance. Conversely, when demand is expected to be low, it adjusts the order quantity to prevent excess inventory. Based on the demand forecast, the purchasing department considers factors such as inventory turnover rate and lead time to determine the optimal order quantity. Furthermore, the purchasing department also selects suppliers. When selecting suppliers, it considers factors such as price, quality, and delivery time to select the most suitable supplier. By automating these processes, the purchasing department achieves efficient and accurate purchase orders. For example, it automatically creates purchase orders based on the results of the demand forecasting model and sends them to suppliers. It also monitors the order status in real time and can modify the order details as needed. This allows the purchasing department to respond quickly to fluctuations in demand and prevent inventory shortages and excess inventory. In addition, the purchasing department can improve the reliability of purchases by facilitating smooth communication with suppliers, confirming delivery dates, and checking quality.

[0033] The Inventory Management Department manages ordered inventory. Specifically, it manages the receipt of ordered inventory and keeps track of the exact quantity of inventory. In receiving management, it verifies the quantity, quality, and delivery date of received goods and registers them in the inventory system. The Inventory Management Department also manages the dispatch of ordered inventory. In dispatch management, it manages the quantity of goods to be dispatched, the destination, and the dispatch date and time, and reflects this in the inventory system. Furthermore, the Inventory Management Department manages the inventory level of ordered inventory and strives to maintain appropriate inventory levels. In inventory level management, it considers inventory turnover rate and efficient use of storage space to maintain an appropriate inventory level. For example, during periods of high demand, inventory is increased to meet demand, and during periods of low demand, inventory is reduced to make effective use of storage space. The Inventory Management Department achieves efficient and accurate inventory management by automating these processes. For example, it uses the inventory system to monitor inventory status in real time and replenish or adjust inventory as needed. The Inventory Management Department also manages the quality of inventory and takes measures to prevent deterioration or damage. This allows the inventory management department to achieve accurate inventory management and maintain quality, improving the overall efficiency and reliability of the system.

[0034] The system also includes a monitoring unit that tracks the activities of competitors. For example, this unit can monitor competitors' price fluctuations. It can also monitor competitors' promotional activities. Furthermore, it can monitor information on new product launches by competitors. This allows the system to adjust its own pricing strategy and promotional measures in a timely manner by monitoring competitor activity.

[0035] The system also includes an adjustment unit that can fine-tune the company's pricing strategy and promotional measures. For example, this unit can adjust the company's prices to match those of competitors. It can also adjust the company's promotional measures to align with those of competitors. Furthermore, it can adjust the company's pricing strategy and promotional measures to match market demand. This allows the system to support rapid decision-making and enable smart business operations by adjusting the company's pricing strategy and promotional measures.

[0036] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, it can identify and apply the most effective data collection method from past data collection history. Furthermore, the data collection unit can analyze data collection history and optimize the timing and frequency of data collection. In addition, based on past data collection history, the data collection unit can improve the data collection method and enhance accuracy. Thus, by analyzing past data collection history, the optimal data collection method can be selected, and the accuracy of data collection can be improved.

[0037] The data collection unit can filter data during collection, taking into account the impact of specific events or campaigns. For example, it can prioritize the collection of data during specific events or campaign periods. Furthermore, the data collection unit can filter data affected by events or campaigns to ensure accurate data collection. In addition, the data collection unit can adjust its data collection methods depending on the type of event or campaign. This allows for accurate data collection by filtering data to account for the impact of specific events or campaigns.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from a specific region based on geographical location information. Furthermore, the data collection unit can filter highly relevant data by considering geographical location information. In addition, the data collection unit can adjust the scope of data collection based on geographical location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant data while considering geographical location information.

[0039] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze social media trends and collect relevant data. It can also analyze social media posts and collect data useful for demand forecasting. Furthermore, the data collection unit can adjust its data collection methods considering the influence of social media. This allows for the collection of data useful for demand forecasting by analyzing social media activity.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. Conversely, the analysis unit can perform a simplified analysis on low-importance data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for optimal allocation of analysis resources by adjusting the level of detail of the analysis based on the importance of the data.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sales forecasting algorithm to sales data. It can also apply a weather forecasting algorithm to weather data. Furthermore, it can apply a competitive analysis algorithm to competitive information. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved.

[0042] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This allows for prioritizing the analysis based on the data submission date, thereby ensuring that the most recent data is analyzed first.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0044] The ordering department can adjust the level of detail in orders based on the importance of the products. For example, it can place detailed orders for high-importance products, and simplified orders for low-importance products. Furthermore, the ordering department can optimally allocate ordering resources according to the importance of the products. This allows for optimal resource allocation by adjusting the level of detail in orders based on the importance of the products.

[0045] The ordering department can apply different ordering algorithms depending on the product category when placing an order. For example, it can apply an ordering algorithm that takes expiration dates into account for food products. It can also apply an ordering algorithm that takes seasonality into account for clothing products. Furthermore, it can apply an ordering algorithm that takes technological advancements into account for electronic devices. By applying different ordering algorithms depending on the product category, the accuracy of orders is improved.

[0046] The ordering department can determine the priority of orders based on the product submission dates. For example, the ordering department can prioritize orders for products with upcoming submission dates. It can also postpone orders for products with later submission dates. Furthermore, the ordering department can adjust the order schedule based on submission dates. This allows for prioritizing orders based on submission dates, enabling priority to be given to products with upcoming submission dates.

[0047] The ordering department can adjust the order of orders based on the relevance of the products. For example, the ordering department can prioritize ordering highly relevant products. It can also postpone ordering less relevant products. Furthermore, the ordering department can optimize the order of orders based on the relevance of the products. This allows for prioritizing the ordering of highly relevant products by adjusting the order based on their relevance.

[0048] The inventory management department can select the optimal inventory management method by referring to past inventory data during inventory management. For example, the inventory management department can identify the optimal inventory management method based on past inventory data. Furthermore, the inventory management department can analyze inventory data and optimize the timing and frequency of inventory management. In addition, the inventory management department can improve inventory management methods based on past inventory data. This allows for the selection of the optimal inventory management method and improvement of inventory management accuracy by referring to past inventory data.

[0049] The inventory management department can apply different inventory management methods depending on the product category. For example, it can apply an inventory management method that takes expiration dates into account for food products. It can also apply an inventory management method that takes seasonality into account for clothing products. Furthermore, it can apply an inventory management method that takes technological advancements into account for electronic devices. By applying different inventory management methods depending on the product category, the accuracy of inventory management is improved.

[0050] The inventory management department can select the optimal inventory management method by considering the geographical location of products during inventory management. For example, the inventory management department can optimize the inventory management method for a specific region based on geographical location information. Furthermore, the inventory management department can determine inventory management priorities by considering geographical location information. In addition, the inventory management department can adjust the scope of inventory management based on geographical location information. This improves the accuracy of inventory management by selecting the optimal inventory management method while considering the geographical location of products.

[0051] The inventory management department can improve the accuracy of its inventory management by referring to relevant literature on products during inventory management. For example, the inventory management department can improve its inventory management methods based on relevant literature. Furthermore, the inventory management department can improve the accuracy of its inventory management by referring to relevant literature. In addition, the inventory management department can optimize the timing and frequency of inventory management based on relevant literature. Thus, by referring to relevant literature on products, the accuracy of inventory management can be improved.

[0052] The monitoring unit can select the optimal monitoring method by referring to past performance data of competitors during monitoring. For example, the monitoring unit can identify the optimal monitoring method based on past performance data of competitors. Furthermore, the monitoring unit can analyze competitor performance data and optimize the timing and frequency of monitoring. In addition, the monitoring unit can improve the monitoring method based on past performance data of competitors. This allows for the selection of the optimal monitoring method and improvement of monitoring accuracy by referring to past performance data of competitors.

[0053] The monitoring unit can apply different monitoring methods depending on the competitor's category during monitoring. For example, the monitoring unit can select an appropriate monitoring method depending on the competitor's category. Furthermore, the monitoring unit can adjust the monitoring method based on the competitor's category. In addition, the monitoring unit can optimally allocate monitoring resources according to the competitor's category. This improves monitoring accuracy by applying different monitoring methods depending on the competitor's category.

[0054] The monitoring unit can select the optimal monitoring method while considering the geographical location information of competitors. For example, the monitoring unit can optimize the monitoring method for competitors in a specific region based on geographical location information. Furthermore, the monitoring unit can determine the priority of competitor monitoring based on geographical location information. In addition, the monitoring unit can adjust the scope of competitor monitoring based on geographical location information. This improves monitoring accuracy by selecting the optimal monitoring method while considering the geographical location information of competitors.

[0055] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature from competitors during monitoring. For example, the monitoring unit can improve the monitoring methods of competitors based on relevant literature. Furthermore, the monitoring unit can improve the accuracy of its monitoring of competitors by referring to relevant literature. In addition, the monitoring unit can optimize the timing and frequency of monitoring competitors based on relevant literature. This allows for improved monitoring accuracy by referring to relevant literature from competitors.

[0056] The adjustment unit can select the optimal adjustment method by referring to past price strategy data during the adjustment process. For example, the adjustment unit can identify the optimal adjustment method based on past price strategy data. Furthermore, the adjustment unit can analyze price strategy data and optimize the timing and frequency of adjustments. In addition, the adjustment unit can improve the adjustment method based on past price strategy data. This allows for the selection of the optimal adjustment method and improvement of adjustment accuracy by referring to past price strategy data.

[0057] The adjustment unit can apply different adjustment methods depending on the category of the promotional measure during the adjustment process. For example, the adjustment unit can select an appropriate adjustment method depending on the category of the promotional measure. Furthermore, the adjustment unit can adjust the adjustment method based on the category of the promotional measure. In addition, the adjustment unit can optimally allocate adjustment resources depending on the category of the promotional measure. This improves the accuracy of the adjustment by applying different adjustment methods depending on the category of the promotional measure.

[0058] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the geographical location information of the pricing strategy. For example, the adjustment unit can optimize the adjustment method for a specific region based on geographical location information. Furthermore, the adjustment unit can determine the priority of price strategy adjustments, taking geographical location information into account. In addition, the adjustment unit can adjust the scope of price strategy adjustments based on geographical location information. This improves the accuracy of adjustments by selecting the optimal adjustment method while considering the geographical location information of the pricing strategy.

[0059] The adjustment unit can improve the accuracy of its adjustments by referring to relevant literature on promotional strategies during the adjustment process. For example, the adjustment unit can improve the method of adjusting promotional strategies based on relevant literature. Furthermore, the adjustment unit can improve the accuracy of its adjustments by referring to relevant literature. In addition, the adjustment unit can optimize the timing and frequency of adjustments to promotional strategies based on relevant literature. This allows for improved adjustment accuracy by referring to relevant literature on promotional strategies.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, it can identify and apply the most effective data collection method from past data collection history. It can also analyze data collection history to optimize the timing and frequency of data collection. Furthermore, it can improve data collection methods and enhance accuracy based on past data collection history. In this way, by analyzing past data collection history, it is possible to select the optimal data collection method and improve the accuracy of data collection.

[0062] The data collection unit can filter data during collection, taking into account the impact of specific events or campaigns. For example, it can prioritize the collection of data during specific events or campaign periods. It can also filter data affected by events or campaigns to collect accurate data. Furthermore, it can adjust the data collection method depending on the type of event or campaign. This allows for accurate data collection by filtering data while considering the impact of specific events or campaigns.

[0063] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, it can prioritize the collection of data from a specific region based on geographical location information. It can also filter highly relevant data by considering geographical location information. Furthermore, it can adjust the scope of data collection based on geographical location information. As a result, the accuracy of data collection is improved by prioritizing the collection of highly relevant data by considering geographical location information.

[0064] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze social media trends and collect relevant data. It can also analyze the content of social media posts and collect data useful for demand forecasting. Furthermore, it can adjust the data collection method considering the influence of social media. This allows for the collection of data useful for demand forecasting by analyzing social media activity.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can optimally allocate analysis resources according to the importance of the data. This means that by adjusting the level of detail of the analysis based on the importance of the data, analysis resources can be optimally allocated.

[0066] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a sales forecasting algorithm can be applied to sales data. Similarly, a weather forecasting algorithm can be applied to weather data. Furthermore, a competitive analysis algorithm can be applied to competitive information. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. For example, it can collect sales data for the past year, data on seasonal events such as Christmas and Halloween, weather information such as temperature and precipitation, data on local festivals and sporting events, and information on competitor pricing and promotions. Step 2: The analysis unit integrates and analyzes the collected data to forecast demand. For example, it can build a demand forecasting model based on the collected data to predict demand fluctuation patterns and peak periods. Step 3: The ordering department automatically places procurement orders based on predicted demand. For example, it can place procurement orders at the appropriate time based on demand forecasts, determine the appropriate order quantity, and select suppliers. Step 4: The inventory management department manages ordered inventory. For example, it can manage the receipt, dispatch, and inventory levels of ordered inventory.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system that optimizes procurement ordering and inventory management for retail stores and restaurants using SmartStock AI. This system collects data such as historical sales data, seasonal events, weather information, local events, and competitor information, and the AI ​​integrates and analyzes this data to predict demand in real time and automatically place orders. This reduces human labor, effectively prevents inventory shortages and excesses, and supports cost reduction and sales maximization. Furthermore, it helps monitor the activities of competitors and adjust the company's pricing strategy and promotional measures in a timely manner. This makes it a powerful tool for retail store and restaurant owners and managers to make quick and informed decisions, supporting smarter business operations and sustainable growth. For example, by streamlining inventory management, unnecessary inventory can be reduced and costs can be lowered. Also, by improving the accuracy of demand forecasting, sales can be maximized. In this way, the system can prevent inventory shortages and excesses, and support cost reduction and sales maximization by integrating and analyzing data such as historical sales data, seasonal events, weather information, local events, and competitor information, predicting demand in real time, and automatically placing orders.

[0070] The system according to this embodiment comprises a data collection unit, an analysis unit, an ordering unit, and an inventory management unit. The data collection unit collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. For example, the data collection unit can collect sales data for the past year. The data collection unit can also collect data on seasonal events such as Christmas and Halloween. Furthermore, the data collection unit can collect weather information such as temperature and precipitation. As for local events, data on local festivals and sporting events can be collected. As for competitor information, price and promotional information of competitors can be collected. The analysis unit integrates and analyzes the collected data to forecast demand. For example, the analysis unit can build a demand forecasting model based on the collected data and forecast demand. Furthermore, the analysis unit can analyze demand fluctuation patterns based on the collected data. Furthermore, the analysis unit can predict the peak period of demand based on the collected data. The ordering unit automatically places purchase orders based on the forecasted demand. For example, the ordering unit can place purchase orders at the appropriate time based on the demand forecast. Furthermore, the ordering department can determine appropriate order quantities based on demand forecasts. Additionally, the ordering department can select suppliers based on demand forecasts. The inventory management department manages ordered inventory. For example, the inventory management department can manage the receipt of ordered inventory. It can also manage the dispatch of ordered inventory. Furthermore, the inventory management department can manage the inventory levels of ordered inventory. As a result, the system according to this embodiment can integrate and analyze data such as historical sales data, seasonal events, weather information, regional events, and competitor information, forecast demand in real time, and automatically place orders, thereby preventing inventory shortages and excesses, and supporting cost reduction and sales maximization.

[0071] The data collection department collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. Specifically, when collecting sales data for the past year, it obtains detailed data such as sales volume, sales amount, and sales date for each product. This allows for an understanding of best-selling products and sales trends. When collecting data on seasonal events, it collects sales data during specific event periods such as Christmas and Halloween and analyzes fluctuations in sales for each event. When collecting weather information, it obtains meteorological data such as temperature, precipitation, and wind speed and analyzes the impact of weather on sales. When collecting data on local events, it collects information such as the dates, scale, and number of participants of local festivals and sporting events and evaluates the impact of these events on sales. When collecting competitor information, it collects prices, promotional information, and sales strategies of competitors and incorporates them into the company's own sales strategy. The data collection department centrally manages this diverse data and updates it in real time, ensuring that the latest information is always available. Furthermore, the data collection department can also collect data from external sources such as the internet and social media, obtaining a wider range of information. As a result, the data collection department can collect comprehensive and detailed data and provide a foundation for data analysis of the entire system.

[0072] The analysis department integrates and analyzes collected data to forecast demand. Specifically, it builds a demand forecasting model based on collected historical sales data, seasonal events, weather information, regional events, and competitor information. The demand forecasting model is built using machine learning algorithms and learns demand patterns from historical data. For example, it combines historical sales data and weather information to predict fluctuations in sales under specific weather conditions. It also analyzes data on seasonal and regional events to evaluate the impact of these events on sales. Furthermore, it analyzes competitor information to predict the impact of competitors' prices and promotions on its own sales. The analysis department integrates this data to predict demand fluctuation patterns and peak periods. For example, it predicts that certain products will sell well during the Christmas season and secures inventory to meet that demand. Also, if a forecast of worsening weather is issued, it predicts a decrease in demand and adjusts procurement accordingly. The analysis department updates these forecast results in real time, always providing the latest demand forecast. Furthermore, the analysis department uses anomaly detection algorithms to detect unusual demand fluctuations early and respond quickly. This allows the analysis unit to perform accurate and rapid demand forecasting, improving the overall efficiency of the system.

[0073] The purchasing department automatically places purchase orders based on predicted demand. Specifically, it places purchase orders at the appropriate time based on the results of a demand forecasting model. For example, when demand is expected to be high, it places orders early to secure the necessary inventory in advance. Conversely, when demand is expected to be low, it adjusts the order quantity to prevent excess inventory. Based on the demand forecast, the purchasing department considers factors such as inventory turnover rate and lead time to determine the optimal order quantity. Furthermore, the purchasing department also selects suppliers. When selecting suppliers, it considers factors such as price, quality, and delivery time to select the most suitable supplier. By automating these processes, the purchasing department achieves efficient and accurate purchase orders. For example, it automatically creates purchase orders based on the results of the demand forecasting model and sends them to suppliers. It also monitors the order status in real time and can modify the order details as needed. This allows the purchasing department to respond quickly to fluctuations in demand and prevent inventory shortages and excess inventory. In addition, the purchasing department can improve the reliability of purchases by facilitating smooth communication with suppliers, confirming delivery dates, and checking quality.

[0074] The Inventory Management Department manages ordered inventory. Specifically, it manages the receipt of ordered inventory and keeps track of the exact quantity of inventory. In receiving management, it verifies the quantity, quality, and delivery date of received goods and registers them in the inventory system. The Inventory Management Department also manages the dispatch of ordered inventory. In dispatch management, it manages the quantity of goods to be dispatched, the destination, and the dispatch date and time, and reflects this in the inventory system. Furthermore, the Inventory Management Department manages the inventory level of ordered inventory and strives to maintain appropriate inventory levels. In inventory level management, it considers inventory turnover rate and efficient use of storage space to maintain an appropriate inventory level. For example, during periods of high demand, inventory is increased to meet demand, and during periods of low demand, inventory is reduced to make effective use of storage space. The Inventory Management Department achieves efficient and accurate inventory management by automating these processes. For example, it uses the inventory system to monitor inventory status in real time and replenish or adjust inventory as needed. The Inventory Management Department also manages the quality of inventory and takes measures to prevent deterioration or damage. This allows the inventory management department to achieve accurate inventory management and maintain quality, improving the overall efficiency and reliability of the system.

[0075] The system also includes a monitoring unit that tracks the activities of competitors. For example, this unit can monitor competitors' price fluctuations. It can also monitor competitors' promotional activities. Furthermore, it can monitor information on new product launches by competitors. This allows the system to adjust its own pricing strategy and promotional measures in a timely manner by monitoring competitor activity.

[0076] The system also includes an adjustment unit that can fine-tune the company's pricing strategy and promotional measures. For example, this unit can adjust the company's prices to match those of competitors. It can also adjust the company's promotional measures to align with those of competitors. Furthermore, it can adjust the company's pricing strategy and promotional measures to match market demand. This allows the system to support rapid decision-making and enable smart business operations by adjusting the company's pricing strategy and promotional measures.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. By adjusting the timing of data collection according to the user's emotions, the burden on the user is reduced, and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, it can identify and apply the most effective data collection method from past data collection history. Furthermore, the data collection unit can analyze data collection history and optimize the timing and frequency of data collection. In addition, based on past data collection history, the data collection unit can improve the data collection method and enhance accuracy. Thus, by analyzing past data collection history, the optimal data collection method can be selected, and the accuracy of data collection can be improved.

[0079] The data collection unit can filter data during collection, taking into account the impact of specific events or campaigns. For example, it can prioritize the collection of data during specific events or campaign periods. Furthermore, the data collection unit can filter data affected by events or campaigns to ensure accurate data collection. In addition, the data collection unit can adjust its data collection methods depending on the type of event or campaign. This allows for accurate data collection by filtering data to account for the impact of specific events or campaigns.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. Similarly, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of important 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from a specific region based on geographical location information. Furthermore, the data collection unit can filter highly relevant data by considering geographical location information. In addition, the data collection unit can adjust the scope of data collection based on geographical location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant data while considering geographical location information.

[0082] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze social media trends and collect relevant data. It can also analyze social media posts and collect data useful for demand forecasting. Furthermore, the data collection unit can adjust its data collection methods considering the influence of social media. This allows for the collection of data useful for demand forecasting by analyzing social media activity.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis result that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis result can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. Conversely, the analysis unit can perform a simplified analysis on low-importance data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for optimal allocation of analysis resources by adjusting the level of detail of the analysis based on the importance of the data.

[0085] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sales forecasting algorithm to sales data. It can also apply a weather forecasting algorithm to weather data. Furthermore, it can apply a competitive analysis algorithm to competitive information. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved.

[0086] 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 easy-to-read display method. If the user is relaxed, the analysis unit can 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. In this way, by adjusting the display method of the analysis results according to the user's emotions, a highly easy-to-read display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This allows for prioritizing the analysis based on the data submission date, thereby ensuring that the most recent data is analyzed first.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0089] The ordering system can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if the user is stressed, the ordering system can delay the order. Conversely, if the user is relaxed, the ordering system can expedite the order. Furthermore, if the user is in a hurry, the ordering system can place the order quickly. By adjusting the timing of orders according to the user's emotions, the system reduces the user's burden and enables efficient ordering. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The ordering department can adjust the level of detail in orders based on the importance of the products. For example, it can place detailed orders for high-importance products, and simplified orders for low-importance products. Furthermore, the ordering department can optimally allocate ordering resources according to the importance of the products. This allows for optimal resource allocation by adjusting the level of detail in orders based on the importance of the products.

[0091] The ordering department can apply different ordering algorithms depending on the product category when placing an order. For example, it can apply an ordering algorithm that takes expiration dates into account for food products. It can also apply an ordering algorithm that takes seasonality into account for clothing products. Furthermore, it can apply an ordering algorithm that takes technological advancements into account for electronic devices. By applying different ordering algorithms depending on the product category, the accuracy of orders is improved.

[0092] The ordering system can estimate the user's emotions and determine order priorities based on those emotions. For example, if the user is stressed, the ordering system can prioritize ordering high-priority items. If the user is relaxed, the ordering system can prioritize detailed orders. Furthermore, if the user is in a hurry, the ordering system can prioritize ordering items that can be ordered quickly. This allows for prioritizing important items by determining order priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The ordering department can determine the priority of orders based on the product submission dates. For example, the ordering department can prioritize orders for products with upcoming submission dates. It can also postpone orders for products with later submission dates. Furthermore, the ordering department can adjust the order schedule based on submission dates. This allows for prioritizing orders based on submission dates, enabling priority to be given to products with upcoming submission dates.

[0094] The ordering department can adjust the order of orders based on the relevance of the products. For example, the ordering department can prioritize ordering highly relevant products. It can also postpone ordering less relevant products. Furthermore, the ordering department can optimize the order of orders based on the relevance of the products. This allows for prioritizing the ordering of highly relevant products by adjusting the order based on their relevance.

[0095] The inventory management department can estimate the user's emotions and adjust inventory management methods based on those estimated emotions. For example, if the user is stressed, the inventory management department can provide a simple inventory management method. If the user is relaxed, the inventory management department can provide a more detailed inventory management method. Furthermore, if the user is in a hurry, the inventory management department can provide a method for quick inventory management. By adjusting inventory management methods according to the user's emotions, the burden on the user is reduced, and efficient inventory management becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The inventory management department can select the optimal inventory management method by referring to past inventory data during inventory management. For example, the inventory management department can identify the optimal inventory management method based on past inventory data. Furthermore, the inventory management department can analyze inventory data and optimize the timing and frequency of inventory management. In addition, the inventory management department can improve inventory management methods based on past inventory data. This allows for the selection of the optimal inventory management method and improvement of inventory management accuracy by referring to past inventory data.

[0097] The inventory management department can apply different inventory management methods depending on the product category. For example, it can apply an inventory management method that takes expiration dates into account for food products. It can also apply an inventory management method that takes seasonality into account for clothing products. Furthermore, it can apply an inventory management method that takes technological advancements into account for electronic devices. By applying different inventory management methods depending on the product category, the accuracy of inventory management is improved.

[0098] The inventory management department can estimate the user's emotions and determine inventory management priorities based on those emotions. For example, if the user is stressed, the inventory management department can prioritize inventory management of high-priority items. Conversely, if the user is relaxed, the inventory management department can prioritize detailed inventory management. Furthermore, if the user is in a hurry, the inventory management department can prioritize inventory management of items that can be managed quickly. This allows for prioritizing inventory management based on the user's emotions, thereby ensuring that important items are prioritized. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The inventory management department can select the optimal inventory management method by considering the geographical location of products during inventory management. For example, the inventory management department can optimize the inventory management method for a specific region based on geographical location information. Furthermore, the inventory management department can determine inventory management priorities by considering geographical location information. In addition, the inventory management department can adjust the scope of inventory management based on geographical location information. This improves the accuracy of inventory management by selecting the optimal inventory management method while considering the geographical location of products.

[0100] The inventory management department can improve the accuracy of its inventory management by referring to relevant literature on products during inventory management. For example, the inventory management department can improve its inventory management methods based on relevant literature. Furthermore, the inventory management department can improve the accuracy of its inventory management by referring to relevant literature. In addition, the inventory management department can optimize the timing and frequency of inventory management based on relevant literature. Thus, by referring to relevant literature on products, the accuracy of inventory management can be improved.

[0101] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can provide a simple monitoring method. If the user is relaxed, the monitoring unit can provide a more detailed monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can provide a method for rapid monitoring. By adjusting the monitoring method according to the user's emotions, the burden on the user is reduced and efficient monitoring becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The monitoring unit can select the optimal monitoring method by referring to past performance data of competitors during monitoring. For example, the monitoring unit can identify the optimal monitoring method based on past performance data of competitors. Furthermore, the monitoring unit can analyze competitor performance data and optimize the timing and frequency of monitoring. In addition, the monitoring unit can improve the monitoring method based on past performance data of competitors. This allows for the selection of the optimal monitoring method and improvement of monitoring accuracy by referring to past performance data of competitors.

[0103] The monitoring unit can apply different monitoring methods depending on the competitor's category during monitoring. For example, the monitoring unit can select an appropriate monitoring method depending on the competitor's category. Furthermore, the monitoring unit can adjust the monitoring method based on the competitor's category. In addition, the monitoring unit can optimally allocate monitoring resources according to the competitor's category. This improves monitoring accuracy by applying different monitoring methods depending on the competitor's category.

[0104] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on those emotions. For example, if the user is stressed, the monitoring unit can prioritize high-priority monitoring. Conversely, if the user is relaxed, the monitoring unit can prioritize detailed monitoring. Furthermore, if the user is in a hurry, the monitoring unit can prioritize items that can be monitored quickly. This allows for prioritizing important monitoring items by determining monitoring priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The monitoring unit can select the optimal monitoring method while considering the geographical location information of competitors. For example, the monitoring unit can optimize the monitoring method for competitors in a specific region based on geographical location information. Furthermore, the monitoring unit can determine the priority of competitor monitoring based on geographical location information. In addition, the monitoring unit can adjust the scope of competitor monitoring based on geographical location information. This improves monitoring accuracy by selecting the optimal monitoring method while considering the geographical location information of competitors.

[0106] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature from competitors during monitoring. For example, the monitoring unit can improve the monitoring methods of competitors based on relevant literature. Furthermore, the monitoring unit can improve the accuracy of its monitoring of competitors by referring to relevant literature. In addition, the monitoring unit can optimize the timing and frequency of monitoring competitors based on relevant literature. This allows for improved monitoring accuracy by referring to relevant literature from competitors.

[0107] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit can provide a simple adjustment method. It can also provide a more detailed adjustment method if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can provide a method for quick adjustment. This reduces the user's burden and enables efficient adjustment by adjusting the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The adjustment unit can select the optimal adjustment method by referring to past price strategy data during the adjustment process. For example, the adjustment unit can identify the optimal adjustment method based on past price strategy data. Furthermore, the adjustment unit can analyze price strategy data and optimize the timing and frequency of adjustments. In addition, the adjustment unit can improve the adjustment method based on past price strategy data. This allows for the selection of the optimal adjustment method and improvement of adjustment accuracy by referring to past price strategy data.

[0109] The adjustment unit can apply different adjustment methods depending on the category of the promotional measure during the adjustment process. For example, the adjustment unit can select an appropriate adjustment method depending on the category of the promotional measure. Furthermore, the adjustment unit can adjust the adjustment method based on the category of the promotional measure. In addition, the adjustment unit can optimally allocate adjustment resources depending on the category of the promotional measure. This improves the accuracy of the adjustment by applying different adjustment methods depending on the category of the promotional measure.

[0110] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit can prioritize high-priority adjustments. If the user is relaxed, the adjustment unit can also prioritize detailed adjustments. Furthermore, if the user is in a hurry, the adjustment unit can prioritize items that can be adjusted quickly. In this way, by determining the priority of adjustments according to the user's emotions, important adjustment items can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the geographical location information of the pricing strategy. For example, the adjustment unit can optimize the adjustment method for a specific region based on geographical location information. Furthermore, the adjustment unit can determine the priority of price strategy adjustments, taking geographical location information into account. In addition, the adjustment unit can adjust the scope of price strategy adjustments based on geographical location information. This improves the accuracy of adjustments by selecting the optimal adjustment method while considering the geographical location information of the pricing strategy.

[0112] The adjustment unit can improve the accuracy of its adjustments by referring to relevant literature on promotional strategies during the adjustment process. For example, the adjustment unit can improve the method of adjusting promotional strategies based on relevant literature. Furthermore, the adjustment unit can improve the accuracy of its adjustments by referring to relevant literature. In addition, the adjustment unit can optimize the timing and frequency of adjustments to promotional strategies based on relevant literature. This allows for improved adjustment accuracy by referring to relevant literature on promotional strategies.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the timing of data collection can be adjusted to quickly collect the necessary data. In this way, by adjusting the timing of data collection according to the user's emotions, the burden on the user is reduced and efficient data collection becomes possible.

[0115] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, it can identify and apply the most effective data collection method from past data collection history. It can also analyze data collection history to optimize the timing and frequency of data collection. Furthermore, it can improve data collection methods and enhance accuracy based on past data collection history. In this way, by analyzing past data collection history, it is possible to select the optimal data collection method and improve the accuracy of data collection.

[0116] The data collection unit can filter data during collection, taking into account the impact of specific events or campaigns. For example, it can prioritize the collection of data during specific events or campaign periods. It can also filter data affected by events or campaigns to collect accurate data. Furthermore, it can adjust the data collection method depending on the type of event or campaign. This allows for accurate data collection by filtering data while considering the impact of specific events or campaigns.

[0117] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on those emotions. For example, if the user is stressed, it can prioritize collecting high-priority data. If the user is relaxed, it can prioritize collecting detailed data. Furthermore, if the user is in a hurry, it can prioritize collecting data that can be retrieved quickly. In this way, by prioritizing the data to be collected according to the user's emotions, important data can be collected preferentially.

[0118] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, it can prioritize the collection of data from a specific region based on geographical location information. It can also filter highly relevant data by considering geographical location information. Furthermore, it can adjust the scope of data collection based on geographical location information. As a result, the accuracy of data collection is improved by prioritizing the collection of highly relevant data by considering geographical location information.

[0119] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze social media trends and collect relevant data. It can also analyze the content of social media posts and collect data useful for demand forecasting. Furthermore, it can adjust the data collection method considering the influence of social media. This allows for the collection of data useful for demand forecasting by analyzing social media activity.

[0120] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide a simple analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide an analysis result that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0121] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can optimally allocate analysis resources according to the importance of the data. This means that by adjusting the level of detail of the analysis based on the importance of the data, analysis resources can be optimally allocated.

[0122] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a sales forecasting algorithm can be applied to sales data. Similarly, a weather forecasting algorithm can be applied to weather data. Furthermore, a competitive analysis algorithm can be applied to competitive information. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category.

[0123] 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, it can provide a simple and easy-to-read display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display method that is easy for the user to understand.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit collects data such as historical sales data, seasonal events, weather information, local events, and competitor information. For example, it can collect sales data for the past year, data on seasonal events such as Christmas and Halloween, weather information such as temperature and precipitation, data on local festivals and sporting events, and information on competitor pricing and promotions. Step 2: The analysis unit integrates and analyzes the collected data to forecast demand. For example, it can build a demand forecasting model based on the collected data to predict demand fluctuation patterns and peak periods. Step 3: The ordering department automatically places procurement orders based on predicted demand. For example, it can place procurement orders at the appropriate time based on demand forecasts, determine the appropriate order quantity, and select suppliers. Step 4: The inventory management department manages ordered inventory. For example, it can manage the receipt, dispatch, and inventory levels of ordered inventory.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data acquisition unit, analysis unit, ordering unit, and inventory management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data acquisition unit collects data using the camera 42 and communication I / F 44 of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which integrates and analyzes the collected data and predicts demand. The ordering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically places purchase orders based on the predicted demand. The inventory management unit is implemented, for example, by the control unit 46A of the smart device 14, which manages the ordered inventory. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data acquisition unit, analysis unit, ordering unit, and inventory management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit collects data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which integrates and analyzes the collected data and predicts demand. The ordering unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which automatically places purchase orders based on the predicted demand. The inventory management unit is implemented, for example, in the control unit 46A of the smart glasses 214, which manages the ordered inventory. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In 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.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 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.

[0161] Each of the multiple elements described above, including the data acquisition unit, analysis unit, ordering unit, and inventory management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit collects data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which integrates and analyzes the collected data and predicts demand. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically places purchase orders based on the predicted demand. The inventory management unit is implemented in the control unit 46A of the headset terminal 314, which manages the ordered inventory. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the data acquisition unit, analysis unit, ordering unit, and inventory management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit collects data using the camera 42 and communication I / F 44 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which integrates and analyzes the collected data and predicts demand. The ordering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically places purchase orders based on the predicted demand. The inventory management unit is implemented by, for example, the control unit 46A of the robot 414, which manages the ordered inventory. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) The data collection department collects data such as past sales data, seasonal events, weather information, local events, and competitor information. An analysis unit integrates and analyzes the data collected by the aforementioned data collection unit to predict demand, An ordering unit that automatically places procurement orders based on the demand predicted by the analysis unit, The system comprises an inventory management unit that manages inventory ordered by the ordering unit. A system characterized by the following features. (Note 2) We will also have a monitoring department to keep track of the activities of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will also have a coordinating department to adjust our company's pricing strategy and promotional measures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data acquisition unit is Analyze past data collection history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data acquisition unit is When collecting data, filter the data to take into account the impact of specific events or campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The ordering department said, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The ordering department said, When placing an order, adjust the level of detail in the order based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 18) The ordering department said, When placing an order, different ordering algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The ordering department said, It estimates user sentiment and determines order priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The ordering department said, When placing an order, we determine the order priority based on the delivery date of the products. The system described in Appendix 1, characterized by the features described herein. (Note 21) The ordering department said, When placing an order, adjust the order of orders based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inventory management department, It estimates user sentiment and adjusts inventory management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inventory management department, When managing inventory, select the optimal inventory management method by referring to past inventory data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned inventory management department, When managing inventory, apply different inventory management methods depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned inventory management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned inventory management department, When managing inventory, select the optimal inventory management method by considering the geographical location of the products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned inventory management department, When managing inventory, refer to relevant literature on products to improve the accuracy of inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected by referring to past performance data of competitors. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, different monitoring methods are applied depending on the category of the competitor. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected by considering the geographical location information of competitors. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant literature from competitors. The system described in Appendix 2, characterized by the features described herein. (Note 34) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The adjustment unit is, During adjustments, the optimal adjustment method is selected by referring to past pricing strategy data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The adjustment unit is, During the adjustment process, different adjustment methods will be applied depending on the category of the promotional initiative. The system described in Appendix 3, characterized by the features described herein. (Note 37) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The adjustment unit is, During adjustments, the optimal adjustment method is selected by considering the geographical location of the pricing strategy. The system described in Appendix 3, characterized by the features described herein. (Note 39) The adjustment unit is, During the adjustment process, we refer to relevant literature on promotional strategies to improve the accuracy of the adjustments. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data such as past sales data, seasonal events, weather information, local events, and competitor information. An analysis unit integrates and analyzes the data collected by the aforementioned data collection unit to predict demand, An ordering unit that automatically places procurement orders based on the demand predicted by the analysis unit, The system comprises an inventory management unit that manages inventory ordered by the ordering unit. A system characterized by the following features.

2. We will also have a monitoring department to keep track of the activities of our competitors. The system according to feature 1.

3. We will also have a coordinating department to adjust our company's pricing strategy and promotional measures. The system according to feature 1.

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

5. The aforementioned data acquisition unit, Analyze past data collection history to select the optimal data collection method. The system according to feature 1.

6. The aforementioned data acquisition unit, When collecting data, filter the data to take into account the impact of specific events or campaigns. The system according to feature 1.

7. The aforementioned data acquisition unit, 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 data acquisition unit, When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

9. The aforementioned data acquisition unit, During data collection, social media activity is analyzed and relevant data is gathered. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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