system

Abstract

The system according to this embodiment aims to streamline inventory management by proposing an optimal inventory level based on past sales data and demand forecast data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand forecast data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal inventory level based on the data analyzed by the analysis unit. The adjustment unit adjusts the inventory based on the inventory level proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, the optimization of inventory levels is not sufficiently performed, and there is a risk of overstocking or shortages.

[0005] The system according to the embodiment aims to propose an optimal inventory level based on past sales data and demand forecast data and improve the efficiency of inventory management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects historical sales data and demand forecast data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal inventory level based on the data analyzed by the analysis unit. The adjustment unit adjusts the inventory based on the inventory level proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal inventory level based on past sales data and demand forecast data, thereby streamlining 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 three or more matters are connected and expressed by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The inventory optimization system according to an embodiment of the present invention is a system that proposes an optimal inventory level using generative AI based on past sales data and demand forecasts. This system analyzes data in real time and responds quickly and flexibly to fluctuations in supply and demand. This enables cost reduction and improved efficiency in inventory management. This service optimizes inventory for various industries such as retail, wholesale, and manufacturing, as well as for businesses operating online. By using this service, each company can achieve efficient and accurate inventory management. For example, it collects past sales data and demand forecast data. This includes each company's sales history and market demand forecast data. For example, sales data for the past year and seasonal demand forecast data are collected. Next, the generative AI analyzes the collected data. Based on the past sales data and demand forecast data, the generative AI predicts future demand. For example, it predicts how much of a particular product will sell and when. Based on the demand data predicted by the generative AI, it proposes an optimal inventory level. This minimizes the risk of excess inventory and stockouts. For example, it adjusts inventory by increasing it when demand is high and decreasing it when demand is low. Furthermore, this system analyzes data in real time and responds flexibly to fluctuations in supply and demand. For example, even in the event of sudden demand fluctuations, the generating AI instantly analyzes the data and proposes optimal inventory levels. This enables rapid and accurate inventory management. This service is suitable for various industries, including retail, wholesale, and manufacturing. For instance, in retail, it streamlines inventory management at each store, preventing unsold inventory and stockouts. In manufacturing, it optimizes inventory management of parts and raw materials, improving production efficiency. It also supports businesses operating online. For example, e-commerce sites can monitor product inventory in real time and adjust inventory levels according to demand, thereby improving customer satisfaction. In this way, the inventory optimization service utilizing generating AI enables efficient and accurate inventory management, leading to cost reductions and improved operational efficiency. By using this service, companies can solve their inventory management challenges and accelerate business growth.This enables inventory optimization systems to achieve efficient and accurate inventory management, leading to cost reductions and improved operational efficiency.

[0029] The inventory optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects historical sales data and demand forecast data. For example, the data collection unit collects sales history data for each company and market demand forecast data. For example, the data collection unit can collect sales data for the past year and seasonal demand forecast data. The data collection unit can also use AI to automatically collect sales history data for each company and market demand forecast data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict future demand based on historical sales data and demand forecast data. For example, the analysis unit can predict how much of a particular product will sell and when. The analysis unit can also use generative AI to predict future demand based on historical sales data and demand forecast data. The proposal unit proposes an optimal inventory level based on the data analyzed by the analysis unit. The proposal unit uses generative AI to propose an optimal inventory level based on the predicted demand data. For example, the proposal unit can make adjustments such as increasing inventory when demand is high and decreasing inventory when demand is low. The proposal unit can also use generation AI to propose an optimal inventory level based on predicted demand data. The adjustment unit adjusts inventory based on the inventory level proposed by the proposal unit. The adjustment unit uses AI to adjust inventory based on the proposed inventory level. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. The adjustment unit can also use AI to adjust inventory based on the proposed inventory level. As a result, the inventory optimization system according to this embodiment can propose an optimal inventory level based on historical sales data and demand forecast data, thereby improving the efficiency of inventory management.

[0030] The data collection unit collects historical sales data and demand forecast data. Specifically, it collects sales history data and market demand forecast data for each company. For example, the data collection unit can collect sales data for the past year and seasonal demand forecast data. This allows the data collection unit to automatically collect sales history data for companies and market demand forecast data. The data collection unit can also use AI to automatically collect sales history data for each company and market demand forecast data. The AI ​​extracts and collects data from publicly available databases on the internet and from internal systems of companies. For example, the AI ​​accesses a company's sales management system to obtain historical sales data. The AI ​​also collects market research reports and demand forecast data from the internet and stores them in the database. This allows the data collection unit to efficiently collect necessary data from a wide range of data sources and provide foundational data for inventory optimization. Furthermore, to ensure data quality, the data collection unit has the functionality to check the integrity and consistency of the collected data and automatically correct inaccurate or missing data. This allows the data collection unit to provide reliable data and improve the accuracy of the analysis and proposal units.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict future demand based on past sales data and demand forecast data. Specifically, the analysis unit can predict how much of a particular product will sell and when. Generative AI learns from past sales data and demand forecast data to predict future demand with high accuracy. For example, based on past sales data, generative AI analyzes seasonal demand patterns and trends to predict future demand. Furthermore, based on demand forecast data, generative AI evaluates the impact of specific events and promotions on demand and performs demand forecasting. This allows the analysis unit to accurately predict future demand and improve the efficiency of inventory management. In addition, the analysis unit can also use generative AI to predict future demand based on past sales data and demand forecast data. Generative AI integrates data collected from multiple data sources to build a demand forecasting model. For example, generative AI integrates past sales data, demand forecast data, weather data, economic indicators, etc., to build a demand forecasting model. This allows the analysis unit to perform highly accurate demand forecasts that consider multiple factors.

[0032] The proposal department proposes optimal inventory levels based on data analyzed by the analysis department. Specifically, the proposal department uses generative AI to propose optimal inventory levels based on predicted demand data. For example, the proposal department can adjust inventory levels by increasing them during periods of high demand and decreasing them during periods of low demand. The generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. This enables the proposal department to achieve flexible inventory management that responds to fluctuations in demand. Furthermore, the proposal department can also use generative AI to propose optimal inventory levels based on predicted demand data. The generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. For example, the generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. This enables the proposal department to achieve flexible inventory management that responds to fluctuations in demand.

[0033] The adjustment unit adjusts inventory based on the inventory levels proposed by the proposal unit. Specifically, the adjustment unit uses AI to adjust inventory based on the proposed inventory levels. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. The AI ​​accesses the inventory management system and automatically adjusts inventory levels. For example, the AI ​​accesses the inventory management system and automatically adjusts inventory levels. This allows the adjustment unit to achieve flexible inventory management that responds to fluctuations in demand. Furthermore, the adjustment unit can also use AI to adjust inventory based on the proposed inventory levels. The AI ​​accesses the inventory management system and automatically adjusts inventory levels. For example, the AI ​​accesses the inventory management system and automatically adjusts inventory levels. This allows the adjustment unit to achieve flexible inventory management that responds to fluctuations in demand.

[0034] The data collection unit can collect sales history and market demand forecast data for each company. For example, the data collection unit can collect sales history such as the date of sale, quantity sold, and price of a product. The data collection unit can also use AI to automatically collect sales history and market demand forecast data for each company. For example, the data collection unit can collect market demand forecast data such as industry-wide demand forecasts and demand forecasts for specific regions. By collecting sales history and market demand forecast data for each company, more accurate demand forecasts become possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sales history and market demand forecast data for each company into the AI ​​and have the AI ​​perform the data collection.

[0035] The analysis unit can predict future demand based on past sales data and demand forecast data. For example, the analysis unit can predict future demand using methods such as time series analysis and regression analysis. The analysis unit can also predict future demand based on past sales data and demand forecast data using generative AI. For example, the analysis unit can predict how much of a particular product will be sold and when. This improves the accuracy of inventory management by predicting future demand based on past sales data and demand forecast data. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input past sales data and demand forecast data into the generative AI and have the generative AI perform the prediction of future demand.

[0036] The proposal unit can propose optimal inventory levels based on predicted demand data. For example, the proposal unit can propose optimal inventory levels using criteria such as inventory turnover based on historical sales data or safety stock levels based on demand forecasts. The proposal unit can also propose optimal inventory levels based on predicted demand data using generative AI. For example, the proposal unit can adjust inventory levels by increasing it during periods of high demand and decreasing it during periods of low demand. This minimizes the risk of excess inventory and stockouts by proposing optimal inventory levels based on predicted demand data. Some or all of the above processes in the proposal unit may be performed using generative AI or not. For example, the proposal unit can input predicted demand data into the generative AI and have the generative AI propose optimal inventory levels.

[0037] The adjustment unit can adjust inventory based on the proposed inventory level. For example, the adjustment unit can adjust inventory based on the proposed inventory level. For example, the adjustment unit can adjust inventory using methods such as placing additional orders for inventory, moving inventory, or reducing inventory. The adjustment unit can also use AI to adjust inventory based on the proposed inventory level. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. This improves the efficiency of inventory management by adjusting inventory based on the proposed inventory level. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the proposed inventory level into the AI ​​and have the AI ​​perform the inventory adjustment.

[0038] The adjustment unit can analyze data in real time and respond flexibly to fluctuations in supply and demand. For example, the adjustment unit can analyze data in real time using methods such as data collection frequency and analysis algorithms. The adjustment unit can also use AI to analyze data in real time and respond flexibly to fluctuations in supply and demand. For example, even if a sudden change in demand occurs, the adjustment unit can immediately analyze the data and propose an optimal inventory level again. This enables rapid and accurate inventory management by analyzing data in real time and responding flexibly to fluctuations in supply and demand. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input data collected in real time into AI and have the AI ​​propose an optimal inventory level again to respond to fluctuations in supply and demand.

[0039] The data collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the data collection unit can identify each company's peak sales period and focus data collection during that time. The data collection unit can also use AI to analyze each company's past sales history and select the optimal data collection method. For example, the data collection unit can analyze data for each company's sales channel and collect data from the most effective channel. This enables efficient data collection by analyzing each company's past sales history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's past sales history into AI and have AI select the optimal data collection method.

[0040] The data collection unit can adjust the types of data collected based on specific seasons or events during data collection. For example, the data collection unit can consider seasonal demand fluctuations and focus on collecting data related to seasonal products. The data collection unit can also use AI to adjust the types of data collected based on specific seasons or events during data collection. For example, the data collection unit can collect data on related products in line with specific events (e.g., Christmas, Black Friday). This improves the accuracy of demand forecasting by adjusting the types of data collected based on specific seasons or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on specific seasons or events into the AI ​​and have the AI ​​perform the data collection.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of demand data in the areas where each company's stores are located. The data collection unit can also use AI to prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can collect data for efficient inventory placement based on the location information of each company's logistics bases. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0042] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can collect campaign information on each company's social media and use it to forecast demand. The data collection unit can also use AI to analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can collect customer feedback on each company's social media and incorporate it into inventory management. This improves the accuracy of demand forecasting by analyzing each company's social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's social media activities into AI and have AI collect relevant data.

[0043] 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 highly important data to improve accuracy. The analysis unit can also use a generative AI to 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 simplified analysis on less important data to prioritize efficiency. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data to grasp trends. The analysis unit can also use a generative AI to apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a regression analysis algorithm to demand forecast data to improve forecast accuracy. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data and perform real-time demand forecasting. The analysis unit can also use generative AI to determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can analyze historical data and grasp long-term trends. This enables real-time demand forecasting by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. The analysis unit can also use generative AI to adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can analyze data with a moderate level of relevance next to balance the overall analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the inventory level. For example, the proposal unit can make more detailed proposals for high-importance inventory to improve accuracy. The proposal unit can also use a generation AI to adjust the level of detail of its proposals based on the importance of the inventory level. For example, the proposal unit can make simplified proposals for low-importance inventory to prioritize efficiency. By adjusting the level of detail of proposals based on the importance of the inventory level, efficient inventory management becomes possible. Some or all of the above processes in the proposal unit may be performed using a generation AI, or not. For example, the proposal unit can input the importance of the inventory level into the generation AI and have the generation AI perform the adjustment of the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the inventory category when making a proposal. For example, the proposal unit can apply a demand forecasting algorithm to consumer goods and propose the optimal inventory level. The proposal unit can also use generative AI to apply different proposal algorithms depending on the inventory category when making a proposal. For example, the proposal unit can apply a production planning algorithm to industrial products and propose efficient inventory management. This improves the accuracy of the proposal by applying different proposal algorithms depending on the inventory category. Some or all of the above processing in the proposal unit may be performed using generative AI or not. For example, the proposal unit can input the inventory category into the generative AI and have the generative AI execute the application of the proposal algorithm.

[0049] The proposal unit can determine the priority of proposals based on the inventory collection timing at the time of proposal. For example, the proposal unit can prioritize the latest inventory data and perform real-time inventory management. The proposal unit can also use a generation AI to determine the priority of proposals based on the inventory collection timing at the time of proposal. For example, the proposal unit can propose long-term inventory management based on historical inventory data. This enables real-time inventory management by determining the priority of proposals based on the inventory collection timing. Some or all of the above processing in the proposal unit may be performed using a generation AI or not. For example, the proposal unit can input the inventory collection timing into the generation AI and have the generation AI perform the determination of proposal priorities.

[0050] The suggestion unit can adjust the order of suggestions based on inventory relevance when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant inventory items to improve accuracy. The suggestion unit can also use generative AI to adjust the order of suggestions based on inventory relevance when making suggestions. For example, the suggestion unit can then suggest inventory items with a moderate level of relevance to balance the overall order. This allows for efficient inventory management by adjusting the order of suggestions based on inventory relevance. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input inventory relevance into the generative AI and have the generative AI perform the adjustment of the suggestion order.

[0051] The adjustment unit can analyze past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can select the most effective adjustment method based on past inventory adjustment history. The adjustment unit can also use AI to analyze past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can eliminate failed adjustment methods from past inventory adjustment history. This enables efficient inventory adjustment by analyzing past inventory adjustment history and selecting the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past inventory adjustment history into AI and have AI select the optimal adjustment method.

[0052] The adjustment unit can customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can analyze the current supply and demand situation in real time and provide the optimal inventory adjustment method. The adjustment unit can also use AI to customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can flexibly change the inventory adjustment methods in response to fluctuations in supply and demand. This enables efficient inventory management by customizing the inventory adjustment methods based on the current supply and demand situation. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the current supply and demand situation into the AI ​​and have the AI ​​perform the customization of the inventory adjustment methods.

[0053] The adjustment unit can select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select the optimal inventory adjustment method by considering the supply and demand situation in the area where each company's stores are located. The adjustment unit can also use AI to select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select an efficient inventory adjustment method based on the location information of each company's logistics base. This enables efficient inventory management by selecting the optimal inventory adjustment method by considering the geographical location information of each company. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the geographical location information of each company into the AI ​​and have the AI ​​select the optimal inventory adjustment method.

[0054] The adjustment unit can analyze each company's social media activities during adjustment and propose inventory adjustment methods. For example, the adjustment unit can propose inventory adjustment methods based on each company's social media campaign information. The adjustment unit can also use AI to analyze each company's social media activities during adjustment and propose inventory adjustment methods. For example, the adjustment unit can propose inventory adjustment methods based on each company's customer feedback on social media. By analyzing each company's social media activities and proposing inventory adjustment methods, efficient inventory management becomes possible. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input each company's social media activities into AI and have AI execute the proposal of inventory adjustment methods.

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

[0056] The data collection unit can analyze each company's past sales history and select the optimal data collection method. For example, it can identify each company's peak sales period and focus data collection during that time. The data collection unit can also use AI to analyze each company's past sales history and select the optimal data collection method. For example, it can analyze data from each company's sales channel and collect data from the most effective channel. This enables efficient data collection by analyzing each company's past sales history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's past sales history into the AI ​​and have the AI ​​select the optimal data collection method.

[0057] The data collection unit can adjust the types of data collected based on specific seasons or events. For example, it can prioritize the collection of data related to seasonal products, taking into account seasonal demand fluctuations. The data collection unit can also use AI to adjust the types of data collected based on specific seasons or events. For example, it can collect data on related products in line with specific events (e.g., Christmas, Black Friday). By adjusting the types of data collected based on specific seasons or events, the accuracy of demand forecasting can be improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on specific seasons or events into the AI ​​and have the AI ​​perform the data collection.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, it can prioritize the collection of demand data in the areas where each company's stores are located. The data collection unit can also use AI to prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, it can collect data for efficient inventory placement based on the location information of each company's logistics bases. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0059] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, it can collect campaign information on each company's social media and use it to help with demand forecasting. The data collection unit can also use AI to analyze each company's social media activities and collect relevant data during data collection. For example, it can collect customer feedback on each company's social media and reflect it in inventory management. By analyzing each company's social media activities and collecting relevant data, the accuracy of demand forecasting is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's social media activities into the AI ​​and have the AI ​​collect relevant data.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data to improve accuracy. The analysis unit can also use a generative AI to adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a simplified analysis on less important data to prioritize efficiency. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0061] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a time series analysis algorithm can be applied to sales data to grasp trends. The analysis unit can also use a generative AI to apply different analysis algorithms depending on the data category during analysis. For example, a regression analysis algorithm can be applied to demand forecast data to improve forecast accuracy. In this way, applying different analysis algorithms depending on the data category improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects historical sales data and demand forecast data. For example, it can collect sales history data and market demand forecast data for each company, and collect sales data for the past year and seasonal demand forecast data. The data collection unit can also use AI to automatically collect sales history data and market demand forecast data for each company. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generating AI to predict future demand based on past sales data and demand forecast data. For example, it can predict how much of a particular product will sell and when. Step 3: The proposal unit proposes the optimal inventory level based on the data analyzed by the analysis unit. The proposal unit uses generation AI to propose the optimal inventory level based on predicted demand data. For example, it can adjust inventory by increasing it during periods of high demand and decreasing it during periods of low demand. Step 4: The adjustment unit adjusts inventory based on the inventory levels proposed by the proposal unit. The adjustment unit uses AI to adjust inventory based on the proposed inventory levels. For example, it can increase inventory during periods of high demand and decrease inventory during periods of low demand.

[0064] (Example of form 2) The inventory optimization system according to an embodiment of the present invention is a system that proposes an optimal inventory level using generative AI based on past sales data and demand forecasts. This system analyzes data in real time and responds quickly and flexibly to fluctuations in supply and demand. This enables cost reduction and improved efficiency in inventory management. This service optimizes inventory for various industries such as retail, wholesale, and manufacturing, as well as for businesses operating online. By using this service, each company can achieve efficient and accurate inventory management. For example, it collects past sales data and demand forecast data. This includes each company's sales history and market demand forecast data. For example, sales data for the past year and seasonal demand forecast data are collected. Next, the generative AI analyzes the collected data. Based on the past sales data and demand forecast data, the generative AI predicts future demand. For example, it predicts how much of a particular product will sell and when. Based on the demand data predicted by the generative AI, it proposes an optimal inventory level. This minimizes the risk of excess inventory and stockouts. For example, it adjusts inventory by increasing it when demand is high and decreasing it when demand is low. Furthermore, this system analyzes data in real time and responds flexibly to fluctuations in supply and demand. For example, even in the event of sudden demand fluctuations, the generating AI instantly analyzes the data and proposes optimal inventory levels. This enables rapid and accurate inventory management. This service is suitable for various industries, including retail, wholesale, and manufacturing. For instance, in retail, it streamlines inventory management at each store, preventing unsold inventory and stockouts. In manufacturing, it optimizes inventory management of parts and raw materials, improving production efficiency. It also supports businesses operating online. For example, e-commerce sites can monitor product inventory in real time and adjust inventory levels according to demand, thereby improving customer satisfaction. In this way, the inventory optimization service utilizing generating AI enables efficient and accurate inventory management, leading to cost reductions and improved operational efficiency. By using this service, companies can solve their inventory management challenges and accelerate business growth.This enables inventory optimization systems to achieve efficient and accurate inventory management, leading to cost reductions and improved operational efficiency.

[0065] The inventory optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects historical sales data and demand forecast data. For example, the data collection unit collects sales history data for each company and market demand forecast data. For example, the data collection unit can collect sales data for the past year and seasonal demand forecast data. The data collection unit can also use AI to automatically collect sales history data for each company and market demand forecast data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict future demand based on historical sales data and demand forecast data. For example, the analysis unit can predict how much of a particular product will sell and when. The analysis unit can also use generative AI to predict future demand based on historical sales data and demand forecast data. The proposal unit proposes an optimal inventory level based on the data analyzed by the analysis unit. The proposal unit uses generative AI to propose an optimal inventory level based on the predicted demand data. For example, the proposal unit can make adjustments such as increasing inventory when demand is high and decreasing inventory when demand is low. The proposal unit can also use generation AI to propose an optimal inventory level based on predicted demand data. The adjustment unit adjusts inventory based on the inventory level proposed by the proposal unit. The adjustment unit uses AI to adjust inventory based on the proposed inventory level. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. The adjustment unit can also use AI to adjust inventory based on the proposed inventory level. As a result, the inventory optimization system according to this embodiment can propose an optimal inventory level based on historical sales data and demand forecast data, thereby improving the efficiency of inventory management.

[0066] The data collection unit collects historical sales data and demand forecast data. Specifically, it collects sales history data and market demand forecast data for each company. For example, the data collection unit can collect sales data for the past year and seasonal demand forecast data. This allows the data collection unit to automatically collect sales history data for companies and market demand forecast data. The data collection unit can also use AI to automatically collect sales history data for each company and market demand forecast data. The AI ​​extracts and collects data from publicly available databases on the internet and from internal systems of companies. For example, the AI ​​accesses a company's sales management system to obtain historical sales data. The AI ​​also collects market research reports and demand forecast data from the internet and stores them in the database. This allows the data collection unit to efficiently collect necessary data from a wide range of data sources and provide foundational data for inventory optimization. Furthermore, to ensure data quality, the data collection unit has the functionality to check the integrity and consistency of the collected data and automatically correct inaccurate or missing data. This allows the data collection unit to provide reliable data and improve the accuracy of the analysis and proposal units.

[0067] The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict future demand based on past sales data and demand forecast data. Specifically, the analysis unit can predict how much of a particular product will sell and when. Generative AI learns from past sales data and demand forecast data to predict future demand with high accuracy. For example, based on past sales data, generative AI analyzes seasonal demand patterns and trends to predict future demand. Furthermore, based on demand forecast data, generative AI evaluates the impact of specific events and promotions on demand and performs demand forecasting. This allows the analysis unit to accurately predict future demand and improve the efficiency of inventory management. In addition, the analysis unit can also use generative AI to predict future demand based on past sales data and demand forecast data. Generative AI integrates data collected from multiple data sources to build a demand forecasting model. For example, generative AI integrates past sales data, demand forecast data, weather data, economic indicators, etc., to build a demand forecasting model. This allows the analysis unit to perform highly accurate demand forecasts that consider multiple factors.

[0068] The proposal department proposes optimal inventory levels based on data analyzed by the analysis department. Specifically, the proposal department uses generative AI to propose optimal inventory levels based on predicted demand data. For example, the proposal department can adjust inventory levels by increasing them during periods of high demand and decreasing them during periods of low demand. The generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. This enables the proposal department to achieve flexible inventory management that responds to fluctuations in demand. Furthermore, the proposal department can also use generative AI to propose optimal inventory levels based on predicted demand data. The generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. For example, the generative AI executes an algorithm to optimize inventory levels based on demand forecast data and proposes the optimal inventory level. This enables the proposal department to achieve flexible inventory management that responds to fluctuations in demand.

[0069] The adjustment unit adjusts inventory based on the inventory levels proposed by the proposal unit. Specifically, the adjustment unit uses AI to adjust inventory based on the proposed inventory levels. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. The AI ​​accesses the inventory management system and automatically adjusts inventory levels. For example, the AI ​​accesses the inventory management system and automatically adjusts inventory levels. This allows the adjustment unit to achieve flexible inventory management that responds to fluctuations in demand. Furthermore, the adjustment unit can also use AI to adjust inventory based on the proposed inventory levels. The AI ​​accesses the inventory management system and automatically adjusts inventory levels. For example, the AI ​​accesses the inventory management system and automatically adjusts inventory levels. This allows the adjustment unit to achieve flexible inventory management that responds to fluctuations in demand.

[0070] The data collection unit can collect sales history and market demand forecast data for each company. For example, the data collection unit can collect sales history such as the date of sale, quantity sold, and price of a product. The data collection unit can also use AI to automatically collect sales history and market demand forecast data for each company. For example, the data collection unit can collect market demand forecast data such as industry-wide demand forecasts and demand forecasts for specific regions. By collecting sales history and market demand forecast data for each company, more accurate demand forecasts become possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sales history and market demand forecast data for each company into the AI ​​and have the AI ​​perform the data collection.

[0071] The analysis unit can predict future demand based on past sales data and demand forecast data. For example, the analysis unit can predict future demand using methods such as time series analysis and regression analysis. The analysis unit can also predict future demand based on past sales data and demand forecast data using generative AI. For example, the analysis unit can predict how much of a particular product will be sold and when. This improves the accuracy of inventory management by predicting future demand based on past sales data and demand forecast data. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input past sales data and demand forecast data into the generative AI and have the generative AI perform the prediction of future demand.

[0072] The proposal unit can propose optimal inventory levels based on predicted demand data. For example, the proposal unit can propose optimal inventory levels using criteria such as inventory turnover based on historical sales data or safety stock levels based on demand forecasts. The proposal unit can also propose optimal inventory levels based on predicted demand data using generative AI. For example, the proposal unit can adjust inventory levels by increasing it during periods of high demand and decreasing it during periods of low demand. This minimizes the risk of excess inventory and stockouts by proposing optimal inventory levels based on predicted demand data. Some or all of the above processes in the proposal unit may be performed using generative AI or not. For example, the proposal unit can input predicted demand data into the generative AI and have the generative AI propose optimal inventory levels.

[0073] The adjustment unit can adjust inventory based on the proposed inventory level. For example, the adjustment unit can adjust inventory based on the proposed inventory level. For example, the adjustment unit can adjust inventory using methods such as placing additional orders for inventory, moving inventory, or reducing inventory. The adjustment unit can also use AI to adjust inventory based on the proposed inventory level. For example, the adjustment unit can increase inventory during periods of high demand and decrease inventory during periods of low demand. This improves the efficiency of inventory management by adjusting inventory based on the proposed inventory level. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the proposed inventory level into the AI ​​and have the AI ​​perform the inventory adjustment.

[0074] The adjustment unit can analyze data in real time and respond flexibly to fluctuations in supply and demand. For example, the adjustment unit can analyze data in real time using methods such as data collection frequency and analysis algorithms. The adjustment unit can also use AI to analyze data in real time and respond flexibly to fluctuations in supply and demand. For example, even if a sudden change in demand occurs, the adjustment unit can immediately analyze the data and propose an optimal inventory level again. This enables rapid and accurate inventory management by analyzing data in real time and responding flexibly to fluctuations in supply and demand. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input data collected in real time into AI and have the AI ​​propose an optimal inventory level again to respond to fluctuations in supply and demand.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary data and immediately send it for analysis. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0076] The data collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the data collection unit can identify each company's peak sales period and focus data collection during that time. The data collection unit can also use AI to analyze each company's past sales history and select the optimal data collection method. For example, the data collection unit can analyze data for each company's sales channel and collect data from the most effective channel. This enables efficient data collection by analyzing each company's past sales history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's past sales history into AI and have AI select the optimal data collection method.

[0077] The data collection unit can adjust the types of data collected based on specific seasons or events during data collection. For example, the data collection unit can consider seasonal demand fluctuations and focus on collecting data related to seasonal products. The data collection unit can also use AI to adjust the types of data collected based on specific seasons or events during data collection. For example, the data collection unit can collect data on related products in line with specific events (e.g., Christmas, Black Friday). This improves the accuracy of demand forecasting by adjusting the types of data collected based on specific seasons or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on specific seasons or events into the AI ​​and have the AI ​​perform the data collection.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This enables efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of demand data in the areas where each company's stores are located. The data collection unit can also use AI to prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can collect data for efficient inventory placement based on the location information of each company's logistics bases. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0080] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can collect campaign information on each company's social media and use it to forecast demand. The data collection unit can also use AI to analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can collect customer feedback on each company's social media and incorporate it into inventory management. This improves the accuracy of demand forecasting by analyzing each company's social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's social media activities into AI and have AI collect relevant data.

[0081] 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 and visually easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result that provides deeper insights. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0082] 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 highly important data to improve accuracy. The analysis unit can also use a generative AI to 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 simplified analysis on less important data to prioritize efficiency. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data to grasp trends. The analysis unit can also use a generative AI to apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a regression analysis algorithm to demand forecast data to improve forecast accuracy. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis, allowing for deeper insights. For example, if the user is in a hurry, the analysis unit can provide a concise analysis that can be quickly understood. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data and perform real-time demand forecasting. The analysis unit can also use generative AI to determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can analyze historical data and grasp long-term trends. This enables real-time demand forecasting by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. The analysis unit can also use generative AI to adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can analyze data with a moderate level of relevance next to balance the overall analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0087] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and visually clear suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions and deeper insights. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0088] The proposal unit can adjust the level of detail of its proposals based on the importance of the inventory level. For example, the proposal unit can make more detailed proposals for high-importance inventory to improve accuracy. The proposal unit can also use a generation AI to adjust the level of detail of its proposals based on the importance of the inventory level. For example, the proposal unit can make simplified proposals for low-importance inventory to prioritize efficiency. By adjusting the level of detail of proposals based on the importance of the inventory level, efficient inventory management becomes possible. Some or all of the above processes in the proposal unit may be performed using a generation AI, or not. For example, the proposal unit can input the importance of the inventory level into the generation AI and have the generation AI perform the adjustment of the level of detail of the proposals.

[0089] The proposal unit can apply different proposal algorithms depending on the inventory category when making a proposal. For example, the proposal unit can apply a demand forecasting algorithm to consumer goods and propose the optimal inventory level. The proposal unit can also use generative AI to apply different proposal algorithms depending on the inventory category when making a proposal. For example, the proposal unit can apply a production planning algorithm to industrial products and propose efficient inventory management. This improves the accuracy of the proposal by applying different proposal algorithms depending on the inventory category. Some or all of the above processing in the proposal unit may be performed using generative AI or not. For example, the proposal unit can input the inventory category into the generative AI and have the generative AI execute the application of the proposal algorithm.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions that offer deeper insights. If the user is in a hurry, the suggestion unit can provide concise suggestions that can be quickly understood. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0091] The proposal unit can determine the priority of proposals based on the inventory collection timing at the time of proposal. For example, the proposal unit can prioritize the latest inventory data and perform real-time inventory management. The proposal unit can also use a generation AI to determine the priority of proposals based on the inventory collection timing at the time of proposal. For example, the proposal unit can propose long-term inventory management based on historical inventory data. This enables real-time inventory management by determining the priority of proposals based on the inventory collection timing. Some or all of the above processing in the proposal unit may be performed using a generation AI or not. For example, the proposal unit can input the inventory collection timing into the generation AI and have the generation AI perform the determination of proposal priorities.

[0092] The suggestion unit can adjust the order of suggestions based on inventory relevance when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant inventory items to improve accuracy. The suggestion unit can also use generative AI to adjust the order of suggestions based on inventory relevance when making suggestions. For example, the suggestion unit can then suggest inventory items with a moderate level of relevance to balance the overall order. This allows for efficient inventory management by adjusting the order of suggestions based on inventory relevance. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input inventory relevance into the generative AI and have the generative AI perform the adjustment of the suggestion order.

[0093] The adjustment unit can estimate the user's emotions and adjust the inventory adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can provide a simple and quick inventory adjustment method. For example, if the user is relaxed, the adjustment unit can provide a detailed inventory adjustment method to improve accuracy. For example, if the user is in a hurry, the adjustment unit can provide a method to perform inventory adjustments quickly. This makes it possible to perform inventory adjustments that are appropriate for the user by adjusting the inventory adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the inventory adjustment method.

[0094] The adjustment unit can analyze past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can select the most effective adjustment method based on past inventory adjustment history. The adjustment unit can also use AI to analyze past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can eliminate failed adjustment methods from past inventory adjustment history. This enables efficient inventory adjustment by analyzing past inventory adjustment history and selecting the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past inventory adjustment history into AI and have AI select the optimal adjustment method.

[0095] The adjustment unit can customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can analyze the current supply and demand situation in real time and provide the optimal inventory adjustment method. The adjustment unit can also use AI to customize the inventory adjustment methods based on the current supply and demand situation during adjustment. For example, the adjustment unit can flexibly change the inventory adjustment methods in response to fluctuations in supply and demand. This enables efficient inventory management by customizing the inventory adjustment methods based on the current supply and demand situation. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the current supply and demand situation into the AI ​​and have the AI ​​perform the customization of the inventory adjustment methods.

[0096] The adjustment unit can estimate the user's emotions and determine the priority of inventory adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize high-priority inventory adjustments. If the user is relaxed, for example, the adjustment unit can perform detailed inventory adjustments to improve accuracy. If the user is in a hurry, for example, the adjustment unit can perform inventory adjustments quickly. This enables efficient inventory management by determining the priority of inventory adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the determination of inventory adjustment priorities.

[0097] The adjustment unit can select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select the optimal inventory adjustment method by considering the supply and demand situation in the area where each company's stores are located. The adjustment unit can also use AI to select the optimal inventory adjustment method by considering the geographical location information of each company during the adjustment process. For example, the adjustment unit can select an efficient inventory adjustment method based on the location information of each company's logistics base. This enables efficient inventory management by selecting the optimal inventory adjustment method by considering the geographical location information of each company. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the geographical location information of each company into the AI ​​and have the AI ​​select the optimal inventory adjustment method.

[0098] The adjustment unit can analyze each company's social media activities during adjustment and propose inventory adjustment methods. For example, the adjustment unit can propose inventory adjustment methods based on each company's social media campaign information. The adjustment unit can also use AI to analyze each company's social media activities during adjustment and propose inventory adjustment methods. For example, the adjustment unit can propose inventory adjustment methods based on each company's customer feedback on social media. By analyzing each company's social media activities and proposing inventory adjustment methods, efficient inventory management becomes possible. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input each company's social media activities into AI and have AI execute the proposal of inventory adjustment methods.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] 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 frequency of data collection can be reduced to lessen the burden. If the user is relaxed, more detailed data can be collected to improve accuracy. If the user is in a hurry, the necessary data can be collected quickly and immediately sent for analysis. By adjusting the timing of data collection based on 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, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0101] 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, it can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result that provides deeper insights. If the user is in a hurry, it can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0102] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and visually easy-to-understand suggestions. If the user is relaxed, it can provide detailed suggestions that offer deeper insights. If the user is in a hurry, it can provide concise suggestions that get straight to the point. In this way, by adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0103] The adjustment unit can estimate the user's emotions and adjust the inventory adjustment method based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and quick inventory adjustment method. If the user is relaxed, it can provide a detailed inventory adjustment method with increased accuracy. If the user is in a hurry, it can provide a method for quick inventory adjustment. In this way, by adjusting the inventory adjustment method based on the user's emotions, it becomes possible to perform inventory adjustments that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the inventory adjustment method.

[0104] The data collection unit can analyze each company's past sales history and select the optimal data collection method. For example, it can identify each company's peak sales period and focus data collection during that time. The data collection unit can also use AI to analyze each company's past sales history and select the optimal data collection method. For example, it can analyze data from each company's sales channel and collect data from the most effective channel. This enables efficient data collection by analyzing each company's past sales history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's past sales history into the AI ​​and have the AI ​​select the optimal data collection method.

[0105] The data collection unit can adjust the types of data collected based on specific seasons or events. For example, it can prioritize the collection of data related to seasonal products, taking into account seasonal demand fluctuations. The data collection unit can also use AI to adjust the types of data collected based on specific seasons or events. For example, it can collect data on related products in line with specific events (e.g., Christmas, Black Friday). By adjusting the types of data collected based on specific seasons or events, the accuracy of demand forecasting can be improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on specific seasons or events into the AI ​​and have the AI ​​perform the data collection.

[0106] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, it can prioritize the collection of demand data in the areas where each company's stores are located. The data collection unit can also use AI to prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, it can collect data for efficient inventory placement based on the location information of each company's logistics bases. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0107] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, it can collect campaign information on each company's social media and use it to help with demand forecasting. The data collection unit can also use AI to analyze each company's social media activities and collect relevant data during data collection. For example, it can collect customer feedback on each company's social media and reflect it in inventory management. By analyzing each company's social media activities and collecting relevant data, the accuracy of demand forecasting is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input each company's social media activities into the AI ​​and have the AI ​​collect relevant data.

[0108] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data to improve accuracy. The analysis unit can also use a generative AI to adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a simplified analysis on less important data to prioritize efficiency. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0109] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a time series analysis algorithm can be applied to sales data to grasp trends. The analysis unit can also use a generative AI to apply different analysis algorithms depending on the data category during analysis. For example, a regression analysis algorithm can be applied to demand forecast data to improve forecast accuracy. In this way, applying different analysis algorithms depending on the data category improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects historical sales data and demand forecast data. For example, it can collect sales history data and market demand forecast data for each company, and collect sales data for the past year and seasonal demand forecast data. The data collection unit can also use AI to automatically collect sales history data and market demand forecast data for each company. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generating AI to predict future demand based on past sales data and demand forecast data. For example, it can predict how much of a particular product will sell and when. Step 3: The proposal unit proposes the optimal inventory level based on the data analyzed by the analysis unit. The proposal unit uses generation AI to propose the optimal inventory level based on predicted demand data. For example, it can adjust inventory by increasing it during periods of high demand and decreasing it during periods of low demand. Step 4: The adjustment unit adjusts inventory based on the inventory levels proposed by the proposal unit. The adjustment unit uses AI to adjust inventory based on the proposed inventory levels. For example, it can increase inventory during periods of high demand and decrease inventory during periods of low demand.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects sales history and demand forecast data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generating AI to forecast future demand. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes an optimal inventory level based on the predicted demand data. The adjustment unit is implemented in the control unit 46A of the smart device 14, and adjusts the inventory based on the proposed inventory level. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects sales history and demand forecast data. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and forecasts future demand. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which proposes an optimal inventory level based on the predicted demand data. The adjustment unit is implemented, for example, in the control unit 46A of the smart glasses 214, which adjusts the inventory based on the proposed inventory level. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects sales history and demand forecast data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI to forecast future demand. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes an optimal inventory level based on the predicted demand data. The adjustment unit is implemented in the control unit 46A of the headset terminal 314, and adjusts the inventory based on the proposed inventory level. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects sales history and demand forecast data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using generated AI and forecasts future demand. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes an optimal inventory level based on the predicted demand data. The adjustment unit is implemented, for example, by the control unit 46A of the robot 414, which adjusts the inventory based on the proposed inventory level. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The data collection department collects past sales data and demand forecast data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes an optimal inventory level based on the data analyzed by the aforementioned analysis unit, The system includes an adjustment unit that adjusts inventory based on the inventory level proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect sales history data and market demand forecast data for each company. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We predict future demand based on past sales data and demand forecast data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the optimal inventory level based on predicted demand data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Adjust inventory based on proposed inventory levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, We analyze data in real time and respond flexibly to fluctuations in supply and demand. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze each company's past sales history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, adjust the types of data collected based on specific seasons or events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of each company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze each company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the inventory level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the inventory category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, prioritize them based on when inventory will be collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on inventory relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, We estimate user sentiment and adjust inventory management methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During adjustments, past inventory adjustment history is analyzed to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During adjustments, customize inventory adjustment methods based on current supply and demand conditions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, The system estimates user sentiment and determines inventory adjustment priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, the optimal inventory adjustment method will be selected, taking into account the geographical location information of each company. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, we analyze each company's social media activity and propose methods for inventory adjustment. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 past sales data and demand forecast data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that proposes an optimal inventory level based on the data analyzed by the aforementioned analysis unit, The system includes an adjustment unit that adjusts inventory based on the inventory level proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect sales history data and market demand forecast data for each company. The system according to feature 1.

3. The aforementioned analysis unit, We predict future demand based on past sales data and demand forecast data. The system according to feature 1.

4. The aforementioned proposal section is, We propose the optimal inventory level based on predicted demand data. The system according to feature 1.

5. The adjustment unit is, Adjust inventory based on proposed inventory levels. The system according to feature 1.

6. The adjustment unit is, We analyze data in real time and respond flexibly to fluctuations in supply and demand. The system according to feature 1.

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

8. The aforementioned collection unit is We analyze each company's past sales history and select the optimal data collection method. The system according to feature 1.

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