system

Abstract

Provide a system. 【Solution means】 Means for collecting and storing inventory data in real time, Means for using an intelligent model that predicts demand based on past data, Means for generating an optimal inventory replenishment plan based on demand prediction, Means for automatically placing orders and shipping, Means for notifying order and shipping information, Means for providing inventory information and replenishment plans via a visual display device, Means for assisting inventory replenishment operations based on voice and image data, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Inventory management is important but very complex in large-scale retail. In particular, shortages or surpluses of inventory can cause losses in sales opportunities and increases in costs. Also, when order placement and inventory replenishment management are done manually, it places a heavy burden on the person in charge and human errors are likely to occur. There is a need to solve such problems and achieve efficient and accurate inventory management.

Means for Solving the Problems

[0005] This invention provides a system for collecting and storing inventory data in real time. Furthermore, it performs highly accurate demand forecasting by using an artificial intelligence model that predicts demand based on historical data. By utilizing the forecast results, it automatically generates an optimal inventory replenishment plan and efficiently carries out ordering and shipping, thereby reducing the workload of staff. In addition, it creates a system that makes it easier for store staff to understand the inventory status by notifying them of ordering and shipping information in a timely manner. This achieves rationalization and efficiency in inventory management.

[0006] "Inventory data" refers to data that includes information about the current inventory status of a product, and it changes in real time.

[0007] "Real-time" refers to data and information being updated instantly, reflecting the current situation immediately.

[0008] An "artificial intelligence model" is a system that uses algorithms to learn patterns based on past data and support prediction and decision-making.

[0009] "Demand forecasting" is the process of predicting future demand for products and services by analyzing past data and market trends.

[0010] An "inventory replenishment plan" refers to a schedule or plan for replenishing goods at the appropriate time and in the appropriate quantity to meet predicted demand.

[0011] "Placing an order" is the process of formally ordering necessary goods from a supplier.

[0012] "Shipping" refers to the process by which goods are sent from the warehouse to their destination via a delivery company.

[0013] A "notification" is a message or alert sent from a system to a user to inform them of information. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The present invention provides a system for streamlining inventory management of mobile devices. This system consists of several main components, and its operation is described below.

[0036] First, the server collects inventory data accessible from each shop in real time and stores it in a dedicated database. This data consists of information such as the status of incoming and outgoing goods, sales information, and past sales history. Because a large amount of data is updated daily, the server uses an efficient database management system to maintain data integrity and accuracy.

[0037] Next, after data collection, the server uses artificial intelligence to analyze past data and predict future demand. This AI model is based on machine learning and has the ability to generate different demand forecasts for each store, taking into account seasonal fluctuations and the impact of promotions.

[0038] Based on predicted demand, the server automatically creates an inventory replenishment plan. This plan includes what to order, when, and how much. Once the plan is finalized, the server automatically places the corresponding orders with suppliers.

[0039] Once an order is placed, the server integrates with the warehouse management system to process the shipment efficiently. Simultaneously, it sends order and shipping notifications to terminals (computers in each shop). This allows store staff to know in advance when new inventory will arrive and prepare accordingly.

[0040] For example, if a popular mobile phone model is expected to run out of stock due to high demand, the server automatically places additional orders based on that information and arranges for delivery before the stock runs out. Because this entire process is automated by the system, users can focus on their daily tasks without worrying about excess inventory or stock shortages.

[0041] As described above, this system enables mobile phone shops to manage their inventory efficiently and accurately, helping them minimize financial losses.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects inventory data from each store in real time and stores it in a database. The data includes product ID, inventory quantity, sales information, and last replenishment date. This data is automatically updated to ensure it is always up-to-date.

[0045] Step 2:

[0046] The server uses an artificial intelligence model to forecast demand based on the collected data. The AI ​​algorithm takes into account past sales data, seasonal factors, marketing campaigns, etc., to predict future demand with high accuracy.

[0047] Step 3:

[0048] The server automatically creates an optimal inventory replenishment plan based on demand forecasts. The plan includes specific actions for each store, such as the required replenishment quantity and the timing of orders.

[0049] Step 4:

[0050] The server automatically places orders for necessary products according to the replenishment plan. The ordering process proceeds by electronically sending orders to suppliers.

[0051] Step 5:

[0052] The server communicates with the warehouse management system to arrange for the rapid shipment of ordered goods. It calculates the optimal delivery route and adjusts the estimated arrival date of the goods.

[0053] Step 6:

[0054] The server notifies the terminal once the order and shipment are complete. The notification includes details of the ordered product and the expected arrival date, allowing the user to efficiently manage store inventory based on this information.

[0055] Step 7:

[0056] Store staff, who are users of the system, receive notifications and understand when inventory needs to be restocked. This allows them to prepare the store and conduct promotional activities as needed.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In inventory management, many companies face the challenge of minimizing losses caused by inventory shortages or excesses. In particular, there is a need for methods to accurately predict fluctuations in demand and efficiently replenish inventory accordingly. Furthermore, automation of ordering and shipping processes is required, but achieving this necessitates advanced information processing capabilities and data analysis.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for instantly collecting and recording inventory information, means for using a machine learning model to predict demand based on historical information, means for generating an optimal inventory replenishment plan based on demand forecasts, means for automatically instructing suppliers to place orders and deliveries, and means for notifying display terminals of order and delivery information. This streamlines the entire inventory management operation, minimizes losses, and enables inventory optimization and process automation.

[0062] "Inventory information" refers to data regarding the quantity and condition of products, and it is required to be updated in real time.

[0063] "Collecting and recording immediately" means taking the time to instantly acquire and save data in order to keep inventory information up-to-date at all times.

[0064] A "machine learning model" is an artificial intelligence technology used to learn regularities and patterns from past data and predict future events.

[0065] An "optimal inventory replenishment plan" is a plan that determines the necessary quantity of goods to prevent inventory shortages or surpluses, based on demand forecasts.

[0066] "Automatically ordering and instructing suppliers to deliver" means that the system executes the process of ordering necessary goods from suppliers and arranging their transportation without requiring human intervention.

[0067] A "display terminal" is an electronic device that can visually display information and plays a role in notifying users of orders and deliveries.

[0068] This system is designed for efficient inventory management and primarily operates through the collaboration of a server and terminals. The server collects real-time inventory information from each store and stores that data using a database management system such as MySQL®. The server has the computing power to process a massive amount of data.

[0069] For data analysis, machine learning libraries such as TENSORFLOW® are used to consider past sales history and external factors. The server inputs this data into an AI model to forecast future demand. As a specific example, a prompt message such as "To forecast demand for the next two months, please use sales data from the past 12 months" is used. This allows the AI ​​model to generate highly accurate demand forecasts that reflect seasonal fluctuations and the impact of promotions.

[0070] The terminal receives notifications from the server, allowing users to prepare based on inventory replenishment plans. For example, it provides users with order and shipping details, enabling efficient management of new inventory.

[0071] Furthermore, the server automatically places orders with suppliers and works in conjunction with the logistics management system to ensure that products are delivered accurately. This system allows users to maintain appropriate inventory levels and minimize the risk of waste due to excess inventory and stockouts.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server collects inventory information from each store's terminal. Specifically, it retrieves product inbound / outbound information and sales data from the store's POS system via API. The input data includes details such as product ID, quantity, and time. The inventory information is stored in a MySQL database, and transaction management is performed to maintain data integrity. After the data is stored, the server outputs the updated database.

[0075] Step 2:

[0076] The server performs demand forecasting using stored data. The input is historical sales data retrieved from the database. Using TensorFlow, the prompt "To forecast demand for the next two months, please use sales data from the past 12 months" is input to the generated AI model. This analysis outputs a future demand forecast for a specific product. The demand forecast is converted into an Excel file or similar format and output in a user-accessible format.

[0077] Step 3:

[0078] The server creates a replenishment plan based on demand forecast data. The inputs are the demand forecast results and the current inventory status. Using SQLAlchemy, it automatically generates a replenishment plan specifying what and how much to order and saves it to the database. In this process, the order quantity and timing are determined for each product, and the results are output to the inventory management system.

[0079] Step 4:

[0080] The server automatically places orders with suppliers based on the replenishment plan. The input is the replenishment plan data. The process includes sending purchase orders to suppliers using the EDI system. Once the order is confirmed, a shipping schedule is created and output to the logistics management system. At this point, the database is updated to confirm the order.

[0081] Step 5:

[0082] The terminal receives order and shipping information from the server and notifies the user. The input is the notification data sent from the server. The terminal displays the expected arrival date and quantity of newly ordered inventory on the screen for the user to check. The user uses this information to secure inventory space and plan sales. The output is notification information that can be checked by shop staff.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Inventory management in logistics centers requires rapid and accurate replenishment, and since it often relies on manual labor, its efficiency is limited. Furthermore, the inability to grasp inventory levels in real time makes it difficult to perform appropriate replenishment based on demand forecasts. Additionally, workers must navigate between multiple management systems to obtain necessary information, resulting in time-consuming and labor-intensive processes.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an intelligent model to predict demand based on historical data, means for providing inventory information and replenishment plans via a visual display device, and means for supporting inventory replenishment operations based on voice and image data. This enables improved efficiency and accuracy of inventory replenishment operations at a logistics center.

[0088] "Inventory data" refers to information regarding the quantity, type, and status of goods being received and shipped.

[0089] "Real-time" refers to a state where information is updated instantly with virtually no time delay.

[0090] "Means of preservation" refers to methods and devices for retaining data and making it available for later reuse.

[0091] "Historical data" refers to information recorded previously and is used for analysis and prediction.

[0092] An "intelligent model for predicting demand" refers to an algorithm that uses machine learning and statistical methods to estimate fluctuations in demand.

[0093] A "visual display device" is a device used to present information visually, and examples include displays and head-mounted displays.

[0094] A "replenishment plan" is the process of scheduling the ordering and delivery of goods necessary to maintain adequate inventory levels.

[0095] "Audio and image data" refers to digitalized sound or visual information used as an interface or support function with the user.

[0096] "Means of supporting replenishment operations" refers to methods or devices that provide workers with the information and instructions necessary to properly manage and replenish inventory.

[0097] In this invention, a server plays a central role in ensuring the inventory management system functions correctly. This server collects inventory data in real time from various sensors and input terminals installed in warehouses and stores, and stores this information in a database. The stored data includes the status of incoming and outgoing goods, the type and quantity of goods, and past sales history.

[0098] The server uses an advanced intelligent model to analyze historical inventory data and newly collected data to predict product demand. This intelligent model is based on machine learning algorithms and updates the predictions in real time, taking into account seasonal fluctuations and the impact of promotions.

[0099] Based on the generated demand forecast, the server automatically develops an optimal inventory replenishment plan. This plan includes the required number of items, the optimal replenishment timing, and the delivery route. The server provides the necessary inventory information and replenishment plan to logistics center workers via visual and audio devices. For example, if a worker is wearing a head-mounted display, the server overlays inventory information within the worker's field of view to help them efficiently find and replenish the necessary items.

[0100] At the same time, necessary information is provided to workers as audio and image data, allowing them to perform tasks hands-free while checking the information. This function includes a prompt system that uses voice prompts to give instructions to workers, such as specific prompts like, "Please tell me the location of the necessary items for the next order," or "Please show me the optimal route within the warehouse." In this way, inventory management and replenishment work at the logistics center is carried out efficiently, and the burden on workers is reduced.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server collects real-time inventory data from sensors and terminals installed in warehouses and stores. Inputs include product quantity, inbound / outbound history, and current product status. The server formats this data appropriately and stores it in a database. This process ensures that accurate and up-to-date information about the current inventory status is always maintained.

[0104] Step 2:

[0105] The server receives historical sales data and current inventory data as input and uses a generating AI model to forecast demand. This model statistically analyzes future demand, taking into account various external factors (e.g., seasons and promotional activities). This allows it to output demand forecasts for each product, providing a foundation for preventing future inventory shortages.

[0106] Step 3:

[0107] The server creates an optimal inventory replenishment plan based on the generated demand forecast. Inputs include demand forecast data, supplier delivery information, and transportation route information. This information is integrated to determine the necessary goods and quantities, the optimal ordering timing, and the delivery route, generating a replenishment plan as output. The replenishment plan aims for efficient resource utilization and stockout avoidance.

[0108] Step 4:

[0109] The server automatically sends purchase orders to suppliers based on the generated replenishment plan. This process includes creating purchase order forms and sending them via email or EDI systems. The input is the replenishment plan, and the output is the purchase order to suppliers. This eliminates the need for manual data entry by users and enables rapid replenishment.

[0110] Step 5:

[0111] The server notifies users of order and shipping information via visual and audio devices. Inputs are order and delivery status data, while outputs are visual and audio instructions for field workers. For example, a worker's head-mounted display shows the next arriving goods and their location. Voice prompts can also be used to provide instructions in case of emergency.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] The system of this invention aims to improve efficiency in inventory management and enable the recognition of user emotions and decision-making based on those emotions. This system has a complex structure that integrates inventory data collection, demand forecasting, optimal inventory replenishment, and an emotion engine.

[0114] First, the server collects inventory data from each store and stores it in a database. This includes product inventory status, sales information, and historical demand history. Data collection is done in real time, making it possible to instantly understand the inventory status of each store.

[0115] Next, the server uses an artificial intelligence model to analyze past data and predict future demand. This model achieves highly accurate demand forecasting by taking into account past sales patterns, seasonal factors, and external marketing initiatives.

[0116] Furthermore, the server incorporates an emotion engine that recognizes user emotions. This engine can analyze text such as user feedback and reviews to identify emotions. For example, if there are many negative reviews about a particular product, the emotion engine will detect this and adjust the system's rating accordingly. This information is then used when developing inventory replenishment plans, contributing to an improved user experience.

[0117] As a concrete example, suppose a user posts a complaint about a specific product through the app. This information is immediately sent to the server, where the sentiment engine analyzes the post and recognizes negative emotions. The server then takes swift action to resolve the issue by adjusting the replenishment plan. Furthermore, it reports the user's feedback and the progress of the countermeasures through their device.

[0118] This system allows inventory management to evolve beyond mere physical product management into a comprehensive service that includes customer satisfaction management. By directly reflecting customer emotions, companies can develop faster and more accurate marketing strategies and strengthen their competitiveness.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server collects inventory data from each store in real time and stores it in a database. This inventory data includes information such as product name, quantity, and sales history. This data is updated with an emphasis on immediacy.

[0122] Step 2:

[0123] The server runs an artificial intelligence model to analyze historical inventory data and sales history, and then performs demand forecasting. This model takes into account past sales trends and seasonal factors to predict future demand with high accuracy.

[0124] Step 3:

[0125] The emotion engine on the server collects user reviews and feedback and performs text analysis. Through this analysis, it recognizes the user's emotional state and identifies products with a high number of negative emotions.

[0126] Step 4:

[0127] The server incorporates the results of sentiment analysis into demand forecasts and creates inventory replenishment plans. An algorithm that takes emotional cues into account adjusts the replenishment quantities of problematic products, optimizing inventory management.

[0128] Step 5:

[0129] The server automatically places orders based on the replenishment plan. It determines the required quantity of products and delivery schedules, and coordinates with warehouses and delivery companies.

[0130] Step 6:

[0131] The terminal notifies users of the progress of orders and shipments. This allows store staff to know when products are expected to arrive and prepare the store accordingly.

[0132] Step 7:

[0133] Users can receive feedback and review the results of emotion recognition. They can obtain information on specific countermeasures and areas for improvement, and provide further feedback to the system as needed.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] In inventory management systems, there is a challenge in simultaneously achieving efficient inventory replenishment and improved customer satisfaction. In particular, relying solely on demand forecasting based on data makes it difficult to achieve inventory replenishment that accurately reflects customer satisfaction.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes means for acquiring and storing inventory information in real time, means for utilizing a machine learning model that predicts demand based on historical information, and means for analyzing user sentiment and incorporating the results into the inventory replenishment plan. This makes it possible to generate demand forecasts and inventory replenishment plans that take user sentiment into account.

[0139] "Inventory information" refers to data about the quantity and condition of goods, which is acquired and stored in real time.

[0140] A "machine learning model" refers to the algorithms and data structures used to analyze historical data and predict future demand.

[0141] "User sentiment" refers to data that shows users' emotional reactions and evaluations, obtained by analyzing user feedback and reviews.

[0142] An "inventory replenishment plan" refers to a plan for ordering and replenishing inventory that is optimized based on demand forecasts and user sentiment.

[0143] A "server" refers to a computer system that acquires and stores inventory information and performs processing such as demand forecasting and sentiment analysis.

[0144] The embodiments for carrying out the invention will now be described. The system of this invention aims to improve efficient inventory management and user experience. Specifically, the configuration involves a server performing the main processing and terminals and users interacting with the system.

[0145] The server retrieves inventory information from each store in real time and stores it in a database. This allows for immediate access to product inventory status, sales figures, and historical demand data. A common database system is used for data management, and MongoDB or MySQL are suitable options.

[0146] The server also uses machine learning models to analyze the collected data and predict future demand. This demand forecast is enhanced by generative AI models that take into account past sales patterns, seasonal factors, and external marketing initiatives. Specific software such as TensorFlow and PyTorch can be used. To effectively execute this process, the server uses prompts to manipulate the AI ​​model. An example of a prompt is, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0147] Furthermore, the server incorporates an emotion analysis engine that utilizes natural language processing (NLP) technology to analyze user emotions. This engine can extract emotion labels from feedback and reviews and incorporate them into inventory replenishment plans. Typically, libraries such as NLTK and spaCy are used for this purpose. When users submit feedback or opinions through the application, the data is immediately sent to the server and analyzed by the emotion analysis engine.

[0148] The device functions as an interface with the user, providing feedback and notifications about inventory status. Examples include smartphones and tablets, utilizing notification technologies such as Firebase Cloud Messaging. This allows users to receive real-time updates on inventory changes and system suggestions.

[0149] This invention's structure enables companies to achieve efficient inventory management and rapid decision-making that directly reflects user sentiment. This not only improves customer satisfaction but also contributes to strengthening competitiveness.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The server collects inventory information from each store in real time. The inputs include inventory status and sales data obtained from the store's POS system. The server uses this information to store it in a database. Specifically, it periodically retrieves data using an API and automatically saves it to a database such as MongoDB or MySQL.

[0153] Step 2:

[0154] The server uses a generative AI model based on the collected data to predict future demand. The inputs are historical inventory and sales data obtained in Step 1, as well as data on external initiatives. The server analyzes this data and executes machine learning algorithms (using TensorFlow or PyTorch) to obtain demand forecast output. In this process, prompt statements are input to the model, for example, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0155] Step 3:

[0156] The server analyzes user feedback using an emotion analysis engine. The input consists of reviews and opinions submitted by users through the app. The server uses natural language processing techniques (such as NLTK and spaCy) to analyze this text information and output emotion labels. These labels are then used in inventory replenishment planning.

[0157] Step 4:

[0158] The server creates an optimal inventory replenishment plan based on forecast data and sentiment analysis results. The inputs are the demand forecast results from step 2 and the sentiment analysis results from step 3. The server uses an algorithm to determine the priority and quantity of inventory replenishment based on this information and outputs the results.

[0159] Step 5:

[0160] The terminal sends notifications to the user regarding inventory and replenishment plans. The input is the replenishment plan and any changes confirmed in step 4. The terminal reports this to the user via email or a notification service (such as Firebase Cloud Messaging). This ensures that the user always receives the latest information.

[0161] (Application Example 2)

[0162] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0163] Traditional inventory management systems only consider data related to product supply and fail to adequately reflect customer feedback and sentiment. This can lead to decreased customer satisfaction and lost sales opportunities. Furthermore, the inability to respond quickly and flexibly to demand fluctuations creates a risk of inventory oversupply or undersupply.

[0164] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0165] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an artificial intelligence model that predicts demand based on historical data, and means for analyzing user feedback and recognizing emotions. This makes it possible to directly incorporate customer feedback into inventory replenishment plans, thereby improving customer satisfaction while enhancing the accuracy of inventory management.

[0166] "Inventory data" refers to a collection of information that includes the inventory status of products at each store and sales location, sales information, and past demand history.

[0167] "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data, seasonal market factors, advertising effectiveness, and other factors.

[0168] An "artificial intelligence model" refers to an algorithm that learns patterns from data and uses them to make predictions and classifications. Specifically, it employs machine learning and deep learning techniques.

[0169] "Emotion recognition" is a technology that analyzes text data such as user feedback and reviews to identify the user's emotions contained within it.

[0170] An "inventory replenishment plan" is a plan to determine the appropriate amount and timing of replenishment of products based on data obtained from demand forecasting and sentiment recognition.

[0171] This invention is embodied as a system for efficiently managing inventory and analyzing customer feedback in physical stores. The server has the function of collecting inventory data in real time from multiple sensors and sales terminals and storing it in a database. This allows for instantaneous understanding of the inventory status of products in stores.

[0172] Next, the server uses artificial intelligence models such as "PyTorch" to analyze historical data and predict demand. The prediction model incorporates elements such as sales history, seasonal factors, and external marketing initiatives, enabling highly accurate predictions.

[0173] Furthermore, the server is equipped with an emotion engine that uses the natural language processing library "Transformers" to analyze user feedback and reviews and recognize emotions. The results of emotion recognition are directly reflected in inventory replenishment plans, making it possible to quickly adjust inventory and revise promotions for products where dissatisfaction is detected.

[0174] This system connects with devices via a smartphone application, allowing store staff to check inventory status and customer feedback, enabling them to respond quickly. Users are also reported on the response measures and progress regarding their feedback, contributing to an improved customer experience.

[0175] As a concrete example, when a new product is introduced in a store, if many negative reviews about that product are received through the user application, the sentiment engine will detect the negative sentiment. Based on this information, the server can optimize inventory and recommend necessary countermeasures to store staff.

[0176] An example of a prompt for a generated AI model is: "Based on customer reviews of the newly introduced product, analyze customer sentiment and propose inventory adjustments to prevent a decline in sales." This prompt enables flexible decision-making using the AI ​​model.

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The server collects real-time inventory data from inventory sensors and sales terminals installed in stores. It receives sensor data and sales data as input and stores them in a database. This allows for an immediate understanding of the store's inventory status.

[0180] Step 2:

[0181] The server uses an artificial intelligence model to forecast demand based on collected inventory data. Using the stored inventory data and historical sales history as input, it calculates future demand using a demand forecasting algorithm (e.g., a PyTorch model) and outputs the predicted demand data.

[0182] Step 3:

[0183] When user feedback or reviews are sent via the device, the server uses an emotion engine to analyze the text data. It receives user reviews as input, recognizes the emotions using a natural language processing library (e.g., Transformers), and outputs the results of the emotion analysis.

[0184] Step 4:

[0185] The server integrates demand forecast data and sentiment analysis results to optimize inventory replenishment plans. Inputs include demand forecast data obtained in the previous step and sentiment data such as negative sentiment. Based on this, it generates instructions to adjust inventory replenishment quantities and timings, outputting an optimal replenishment plan.

[0186] Step 5:

[0187] The server automatically manages orders and shipments based on replenishment plans and notifies terminals of relevant information. It uses optimized replenishment plans as input, processes orders and issues shipping instructions, and outputs this information to terminals. This ensures smooth inventory management and customer service.

[0188] 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.

[0189] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0190] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0195] 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.

[0196] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0197] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0198] 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.

[0199] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0200] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The 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.

[0202] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0203] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0204] The present invention provides a system for streamlining inventory management of mobile devices. This system consists of several main components, and its operation is described below.

[0205] First, the server collects inventory data accessible from each shop in real time and stores it in a dedicated database. This data consists of information such as the status of incoming and outgoing goods, sales information, and past sales history. Because a large amount of data is updated daily, the server uses an efficient database management system to maintain data integrity and accuracy.

[0206] Next, after data collection, the server uses artificial intelligence to analyze past data and predict future demand. This AI model is based on machine learning and has the ability to generate different demand forecasts for each store, taking into account seasonal fluctuations and the impact of promotions.

[0207] Based on predicted demand, the server automatically creates an inventory replenishment plan. This plan includes what to order, when, and how much. Once the plan is finalized, the server automatically places the corresponding orders with suppliers.

[0208] Once an order is placed, the server integrates with the warehouse management system to process the shipment efficiently. Simultaneously, it sends order and shipping notifications to terminals (computers in each shop). This allows store staff to know in advance when new inventory will arrive and prepare accordingly.

[0209] For example, if a popular mobile phone model is expected to run out of stock due to high demand, the server automatically places additional orders based on that information and arranges for delivery before the stock runs out. Because this entire process is automated by the system, users can focus on their daily tasks without worrying about excess inventory or stock shortages.

[0210] As described above, this system enables mobile phone shops to manage their inventory efficiently and accurately, helping them minimize financial losses.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The server collects inventory data from each store in real time and stores it in a database. The data includes product ID, inventory quantity, sales information, and last replenishment date. This data is automatically updated to ensure it is always up-to-date.

[0214] Step 2:

[0215] The server uses an artificial intelligence model to forecast demand based on the collected data. The AI ​​algorithm takes into account past sales data, seasonal factors, marketing campaigns, etc., to predict future demand with high accuracy.

[0216] Step 3:

[0217] The server automatically creates an optimal inventory replenishment plan based on demand forecasts. The plan includes specific actions for each store, such as the required replenishment quantity and the timing of orders.

[0218] Step 4:

[0219] The server automatically places orders for necessary products according to the replenishment plan. The ordering process proceeds by electronically sending orders to suppliers.

[0220] Step 5:

[0221] The server communicates with the warehouse management system to arrange for the rapid shipment of ordered goods. It calculates the optimal delivery route and adjusts the estimated arrival date of the goods.

[0222] Step 6:

[0223] The server notifies the terminal once the order and shipment are complete. The notification includes details of the ordered product and the expected arrival date, allowing the user to efficiently manage store inventory based on this information.

[0224] Step 7:

[0225] Store staff, who are users of the system, receive notifications and understand when inventory needs to be restocked. This allows them to prepare the store and conduct promotional activities as needed.

[0226] (Example 1)

[0227] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] In inventory management, many companies face the challenge of minimizing losses caused by inventory shortages or excesses. In particular, there is a need for methods to accurately predict fluctuations in demand and efficiently replenish inventory accordingly. Furthermore, automation of ordering and shipping processes is required, but achieving this necessitates advanced information processing capabilities and data analysis.

[0229] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0230] In this invention, the server includes means for instantly collecting and recording inventory information, means for using a machine learning model to predict demand based on historical information, means for generating an optimal inventory replenishment plan based on demand forecasts, means for automatically instructing suppliers to place orders and deliveries, and means for notifying display terminals of order and delivery information. This streamlines the entire inventory management operation, minimizes losses, and enables inventory optimization and process automation.

[0231] "Inventory information" refers to data regarding the quantity and condition of products, and it is required to be updated in real time.

[0232] "Collecting and recording immediately" means taking the time to instantly acquire and save data in order to keep inventory information up-to-date at all times.

[0233] A "machine learning model" is an artificial intelligence technology used to learn regularities and patterns from past data and predict future events.

[0234] An "optimal inventory replenishment plan" is a plan that determines the necessary quantity of goods to prevent inventory shortages or surpluses, based on demand forecasts.

[0235] "Automatically ordering and instructing suppliers to deliver" means that the system executes the process of ordering necessary goods from suppliers and arranging their transportation without requiring human intervention.

[0236] A "display terminal" is an electronic device that can visually display information and plays a role in notifying users of orders and deliveries.

[0237] This system is designed for efficient inventory management and primarily operates through the collaboration of a server and terminals. The server collects real-time inventory information from each store and stores this data using a database management system such as MySQL. The server has the computing power to process a massive amount of data.

[0238] For data analysis, machine learning libraries such as TensorFlow are used to consider past sales history and external factors. The server inputs this data into an AI model to forecast future demand. As a concrete example, a prompt message such as "To forecast demand for the next two months, please use sales data from the past 12 months" is used. This allows the AI ​​model to generate highly accurate demand forecasts that reflect seasonal fluctuations and the impact of promotions.

[0239] The terminal receives notifications from the server, allowing users to prepare based on inventory replenishment plans. For example, it provides users with order and shipping details, enabling efficient management of new inventory.

[0240] Furthermore, the server automatically places orders with suppliers and works in conjunction with the logistics management system to ensure that products are delivered accurately. This system allows users to maintain appropriate inventory levels and minimize the risk of waste due to excess inventory and stockouts.

[0241] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0242] Step 1:

[0243] The server collects inventory information from each store's terminal. Specifically, it retrieves product inbound / outbound information and sales data from the store's POS system via API. The input data includes details such as product ID, quantity, and time. The inventory information is stored in a MySQL database, and transaction management is performed to maintain data integrity. After the data is stored, the server outputs the updated database.

[0244] Step 2:

[0245] The server performs demand forecasting using stored data. The input is historical sales data retrieved from the database. Using TensorFlow, the prompt "To forecast demand for the next two months, please use sales data from the past 12 months" is input to the generated AI model. This analysis outputs a future demand forecast for a specific product. The demand forecast is converted into an Excel file or similar format and output in a user-accessible format.

[0246] Step 3:

[0247] The server creates a replenishment plan based on demand forecast data. The inputs are the demand forecast results and the current inventory status. Using SQLAlchemy, it automatically generates a replenishment plan specifying what and how much to order and saves it to the database. In this process, the order quantity and timing are determined for each product, and the results are output to the inventory management system.

[0248] Step 4:

[0249] The server automatically places orders with suppliers based on the replenishment plan. The input is the replenishment plan data. The process includes sending purchase orders to suppliers using the EDI system. Once the order is confirmed, a shipping schedule is created and output to the logistics management system. At this point, the database is updated to confirm the order.

[0250] Step 5:

[0251] The terminal receives order and shipping information from the server and notifies the user. The input is the notification data sent from the server. The terminal displays the expected arrival date and quantity of newly ordered inventory on the screen for the user to check. The user uses this information to secure inventory space and plan sales. The output is notification information that can be checked by shop staff.

[0252] (Application Example 1)

[0253] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] Inventory management in logistics centers requires rapid and accurate replenishment, and since it often relies on manual labor, its efficiency is limited. Furthermore, the inability to grasp inventory levels in real time makes it difficult to perform appropriate replenishment based on demand forecasts. Additionally, workers must navigate between multiple management systems to obtain necessary information, resulting in time-consuming and labor-intensive processes.

[0255] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0256] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an intelligent model to predict demand based on historical data, means for providing inventory information and replenishment plans via a visual display device, and means for supporting inventory replenishment operations based on voice and image data. This enables improved efficiency and accuracy of inventory replenishment operations at a logistics center.

[0257] "Inventory data" refers to information regarding the quantity, type, and status of goods being received and shipped.

[0258] "Real-time" refers to a state where information is updated instantly with virtually no time delay.

[0259] "Means of preservation" refers to methods and devices for retaining data and making it available for later reuse.

[0260] "Historical data" refers to information recorded previously and is used for analysis and prediction.

[0261] An "intelligent model for predicting demand" refers to an algorithm that uses machine learning and statistical methods to estimate fluctuations in demand.

[0262] A "visual display device" is a device used to present information visually, and examples include displays and head-mounted displays.

[0263] A "replenishment plan" is the process of scheduling the ordering and delivery of goods necessary to maintain adequate inventory levels.

[0264] "Audio and image data" refers to digitalized sound or visual information used as an interface or support function with the user.

[0265] "Means of supporting replenishment operations" refers to methods or devices that provide workers with the information and instructions necessary to properly manage and replenish inventory.

[0266] In this invention, a server plays a central role in ensuring the inventory management system functions correctly. This server collects inventory data in real time from various sensors and input terminals installed in warehouses and stores, and stores this information in a database. The stored data includes the status of incoming and outgoing goods, the type and quantity of goods, and past sales history.

[0267] The server uses an advanced intelligent model to analyze historical inventory data and newly collected data to predict product demand. This intelligent model is based on machine learning algorithms and updates the predictions in real time, taking into account seasonal fluctuations and the impact of promotions.

[0268] Based on the generated demand forecast, the server automatically develops an optimal inventory replenishment plan. This plan includes the required number of items, the optimal replenishment timing, and the delivery route. The server provides the necessary inventory information and replenishment plan to logistics center workers via visual and audio devices. For example, if a worker is wearing a head-mounted display, the server overlays inventory information within the worker's field of view to help them efficiently find and replenish the necessary items.

[0269] At the same time, necessary information is provided to workers as audio and image data, allowing them to perform tasks hands-free while checking the information. This function includes a prompt system that uses voice prompts to give instructions to workers, such as specific prompts like, "Please tell me the location of the necessary items for the next order," or "Please show me the optimal route within the warehouse." In this way, inventory management and replenishment work at the logistics center is carried out efficiently, and the burden on workers is reduced.

[0270] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0271] Step 1:

[0272] The server collects real-time inventory data from sensors and terminals installed in warehouses and stores. Inputs include product quantity, inbound / outbound history, and current product status. The server formats this data appropriately and stores it in a database. This process ensures that accurate and up-to-date information about the current inventory status is always maintained.

[0273] Step 2:

[0274] The server receives historical sales data and current inventory data as input and uses a generating AI model to forecast demand. This model statistically analyzes future demand, taking into account various external factors (e.g., seasons and promotional activities). This allows it to output demand forecasts for each product, providing a foundation for preventing future inventory shortages.

[0275] Step 3:

[0276] The server creates an optimal inventory replenishment plan based on the generated demand forecast. Inputs include demand forecast data, supplier delivery information, and transportation route information. This information is integrated to determine the necessary goods and quantities, the optimal ordering timing, and the delivery route, generating a replenishment plan as output. The replenishment plan aims for efficient resource utilization and stockout avoidance.

[0277] Step 4:

[0278] The server automatically sends purchase orders to suppliers based on the generated replenishment plan. This process includes creating purchase order forms and sending them via email or EDI systems. The input is the replenishment plan, and the output is the purchase order to suppliers. This eliminates the need for manual data entry by users and enables rapid replenishment.

[0279] Step 5:

[0280] The server notifies users of order and shipping information via visual and audio devices. Inputs are order and delivery status data, while outputs are visual and audio instructions for field workers. For example, a worker's head-mounted display shows the next arriving goods and their location. Voice prompts can also be used to provide instructions in case of emergency.

[0281] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0282] The system of the present invention aims to improve efficiency in inventory management, recognize the user's emotion, and enable decision-making based on it. This system has a composite structure that integrates inventory data collection, demand forecasting, optimal inventory replenishment, and an emotion engine.

[0283] First, the server collects inventory data from each store and stores it in a database. This includes the inventory status of products, sales information, and past demand history. The data collection is performed in real time, enabling immediate grasp of the inventory status of the store.

[0284] Next, the server analyzes past data using an artificial intelligence model to predict future demand. This model realizes highly accurate demand forecasting by considering past sales patterns, seasonal factors, external marketing measures, etc.

[0285] Furthermore, an emotion engine for recognizing the user's emotion is incorporated into the server. This engine can analyze text such as feedback and reviews from the user to identify the emotion. For example, when there are many dissatisfied reviews regarding the use of a certain product, the emotion engine detects this and adjusts the corresponding evaluation within the system. This information is reflected when devising an inventory replenishment plan, contributing to the improvement of the user experience.

[0286] As a specific example, assume that the user posts dissatisfaction regarding a specific product through an app. This information is immediately sent to the server, and the emotion engine analyzes the post and recognizes the negative emotion. The server receives this and quickly takes action towards solving the problem by adjusting the replenishment plan. Furthermore, the terminal reports to the user the confirmation of the feedback and the progress of the countermeasures.

[0287] With this system, inventory management can evolve from mere physical product management to comprehensive services that include customer satisfaction management. By directly reflecting customer sentiment, companies can build more rapid and accurate marketing strategies and enhance their competitiveness.

[0288] The processing flow will be described below.

[0289] Step 1:

[0290] The server collects real-time inventory data from each store and stores it in the database. The inventory data includes information such as product name, quantity, and sales history. This data is updated with an emphasis on immediacy.

[0291] Step 2:

[0292] The server runs an artificial intelligence model to analyze past inventory data and sales history and performs demand forecasting. This model takes into account past sales trends and seasonal factors to accurately predict future demand.

[0293] Step 3:

[0294] The emotion engine in the server collects user reviews and feedback and performs text analysis. Through this analysis, it recognizes the user's emotional state and identifies products with a high level of negative sentiment.

[0295] Step 4:

[0296] The server reflects the emotion analysis results in demand forecasting and creates an inventory replenishment plan. Using an algorithm that takes into account emotional cues, it adjusts the replenishment quantity of problematic products to optimize inventory management.

[0297] Step 5:

[0298] The server automatically places orders based on the replenishment plan. It determines the quantity of products needed and the delivery schedule, and coordinates with the warehouse and the delivery provider.

[0299] Step 6:

[0300] The terminal notifies the user of the progress of the order placement and shipment. As a result, the store staff can grasp the expected arrival time of the products and make preparations within the store.

[0301] Step 7:

[0302] The user receives feedback and can confirm the results based on emotion recognition. The user obtains information about specific countermeasures and improvement points, and provides further feedback to the system if necessary.

[0303] (Example 2)

[0304] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0305] In the inventory management system, there is a problem that it is difficult to simultaneously achieve efficient inventory replenishment and improved customer satisfaction. In particular, it is difficult to realize inventory replenishment that accurately reflects customer satisfaction based only on demand prediction based on simple data.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes means for acquiring and storing inventory information in real time, means for using a machine learning model to predict demand based on past information, and means for analyzing the user's emotion and incorporating the results into the inventory replenishment plan. As a result, it becomes possible to perform demand prediction and generate an inventory replenishment plan considering the user's emotion.

[0308] "Inventory information" refers to data about the quantity and condition of goods, which is acquired and stored in real time.

[0309] A "machine learning model" refers to the algorithms and data structures used to analyze historical data and predict future demand.

[0310] "User sentiment" refers to data that shows users' emotional reactions and evaluations, obtained by analyzing user feedback and reviews.

[0311] An "inventory replenishment plan" refers to a plan for ordering and replenishing inventory that is optimized based on demand forecasts and user sentiment.

[0312] A "server" refers to a computer system that acquires and stores inventory information and performs processing such as demand forecasting and sentiment analysis.

[0313] The embodiments for carrying out the invention will now be described. The system of this invention aims to improve efficient inventory management and user experience. Specifically, the configuration involves a server performing the main processing and terminals and users interacting with the system.

[0314] The server retrieves inventory information from each store in real time and stores it in a database. This allows for immediate access to product inventory status, sales figures, and historical demand data. A common database system is used for data management, and MongoDB or MySQL are suitable options.

[0315] The server also uses machine learning models to analyze the collected data and predict future demand. This demand forecast is enhanced by generative AI models that take into account past sales patterns, seasonal factors, and external marketing initiatives. Specific software such as TensorFlow and PyTorch can be used. To effectively execute this process, the server uses prompts to manipulate the AI ​​model. An example of a prompt is, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0316] Furthermore, the server incorporates an emotion analysis engine that utilizes natural language processing (NLP) technology to analyze user emotions. This engine can extract emotion labels from feedback and reviews and incorporate them into inventory replenishment plans. Typically, libraries such as NLTK and spaCy are used for this purpose. When users submit feedback or opinions through the application, the data is immediately sent to the server and analyzed by the emotion analysis engine.

[0317] The device functions as an interface with the user, providing feedback and notifications about inventory status. Examples include smartphones and tablets, utilizing notification technologies such as Firebase Cloud Messaging. This allows users to receive real-time updates on inventory changes and system suggestions.

[0318] This invention's structure enables companies to achieve efficient inventory management and rapid decision-making that directly reflects user sentiment. This not only improves customer satisfaction but also contributes to strengthening competitiveness.

[0319] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0320] Step 1:

[0321] The server collects inventory information from each store in real time. The inputs include inventory status and sales data obtained from the store's POS system. The server uses this information to store it in a database. Specifically, it periodically retrieves data using an API and automatically saves it to a database such as MongoDB or MySQL.

[0322] Step 2:

[0323] The server uses a generative AI model based on the collected data to predict future demand. The inputs are historical inventory and sales data obtained in Step 1, as well as data on external initiatives. The server analyzes this data and executes machine learning algorithms (using TensorFlow or PyTorch) to obtain demand forecast output. In this process, prompt statements are input to the model, for example, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0324] Step 3:

[0325] The server analyzes user feedback using an emotion analysis engine. The input consists of reviews and opinions submitted by users through the app. The server uses natural language processing techniques (such as NLTK and spaCy) to analyze this text information and output emotion labels. These labels are then used in inventory replenishment planning.

[0326] Step 4:

[0327] The server creates an optimal inventory replenishment plan based on forecast data and sentiment analysis results. The inputs are the demand forecast results from step 2 and the sentiment analysis results from step 3. The server uses an algorithm to determine the priority and quantity of inventory replenishment based on this information and outputs the results.

[0328] Step 5:

[0329] The terminal sends notifications to the user regarding inventory and replenishment plans. The input is the replenishment plan and any changes confirmed in step 4. The terminal reports this to the user via email or a notification service (such as Firebase Cloud Messaging). This ensures that the user always receives the latest information.

[0330] (Application Example 2)

[0331] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0332] Traditional inventory management systems only consider data related to product supply and fail to adequately reflect customer feedback and sentiment. This can lead to decreased customer satisfaction and lost sales opportunities. Furthermore, the inability to respond quickly and flexibly to demand fluctuations creates a risk of inventory oversupply or undersupply.

[0333] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0334] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an artificial intelligence model that predicts demand based on historical data, and means for analyzing user feedback and recognizing emotions. This makes it possible to directly incorporate customer feedback into inventory replenishment plans, thereby improving customer satisfaction while enhancing the accuracy of inventory management.

[0335] "Inventory data" refers to a collection of information that includes the inventory status of products at each store and sales location, sales information, and past demand history.

[0336] "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data, seasonal market factors, advertising effectiveness, and other factors.

[0337] An "artificial intelligence model" refers to an algorithm that learns patterns from data and uses them to make predictions and classifications. Specifically, it employs machine learning and deep learning techniques.

[0338] "Emotion recognition" is a technology that analyzes text data such as user feedback and reviews to identify the user's emotions contained within it.

[0339] An "inventory replenishment plan" is a plan to determine the appropriate amount and timing of replenishment of products based on data obtained from demand forecasting and sentiment recognition.

[0340] This invention is embodied as a system for efficiently managing inventory and analyzing customer feedback in physical stores. The server has the function of collecting inventory data in real time from multiple sensors and sales terminals and storing it in a database. This allows for instantaneous understanding of the inventory status of products in stores.

[0341] Next, the server uses artificial intelligence models such as "PyTorch" to analyze historical data and predict demand. The prediction model incorporates elements such as sales history, seasonal factors, and external marketing initiatives, enabling highly accurate predictions.

[0342] Furthermore, the server is equipped with an emotion engine that uses the natural language processing library "Transformers" to analyze user feedback and reviews and recognize emotions. The results of emotion recognition are directly reflected in inventory replenishment plans, making it possible to quickly adjust inventory and revise promotions for products where dissatisfaction is detected.

[0343] This system connects with devices via a smartphone application, allowing store staff to check inventory status and customer feedback, enabling them to respond quickly. Users are also reported on the response measures and progress regarding their feedback, contributing to an improved customer experience.

[0344] As a concrete example, when a new product is introduced in a store, if many negative reviews about that product are received through the user application, the sentiment engine will detect the negative sentiment. Based on this information, the server can optimize inventory and recommend necessary countermeasures to store staff.

[0345] An example of a prompt for a generated AI model is: "Based on customer reviews of the newly introduced product, analyze customer sentiment and propose inventory adjustments to prevent a decline in sales." This prompt enables flexible decision-making using the AI ​​model.

[0346] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0347] Step 1:

[0348] The server collects real-time inventory data from inventory sensors and sales terminals installed in stores. It receives sensor data and sales data as input and stores them in a database. This allows for an immediate understanding of the store's inventory status.

[0349] Step 2:

[0350] The server uses an artificial intelligence model to forecast demand based on collected inventory data. Using the stored inventory data and historical sales history as input, it calculates future demand using a demand forecasting algorithm (e.g., a PyTorch model) and outputs the predicted demand data.

[0351] Step 3:

[0352] When user feedback or reviews are sent via the device, the server uses an emotion engine to analyze the text data. It receives user reviews as input, recognizes the emotions using a natural language processing library (e.g., Transformers), and outputs the results of the emotion analysis.

[0353] Step 4:

[0354] The server integrates demand forecast data and sentiment analysis results to optimize inventory replenishment plans. Inputs include demand forecast data obtained in the previous step and sentiment data such as negative sentiment. Based on this, it generates instructions to adjust inventory replenishment quantities and timings, outputting an optimal replenishment plan.

[0355] Step 5:

[0356] The server automatically manages orders and shipments based on replenishment plans and notifies terminals of relevant information. It uses optimized replenishment plans as input, processes orders and issues shipping instructions, and outputs this information to terminals. This ensures smooth inventory management and customer service.

[0357] 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.

[0358] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0359] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0360] [Third Embodiment]

[0361] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0362] 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.

[0363] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0364] 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.

[0365] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0366] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0367] 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.

[0368] 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.

[0369] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0370] The 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.

[0371] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0372] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0373] The present invention provides a system for streamlining inventory management of mobile devices. This system consists of several main components, and its operation is described below.

[0374] First, the server collects inventory data accessible from each shop in real time and stores it in a dedicated database. This data consists of information such as the status of incoming and outgoing goods, sales information, and past sales history. Because a large amount of data is updated daily, the server uses an efficient database management system to maintain data integrity and accuracy.

[0375] Next, after data collection, the server uses artificial intelligence to analyze past data and predict future demand. This AI model is based on machine learning and has the ability to generate different demand forecasts for each store, taking into account seasonal fluctuations and the impact of promotions.

[0376] Based on predicted demand, the server automatically creates an inventory replenishment plan. This plan includes what to order, when, and how much. Once the plan is finalized, the server automatically places the corresponding orders with suppliers.

[0377] Once an order is placed, the server integrates with the warehouse management system to process the shipment efficiently. Simultaneously, it sends order and shipping notifications to terminals (computers in each shop). This allows store staff to know in advance when new inventory will arrive and prepare accordingly.

[0378] For example, if a popular mobile phone model is expected to run out of stock due to high demand, the server automatically places additional orders based on that information and arranges for delivery before the stock runs out. Because this entire process is automated by the system, users can focus on their daily tasks without worrying about excess inventory or stock shortages.

[0379] As described above, this system enables mobile phone shops to manage their inventory efficiently and accurately, helping them minimize financial losses.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The server collects inventory data from each store in real time and stores it in a database. The data includes product ID, inventory quantity, sales information, and last replenishment date. This data is automatically updated to ensure it is always up-to-date.

[0383] Step 2:

[0384] The server uses an artificial intelligence model to forecast demand based on the collected data. The AI ​​algorithm takes into account past sales data, seasonal factors, marketing campaigns, etc., to predict future demand with high accuracy.

[0385] Step 3:

[0386] The server automatically creates an optimal inventory replenishment plan based on demand forecasts. The plan includes specific actions for each store, such as the required replenishment quantity and the timing of orders.

[0387] Step 4:

[0388] The server automatically places orders for necessary products according to the replenishment plan. The ordering process proceeds by electronically sending orders to suppliers.

[0389] Step 5:

[0390] The server communicates with the warehouse management system to arrange for the rapid shipment of ordered goods. It calculates the optimal delivery route and adjusts the estimated arrival date of the goods.

[0391] Step 6:

[0392] The server notifies the terminal once the order and shipment are complete. The notification includes details of the ordered product and the expected arrival date, allowing the user to efficiently manage store inventory based on this information.

[0393] Step 7:

[0394] Store staff, who are users of the system, receive notifications and understand when inventory needs to be restocked. This allows them to prepare the store and conduct promotional activities as needed.

[0395] (Example 1)

[0396] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0397] In inventory management, many companies face the challenge of minimizing losses caused by inventory shortages or excesses. In particular, there is a need for methods to accurately predict fluctuations in demand and efficiently replenish inventory accordingly. Furthermore, automation of ordering and shipping processes is required, but achieving this necessitates advanced information processing capabilities and data analysis.

[0398] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0399] In this invention, the server includes means for instantly collecting and recording inventory information, means for using a machine learning model to predict demand based on historical information, means for generating an optimal inventory replenishment plan based on demand forecasts, means for automatically instructing suppliers to place orders and deliveries, and means for notifying display terminals of order and delivery information. This streamlines the entire inventory management operation, minimizes losses, and enables inventory optimization and process automation.

[0400] "Inventory information" refers to data regarding the quantity and condition of products, and it is required to be updated in real time.

[0401] "Collecting and recording immediately" means taking the time to instantly acquire and save data in order to keep inventory information up-to-date at all times.

[0402] A "machine learning model" is an artificial intelligence technology used to learn regularities and patterns from past data and predict future events.

[0403] An "optimal inventory replenishment plan" is a plan that determines the necessary quantity of goods to prevent inventory shortages or surpluses, based on demand forecasts.

[0404] "Automatically ordering and instructing suppliers to deliver" means that the system executes the process of ordering necessary goods from suppliers and arranging their transportation without requiring human intervention.

[0405] A "display terminal" is an electronic device that can visually display information and plays a role in notifying users of orders and deliveries.

[0406] This system is designed for efficient inventory management and primarily operates through the collaboration of a server and terminals. The server collects real-time inventory information from each store and stores this data using a database management system such as MySQL. The server has the computing power to process a massive amount of data.

[0407] For data analysis, machine learning libraries such as TensorFlow are used to consider past sales history and external factors. The server inputs this data into an AI model to forecast future demand. As a concrete example, a prompt message such as "To forecast demand for the next two months, please use sales data from the past 12 months" is used. This allows the AI ​​model to generate highly accurate demand forecasts that reflect seasonal fluctuations and the impact of promotions.

[0408] The terminal receives notifications from the server, allowing users to prepare based on inventory replenishment plans. For example, it provides users with order and shipping details, enabling efficient management of new inventory.

[0409] Furthermore, the server automatically places orders with suppliers and works in conjunction with the logistics management system to ensure that products are delivered accurately. This system allows users to maintain appropriate inventory levels and minimize the risk of waste due to excess inventory and stockouts.

[0410] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0411] Step 1:

[0412] The server collects inventory information from each store's terminal. Specifically, it retrieves product inbound / outbound information and sales data from the store's POS system via API. The input data includes details such as product ID, quantity, and time. The inventory information is stored in a MySQL database, and transaction management is performed to maintain data integrity. After the data is stored, the server outputs the updated database.

[0413] Step 2:

[0414] The server performs demand forecasting using stored data. The input is historical sales data retrieved from the database. Using TensorFlow, the prompt "To forecast demand for the next two months, please use sales data from the past 12 months" is input to the generated AI model. This analysis outputs a future demand forecast for a specific product. The demand forecast is converted into an Excel file or similar format and output in a user-accessible format.

[0415] Step 3:

[0416] The server creates a replenishment plan based on demand forecast data. The inputs are the demand forecast results and the current inventory status. Using SQLAlchemy, it automatically generates a replenishment plan specifying what and how much to order and saves it to the database. In this process, the order quantity and timing are determined for each product, and the results are output to the inventory management system.

[0417] Step 4:

[0418] The server automatically places orders with suppliers based on the replenishment plan. The input is the replenishment plan data. The process includes sending purchase orders to suppliers using the EDI system. Once the order is confirmed, a shipping schedule is created and output to the logistics management system. At this point, the database is updated to confirm the order.

[0419] Step 5:

[0420] The terminal receives order and shipping information from the server and notifies the user. The input is the notification data sent from the server. The terminal displays the expected arrival date and quantity of newly ordered inventory on the screen for the user to check. The user uses this information to secure inventory space and plan sales. The output is notification information that can be checked by shop staff.

[0421] (Application Example 1)

[0422] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0423] Inventory management in logistics centers requires rapid and accurate replenishment, and since it often relies on manual labor, its efficiency is limited. Furthermore, the inability to grasp inventory levels in real time makes it difficult to perform appropriate replenishment based on demand forecasts. Additionally, workers must navigate between multiple management systems to obtain necessary information, resulting in time-consuming and labor-intensive processes.

[0424] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0425] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an intelligent model to predict demand based on historical data, means for providing inventory information and replenishment plans via a visual display device, and means for supporting inventory replenishment operations based on voice and image data. This enables improved efficiency and accuracy of inventory replenishment operations at a logistics center.

[0426] "Inventory data" refers to information regarding the quantity, type, and status of goods being received and shipped.

[0427] "Real-time" refers to a state where information is updated instantly with virtually no time delay.

[0428] "Means of preservation" refers to methods and devices for retaining data and making it available for later reuse.

[0429] "Historical data" refers to information recorded previously and is used for analysis and prediction.

[0430] An "intelligent model for predicting demand" refers to an algorithm that uses machine learning and statistical methods to estimate fluctuations in demand.

[0431] A "visual display device" is a device used to present information visually, and examples include displays and head-mounted displays.

[0432] A "replenishment plan" is the process of scheduling the ordering and delivery of goods necessary to maintain adequate inventory levels.

[0433] "Audio and image data" refers to digitalized sound or visual information used as an interface or support function with the user.

[0434] "Means of supporting replenishment operations" refers to methods or devices that provide workers with the information and instructions necessary to properly manage and replenish inventory.

[0435] In this invention, a server plays a central role in ensuring the inventory management system functions correctly. This server collects inventory data in real time from various sensors and input terminals installed in warehouses and stores, and stores this information in a database. The stored data includes the status of incoming and outgoing goods, the type and quantity of goods, and past sales history.

[0436] The server uses an advanced intelligent model to analyze historical inventory data and newly collected data to predict product demand. This intelligent model is based on machine learning algorithms and updates the predictions in real time, taking into account seasonal fluctuations and the impact of promotions.

[0437] Based on the generated demand forecast, the server automatically develops an optimal inventory replenishment plan. This plan includes the required number of items, the optimal replenishment timing, and the delivery route. The server provides the necessary inventory information and replenishment plan to logistics center workers via visual and audio devices. For example, if a worker is wearing a head-mounted display, the server overlays inventory information within the worker's field of view to help them efficiently find and replenish the necessary items.

[0438] At the same time, necessary information is provided to workers as audio and image data, allowing them to perform tasks hands-free while checking the information. This function includes a prompt system that uses voice prompts to give instructions to workers, such as specific prompts like, "Please tell me the location of the necessary items for the next order," or "Please show me the optimal route within the warehouse." In this way, inventory management and replenishment work at the logistics center is carried out efficiently, and the burden on workers is reduced.

[0439] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0440] Step 1:

[0441] The server collects real-time inventory data from sensors and terminals installed in warehouses and stores. Inputs include product quantity, inbound / outbound history, and current product status. The server formats this data appropriately and stores it in a database. This process ensures that accurate and up-to-date information about the current inventory status is always maintained.

[0442] Step 2:

[0443] The server receives historical sales data and current inventory data as input and uses a generating AI model to forecast demand. This model statistically analyzes future demand, taking into account various external factors (e.g., seasons and promotional activities). This allows it to output demand forecasts for each product, providing a foundation for preventing future inventory shortages.

[0444] Step 3:

[0445] The server creates an optimal inventory replenishment plan based on the generated demand forecast. Inputs include demand forecast data, supplier delivery information, and transportation route information. This information is integrated to determine the necessary goods and quantities, the optimal ordering timing, and the delivery route, generating a replenishment plan as output. The replenishment plan aims for efficient resource utilization and stockout avoidance.

[0446] Step 4:

[0447] The server automatically sends purchase orders to suppliers based on the generated replenishment plan. This process includes creating purchase order forms and sending them via email or EDI systems. The input is the replenishment plan, and the output is the purchase order to suppliers. This eliminates the need for manual data entry by users and enables rapid replenishment.

[0448] Step 5:

[0449] The server notifies users of order and shipping information via visual and audio devices. Inputs are order and delivery status data, while outputs are visual and audio instructions for field workers. For example, a worker's head-mounted display shows the next arriving goods and their location. Voice prompts can also be used to provide instructions in case of emergency.

[0450] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0451] The system of this invention aims to improve efficiency in inventory management and enable the recognition of user emotions and decision-making based on those emotions. This system has a complex structure that integrates inventory data collection, demand forecasting, optimal inventory replenishment, and an emotion engine.

[0452] First, the server collects inventory data from each store and stores it in a database. This includes product inventory status, sales information, and historical demand history. Data collection is done in real time, making it possible to instantly understand the inventory status of each store.

[0453] Next, the server uses an artificial intelligence model to analyze past data and predict future demand. This model achieves highly accurate demand forecasting by taking into account past sales patterns, seasonal factors, and external marketing initiatives.

[0454] Furthermore, the server incorporates an emotion engine that recognizes user emotions. This engine can analyze text such as user feedback and reviews to identify emotions. For example, if there are many negative reviews about a particular product, the emotion engine will detect this and adjust the system's rating accordingly. This information is then used when developing inventory replenishment plans, contributing to an improved user experience.

[0455] As a concrete example, suppose a user posts a complaint about a specific product through the app. This information is immediately sent to the server, where the sentiment engine analyzes the post and recognizes negative emotions. The server then takes swift action to resolve the issue by adjusting the replenishment plan. Furthermore, it reports the user's feedback and the progress of the countermeasures through their device.

[0456] This system allows inventory management to evolve beyond mere physical product management into a comprehensive service that includes customer satisfaction management. By directly reflecting customer emotions, companies can develop faster and more accurate marketing strategies and strengthen their competitiveness.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The server collects inventory data from each store in real time and stores it in a database. This inventory data includes information such as product name, quantity, and sales history. This data is updated with an emphasis on immediacy.

[0460] Step 2:

[0461] The server runs an artificial intelligence model to analyze historical inventory data and sales history, and then performs demand forecasting. This model takes into account past sales trends and seasonal factors to predict future demand with high accuracy.

[0462] Step 3:

[0463] The emotion engine on the server collects user reviews and feedback and performs text analysis. Through this analysis, it recognizes the user's emotional state and identifies products with a high number of negative emotions.

[0464] Step 4:

[0465] The server incorporates the results of sentiment analysis into demand forecasts and creates inventory replenishment plans. An algorithm that takes emotional cues into account adjusts the replenishment quantities of problematic products, optimizing inventory management.

[0466] Step 5:

[0467] The server automatically places orders based on the replenishment plan. It determines the required quantity of products and delivery schedules, and coordinates with warehouses and delivery companies.

[0468] Step 6:

[0469] The terminal notifies users of the progress of orders and shipments. This allows store staff to know when products are expected to arrive and prepare the store accordingly.

[0470] Step 7:

[0471] Users can receive feedback and review the results of emotion recognition. They can obtain information on specific countermeasures and areas for improvement, and provide further feedback to the system as needed.

[0472] (Example 2)

[0473] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In inventory management systems, there is a challenge in simultaneously achieving efficient inventory replenishment and improved customer satisfaction. In particular, relying solely on demand forecasting based on data makes it difficult to achieve inventory replenishment that accurately reflects customer satisfaction.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0476] In this invention, the server includes means for acquiring and storing inventory information in real time, means for utilizing a machine learning model that predicts demand based on historical information, and means for analyzing user sentiment and incorporating the results into the inventory replenishment plan. This makes it possible to generate demand forecasts and inventory replenishment plans that take user sentiment into account.

[0477] "Inventory information" refers to data about the quantity and condition of goods, which is acquired and stored in real time.

[0478] A "machine learning model" refers to the algorithms and data structures used to analyze historical data and predict future demand.

[0479] "User sentiment" refers to data that shows users' emotional reactions and evaluations, obtained by analyzing user feedback and reviews.

[0480] An "inventory replenishment plan" refers to a plan for ordering and replenishing inventory that is optimized based on demand forecasts and user sentiment.

[0481] A "server" refers to a computer system that acquires and stores inventory information and performs processing such as demand forecasting and sentiment analysis.

[0482] The embodiments for carrying out the invention will now be described. The system of this invention aims to improve efficient inventory management and user experience. Specifically, the configuration involves a server performing the main processing and terminals and users interacting with the system.

[0483] The server retrieves inventory information from each store in real time and stores it in a database. This allows for immediate access to product inventory status, sales figures, and historical demand data. A common database system is used for data management, and MongoDB or MySQL are suitable options.

[0484] The server also uses machine learning models to analyze the collected data and predict future demand. This demand forecast is enhanced by generative AI models that take into account past sales patterns, seasonal factors, and external marketing initiatives. Specific software such as TensorFlow and PyTorch can be used. To effectively execute this process, the server uses prompts to manipulate the AI ​​model. An example of a prompt is, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0485] Furthermore, the server incorporates an emotion analysis engine that utilizes natural language processing (NLP) technology to analyze user emotions. This engine can extract emotion labels from feedback and reviews and incorporate them into inventory replenishment plans. Typically, libraries such as NLTK and spaCy are used for this purpose. When users submit feedback or opinions through the application, the data is immediately sent to the server and analyzed by the emotion analysis engine.

[0486] The device functions as an interface with the user, providing feedback and notifications about inventory status. Examples include smartphones and tablets, utilizing notification technologies such as Firebase Cloud Messaging. This allows users to receive real-time updates on inventory changes and system suggestions.

[0487] This invention's structure enables companies to achieve efficient inventory management and rapid decision-making that directly reflects user sentiment. This not only improves customer satisfaction but also contributes to strengthening competitiveness.

[0488] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0489] Step 1:

[0490] The server collects inventory information from each store in real time. The inputs include inventory status and sales data obtained from the store's POS system. The server uses this information to store it in a database. Specifically, it periodically retrieves data using an API and automatically saves it to a database such as MongoDB or MySQL.

[0491] Step 2:

[0492] The server uses a generative AI model based on the collected data to predict future demand. The inputs are historical inventory and sales data obtained in Step 1, as well as data on external initiatives. The server analyzes this data and executes machine learning algorithms (using TensorFlow or PyTorch) to obtain demand forecast output. In this process, prompt statements are input to the model, for example, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0493] Step 3:

[0494] The server analyzes user feedback using an emotion analysis engine. The input consists of reviews and opinions submitted by users through the app. The server uses natural language processing techniques (such as NLTK and spaCy) to analyze this text information and output emotion labels. These labels are then used in inventory replenishment planning.

[0495] Step 4:

[0496] The server creates an optimal inventory replenishment plan based on forecast data and sentiment analysis results. The inputs are the demand forecast results from step 2 and the sentiment analysis results from step 3. The server uses an algorithm to determine the priority and quantity of inventory replenishment based on this information and outputs the results.

[0497] Step 5:

[0498] The terminal sends notifications to the user regarding inventory and replenishment plans. The input is the replenishment plan and any changes confirmed in step 4. The terminal reports this to the user via email or a notification service (such as Firebase Cloud Messaging). This ensures that the user always receives the latest information.

[0499] (Application Example 2)

[0500] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0501] Traditional inventory management systems only consider data related to product supply and fail to adequately reflect customer feedback and sentiment. This can lead to decreased customer satisfaction and lost sales opportunities. Furthermore, the inability to respond quickly and flexibly to demand fluctuations creates a risk of inventory oversupply or undersupply.

[0502] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0503] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an artificial intelligence model that predicts demand based on historical data, and means for analyzing user feedback and recognizing emotions. This makes it possible to directly incorporate customer feedback into inventory replenishment plans, thereby improving customer satisfaction while enhancing the accuracy of inventory management.

[0504] "Inventory data" refers to a collection of information that includes the inventory status of products at each store and sales location, sales information, and past demand history.

[0505] "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data, seasonal market factors, advertising effectiveness, and other factors.

[0506] An "artificial intelligence model" refers to an algorithm that learns patterns from data and uses them to make predictions and classifications. Specifically, it employs machine learning and deep learning techniques.

[0507] "Emotion recognition" is a technology that analyzes text data such as user feedback and reviews to identify the user's emotions contained within it.

[0508] An "inventory replenishment plan" is a plan to determine the appropriate amount and timing of replenishment of products based on data obtained from demand forecasting and sentiment recognition.

[0509] This invention is embodied as a system for efficiently managing inventory and analyzing customer feedback in physical stores. The server has the function of collecting inventory data in real time from multiple sensors and sales terminals and storing it in a database. This allows for instantaneous understanding of the inventory status of products in stores.

[0510] Next, the server uses artificial intelligence models such as "PyTorch" to analyze historical data and predict demand. The prediction model incorporates elements such as sales history, seasonal factors, and external marketing initiatives, enabling highly accurate predictions.

[0511] Furthermore, the server is equipped with an emotion engine that uses the natural language processing library "Transformers" to analyze user feedback and reviews and recognize emotions. The results of emotion recognition are directly reflected in inventory replenishment plans, making it possible to quickly adjust inventory and revise promotions for products where dissatisfaction is detected.

[0512] This system connects with devices via a smartphone application, allowing store staff to check inventory status and customer feedback, enabling them to respond quickly. Users are also reported on the response measures and progress regarding their feedback, contributing to an improved customer experience.

[0513] As a concrete example, when a new product is introduced in a store, if many negative reviews about that product are received through the user application, the sentiment engine will detect the negative sentiment. Based on this information, the server can optimize inventory and recommend necessary countermeasures to store staff.

[0514] An example of a prompt for a generated AI model is: "Based on customer reviews of the newly introduced product, analyze customer sentiment and propose inventory adjustments to prevent a decline in sales." This prompt enables flexible decision-making using the AI ​​model.

[0515] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0516] Step 1:

[0517] The server collects real-time inventory data from inventory sensors and sales terminals installed in stores. It receives sensor data and sales data as input and stores them in a database. This allows for an immediate understanding of the store's inventory status.

[0518] Step 2:

[0519] The server uses an artificial intelligence model to forecast demand based on collected inventory data. Using the stored inventory data and historical sales history as input, it calculates future demand using a demand forecasting algorithm (e.g., a PyTorch model) and outputs the predicted demand data.

[0520] Step 3:

[0521] When user feedback or reviews are sent via the device, the server uses an emotion engine to analyze the text data. It receives user reviews as input, recognizes the emotions using a natural language processing library (e.g., Transformers), and outputs the results of the emotion analysis.

[0522] Step 4:

[0523] The server integrates demand forecast data and sentiment analysis results to optimize inventory replenishment plans. Inputs include demand forecast data obtained in the previous step and sentiment data such as negative sentiment. Based on this, it generates instructions to adjust inventory replenishment quantities and timings, outputting an optimal replenishment plan.

[0524] Step 5:

[0525] The server automatically manages orders and shipments based on replenishment plans and notifies terminals of relevant information. It uses optimized replenishment plans as input, processes orders and issues shipping instructions, and outputs this information to terminals. This ensures smooth inventory management and customer service.

[0526] 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.

[0527] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0528] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0529] [Fourth Embodiment]

[0530] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0531] 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.

[0532] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0533] 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.

[0534] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0535] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0536] 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.

[0537] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0538] 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.

[0539] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0540] The 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.

[0541] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0542] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0543] The present invention provides a system for streamlining inventory management of mobile devices. This system consists of several main components, and its operation is described below.

[0544] First, the server collects inventory data accessible from each shop in real time and stores it in a dedicated database. This data consists of information such as the status of incoming and outgoing goods, sales information, and past sales history. Because a large amount of data is updated daily, the server uses an efficient database management system to maintain data integrity and accuracy.

[0545] Next, after data collection, the server uses artificial intelligence to analyze past data and predict future demand. This AI model is based on machine learning and has the ability to generate different demand forecasts for each store, taking into account seasonal fluctuations and the impact of promotions.

[0546] Based on predicted demand, the server automatically creates an inventory replenishment plan. This plan includes what to order, when, and how much. Once the plan is finalized, the server automatically places the corresponding orders with suppliers.

[0547] Once an order is placed, the server integrates with the warehouse management system to process the shipment efficiently. Simultaneously, it sends order and shipping notifications to terminals (computers in each shop). This allows store staff to know in advance when new inventory will arrive and prepare accordingly.

[0548] For example, if a popular mobile phone model is expected to run out of stock due to high demand, the server automatically places additional orders based on that information and arranges for delivery before the stock runs out. Because this entire process is automated by the system, users can focus on their daily tasks without worrying about excess inventory or stock shortages.

[0549] As described above, this system enables mobile phone shops to manage their inventory efficiently and accurately, helping them minimize financial losses.

[0550] The following describes the processing flow.

[0551] Step 1:

[0552] The server collects inventory data from each store in real time and stores it in a database. The data includes product ID, inventory quantity, sales information, and last replenishment date. This data is automatically updated to ensure it is always up-to-date.

[0553] Step 2:

[0554] The server uses an artificial intelligence model to forecast demand based on the collected data. The AI ​​algorithm takes into account past sales data, seasonal factors, marketing campaigns, etc., to predict future demand with high accuracy.

[0555] Step 3:

[0556] The server automatically creates an optimal inventory replenishment plan based on demand forecasts. The plan includes specific actions for each store, such as the required replenishment quantity and the timing of orders.

[0557] Step 4:

[0558] The server automatically places orders for necessary products according to the replenishment plan. The ordering process proceeds by electronically sending orders to suppliers.

[0559] Step 5:

[0560] The server communicates with the warehouse management system to arrange for the rapid shipment of ordered goods. It calculates the optimal delivery route and adjusts the estimated arrival date of the goods.

[0561] Step 6:

[0562] The server notifies the terminal once the order and shipment are complete. The notification includes details of the ordered product and the expected arrival date, allowing the user to efficiently manage store inventory based on this information.

[0563] Step 7:

[0564] Store staff, who are users of the system, receive notifications and understand when inventory needs to be restocked. This allows them to prepare the store and conduct promotional activities as needed.

[0565] (Example 1)

[0566] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0567] In inventory management, many companies face the challenge of minimizing losses caused by inventory shortages or excesses. In particular, there is a need for methods to accurately predict fluctuations in demand and efficiently replenish inventory accordingly. Furthermore, automation of ordering and shipping processes is required, but achieving this necessitates advanced information processing capabilities and data analysis.

[0568] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0569] In this invention, the server includes means for instantly collecting and recording inventory information, means for using a machine learning model to predict demand based on historical information, means for generating an optimal inventory replenishment plan based on demand forecasts, means for automatically instructing suppliers to place orders and deliveries, and means for notifying display terminals of order and delivery information. This streamlines the entire inventory management operation, minimizes losses, and enables inventory optimization and process automation.

[0570] "Inventory information" refers to data regarding the quantity and condition of products, and it is required to be updated in real time.

[0571] "Collecting and recording immediately" means taking the time to instantly acquire and save data in order to keep inventory information up-to-date at all times.

[0572] A "machine learning model" is an artificial intelligence technology used to learn regularities and patterns from past data and predict future events.

[0573] An "optimal inventory replenishment plan" is a plan that determines the necessary quantity of goods to prevent inventory shortages or surpluses, based on demand forecasts.

[0574] "Automatically ordering and instructing suppliers to deliver" means that the system executes the process of ordering necessary goods from suppliers and arranging their transportation without requiring human intervention.

[0575] A "display terminal" is an electronic device that can visually display information and plays a role in notifying users of orders and deliveries.

[0576] This system is designed for efficient inventory management and primarily operates through the collaboration of a server and terminals. The server collects real-time inventory information from each store and stores this data using a database management system such as MySQL. The server has the computing power to process a massive amount of data.

[0577] For data analysis, machine learning libraries such as TensorFlow are used to consider past sales history and external factors. The server inputs this data into an AI model to forecast future demand. As a concrete example, a prompt message such as "To forecast demand for the next two months, please use sales data from the past 12 months" is used. This allows the AI ​​model to generate highly accurate demand forecasts that reflect seasonal fluctuations and the impact of promotions.

[0578] The terminal receives notifications from the server, allowing users to prepare based on inventory replenishment plans. For example, it provides users with order and shipping details, enabling efficient management of new inventory.

[0579] Furthermore, the server automatically places orders with suppliers and works in conjunction with the logistics management system to ensure that products are delivered accurately. This system allows users to maintain appropriate inventory levels and minimize the risk of waste due to excess inventory and stockouts.

[0580] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0581] Step 1:

[0582] The server collects inventory information from each store's terminal. Specifically, it retrieves product inbound / outbound information and sales data from the store's POS system via API. The input data includes details such as product ID, quantity, and time. The inventory information is stored in a MySQL database, and transaction management is performed to maintain data integrity. After the data is stored, the server outputs the updated database.

[0583] Step 2:

[0584] The server performs demand forecasting using stored data. The input is historical sales data retrieved from the database. Using TensorFlow, the prompt "To forecast demand for the next two months, please use sales data from the past 12 months" is input to the generated AI model. This analysis outputs a future demand forecast for a specific product. The demand forecast is converted into an Excel file or similar format and output in a user-accessible format.

[0585] Step 3:

[0586] The server creates a replenishment plan based on demand forecast data. The inputs are the demand forecast results and the current inventory status. Using SQLAlchemy, it automatically generates a replenishment plan specifying what and how much to order and saves it to the database. In this process, the order quantity and timing are determined for each product, and the results are output to the inventory management system.

[0587] Step 4:

[0588] The server automatically places orders with suppliers based on the replenishment plan. The input is the replenishment plan data. The process includes sending purchase orders to suppliers using the EDI system. Once the order is confirmed, a shipping schedule is created and output to the logistics management system. At this point, the database is updated to confirm the order.

[0589] Step 5:

[0590] The terminal receives order and shipping information from the server and notifies the user. The input is the notification data sent from the server. The terminal displays the expected arrival date and quantity of newly ordered inventory on the screen for the user to check. The user uses this information to secure inventory space and plan sales. The output is notification information that can be checked by shop staff.

[0591] (Application Example 1)

[0592] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0593] Inventory management in logistics centers requires rapid and accurate replenishment, and since it often relies on manual labor, its efficiency is limited. Furthermore, the inability to grasp inventory levels in real time makes it difficult to perform appropriate replenishment based on demand forecasts. Additionally, workers must navigate between multiple management systems to obtain necessary information, resulting in time-consuming and labor-intensive processes.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0595] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an intelligent model to predict demand based on historical data, means for providing inventory information and replenishment plans via a visual display device, and means for supporting inventory replenishment operations based on voice and image data. This enables improved efficiency and accuracy of inventory replenishment operations at a logistics center.

[0596] "Inventory data" refers to information regarding the quantity, type, and status of goods being received and shipped.

[0597] "Real-time" refers to a state where information is updated instantly with virtually no time delay.

[0598] "Means of preservation" refers to methods and devices for retaining data and making it available for later reuse.

[0599] "Historical data" refers to information recorded previously and is used for analysis and prediction.

[0600] An "intelligent model for predicting demand" refers to an algorithm that uses machine learning and statistical methods to estimate fluctuations in demand.

[0601] A "visual display device" is a device used to present information visually, and examples include displays and head-mounted displays.

[0602] A "replenishment plan" is the process of scheduling the ordering and delivery of goods necessary to maintain adequate inventory levels.

[0603] "Audio and image data" refers to digitalized sound or visual information used as an interface or support function with the user.

[0604] "Means of supporting replenishment operations" refers to methods or devices that provide workers with the information and instructions necessary to properly manage and replenish inventory.

[0605] In this invention, a server plays a central role in ensuring the inventory management system functions correctly. This server collects inventory data in real time from various sensors and input terminals installed in warehouses and stores, and stores this information in a database. The stored data includes the status of incoming and outgoing goods, the type and quantity of goods, and past sales history.

[0606] The server uses an advanced intelligent model to analyze historical inventory data and newly collected data to predict product demand. This intelligent model is based on machine learning algorithms and updates the predictions in real time, taking into account seasonal fluctuations and the impact of promotions.

[0607] Based on the generated demand forecast, the server automatically develops an optimal inventory replenishment plan. This plan includes the required number of items, the optimal replenishment timing, and the delivery route. The server provides the necessary inventory information and replenishment plan to logistics center workers via visual and audio devices. For example, if a worker is wearing a head-mounted display, the server overlays inventory information within the worker's field of view to help them efficiently find and replenish the necessary items.

[0608] At the same time, necessary information is provided to workers as audio and image data, allowing them to perform tasks hands-free while checking the information. This function includes a prompt system that uses voice prompts to give instructions to workers, such as specific prompts like, "Please tell me the location of the necessary items for the next order," or "Please show me the optimal route within the warehouse." In this way, inventory management and replenishment work at the logistics center is carried out efficiently, and the burden on workers is reduced.

[0609] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0610] Step 1:

[0611] The server collects real-time inventory data from sensors and terminals installed in warehouses and stores. Inputs include product quantity, inbound / outbound history, and current product status. The server formats this data appropriately and stores it in a database. This process ensures that accurate and up-to-date information about the current inventory status is always maintained.

[0612] Step 2:

[0613] The server receives historical sales data and current inventory data as input and uses a generating AI model to forecast demand. This model statistically analyzes future demand, taking into account various external factors (e.g., seasons and promotional activities). This allows it to output demand forecasts for each product, providing a foundation for preventing future inventory shortages.

[0614] Step 3:

[0615] The server creates an optimal inventory replenishment plan based on the generated demand forecast. Inputs include demand forecast data, supplier delivery information, and transportation route information. This information is integrated to determine the necessary goods and quantities, the optimal ordering timing, and the delivery route, generating a replenishment plan as output. The replenishment plan aims for efficient resource utilization and stockout avoidance.

[0616] Step 4:

[0617] The server automatically sends purchase orders to suppliers based on the generated replenishment plan. This process includes creating purchase order forms and sending them via email or EDI systems. The input is the replenishment plan, and the output is the purchase order to suppliers. This eliminates the need for manual data entry by users and enables rapid replenishment.

[0618] Step 5:

[0619] The server notifies users of order and shipping information via visual and audio devices. Inputs are order and delivery status data, while outputs are visual and audio instructions for field workers. For example, a worker's head-mounted display shows the next arriving goods and their location. Voice prompts can also be used to provide instructions in case of emergency.

[0620] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0621] The system of this invention aims to improve efficiency in inventory management and enable the recognition of user emotions and decision-making based on those emotions. This system has a complex structure that integrates inventory data collection, demand forecasting, optimal inventory replenishment, and an emotion engine.

[0622] First, the server collects inventory data from each store and stores it in a database. This includes product inventory status, sales information, and historical demand history. Data collection is done in real time, making it possible to instantly understand the inventory status of each store.

[0623] Next, the server uses an artificial intelligence model to analyze past data and predict future demand. This model achieves highly accurate demand forecasting by taking into account past sales patterns, seasonal factors, and external marketing initiatives.

[0624] Furthermore, the server incorporates an emotion engine that recognizes user emotions. This engine can analyze text such as user feedback and reviews to identify emotions. For example, if there are many negative reviews about a particular product, the emotion engine will detect this and adjust the system's rating accordingly. This information is then used when developing inventory replenishment plans, contributing to an improved user experience.

[0625] As a concrete example, suppose a user posts a complaint about a specific product through the app. This information is immediately sent to the server, where the sentiment engine analyzes the post and recognizes negative emotions. The server then takes swift action to resolve the issue by adjusting the replenishment plan. Furthermore, it reports the user's feedback and the progress of the countermeasures through their device.

[0626] This system allows inventory management to evolve beyond mere physical product management into a comprehensive service that includes customer satisfaction management. By directly reflecting customer emotions, companies can develop faster and more accurate marketing strategies and strengthen their competitiveness.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The server collects inventory data from each store in real time and stores it in a database. This inventory data includes information such as product name, quantity, and sales history. This data is updated with an emphasis on immediacy.

[0630] Step 2:

[0631] The server runs an artificial intelligence model to analyze historical inventory data and sales history, and then performs demand forecasting. This model takes into account past sales trends and seasonal factors to predict future demand with high accuracy.

[0632] Step 3:

[0633] The emotion engine on the server collects user reviews and feedback and performs text analysis. Through this analysis, it recognizes the user's emotional state and identifies products with a high number of negative emotions.

[0634] Step 4:

[0635] The server incorporates the results of sentiment analysis into demand forecasts and creates inventory replenishment plans. An algorithm that takes emotional cues into account adjusts the replenishment quantities of problematic products, optimizing inventory management.

[0636] Step 5:

[0637] The server automatically places orders based on the replenishment plan. It determines the required quantity of products and delivery schedules, and coordinates with warehouses and delivery companies.

[0638] Step 6:

[0639] The terminal notifies users of the progress of orders and shipments. This allows store staff to know when products are expected to arrive and prepare the store accordingly.

[0640] Step 7:

[0641] Users can receive feedback and review the results of emotion recognition. They can obtain information on specific countermeasures and areas for improvement, and provide further feedback to the system as needed.

[0642] (Example 2)

[0643] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] In inventory management systems, there is a challenge in simultaneously achieving efficient inventory replenishment and improved customer satisfaction. In particular, relying solely on demand forecasting based on data makes it difficult to achieve inventory replenishment that accurately reflects customer satisfaction.

[0645] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0646] In this invention, the server includes means for acquiring and storing inventory information in real time, means for utilizing a machine learning model that predicts demand based on historical information, and means for analyzing user sentiment and incorporating the results into the inventory replenishment plan. This makes it possible to generate demand forecasts and inventory replenishment plans that take user sentiment into account.

[0647] "Inventory information" refers to data about the quantity and condition of goods, which is acquired and stored in real time.

[0648] A "machine learning model" refers to the algorithms and data structures used to analyze historical data and predict future demand.

[0649] "User sentiment" refers to data that shows users' emotional reactions and evaluations, obtained by analyzing user feedback and reviews.

[0650] An "inventory replenishment plan" refers to a plan for ordering and replenishing inventory that is optimized based on demand forecasts and user sentiment.

[0651] A "server" refers to a computer system that acquires and stores inventory information and performs processing such as demand forecasting and sentiment analysis.

[0652] The embodiments for carrying out the invention will now be described. The system of this invention aims to improve efficient inventory management and user experience. Specifically, the configuration involves a server performing the main processing and terminals and users interacting with the system.

[0653] The server retrieves inventory information from each store in real time and stores it in a database. This allows for immediate access to product inventory status, sales figures, and historical demand data. A common database system is used for data management, and MongoDB or MySQL are suitable options.

[0654] The server also uses machine learning models to analyze the collected data and predict future demand. This demand forecast is enhanced by generative AI models that take into account past sales patterns, seasonal factors, and external marketing initiatives. Specific software such as TensorFlow and PyTorch can be used. To effectively execute this process, the server uses prompts to manipulate the AI ​​model. An example of a prompt is, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0655] Furthermore, the server incorporates an emotion analysis engine that utilizes natural language processing (NLP) technology to analyze user emotions. This engine can extract emotion labels from feedback and reviews and incorporate them into inventory replenishment plans. Typically, libraries such as NLTK and spaCy are used for this purpose. When users submit feedback or opinions through the application, the data is immediately sent to the server and analyzed by the emotion analysis engine.

[0656] The device functions as an interface with the user, providing feedback and notifications about inventory status. Examples include smartphones and tablets, utilizing notification technologies such as Firebase Cloud Messaging. This allows users to receive real-time updates on inventory changes and system suggestions.

[0657] This invention's structure enables companies to achieve efficient inventory management and rapid decision-making that directly reflects user sentiment. This not only improves customer satisfaction but also contributes to strengthening competitiveness.

[0658] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0659] Step 1:

[0660] The server collects inventory information from each store in real time. The inputs include inventory status and sales data obtained from the store's POS system. The server uses this information to store it in a database. Specifically, it periodically retrieves data using an API and automatically saves it to a database such as MongoDB or MySQL.

[0661] Step 2:

[0662] The server uses a generative AI model based on the collected data to predict future demand. The inputs are historical inventory and sales data obtained in Step 1, as well as data on external initiatives. The server analyzes this data and executes machine learning algorithms (using TensorFlow or PyTorch) to obtain demand forecast output. In this process, prompt statements are input to the model, for example, "Suggest a demand forecast and inventory replenishment plan for the next period based on sales data and user reviews from the past year."

[0663] Step 3:

[0664] The server analyzes user feedback using an emotion analysis engine. The input consists of reviews and opinions submitted by users through the app. The server uses natural language processing techniques (such as NLTK and spaCy) to analyze this text information and output emotion labels. These labels are then used in inventory replenishment planning.

[0665] Step 4:

[0666] The server creates an optimal inventory replenishment plan based on forecast data and sentiment analysis results. The inputs are the demand forecast results from step 2 and the sentiment analysis results from step 3. The server uses an algorithm to determine the priority and quantity of inventory replenishment based on this information and outputs the results.

[0667] Step 5:

[0668] The terminal sends notifications to the user regarding inventory and replenishment plans. The input is the replenishment plan and any changes confirmed in step 4. The terminal reports this to the user via email or a notification service (such as Firebase Cloud Messaging). This ensures that the user always receives the latest information.

[0669] (Application Example 2)

[0670] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] Traditional inventory management systems only consider data related to product supply and fail to adequately reflect customer feedback and sentiment. This can lead to decreased customer satisfaction and lost sales opportunities. Furthermore, the inability to respond quickly and flexibly to demand fluctuations creates a risk of inventory oversupply or undersupply.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0673] In this invention, the server includes means for collecting and storing inventory data in real time, means for using an artificial intelligence model that predicts demand based on historical data, and means for analyzing user feedback and recognizing emotions. This makes it possible to directly incorporate customer feedback into inventory replenishment plans, thereby improving customer satisfaction while enhancing the accuracy of inventory management.

[0674] "Inventory data" refers to a collection of information that includes the inventory status of products at each store and sales location, sales information, and past demand history.

[0675] "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data, seasonal market factors, advertising effectiveness, and other factors.

[0676] An "artificial intelligence model" refers to an algorithm that learns patterns from data and uses them to make predictions and classifications. Specifically, it employs machine learning and deep learning techniques.

[0677] "Emotion recognition" is a technology that analyzes text data such as user feedback and reviews to identify the user's emotions contained within it.

[0678] An "inventory replenishment plan" is a plan to determine the appropriate amount and timing of replenishment of products based on data obtained from demand forecasting and sentiment recognition.

[0679] This invention is embodied as a system for efficiently managing inventory and analyzing customer feedback in physical stores. The server has the function of collecting inventory data in real time from multiple sensors and sales terminals and storing it in a database. This allows for instantaneous understanding of the inventory status of products in stores.

[0680] Next, the server uses artificial intelligence models such as "PyTorch" to analyze historical data and predict demand. The prediction model incorporates elements such as sales history, seasonal factors, and external marketing initiatives, enabling highly accurate predictions.

[0681] Furthermore, the server is equipped with an emotion engine that uses the natural language processing library "Transformers" to analyze user feedback and reviews and recognize emotions. The results of emotion recognition are directly reflected in inventory replenishment plans, making it possible to quickly adjust inventory and revise promotions for products where dissatisfaction is detected.

[0682] This system connects with devices via a smartphone application, allowing store staff to check inventory status and customer feedback, enabling them to respond quickly. Users are also reported on the response measures and progress regarding their feedback, contributing to an improved customer experience.

[0683] As a concrete example, when a new product is introduced in a store, if many negative reviews about that product are received through the user application, the sentiment engine will detect the negative sentiment. Based on this information, the server can optimize inventory and recommend necessary countermeasures to store staff.

[0684] An example of a prompt for a generated AI model is: "Based on customer reviews of the newly introduced product, analyze customer sentiment and propose inventory adjustments to prevent a decline in sales." This prompt enables flexible decision-making using the AI ​​model.

[0685] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0686] Step 1:

[0687] The server collects real-time inventory data from inventory sensors and sales terminals installed in stores. It receives sensor data and sales data as input and stores them in a database. This allows for an immediate understanding of the store's inventory status.

[0688] Step 2:

[0689] The server uses an artificial intelligence model to forecast demand based on collected inventory data. Using the stored inventory data and historical sales history as input, it calculates future demand using a demand forecasting algorithm (e.g., a PyTorch model) and outputs the predicted demand data.

[0690] Step 3:

[0691] When user feedback or reviews are sent via the device, the server uses an emotion engine to analyze the text data. It receives user reviews as input, recognizes the emotions using a natural language processing library (e.g., Transformers), and outputs the results of the emotion analysis.

[0692] Step 4:

[0693] The server integrates demand forecast data and sentiment analysis results to optimize inventory replenishment plans. Inputs include demand forecast data obtained in the previous step and sentiment data such as negative sentiment. Based on this, it generates instructions to adjust inventory replenishment quantities and timings, outputting an optimal replenishment plan.

[0694] Step 5:

[0695] The server automatically manages orders and shipments based on replenishment plans and notifies terminals of relevant information. It uses optimized replenishment plans as input, processes orders and issues shipping instructions, and outputs this information to terminals. This ensures smooth inventory management and customer service.

[0696] 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.

[0697] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0698] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0699] 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.

[0700] Figure 9 shows an 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.

[0701] 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.

[0702] 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.

[0703] 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, motorcycles, etc., 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, for example, based 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.

[0704] 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."

[0705] 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.

[0706] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0707] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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 the like 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.

[0716] 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.

[0717] The following is further disclosed regarding the embodiments described above.

[0718] (Claim 1)

[0719] A means of collecting and storing inventory data in real time,

[0720] A means of using an artificial intelligence model to predict demand based on past data,

[0721] A means for generating an optimal inventory replenishment plan based on demand forecasts,

[0722] A means of automatically placing orders and shipping,

[0723] A system that includes means for notifying order and shipping information.

[0724] (Claim 2)

[0725] The system according to claim 1, which adjusts the predictive model to take external factors into consideration.

[0726] (Claim 3)

[0727] The system according to claim 1, which is linked with a warehouse management system for inventory management.

[0728] "Example 1"

[0729] (Claim 1)

[0730] Means for instantly collecting and recording inventory information,

[0731] A means of using machine learning models to predict demand based on past information,

[0732] A means for generating an optimal inventory replenishment plan based on demand forecasts,

[0733] A means of automatically instructing suppliers to place orders and deliver,

[0734] A system that includes means for notifying a display terminal of order and delivery information.

[0735] (Claim 2)

[0736] The system according to claim 1, wherein the prediction algorithm is adjusted to take external factors into consideration.

[0737] (Claim 3)

[0738] The system according to claim 1, which is linked with a logistics management system for inventory management.

[0739] "Application Example 1"

[0740] (Claim 1)

[0741] A means of collecting and storing inventory data in real time,

[0742] A means of using intelligent models to predict demand based on past data,

[0743] A means for generating an optimal inventory replenishment plan based on demand forecasts,

[0744] A means of automatically placing orders and shipping,

[0745] Means for notifying order and shipping information,

[0746] A means of providing inventory information and replenishment plans via a visual display device,

[0747] A means of supporting inventory replenishment work based on audio and image data.

[0748] A system that includes this.

[0749] (Claim 2)

[0750] The system according to claim 1, which adjusts the predictive model to take external factors into consideration.

[0751] (Claim 3)

[0752] The system according to claim 1, which is linked with a transportation management system for inventory management.

[0753] "Example 2 of combining an emotion engine"

[0754] (Claim 1)

[0755] A means of acquiring and storing inventory information in real time,

[0756] A method that utilizes machine learning models to predict demand based on past information,

[0757] A means for creating an optimized inventory replenishment plan based on demand forecasts,

[0758] A method for analyzing user emotions and incorporating the results into inventory replenishment plans,

[0759] A system including means for issuing execution instructions and notifying the status of execution.

[0760] (Claim 2)

[0761] The system according to claim 1, which adjusts the predictive model to take into account external factors and user sentiment.

[0762] (Claim 3)

[0763] The system according to claim 1, which is linked with an item storage system for item management.

[0764] "Application example 2 when combining with an emotional engine"

[0765] (Claim 1)

[0766] A means of collecting and storing inventory data in real time,

[0767] A means of using an artificial intelligence model to predict demand based on past data,

[0768] A means for generating an optimal inventory replenishment plan based on demand forecasts,

[0769] A means of analyzing user feedback to recognize emotions,

[0770] A means of adjusting inventory replenishment plans based on perceived emotions,

[0771] A means of placing orders and shipping based on that,

[0772] A system that includes means for notifying order and shipping information.

[0773] (Claim 2)

[0774] The system according to claim 1, which adjusts the predictive model to take external factors into consideration.

[0775] (Claim 3)

[0776] The system according to claim 1, which is linked with a warehouse management system for inventory management and provides information that reflects the user's emotions. [Explanation of symbols]

[0777] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and storing inventory data in real time, A means of using intelligent models to predict demand based on past data, A means for generating an optimal inventory replenishment plan based on demand forecasts, A means of automatically placing orders and shipping, Means for notifying order and shipping information, A means of providing inventory information and replenishment plans via a visual display device, A means of supporting inventory replenishment work based on audio and image data, A system that includes this.

2. The system according to claim 1, wherein the predictive model is adjusted to take external factors into consideration.

3. The system according to claim 1, which is linked with a transportation management system for inventory management.

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