system

Abstract

We provide the system. [Solution] A measuring means for measuring the weight of a product, A receiving means for receiving data from the measuring means, An order instruction generation means that generates an order instruction based on the data received by the receiving means, A transmission means for transmitting the order instructions generated from the order instruction generation means to a supplier, A voice recognition means for inquiring about inventory information via voice commands, A feedback analysis tool that collects customer feedback and formulates the next inventory strategy, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a conventional inventory management system, human errors and time lags are likely to occur, resulting in problems of overstocking or understocking. In addition, due to the insufficient efficient utilization of inventory confirmation by voice and customer feedback, means for improving the business efficiency of stores are required. 【Means for Solving the Problems】 【0005】 This invention enables real-time monitoring of inventory fluctuations using a measuring means for measuring product weight, and accurate management of in-store inventory status using a receiving means that receives data from the measuring means. Furthermore, by including an order instruction generation means that generates order instructions based on the data received by the receiving means, it is possible to automatically send order instructions to suppliers when inventory falls below a set threshold. In addition, it enables immediate inquiry of inventory information via voice commands from users using a voice recognition means, and utilizes a feedback analysis means that collects and analyzes customer feedback to improve inventory strategy, thereby increasing the efficiency of store operations and enhancing customer service capabilities. 【0006】 "Measuring means for measuring product weight" refers to a device or mechanism for measuring the weight of a product in real time and providing that data to a system. 【0007】 "Receiving means for receiving data from the measuring means" refers to a device or mechanism for accurately receiving weight data transmitted from the measuring means. 【0008】 "Order instruction generation means" refers to a process or device for automatically creating order instructions based on received data. 【0009】 "Transmission means" refers to a communication device or system for transmitting order instructions to a supplier. 【0010】 "Voice recognition means" refers to a process or device for recognizing voice instructions from a user and providing necessary inventory information. 【0011】 A "feedback analysis tool" is a means of collecting feedback data from customers and analyzing it to formulate the next inventory strategy. [Brief explanation of the drawing] 【0012】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, the numbered 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. 【0016】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. 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. 【0018】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 This inventory management system begins with the use of a measuring device that measures the weight of products installed on store shelves. First, a sensor continuously measures the weight of the products and transmits the data to a server. The server receives this data through a receiving device, and if it determines that the weight falls below a set weight threshold, it automatically creates an order for new inventory using an order instruction generation device. 【0034】 Specifically, the server uses this information to send order instructions to suppliers using a transmission device. This process allows for replenishment of goods before inventory runs out. Furthermore, the terminal is equipped with voice recognition, allowing users to check inventory status by voice. When a user says "check inventory" to the terminal, the terminal immediately communicates with the server and provides the latest inventory information. 【0035】 The system also incorporates a mechanism for collecting feedback when customers change products. This feedback is analyzed by the server and incorporated into the next inventory strategy using feedback analysis tools. As a result of this analysis, popular products and colors can be identified and reflected in the next ordering plan, making it possible to accurately grasp customer needs. In this way, the goal is to improve the accuracy and efficiency of inventory management through collaboration between the user and the server. 【0036】 The following describes the processing flow. 【0037】 Step 1: 【0038】 The sensor sheet continuously measures the weight of the products on the shelf and transmits the data to the server in real time. 【0039】 Step 2: 【0040】 The server receives weight data transmitted from the sensor sheet and compares the current inventory weight to a predetermined baseline value. If it detects that the weight has fallen below the baseline value, it generates an alert. 【0041】 Step 3: 【0042】 The server initiates the automated ordering process based on the alert. It creates an order list using an order instruction generation mechanism and electronically sends the order instructions to the designated suppliers. 【0043】 Step 4: 【0044】 When a user requests an inventory check via voice to the terminal, the terminal's voice recognition system recognizes the request and queries the server for current inventory information. 【0045】 Step 5: 【0046】 The server sends the requested inventory information to the terminal, and the terminal displays real-time inventory information to the user. 【0047】 Step 6: 【0048】 When a user answers a survey during a device upgrade, the device sends this feedback data to the server. 【0049】 Step 7: 【0050】 The server collects feedback data and analyzes it using feedback analysis tools. Based on the analysis results, adjustments are made to reflect the findings in the next inventory strategy. 【0051】 (Example 1) 【0052】 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." 【0053】 In inventory management, there is a challenge in accurately understanding the real-time inventory status of products and preventing stockouts. Furthermore, it is difficult to develop inventory strategies that accurately reflect customer needs. 【0054】 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. 【0055】 In this invention, the server includes a measuring means for measuring the weight of goods, an information processing means for receiving data from the measuring means, and an instruction generation means for generating replenishment instructions based on the data received by the information processing means. This makes it possible to accurately grasp the inventory status in real time and prevent stockouts. Furthermore, by including a data analysis means for analyzing user feedback and planning the next inventory strategy, inventory management that accurately reflects customer needs can be realized. 【0056】 "Measuring means" refers to devices or equipment used to accurately measure the weight of a product. 【0057】 "Information processing means" refers to means for analyzing data received from measurement means and extracting necessary information. 【0058】 "Instruction generation means" refers to a part of a system that automatically generates instructions, such as inventory replenishment, based on the results of information processing means. 【0059】 "Information transmission means" refers to a configuration that includes communication means for transmitting generated replenishment instructions to the supplier. 【0060】 "Voice recognition means" refers to technology that enables a system to identify user questions and instructions based on voice input and respond appropriately. 【0061】 "Data analysis means" refers to analytical methods or techniques for analyzing collected user feedback to optimize future inventory strategies. 【0062】 The embodiment of this invention is a system designed to automate and improve the efficiency of store inventory management processes. Servers, terminals, and users interact with each other to enable optimal inventory management. 【0063】 First, the server continuously receives product weight data from sensors installed on store shelves. These sensors use high-precision "weight measuring devices" to measure weight changes in real time and transmit the data to the server. The server receives this data and determines whether it falls below a set threshold. 【0064】 If the level falls below the baseline, the server automatically generates a replenishment order using the order generation mechanism. This replenishment order includes details such as the type of product, the quantity to be replenished, and the desired delivery date and time. Next, the server uses the information transmission mechanism to send the generated replenishment order to the supplier. A general communication module and communication device are used for this information transmission. 【0065】 Furthermore, the terminal is equipped with voice recognition functionality, allowing users to check inventory status via voice commands. The terminal uses voice recognition software to instantly retrieve the latest inventory information from the server in response to user inquiries. For example, if a user says "check inventory" to the terminal, it communicates with the server and provides the most recent inventory count. 【0066】 In addition, the server collects and analyzes user feedback using data analysis tools and utilizes it to formulate future inventory strategies. This analysis allows the server to identify popular products and colors and reflect them in future ordering plans. This enables inventory adjustments that reflect customer preferences. 【0067】 As a concrete example, if the stock of orange juice on a store's beverage shelf decreases, the server automatically instructs the supplier to order an additional 20 bottles. Users can instantly check the stock status by simply saying "Check orange juice stock" to their terminal. 【0068】 An example of a prompt statement input to a generated AI model is, "Describe the process by which a server analyzes product data and sends an inventory replenishment instruction to a supplier." This prompt statement allows the model to output the exact steps. 【0069】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0070】 Step 1: 【0071】 The server receives product weight data from weighing devices installed on store shelves. It receives real-time weight data from the sensors as input and stores it in a database for comparison with a baseline. For data processing, it converts the received data to integers and calculates trends over time. As output, it retrieves the latest weight data for each product. 【0072】 Step 2: 【0073】 The server evaluates whether the weight has fallen below a certain threshold based on the stored weight data. Specifically, it compares the current weight of the product with a predetermined threshold and determines whether the decrease is within the threshold. The input is the weight data stored in step 1, and the output is the result of the determination of whether it has fallen below the threshold. 【0074】 Step 3: 【0075】 If the level falls below the threshold, the server generates a replenishment order. Specifically, it calculates the required quantity and creates an order form to send to the supplier. Using the evaluation results and order template data within the server as input, it generates an order form specifying the type and quantity of goods required as output. 【0076】 Step 4: 【0077】 The server transmits the generated order instructions to the supplier using an information transmission method. Specifically, data communication takes place via email or a dedicated EDI system. The input is replenishment instruction data, and the output is the completion of sending the instructions to the supplier's inbox. 【0078】 Step 5: 【0079】 The user uses the terminal's voice recognition function to inquire about inventory status. Specifically, the user speaks "Check inventory" into the terminal. The system receives the user's voice signal as input and generates an inventory information confirmation request as output. 【0080】 Step 6: 【0081】 The server retrieves the latest inventory data based on inventory check requests from terminals and sends the results to the terminals. Specifically, it extracts necessary information from the inventory database, converts it into a voice response format, and sends it back to the terminal. It receives inventory check request data as input and generates information including inventory levels and replenishment status as output, which is then output as voice. 【0082】 Step 7: 【0083】 The server collects user feedback through data analysis and uses it to inform future inventory strategies. Specifically, it analyzes feedback data to identify popular products and areas for improvement. It receives user feedback data as input and generates analysis results as materials for strategic meetings as output. 【0084】 (Application Example 1) 【0085】 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." 【0086】 Traditional inventory management systems did not update store inventory information in real time, sometimes leading to stock shortages or excesses. Furthermore, the difficulty in using voice commands for inventory inquiries made efficient inventory management challenging. Additionally, there was insufficient mechanism for utilizing customer feedback to inform future ordering strategies. 【0087】 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. 【0088】 In this invention, the server includes measuring means for measuring product weight, receiving means for receiving data from the measuring means, order instruction generation means for generating order instructions based on the data received by the receiving means, transmitting means for sending the order instructions generated by the order instruction generation means to suppliers, voice recognition means for querying inventory information based on voice instructions and providing the information in real time, feedback analysis means for collecting customer feedback and reflecting it in the next inventory strategy, and user interface means for managing inventory status using a smart device. This enables real-time updating of inventory information and efficient inventory inquiry by voice instructions, and further enables the construction of an effective ordering strategy that utilizes customer feedback. 【0089】 "Measuring means" refers to devices or methods for continuously measuring the weight of a product. 【0090】 A "receiving means" is a device or system that receives weight data sent from a measuring means. 【0091】 The "order instruction generation means" is a system that generates data to instruct the ordering of new inventory based on the received weight data. 【0092】 "Transmission means" refers to devices or communication methods used to transmit generated order instructions to suppliers. 【0093】 "Voice recognition means" refers to technology that interprets user voice commands and provides inventory information in real time. 【0094】 A "feedback analysis system" is a mechanism that collects customer opinions and comments and analyzes them to reflect them in future inventory strategies. 【0095】 A "user interface means" refers to a screen or system that manages inventory status via a smart device and allows users to operate it intuitively. 【0096】 The system for implementing this invention provides integrated technology for measuring product weight in real time and efficiently managing store inventory. A server receives product weight data from measuring devices installed on store shelves and continuously monitors this data. When the product weight falls below a set threshold, the server automatically uses an order instruction generation device to send an order instruction for new inventory to the supplier. This enables automatic replenishment before inventory runs out. 【0097】 The terminal is equipped with voice recognition capabilities. When a user gives a voice command such as "check inventory" to the terminal, the terminal analyzes the voice data and communicates with a server to immediately provide the latest inventory status. Furthermore, customer feedback is collected through feedback analysis capabilities and used to inform future inventory strategies. In this way, inventory management that reflects customer needs is realized. 【0098】 As a concrete example, consider its use in a supermarket. Sensors installed on the shelves measure the weight of products, and terminals provide inventory information via voice commands from staff. Simultaneously, customer feedback after purchase is analyzed to identify popular products and incorporate this information into future orders. This ensures that the necessary products are always available on the shelves. 【0099】 An example of a prompt message could be: "Please describe a smartphone app that streamlines inventory management in retail stores. Please specifically show how to utilize features such as automatic inventory data acquisition and voice recognition." This is how the input to the generating AI model might look. 【0100】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0101】 Step 1: 【0102】 The server receives product weight data from measuring devices placed on shelves. The input is the product weight measured by the measuring device, and the output is continuously updated weight data. By receiving this weight data, the server can instantly grasp the current inventory status. 【0103】 Step 2: 【0104】 The server compares the received weight data to a set baseline value. The input is the weight data obtained in step 1, and the output is the result of determining whether it falls below the baseline value. Based on this determination, the server determines whether there is a shortage of inventory. 【0105】 Step 3: 【0106】 If the server determines that the threshold value has fallen below a certain level, it automatically generates a new order using the order order generation mechanism. The input is the result of the determination in step 2, and the output is the generated order order data. The order order data is used to place orders with suppliers. 【0107】 Step 4: 【0108】 The user gives voice commands to the terminal to check the inventory status. The input is the user's voice commands, and the output is voice data. The terminal analyzes the voice data and converts the voice commands into text data. 【0109】 Step 5: 【0110】 The terminal sends text data to the server and retrieves the latest inventory information. The input is the text data converted in step 4, and the output is the latest inventory information sent from the server. The terminal displays the latest inventory information to the user. 【0111】 Step 6: 【0112】 The user inputs feedback from their purchase at the store into a terminal. The input is customer feedback data, and the output is a feedback record for analysis by the server. The server uses feedback analysis tools to utilize this data for future inventory strategies. 【0113】 The specific implementation and input considerations for the generated AI model and prompt statements are utilized in the data processing related to each of the steps described above. This processing flow enables servers and terminals to perform inventory management quickly and accurately. 【0114】 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. 【0115】 This invention aims to optimize inventory management based on user emotions by incorporating an emotion engine into a conventional inventory management system. The inventory management system includes a measuring means for measuring product weight, a receiving means for receiving data from the measuring means, an order instruction generating means for generating order instructions, and a transmitting means for sending the generated order instructions to suppliers. Furthermore, it includes a voice recognition means for querying inventory information via voice commands and an emotion engine for recognizing and analyzing user emotions. 【0116】 Specifically, the device receives voice commands from the user using voice recognition, and simultaneously analyzes the user's emotional state using an emotion engine. If the user is, for example, feeling stressed, the emotion engine sends this information to the server in real time. Based on this emotional data, the server adjusts how inventory information is provided and controls the device to present more user-friendly information. 【0117】 For example, when a user gives a voice command such as "I need to check this urgently!", the emotion engine recognizes the urgency and communicates this to the server. The server then displays the inventory information on the terminal in a quick and easy-to-use format and, if necessary, also presents immediate support options. 【0118】 Furthermore, the emotion engine is also used to analyze user feedback. For example, if a user expresses a positive emotion towards a product, the server analyzes that information and incorporates it into the strategy as a product that should be increased in inventory. In this way, incorporating an emotion engine can significantly improve the accuracy of inventory management and the quality of user support. 【0119】 The following describes the processing flow. 【0120】 Step 1: 【0121】 The device uses voice recognition to receive voice commands from the user. At this time, the voice data is transmitted to the emotion engine in real time. 【0122】 Step 2: 【0123】 The emotion engine analyzes the user's emotional state from the received audio data, determining levels of tension, excitement, calmness, etc. The analysis results are then sent to the server as numerical data. 【0124】 Step 3: 【0125】 Based on the emotional data received from the emotion engine, the server determines how to provide inventory information while considering the user's emotional state. If the situation is urgent, the information is made concise, and reassuring information is added. 【0126】 Step 4: 【0127】 The server sends the adjusted inventory information to the terminal. The terminal receives this information and presents it to the user visually or through audio announcements. 【0128】 Step 5: 【0129】 The user checks the inventory information provided via the terminal and gives additional voice instructions if further action is required. The terminal receives these instructions again using voice recognition and sends a new request to the server. 【0130】 Step 6: 【0131】 User feedback is sent to the server via the device. The server uses feedback analysis tools to analyze the benefits and challenges, including sentiment data. 【0132】 Step 7: 【0133】 Based on the analysis results, the server formulates the next inventory strategy and adjusts inventory according to demand. This process is performed regularly to maintain the efficient operation of the system. 【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 will be referred to as the "terminal." 【0136】 Traditional inventory management systems lacked the ability to provide information and adjust inventory levels based on user emotions, making it difficult to improve user satisfaction and inventory efficiency. Furthermore, they were insufficient in providing real-time inventory information that considered user urgency and emotional states, highlighting the need for improved user experience. 【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 a voice recognition means for querying inventory information via voice commands, an emotion analysis means for analyzing the emotions associated with the voice commands, and an information provision adjustment means for adjusting the information provision method based on the analyzed emotion data. This enables flexible information provision and inventory adjustment based on the user's emotions. 【0139】 "Measuring means" refers to a device or function for accurately measuring the weight of a product. 【0140】 A "receiving device" refers to a device or function that plays the role of receiving data acquired from a measuring device. 【0141】 A "order instruction generation device" refers to a device or function that automatically creates necessary order instructions based on data from a receiving device. 【0142】 "Transmission device" refers to a device or function for transmitting generated order instructions to the supplier. 【0143】 A "voice recognition device" refers to a device or function that analyzes a user's voice commands and processes them as text data. 【0144】 "Emotional analysis means" refers to a device or function for identifying and analyzing the user's emotions associated with voice commands. 【0145】 "Information provision adjustment means" refers to a device or function for adjusting the format and content of information provided to users based on analyzed sentiment data. 【0146】 A "feedback analysis device" refers to a device or function that analyzes feedback collected from customers and uses it to formulate future inventory policies. 【0147】 This invention aims to realize an advanced inventory management system that utilizes user emotions. Specific embodiments of this system are described below. 【0148】 The terminal uses a speech recognition device to receive voice commands from the user. This device uses a common voice input device (e.g., a voice assistant device) to convert speech into text data. A "speech recognition API" is used as the technology supporting this process. This text data is then sent to a sentiment analysis system. 【0149】 The emotion analysis method uses emotion analysis software (e.g., an emotion analysis SDK) to analyze the emotions associated with the text. The emotion data obtained from this analysis is transmitted from the terminal to the server. 【0150】 Based on the received emotional data and voice instructions, the server utilizes information provision adjustment mechanisms to determine the optimal method for providing inventory information to the user. Specifically, it retrieves inventory information using a database management system and provides information tailored to urgency and emotional needs. In some cases, it provides information quickly and offers additional support options as needed. 【0151】 Furthermore, user feedback is collected through a feedback analysis device. This device analyzes the feedback to incorporate it into the next inventory policy, and the server modifies the inventory strategy based on the results. 【0152】 For example, if a user gives a voice command saying, "I want to quickly check inventory information," the sentiment analysis tool will identify that the command is urgent. The server will then present detailed and immediate inventory information and promptly provide the necessary support. 【0153】 An example of a prompt message is, "How should inventory data be provided when a user expresses an urgent need?" This system enables efficient inventory management while improving the user experience. 【0154】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0155】 Step 1: 【0156】 The terminal accepts voice commands from the user. When the user says, "I want to know the stock status of product A," the voice recognition system captures the voice and converts it into digital voice data. From this input data, text data is generated using the voice recognition system (e.g., a voice recognition API). The resulting output is text data related to the voice command. 【0157】 Step 2: 【0158】 The terminal processes text data using sentiment analysis tools. Here, sentiment analysis software (e.g., sentiment analysis SDK) is used to analyze the user's emotional state. The input is the text data obtained in step 1, and by analyzing the urgency and emotional nuances of the voice, the output is generated as user sentiment data. 【0159】 Step 3: 【0160】 The terminal sends analyzed sentiment data to the server. The server receives text data and sentiment data based on the user's voice instructions as input. Based on this data, the server determines the user's state and decides how to provide inventory information. The output is an instruction to provide appropriate inventory information. 【0161】 Step 4: 【0162】 The server retrieves inventory information from the database according to the determined delivery method. The input consists of information about the product requested by the user (e.g., product ID) and an assessment of its urgency. The server uses a database management system to extract the necessary inventory information, processes and formats it, and sends it to the terminal as output. 【0163】 Step 5: 【0164】 The terminal displays the acquired inventory information to the user. The input is inventory information sent from the server. The terminal provides this to the user via screen display or audio. The output is the display of inventory information to the user, and additional support is provided as needed. 【0165】 Step 6: 【0166】 The user provides feedback on the provided inventory information. The terminal captures this feedback and sends it back as text data to a sentiment analysis system. The input is text data about the user's feedback, and the output is data for analysis to inform future strategies. 【0167】 Step 7: 【0168】 The server analyzes feedback data and incorporates it into the next inventory strategy. The input is text data of user feedback. A feedback analysis device is used to identify positive or negative sentiment and adjust the inventory plan accordingly. The output is updated inventory strategy information. 【0169】 (Application Example 2) 【0170】 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". 【0171】 Modern inventory management systems require information that takes customer emotions into account to improve the customer experience. However, traditional systems fail to grasp customer emotions, making it difficult to respond effectively according to customer urgency and satisfaction levels, potentially leading to decreased customer satisfaction. Solving this problem requires flexible inventory information provision tailored to customer needs and strategic inventory adjustments. 【0172】 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. 【0173】 In this invention, the server includes means for evaluating products, emotion analysis means for analyzing customer emotions and optimizing the method of providing information, and speech recognition means for querying information based on voice instructions from customers. This makes it possible to provide information and optimize inventory strategies according to the customer's emotional state. 【0174】 "Means of evaluating products" refers to functions for inspecting and evaluating the condition and characteristics of a product. 【0175】 A "receiving means" is a function for receiving data sent from an external source. 【0176】 The "order instruction generation means" is a function that creates instructions for ordering goods and supplies based on received data. 【0177】 "Transmission means" refers to the function for sending the generated order instructions to external suppliers. 【0178】 "Voice recognition means" refers to a function that recognizes voice commands from the user and converts them into digital data. 【0179】 "Emotional analysis tools" are functions that analyze the user's emotions and optimize the way information is provided based on the results. 【0180】 This invention is a system that enables inventory management based on user sentiment information through data processing between a server and a terminal. The server evaluates the status of products and generates order instructions as needed. The server also receives instructions from the user via speech recognition and uses speech recognition software to convert them into text format. Here, Google® Cloud Speech-to-Text API is used. The received speech data is subjected to sentiment analysis using IBM Watson® Tone Analyzer. 【0181】 The terminal provides inventory information based on user input and offers real-time information tailored to the user's emotional state. When a user searches for products by voice, the terminal displays quick and appropriate information based on the received data. 【0182】 For example, if a user says, "I need to quickly check the stock of this product," the terminal converts this instruction into text and sends it to the server. The server performs sentiment analysis and quickly sends concise stock information to the terminal, which then immediately provides that information to the user. 【0183】 To utilize a generative AI model, a prompt is required. An example of a prompt is, "Please tell me how to determine if this customer's voice instruction is urgent and how to provide appropriate inventory information and support options." 【0184】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0185】 Step 1: 【0186】 The user requests product availability via voice command to the terminal. This voice input is collected by the microphone built into the terminal. The terminal receives this voice data and sends it to the server in digital format. 【0187】 Step 2: 【0188】 The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This text data is then preprocessed and parsed to make it easier to understand the user's intent. 【0189】 Step 3: 【0190】 Based on the text data received by the server, IBM Watson Tone Analyzer is used to analyze the user's emotions. The input for this emotion analysis is text data, and the output is an analysis result indicating the user's emotions. Based on the analysis result, it is determined whether urgency or positive emotions are present. 【0191】 Step 4: 【0192】 The server uses sentiment analysis results to determine how to provide inventory information. If the analysis results indicate urgency, the server quickly extracts the necessary information from the inventory database. If necessary, it also provides support information that can be used immediately. 【0193】 Step 5: 【0194】 The server sends extracted inventory and support information to the terminal. This data is presented in the most user-friendly and organized format. The terminal receives this information and displays it visually to the user. 【0195】 Step 6: 【0196】 The user checks the inventory information displayed on the terminal. They can request further assistance if needed. The terminal receives these requests and prepares to communicate with the server again. 【0197】 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. 【0198】 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. 【0199】 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. 【0200】 [Second Embodiment] 【0201】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0202】 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. 【0203】 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). 【0204】 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. 【0205】 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. 【0206】 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). 【0207】 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. 【0208】 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. 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 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". 【0213】 This inventory management system begins with the use of a measuring device that measures the weight of products installed on store shelves. First, a sensor continuously measures the weight of the products and transmits the data to a server. The server receives this data through a receiving device, and if it determines that the weight falls below a set weight threshold, it automatically creates an order for new inventory using an order instruction generation device. 【0214】 Specifically, the server uses this information to send order instructions to suppliers using a transmission device. This process allows for replenishment of goods before inventory runs out. Furthermore, the terminal is equipped with voice recognition, allowing users to check inventory status by voice. When a user says "check inventory" to the terminal, the terminal immediately communicates with the server and provides the latest inventory information. 【0215】 The system also incorporates a mechanism for collecting feedback when customers change products. This feedback is analyzed by the server and incorporated into the next inventory strategy using feedback analysis tools. As a result of this analysis, popular products and colors can be identified and reflected in the next ordering plan, making it possible to accurately grasp customer needs. In this way, the goal is to improve the accuracy and efficiency of inventory management through collaboration between the user and the server. 【0216】 The following describes the processing flow. 【0217】 Step 1: 【0218】 The sensor sheet continuously measures the weight of the products on the shelf and transmits the data to the server in real time. 【0219】 Step 2: 【0220】 The server receives weight data transmitted from the sensor sheet and compares the current inventory weight to a predetermined baseline value. If it detects that the weight has fallen below the baseline value, it generates an alert. 【0221】 Step 3: 【0222】 The server initiates the automated ordering process based on the alert. It creates an order list using an order instruction generation mechanism and electronically sends the order instructions to the designated suppliers. 【0223】 Step 4: 【0224】 When a user requests an inventory check via voice to the terminal, the terminal's voice recognition system recognizes the request and queries the server for current inventory information. 【0225】 Step 5: 【0226】 The server sends the requested inventory information to the terminal, and the terminal displays real-time inventory information to the user. 【0227】 Step 6: 【0228】 When a user answers a survey during a device upgrade, the device sends this feedback data to the server. 【0229】 Step 7: 【0230】 The server collects feedback data and analyzes it using feedback analysis tools. Based on the analysis results, adjustments are made to reflect the findings in the next inventory strategy. 【0231】 (Example 1) 【0232】 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." 【0233】 In inventory management, there is a challenge in accurately understanding the real-time inventory status of products and preventing stockouts. Furthermore, it is difficult to develop inventory strategies that accurately reflect customer needs. 【0234】 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. 【0235】 In this invention, the server includes a measuring means for measuring the weight of goods, an information processing means for receiving data from the measuring means, and an instruction generation means for generating replenishment instructions based on the data received by the information processing means. This makes it possible to accurately grasp the inventory status in real time and prevent stockouts. Furthermore, by including a data analysis means for analyzing user feedback and planning the next inventory strategy, inventory management that accurately reflects customer needs can be realized. 【0236】 "Measuring means" refers to devices or equipment used to accurately measure the weight of a product. 【0237】 "Information processing means" refers to means for analyzing data received from measurement means and extracting necessary information. 【0238】 "Instruction generation means" refers to a part of a system that automatically generates instructions, such as inventory replenishment, based on the results of information processing means. 【0239】 "Information transmission means" refers to a configuration that includes communication means for transmitting generated replenishment instructions to the supplier. 【0240】 "Voice recognition means" refers to technology that enables a system to identify user questions and instructions based on voice input and respond appropriately. 【0241】 "Data analysis means" refers to analytical methods or techniques for analyzing collected user feedback to optimize future inventory strategies. 【0242】 The embodiment of this invention is a system designed to automate and improve the efficiency of store inventory management processes. Servers, terminals, and users interact with each other to enable optimal inventory management. 【0243】 First, the server continuously receives product weight data from sensors installed on store shelves. These sensors use high-precision "weight measuring devices" to measure weight changes in real time and transmit the data to the server. The server receives this data and determines whether it falls below a set threshold. 【0244】 If the level falls below the baseline, the server automatically generates a replenishment order using the order generation mechanism. This replenishment order includes details such as the type of product, the quantity to be replenished, and the desired delivery date and time. Next, the server uses the information transmission mechanism to send the generated replenishment order to the supplier. A general communication module and communication device are used for this information transmission. 【0245】 Furthermore, the terminal is equipped with voice recognition functionality, allowing users to check inventory status via voice commands. The terminal uses voice recognition software to instantly retrieve the latest inventory information from the server in response to user inquiries. For example, if a user says "check inventory" to the terminal, it communicates with the server and provides the most recent inventory count. 【0246】 In addition, the server collects and analyzes user feedback using data analysis tools and utilizes it to formulate future inventory strategies. This analysis allows the server to identify popular products and colors and reflect them in future ordering plans. This enables inventory adjustments that reflect customer preferences. 【0247】 As a concrete example, if the stock of orange juice on a store's beverage shelf decreases, the server automatically instructs the supplier to order an additional 20 bottles. Users can instantly check the stock status by simply saying "Check orange juice stock" to their terminal. 【0248】 An example of a prompt statement input to a generated AI model is, "Describe the process by which a server analyzes product data and sends an inventory replenishment instruction to a supplier." This prompt statement allows the model to output the exact steps. 【0249】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0250】 Step 1: 【0251】 The server receives product weight data from weighing devices installed on store shelves. It receives real-time weight data from the sensors as input and stores it in a database for comparison with a baseline. For data processing, it converts the received data to integers and calculates trends over time. As output, it retrieves the latest weight data for each product. 【0252】 Step 2: 【0253】 The server evaluates whether the weight has fallen below a certain threshold based on the stored weight data. Specifically, it compares the current weight of the product with a predetermined threshold and determines whether the decrease is within the threshold. The input is the weight data stored in step 1, and the output is the result of the determination of whether it has fallen below the threshold. 【0254】 Step 3: 【0255】 If the level falls below the threshold, the server generates a replenishment order. Specifically, it calculates the required quantity and creates an order form to send to the supplier. Using the evaluation results and order template data within the server as input, it generates an order form specifying the type and quantity of goods required as output. 【0256】 Step 4: 【0257】 The server transmits the generated order instructions to the supplier using an information transmission method. Specifically, data communication takes place via email or a dedicated EDI system. The input is replenishment instruction data, and the output is the completion of sending the instructions to the supplier's inbox. 【0258】 Step 5: 【0259】 The user uses the terminal's voice recognition function to inquire about inventory status. Specifically, the user speaks "Check inventory" into the terminal. The system receives the user's voice signal as input and generates an inventory information confirmation request as output. 【0260】 Step 6: 【0261】 The server retrieves the latest inventory data based on inventory check requests from terminals and sends the results to the terminals. Specifically, it extracts necessary information from the inventory database, converts it into a voice response format, and sends it back to the terminal. It receives inventory check request data as input and generates information including inventory levels and replenishment status as output, which is then output as voice. 【0262】 Step 7: 【0263】 The server collects user feedback through data analysis and uses it to inform future inventory strategies. Specifically, it analyzes feedback data to identify popular products and areas for improvement. It receives user feedback data as input and generates analysis results as materials for strategic meetings as output. 【0264】 (Application Example 1) 【0265】 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." 【0266】 Traditional inventory management systems did not update store inventory information in real time, sometimes leading to stock shortages or excesses. Furthermore, the difficulty in using voice commands for inventory inquiries made efficient inventory management challenging. Additionally, there was insufficient mechanism for utilizing customer feedback to inform future ordering strategies. 【0267】 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. 【0268】 In this invention, the server includes measuring means for measuring product weight, receiving means for receiving data from the measuring means, order instruction generation means for generating order instructions based on the data received by the receiving means, transmitting means for sending the order instructions generated by the order instruction generation means to suppliers, voice recognition means for querying inventory information based on voice instructions and providing the information in real time, feedback analysis means for collecting customer feedback and reflecting it in the next inventory strategy, and user interface means for managing inventory status using a smart device. This enables real-time updating of inventory information and efficient inventory inquiry by voice instructions, and further enables the construction of an effective ordering strategy that utilizes customer feedback. 【0269】 "Measuring means" refers to devices or methods for continuously measuring the weight of a product. 【0270】 A "receiving means" is a device or system that receives weight data sent from a measuring means. 【0271】 The "order instruction generation means" is a system that generates data to instruct the ordering of new inventory based on the received weight data. 【0272】 "Transmission means" refers to devices or communication methods used to transmit generated order instructions to suppliers. 【0273】 "Voice recognition means" refers to technology that interprets user voice commands and provides inventory information in real time. 【0274】 A "feedback analysis system" is a mechanism that collects customer opinions and comments and analyzes them to reflect them in future inventory strategies. 【0275】 A "user interface means" refers to a screen or system that manages inventory status via a smart device and allows users to operate it intuitively. 【0276】 The system for implementing this invention provides integrated technology for measuring product weight in real time and efficiently managing store inventory. A server receives product weight data from measuring devices installed on store shelves and continuously monitors this data. When the product weight falls below a set threshold, the server automatically uses an order instruction generation device to send an order instruction for new inventory to the supplier. This enables automatic replenishment before inventory runs out. 【0277】 The terminal is equipped with voice recognition capabilities. When a user gives a voice command such as "check inventory" to the terminal, the terminal analyzes the voice data and communicates with a server to immediately provide the latest inventory status. Furthermore, customer feedback is collected through feedback analysis capabilities and used to inform future inventory strategies. In this way, inventory management that reflects customer needs is realized. 【0278】 As a concrete example, consider its use in a supermarket. Sensors installed on the shelves measure the weight of products, and terminals provide inventory information via voice commands from staff. Simultaneously, customer feedback after purchase is analyzed to identify popular products and incorporate this information into future orders. This ensures that the necessary products are always available on the shelves. 【0279】 An example of a prompt message could be: "Please describe a smartphone app that streamlines inventory management in retail stores. Please specifically show how to utilize features such as automatic inventory data acquisition and voice recognition." This is how the input to the generating AI model might look. 【0280】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0281】 Step 1: 【0282】 The server receives the weight data of the product from the measuring means arranged on the shelf. The input is the weight of the product measured by the measuring means, and the output is the continuously updated weight data. By receiving this weight data, the server can immediately grasp the current inventory status. 【0283】 Step 2: 【0284】 The server compares the received weight data with the set reference value. The input is the weight data obtained in Step 1, and the output is the determination result of whether it is below the reference value. By this determination, the server determines whether the inventory is insufficient. 【0285】 Step 3: 【0286】 If the server determines that it is below the reference value, it automatically creates a new order instruction using the order instruction generation means. The input is the determination result of Step 2, and the output is the generated order instruction data. The order instruction data is used for placing an order with the supplier. 【0287】 Step 4: 【0288】 The user gives a voice instruction to check the inventory status towards the terminal. The input is the user's voice instruction, and the output is the voice data. The terminal analyzes the voice data and converts the voice instruction into character data. 【0289】 Step 5: 【0290】 The terminal transmits the character data to the server and obtains the latest inventory information. The input is the character data converted in Step 4, and the output is the latest inventory information transmitted from the server. The terminal displays the latest inventory information to the user. 【0291】 Step 6: 【0292】 The user inputs feedback from their purchase at the store into a terminal. The input is customer feedback data, and the output is a feedback record for analysis by the server. The server uses feedback analysis tools to utilize this data for future inventory strategies. 【0293】 The specific implementation and input considerations for the generated AI model and prompt statements are utilized in the data processing related to each of the steps described above. This processing flow enables servers and terminals to perform inventory management quickly and accurately. 【0294】 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. 【0295】 This invention aims to optimize inventory management based on user emotions by incorporating an emotion engine into a conventional inventory management system. The inventory management system includes a measuring means for measuring product weight, a receiving means for receiving data from the measuring means, an order instruction generating means for generating order instructions, and a transmitting means for sending the generated order instructions to suppliers. Furthermore, it includes a voice recognition means for querying inventory information via voice commands and an emotion engine for recognizing and analyzing user emotions. 【0296】 Specifically, the device receives voice commands from the user using voice recognition, and simultaneously analyzes the user's emotional state using an emotion engine. If the user is, for example, feeling stressed, the emotion engine sends this information to the server in real time. Based on this emotional data, the server adjusts how inventory information is provided and controls the device to present more user-friendly information. 【0297】 For example, when a user gives a voice command such as "I need to check this urgently!", the emotion engine recognizes the urgency and communicates this to the server. The server then displays the inventory information on the terminal in a quick and easy-to-use format and, if necessary, also presents immediate support options. 【0298】 Furthermore, the emotion engine is also used to analyze user feedback. For example, if a user expresses a positive emotion towards a product, the server analyzes that information and incorporates it into the strategy as a product that should be increased in inventory. In this way, incorporating an emotion engine can significantly improve the accuracy of inventory management and the quality of user support. 【0299】 The following describes the processing flow. 【0300】 Step 1: 【0301】 The device uses voice recognition to receive voice commands from the user. At this time, the voice data is transmitted to the emotion engine in real time. 【0302】 Step 2: 【0303】 The emotion engine analyzes the user's emotional state from the received audio data, determining levels of tension, excitement, calmness, etc. The analysis results are then sent to the server as numerical data. 【0304】 Step 3: 【0305】 Based on the emotional data received from the emotion engine, the server determines how to provide inventory information while considering the user's emotional state. If the situation is urgent, the information is made concise, and reassuring information is added. 【0306】 Step 4: 【0307】 The server sends the adjusted inventory information to the terminal. The terminal receives this information and presents it to the user visually or through audio announcements. 【0308】 Step 5: 【0309】 If the user checks the inventory information provided via the terminal and requests additional actions if necessary, additional voice instructions are given. The terminal receives this instruction again using the voice recognition means and sends a new request to the server. 【0310】 Step 6: 【0311】 Feedback provided by the user is sent to the server through the terminal. The server uses feedback analysis means to analyze advantages and issues including emotional data. 【0312】 Step 7: 【0313】 Based on the analysis results, the server formulates the next inventory strategy and adjusts the inventory according to demand. This process is regularly executed to maintain the efficient operation of the system. 【0314】 (Example 2) 【0315】 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". 【0316】 In the conventional inventory management system, since information provision and inventory adjustment based on the user's emotions were not performed, it was difficult to improve the user's satisfaction and inventory efficiency. Also, the provision of inventory information considering the user's urgency and emotional state in real time was insufficient, and an improvement in the user experience was required. 【0317】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0318】 In this invention, the server includes a voice recognition means for querying inventory information via voice commands, an emotion analysis means for analyzing the emotions associated with the voice commands, and an information provision adjustment means for adjusting the information provision method based on the analyzed emotion data. This enables flexible information provision and inventory adjustment based on the user's emotions. 【0319】 "Measuring means" refers to a device or function for accurately measuring the weight of a product. 【0320】 A "receiving device" refers to a device or function that plays the role of receiving data acquired from a measuring device. 【0321】 A "order instruction generation device" refers to a device or function that automatically creates necessary order instructions based on data from a receiving device. 【0322】 "Transmission device" refers to a device or function for transmitting generated order instructions to the supplier. 【0323】 A "voice recognition device" refers to a device or function that analyzes a user's voice commands and processes them as text data. 【0324】 "Emotional analysis means" refers to a device or function for identifying and analyzing the user's emotions associated with voice commands. 【0325】 "Information provision adjustment means" refers to a device or function for adjusting the format and content of information provided to users based on analyzed sentiment data. 【0326】 A "feedback analysis device" refers to a device or function that analyzes feedback collected from customers and uses it to formulate future inventory policies. 【0327】 This invention aims to realize an advanced inventory management system that utilizes user emotions. Specific embodiments of this system are described below. 【0328】 The terminal uses a speech recognition device to receive voice commands from the user. This device uses a common voice input device (e.g., a voice assistant device) to convert speech into text data. A "speech recognition API" is used as the technology supporting this process. This text data is then sent to a sentiment analysis system. 【0329】 The emotion analysis method uses emotion analysis software (e.g., an emotion analysis SDK) to analyze the emotions associated with the text. The emotion data obtained from this analysis is transmitted from the terminal to the server. 【0330】 Based on the received emotional data and voice instructions, the server utilizes information provision adjustment mechanisms to determine the optimal method for providing inventory information to the user. Specifically, it retrieves inventory information using a database management system and provides information tailored to urgency and emotional needs. In some cases, it provides information quickly and offers additional support options as needed. 【0331】 Furthermore, user feedback is collected through a feedback analysis device. This device analyzes the feedback to incorporate it into the next inventory policy, and the server modifies the inventory strategy based on the results. 【0332】 For example, if a user gives a voice command saying, "I want to quickly check inventory information," the sentiment analysis tool will identify that the command is urgent. The server will then present detailed and immediate inventory information and promptly provide the necessary support. 【0333】 An example of a prompt message is, "How should inventory data be provided when a user expresses an urgent need?" This system enables efficient inventory management while improving the user experience. 【0334】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0335】 Step 1: 【0336】 The terminal accepts voice commands from the user. When the user says, "I want to know the stock status of product A," the voice recognition system captures the voice and converts it into digital voice data. From this input data, text data is generated using the voice recognition system (e.g., a voice recognition API). The resulting output is text data related to the voice command. 【0337】 Step 2: 【0338】 The terminal processes text data using sentiment analysis tools. Here, sentiment analysis software (e.g., sentiment analysis SDK) is used to analyze the user's emotional state. The input is the text data obtained in step 1, and by analyzing the urgency and emotional nuances of the voice, the output is generated as user sentiment data. 【0339】 Step 3: 【0340】 The terminal sends analyzed sentiment data to the server. The server receives text data and sentiment data based on the user's voice instructions as input. Based on this data, the server determines the user's state and decides how to provide inventory information. The output is an instruction to provide appropriate inventory information. 【0341】 Step 4: 【0342】 The server retrieves inventory information from the database according to the determined delivery method. The input consists of information about the product requested by the user (e.g., product ID) and an assessment of its urgency. The server uses a database management system to extract the necessary inventory information, processes and formats it, and sends it to the terminal as output. 【0343】 Step 5: 【0344】 The terminal displays the acquired inventory information to the user. The input is inventory information sent from the server. The terminal provides this to the user via screen display or audio. The output is the display of inventory information to the user, and additional support is provided as needed. 【0345】 Step 6: 【0346】 The user provides feedback on the provided inventory information. The terminal captures this feedback and sends it back as text data to a sentiment analysis system. The input is text data about the user's feedback, and the output is data for analysis to inform future strategies. 【0347】 Step 7: 【0348】 The server analyzes feedback data and incorporates it into the next inventory strategy. The input is text data of user feedback. A feedback analysis device is used to identify positive or negative sentiment and adjust the inventory plan accordingly. The output is updated inventory strategy information. 【0349】 (Application Example 2) 【0350】 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." 【0351】 Modern inventory management systems require information that takes customer emotions into account to improve the customer experience. However, traditional systems fail to grasp customer emotions, making it difficult to respond effectively according to customer urgency and satisfaction levels, potentially leading to decreased customer satisfaction. Solving this problem requires flexible inventory information provision tailored to customer needs and strategic inventory adjustments. 【0352】 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. 【0353】 In this invention, the server includes means for evaluating products, emotion analysis means for analyzing customer emotions and optimizing the method of providing information, and speech recognition means for querying information based on voice instructions from customers. This makes it possible to provide information and optimize inventory strategies according to the customer's emotional state. 【0354】 "Means of evaluating products" refers to functions for inspecting and evaluating the condition and characteristics of a product. 【0355】 A "receiving means" is a function for receiving data sent from an external source. 【0356】 The "order instruction generation means" is a function that creates instructions for ordering goods and supplies based on received data. 【0357】 "Transmission means" refers to the function for sending the generated order instructions to external suppliers. 【0358】 "Voice recognition means" refers to a function that recognizes voice commands from the user and converts them into digital data. 【0359】 "Emotional analysis tools" are functions that analyze the user's emotions and optimize the way information is provided based on the results. 【0360】 This invention is a system that enables inventory management based on user sentiment information through data processing between a server and a terminal. The server evaluates the condition of the products and generates order instructions as needed. The server also receives instructions from the user via speech recognition and uses speech recognition software to convert them into text format. Here, the Google Cloud Speech-to-Text API is used. The received speech data is subjected to sentiment analysis using IBM Watson Tone Analyzer. 【0361】 The terminal provides inventory information based on user input and offers real-time information tailored to the user's emotional state. When a user searches for products by voice, the terminal displays quick and appropriate information based on the received data. 【0362】 For example, if a user says, "I need to quickly check the stock of this product," the terminal converts this instruction into text and sends it to the server. The server performs sentiment analysis and quickly sends concise stock information to the terminal, which then immediately provides that information to the user. 【0363】 To utilize a generative AI model, a prompt is required. An example of a prompt is, "Please tell me how to determine if this customer's voice instruction is urgent and how to provide appropriate inventory information and support options." 【0364】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0365】 Step 1: 【0366】 The user requests product availability via voice command to the terminal. This voice input is collected by the microphone built into the terminal. The terminal receives this voice data and sends it to the server in digital format. 【0367】 Step 2: 【0368】 The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This text data is then preprocessed and parsed to make it easier to understand the user's intent. 【0369】 Step 3: 【0370】 Based on the text data received by the server, IBM Watson Tone Analyzer is used to analyze the user's emotions. The input for this emotion analysis is text data, and the output is an analysis result indicating the user's emotions. Based on the analysis result, it is determined whether urgency or positive emotions are present. 【0371】 Step 4: 【0372】 The server uses sentiment analysis results to determine how to provide inventory information. If the analysis results indicate urgency, the server quickly extracts the necessary information from the inventory database. If necessary, it also provides support information that can be used immediately. 【0373】 Step 5: 【0374】 The server sends extracted inventory and support information to the terminal. This data is presented in the most user-friendly and organized format. The terminal receives this information and displays it visually to the user. 【0375】 Step 6: 【0376】 The user checks the inventory information displayed on the terminal. They can request further assistance if needed. The terminal receives these requests and prepares to communicate with the server again. 【0377】 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. 【0378】 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. 【0379】 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. 【0380】 [Third Embodiment] 【0381】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0382】 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. 【0383】 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). 【0384】 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. 【0385】 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. 【0386】 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). 【0387】 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. 【0388】 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. 【0389】 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. 【0390】 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. 【0391】 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. 【0392】 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". 【0393】 This inventory management system begins with the use of a measuring device that measures the weight of products installed on store shelves. First, a sensor continuously measures the weight of the products and transmits the data to a server. The server receives this data through a receiving device, and if it determines that the weight falls below a set weight threshold, it automatically creates an order for new inventory using an order instruction generation device. 【0394】 Specifically, the server uses this information to send order instructions to suppliers using a transmission device. This process allows for replenishment of goods before inventory runs out. Furthermore, the terminal is equipped with voice recognition, allowing users to check inventory status by voice. When a user says "check inventory" to the terminal, the terminal immediately communicates with the server and provides the latest inventory information. 【0395】 The system also incorporates a mechanism for collecting feedback when customers change products. This feedback is analyzed by the server and incorporated into the next inventory strategy using feedback analysis tools. As a result of this analysis, popular products and colors can be identified and reflected in the next ordering plan, making it possible to accurately grasp customer needs. In this way, the goal is to improve the accuracy and efficiency of inventory management through collaboration between the user and the server. 【0396】 The following describes the processing flow. 【0397】 Step 1: 【0398】 The sensor sheet continuously measures the weight of the products on the shelf and transmits the data to the server in real time. 【0399】 Step 2: 【0400】 The server receives weight data transmitted from the sensor sheet and compares the current inventory weight to a predetermined baseline value. If it detects that the weight has fallen below the baseline value, it generates an alert. 【0401】 Step 3: 【0402】 The server initiates the automated ordering process based on the alert. It creates an order list using an order instruction generation mechanism and electronically sends the order instructions to the designated suppliers. 【0403】 Step 4: 【0404】 When a user requests an inventory check via voice to the terminal, the terminal's voice recognition system recognizes the request and queries the server for current inventory information. 【0405】 Step 5: 【0406】 The server sends the requested inventory information to the terminal, and the terminal displays real-time inventory information to the user. 【0407】 Step 6: 【0408】 When a user answers a survey during a device upgrade, the device sends this feedback data to the server. 【0409】 Step 7: 【0410】 The server collects feedback data and analyzes it using feedback analysis tools. Based on the analysis results, adjustments are made to reflect the findings in the next inventory strategy. 【0411】 (Example 1) 【0412】 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." 【0413】 In inventory management, there is a challenge in accurately understanding the real-time inventory status of products and preventing stockouts. Furthermore, it is difficult to develop inventory strategies that accurately reflect customer needs. 【0414】 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. 【0415】 In this invention, the server includes a measuring means for measuring the weight of goods, an information processing means for receiving data from the measuring means, and an instruction generation means for generating replenishment instructions based on the data received by the information processing means. This makes it possible to accurately grasp the inventory status in real time and prevent stockouts. Furthermore, by including a data analysis means for analyzing user feedback and planning the next inventory strategy, inventory management that accurately reflects customer needs can be realized. 【0416】 "Measuring means" refers to devices or equipment used to accurately measure the weight of a product. 【0417】 "Information processing means" refers to means for analyzing data received from measurement means and extracting necessary information. 【0418】 "Instruction generation means" refers to a part of a system that automatically generates instructions, such as inventory replenishment, based on the results of information processing means. 【0419】 "Information transmission means" refers to a configuration that includes communication means for transmitting generated replenishment instructions to the supplier. 【0420】 "Voice recognition means" refers to technology that enables a system to identify user questions and instructions based on voice input and respond appropriately. 【0421】 "Data analysis means" refers to analytical methods or techniques for analyzing collected user feedback to optimize future inventory strategies. 【0422】 The embodiment of this invention is a system designed to automate and improve the efficiency of store inventory management processes. Servers, terminals, and users interact with each other to enable optimal inventory management. 【0423】 First, the server continuously receives product weight data from sensors installed on store shelves. These sensors use high-precision "weight measuring devices" to measure weight changes in real time and transmit the data to the server. The server receives this data and determines whether it falls below a set threshold. 【0424】 If the level falls below the baseline, the server automatically generates a replenishment order using the order generation mechanism. This replenishment order includes details such as the type of product, the quantity to be replenished, and the desired delivery date and time. Next, the server uses the information transmission mechanism to send the generated replenishment order to the supplier. A general communication module and communication device are used for this information transmission. 【0425】 Furthermore, the terminal is equipped with voice recognition functionality, allowing users to check inventory status via voice commands. The terminal uses voice recognition software to instantly retrieve the latest inventory information from the server in response to user inquiries. For example, if a user says "check inventory" to the terminal, it communicates with the server and provides the most recent inventory count. 【0426】 In addition, the server collects and analyzes user feedback using data analysis tools and utilizes it to formulate future inventory strategies. This analysis allows the server to identify popular products and colors and reflect them in future ordering plans. This enables inventory adjustments that reflect customer preferences. 【0427】 As a concrete example, if the stock of orange juice on a store's beverage shelf decreases, the server automatically instructs the supplier to order an additional 20 bottles. Users can instantly check the stock status by simply saying "Check orange juice stock" to their terminal. 【0428】 An example of a prompt statement input to a generated AI model is, "Describe the process by which a server analyzes product data and sends an inventory replenishment instruction to a supplier." This prompt statement allows the model to output the exact steps. 【0429】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0430】 Step 1: 【0431】 The server receives product weight data from weighing devices installed on store shelves. It receives real-time weight data from the sensors as input and stores it in a database for comparison with a baseline. For data processing, it converts the received data to integers and calculates trends over time. As output, it retrieves the latest weight data for each product. 【0432】 Step 2: 【0433】 The server evaluates whether the weight has fallen below a certain threshold based on the stored weight data. Specifically, it compares the current weight of the product with a predetermined threshold and determines whether the decrease is within the threshold. The input is the weight data stored in step 1, and the output is the result of the determination of whether it has fallen below the threshold. 【0434】 Step 3: 【0435】 If the level falls below the threshold, the server generates a replenishment order. Specifically, it calculates the required quantity and creates an order form to send to the supplier. Using the evaluation results and order template data within the server as input, it generates an order form specifying the type and quantity of goods required as output. 【0436】 Step 4: 【0437】 The server transmits the generated order instructions to the supplier using an information transmission method. Specifically, data communication takes place via email or a dedicated EDI system. The input is replenishment instruction data, and the output is the completion of sending the instructions to the supplier's inbox. 【0438】 Step 5: 【0439】 The user uses the terminal's voice recognition function to inquire about inventory status. Specifically, the user speaks "Check inventory" into the terminal. The system receives the user's voice signal as input and generates an inventory information confirmation request as output. 【0440】 Step 6: 【0441】 The server retrieves the latest inventory data based on inventory check requests from terminals and sends the results to the terminals. Specifically, it extracts necessary information from the inventory database, converts it into a voice response format, and sends it back to the terminal. It receives inventory check request data as input and generates information including inventory levels and replenishment status as output, which is then output as voice. 【0442】 Step 7: 【0443】 The server collects user feedback through data analysis and uses it to inform future inventory strategies. Specifically, it analyzes feedback data to identify popular products and areas for improvement. It receives user feedback data as input and generates analysis results as materials for strategic meetings as output. 【0444】 (Application Example 1) 【0445】 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." 【0446】 Traditional inventory management systems did not update store inventory information in real time, sometimes leading to stock shortages or excesses. Furthermore, the difficulty in using voice commands for inventory inquiries made efficient inventory management challenging. Additionally, there was insufficient mechanism for utilizing customer feedback to inform future ordering strategies. 【0447】 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. 【0448】 In this invention, the server includes measuring means for measuring product weight, receiving means for receiving data from the measuring means, order instruction generation means for generating order instructions based on the data received by the receiving means, transmitting means for sending the order instructions generated by the order instruction generation means to suppliers, voice recognition means for querying inventory information based on voice instructions and providing the information in real time, feedback analysis means for collecting customer feedback and reflecting it in the next inventory strategy, and user interface means for managing inventory status using a smart device. This enables real-time updating of inventory information and efficient inventory inquiry by voice instructions, and further enables the construction of an effective ordering strategy that utilizes customer feedback. 【0449】 "Measuring means" refers to devices or methods for continuously measuring the weight of a product. 【0450】 A "receiving means" is a device or system that receives weight data sent from a measuring means. 【0451】 The "order instruction generation means" is a system that generates data to instruct the ordering of new inventory based on the received weight data. 【0452】 "Transmission means" refers to devices or communication methods used to transmit generated order instructions to suppliers. 【0453】 "Voice recognition means" refers to technology that interprets user voice commands and provides inventory information in real time. 【0454】 A "feedback analysis system" is a mechanism that collects customer opinions and comments and analyzes them to reflect them in future inventory strategies. 【0455】 A "user interface means" refers to a screen or system that manages inventory status via a smart device and allows users to operate it intuitively. 【0456】 The system for implementing this invention provides integrated technology for measuring product weight in real time and efficiently managing store inventory. A server receives product weight data from measuring devices installed on store shelves and continuously monitors this data. When the product weight falls below a set threshold, the server automatically uses an order instruction generation device to send an order instruction for new inventory to the supplier. This enables automatic replenishment before inventory runs out. 【0457】 The terminal is equipped with voice recognition capabilities. When a user gives a voice command such as "check inventory" to the terminal, the terminal analyzes the voice data and communicates with a server to immediately provide the latest inventory status. Furthermore, customer feedback is collected through feedback analysis capabilities and used to inform future inventory strategies. In this way, inventory management that reflects customer needs is realized. 【0458】 As a concrete example, consider its use in a supermarket. Sensors installed on the shelves measure the weight of products, and terminals provide inventory information via voice commands from staff. Simultaneously, customer feedback after purchase is analyzed to identify popular products and incorporate this information into future orders. This ensures that the necessary products are always available on the shelves. 【0459】 An example of a prompt message could be: "Please describe a smartphone app that streamlines inventory management in retail stores. Please specifically show how to utilize features such as automatic inventory data acquisition and voice recognition." This is how the input to the generating AI model might look. 【0460】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0461】 Step 1: 【0462】 The server receives product weight data from measuring devices placed on shelves. The input is the product weight measured by the measuring device, and the output is continuously updated weight data. By receiving this weight data, the server can instantly grasp the current inventory status. 【0463】 Step 2: 【0464】 The server compares the received weight data to a set baseline value. The input is the weight data obtained in step 1, and the output is the result of determining whether it falls below the baseline value. Based on this determination, the server determines whether there is a shortage of inventory. 【0465】 Step 3: 【0466】 If the server determines that the threshold value has fallen below a certain level, it automatically generates a new order using the order order generation mechanism. The input is the result of the determination in step 2, and the output is the generated order order data. The order order data is used to place orders with suppliers. 【0467】 Step 4: 【0468】 The user gives voice commands to the terminal to check the inventory status. The input is the user's voice commands, and the output is voice data. The terminal analyzes the voice data and converts the voice commands into text data. 【0469】 Step 5: 【0470】 The terminal sends text data to the server and retrieves the latest inventory information. The input is the text data converted in step 4, and the output is the latest inventory information sent from the server. The terminal displays the latest inventory information to the user. 【0471】 Step 6: 【0472】 The user inputs feedback from their purchase at the store into a terminal. The input is customer feedback data, and the output is a feedback record for analysis by the server. The server uses feedback analysis tools to utilize this data for future inventory strategies. 【0473】 The specific implementation and input considerations for the generated AI model and prompt statements are utilized in the data processing related to each of the steps described above. This processing flow enables servers and terminals to perform inventory management quickly and accurately. 【0474】 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. 【0475】 This invention aims to optimize inventory management based on user emotions by incorporating an emotion engine into a conventional inventory management system. The inventory management system includes a measuring means for measuring product weight, a receiving means for receiving data from the measuring means, an order instruction generating means for generating order instructions, and a transmitting means for sending the generated order instructions to suppliers. Furthermore, it includes a voice recognition means for querying inventory information via voice commands and an emotion engine for recognizing and analyzing user emotions. 【0476】 Specifically, the device receives voice commands from the user using voice recognition, and simultaneously analyzes the user's emotional state using an emotion engine. If the user is, for example, feeling stressed, the emotion engine sends this information to the server in real time. Based on this emotional data, the server adjusts how inventory information is provided and controls the device to present more user-friendly information. 【0477】 For example, when a user gives a voice command such as "I need to check this urgently!", the emotion engine recognizes the urgency and communicates this to the server. The server then displays the inventory information on the terminal in a quick and easy-to-use format and, if necessary, also presents immediate support options. 【0478】 Furthermore, the emotion engine is also used to analyze user feedback. For example, if a user expresses a positive emotion towards a product, the server analyzes that information and incorporates it into the strategy as a product that should be increased in inventory. In this way, incorporating an emotion engine can significantly improve the accuracy of inventory management and the quality of user support. 【0479】 The following describes the processing flow. 【0480】 Step 1: 【0481】 The device uses voice recognition to receive voice commands from the user. At this time, the voice data is transmitted to the emotion engine in real time. 【0482】 Step 2: 【0483】 The emotion engine analyzes the user's emotional state from the received audio data, determining levels of tension, excitement, calmness, etc. The analysis results are then sent to the server as numerical data. 【0484】 Step 3: 【0485】 Based on the emotional data received from the emotion engine, the server determines how to provide inventory information while considering the user's emotional state. If the situation is urgent, the information is made concise, and reassuring information is added. 【0486】 Step 4: 【0487】 The server sends the adjusted inventory information to the terminal. The terminal receives this information and presents it to the user visually or through audio announcements. 【0488】 Step 5: 【0489】 The user checks the inventory information provided via the terminal and gives additional voice instructions if further action is required. The terminal receives these instructions again using voice recognition and sends a new request to the server. 【0490】 Step 6: 【0491】 User feedback is sent to the server via the device. The server uses feedback analysis tools to analyze the benefits and challenges, including sentiment data. 【0492】 Step 7: 【0493】 Based on the analysis results, the server formulates the next inventory strategy and adjusts inventory according to demand. This process is performed regularly to maintain the efficient operation of the system. 【0494】 (Example 2) 【0495】 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." 【0496】 Traditional inventory management systems lacked the ability to provide information and adjust inventory levels based on user emotions, making it difficult to improve user satisfaction and inventory efficiency. Furthermore, they were insufficient in providing real-time inventory information that considered user urgency and emotional states, highlighting the need for improved user experience. 【0497】 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. 【0498】 In this invention, the server includes a voice recognition means for querying inventory information via voice commands, an emotion analysis means for analyzing the emotions associated with the voice commands, and an information provision adjustment means for adjusting the information provision method based on the analyzed emotion data. This enables flexible information provision and inventory adjustment based on the user's emotions. 【0499】 "Measuring means" refers to a device or function for accurately measuring the weight of a product. 【0500】 A "receiving device" refers to a device or function that plays the role of receiving data acquired from a measuring device. 【0501】 A "order instruction generation device" refers to a device or function that automatically creates necessary order instructions based on data from a receiving device. 【0502】 "Transmission device" refers to a device or function for transmitting generated order instructions to the supplier. 【0503】 A "voice recognition device" refers to a device or function that analyzes a user's voice commands and processes them as text data. 【0504】 "Emotional analysis means" refers to a device or function for identifying and analyzing the user's emotions associated with voice commands. 【0505】 "Information provision adjustment means" refers to a device or function for adjusting the format and content of information provided to users based on analyzed sentiment data. 【0506】 A "feedback analysis device" refers to a device or function that analyzes feedback collected from customers and uses it to formulate future inventory policies. 【0507】 This invention aims to realize an advanced inventory management system that utilizes user emotions. Specific embodiments of this system are described below. 【0508】 The terminal uses a speech recognition device to receive voice commands from the user. This device uses a common voice input device (e.g., a voice assistant device) to convert speech into text data. A "speech recognition API" is used as the technology supporting this process. This text data is then sent to a sentiment analysis system. 【0509】 The emotion analysis method uses emotion analysis software (e.g., an emotion analysis SDK) to analyze the emotions associated with the text. The emotion data obtained from this analysis is transmitted from the terminal to the server. 【0510】 Based on the received emotional data and voice instructions, the server utilizes information provision adjustment mechanisms to determine the optimal method for providing inventory information to the user. Specifically, it retrieves inventory information using a database management system and provides information tailored to urgency and emotional needs. In some cases, it provides information quickly and offers additional support options as needed. 【0511】 Furthermore, user feedback is collected through a feedback analysis device. This device analyzes the feedback to incorporate it into the next inventory policy, and the server modifies the inventory strategy based on the results. 【0512】 For example, if a user gives a voice command saying, "I want to quickly check inventory information," the sentiment analysis tool will identify that the command is urgent. The server will then present detailed and immediate inventory information and promptly provide the necessary support. 【0513】 An example of a prompt message is, "How should inventory data be provided when a user expresses an urgent need?" This system enables efficient inventory management while improving the user experience. 【0514】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0515】 Step 1: 【0516】 The terminal accepts voice commands from the user. When the user says, "I want to know the stock status of product A," the voice recognition system captures the voice and converts it into digital voice data. From this input data, text data is generated using the voice recognition system (e.g., a voice recognition API). The resulting output is text data related to the voice command. 【0517】 Step 2: 【0518】 The terminal processes text data using sentiment analysis tools. Here, sentiment analysis software (e.g., sentiment analysis SDK) is used to analyze the user's emotional state. The input is the text data obtained in step 1, and by analyzing the urgency and emotional nuances of the voice, the output is generated as user sentiment data. 【0519】 Step 3: 【0520】 The terminal sends analyzed sentiment data to the server. The server receives text data and sentiment data based on the user's voice instructions as input. Based on this data, the server determines the user's state and decides how to provide inventory information. The output is an instruction to provide appropriate inventory information. 【0521】 Step 4: 【0522】 The server retrieves inventory information from the database according to the determined delivery method. The input consists of information about the product requested by the user (e.g., product ID) and an assessment of its urgency. The server uses a database management system to extract the necessary inventory information, processes and formats it, and sends it to the terminal as output. 【0523】 Step 5: 【0524】 The terminal displays the acquired inventory information to the user. The input is inventory information sent from the server. The terminal provides this to the user via screen display or audio. The output is the display of inventory information to the user, and additional support is provided as needed. 【0525】 Step 6: 【0526】 The user provides feedback on the provided inventory information. The terminal captures this feedback and sends it back as text data to a sentiment analysis system. The input is text data about the user's feedback, and the output is data for analysis to inform future strategies. 【0527】 Step 7: 【0528】 The server analyzes feedback data and incorporates it into the next inventory strategy. The input is text data of user feedback. A feedback analysis device is used to identify positive or negative sentiment and adjust the inventory plan accordingly. The output is updated inventory strategy information. 【0529】 (Application Example 2) 【0530】 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." 【0531】 Modern inventory management systems require information that takes customer emotions into account to improve the customer experience. However, traditional systems fail to grasp customer emotions, making it difficult to respond effectively according to customer urgency and satisfaction levels, potentially leading to decreased customer satisfaction. Solving this problem requires flexible inventory information provision tailored to customer needs and strategic inventory adjustments. 【0532】 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. 【0533】 In this invention, the server includes means for evaluating products, emotion analysis means for analyzing customer emotions and optimizing the method of providing information, and speech recognition means for querying information based on voice instructions from customers. This makes it possible to provide information and optimize inventory strategies according to the customer's emotional state. 【0534】 "Means of evaluating products" refers to functions for inspecting and evaluating the condition and characteristics of a product. 【0535】 A "receiving means" is a function for receiving data sent from an external source. 【0536】 The "order instruction generation means" is a function that creates instructions for ordering goods and supplies based on received data. 【0537】 "Transmission means" refers to the function for sending the generated order instructions to external suppliers. 【0538】 "Voice recognition means" refers to a function that recognizes voice commands from the user and converts them into digital data. 【0539】 "Emotional analysis tools" are functions that analyze the user's emotions and optimize the way information is provided based on the results. 【0540】 This invention is a system that enables inventory management based on user sentiment information through data processing between a server and a terminal. The server evaluates the condition of the products and generates order instructions as needed. The server also receives instructions from the user via speech recognition and uses speech recognition software to convert them into text format. Here, the Google Cloud Speech-to-Text API is used. The received speech data is subjected to sentiment analysis using IBM Watson Tone Analyzer. 【0541】 The terminal provides inventory information based on user input and offers real-time information tailored to the user's emotional state. When a user searches for products by voice, the terminal displays quick and appropriate information based on the received data. 【0542】 For example, if a user says, "I need to quickly check the stock of this product," the terminal converts this instruction into text and sends it to the server. The server performs sentiment analysis and quickly sends concise stock information to the terminal, which then immediately provides that information to the user. 【0543】 To utilize a generative AI model, a prompt is required. An example of a prompt is, "Please tell me how to determine if this customer's voice instruction is urgent and how to provide appropriate inventory information and support options." 【0544】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0545】 Step 1: 【0546】 The user requests product availability via voice command to the terminal. This voice input is collected by the microphone built into the terminal. The terminal receives this voice data and sends it to the server in digital format. 【0547】 Step 2: 【0548】 The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This text data is then preprocessed and parsed to make it easier to understand the user's intent. 【0549】 Step 3: 【0550】 Based on the text data received by the server, IBM Watson Tone Analyzer is used to analyze the user's emotions. The input for this emotion analysis is text data, and the output is an analysis result indicating the user's emotions. Based on the analysis result, it is determined whether urgency or positive emotions are present. 【0551】 Step 4: 【0552】 The server uses sentiment analysis results to determine how to provide inventory information. If the analysis results indicate urgency, the server quickly extracts the necessary information from the inventory database. If necessary, it also provides support information that can be used immediately. 【0553】 Step 5: 【0554】 The server sends extracted inventory and support information to the terminal. This data is presented in the most user-friendly and organized format. The terminal receives this information and displays it visually to the user. 【0555】 Step 6: 【0556】 The user checks the inventory information displayed on the terminal. They can request further assistance if needed. The terminal receives these requests and prepares to communicate with the server again. 【0557】 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. 【0558】 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. 【0559】 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. 【0560】 [Fourth Embodiment] 【0561】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0562】 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. 【0563】 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). 【0564】 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. 【0565】 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. 【0566】 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). 【0567】 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. 【0568】 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. 【0569】 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. 【0570】 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. 【0571】 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. 【0572】 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. 【0573】 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". 【0574】 This inventory management system begins with the use of a measuring device that measures the weight of products installed on store shelves. First, a sensor continuously measures the weight of the products and transmits the data to a server. The server receives this data through a receiving device, and if it determines that the weight falls below a set weight threshold, it automatically creates an order for new inventory using an order instruction generation device. 【0575】 Specifically, the server uses this information to send order instructions to suppliers using a transmission device. This process allows for replenishment of goods before inventory runs out. Furthermore, the terminal is equipped with voice recognition, allowing users to check inventory status by voice. When a user says "check inventory" to the terminal, the terminal immediately communicates with the server and provides the latest inventory information. 【0576】 The system also incorporates a mechanism for collecting feedback when customers change products. This feedback is analyzed by the server and incorporated into the next inventory strategy using feedback analysis tools. As a result of this analysis, popular products and colors can be identified and reflected in the next ordering plan, making it possible to accurately grasp customer needs. In this way, the goal is to improve the accuracy and efficiency of inventory management through collaboration between the user and the server. 【0577】 The following describes the processing flow. 【0578】 Step 1: 【0579】 The sensor sheet continuously measures the weight of the products on the shelf and transmits the data to the server in real time. 【0580】 Step 2: 【0581】 The server receives weight data transmitted from the sensor sheet and compares the current inventory weight to a predetermined baseline value. If it detects that the weight has fallen below the baseline value, it generates an alert. 【0582】 Step 3: 【0583】 The server initiates the automated ordering process based on the alert. It creates an order list using an order instruction generation mechanism and electronically sends the order instructions to the designated suppliers. 【0584】 Step 4: 【0585】 When a user requests an inventory check via voice to the terminal, the terminal's voice recognition system recognizes the request and queries the server for current inventory information. 【0586】 Step 5: 【0587】 The server sends the requested inventory information to the terminal, and the terminal displays real-time inventory information to the user. 【0588】 Step 6: 【0589】 When a user answers a survey during a device upgrade, the device sends this feedback data to the server. 【0590】 Step 7: 【0591】 The server collects feedback data and analyzes it using feedback analysis tools. Based on the analysis results, adjustments are made to reflect the findings in the next inventory strategy. 【0592】 (Example 1) 【0593】 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". 【0594】 In inventory management, there is a challenge in accurately understanding the real-time inventory status of products and preventing stockouts. Furthermore, it is difficult to develop inventory strategies that accurately reflect customer needs. 【0595】 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. 【0596】 In this invention, the server includes a measuring means for measuring the weight of goods, an information processing means for receiving data from the measuring means, and an instruction generation means for generating replenishment instructions based on the data received by the information processing means. This makes it possible to accurately grasp the inventory status in real time and prevent stockouts. Furthermore, by including a data analysis means for analyzing user feedback and planning the next inventory strategy, inventory management that accurately reflects customer needs can be realized. 【0597】 "Measuring means" refers to devices or equipment used to accurately measure the weight of a product. 【0598】 "Information processing means" refers to means for analyzing data received from measurement means and extracting necessary information. 【0599】 "Instruction generation means" refers to a part of a system that automatically generates instructions, such as inventory replenishment, based on the results of information processing means. 【0600】 "Information transmission means" refers to a configuration that includes communication means for transmitting generated replenishment instructions to the supplier. 【0601】 "Voice recognition means" refers to technology that enables a system to identify user questions and instructions based on voice input and respond appropriately. 【0602】 "Data analysis means" refers to analytical methods or techniques for analyzing collected user feedback to optimize future inventory strategies. 【0603】 The embodiment of this invention is a system designed to automate and improve the efficiency of store inventory management processes. Servers, terminals, and users interact with each other to enable optimal inventory management. 【0604】 First, the server continuously receives product weight data from sensors installed on store shelves. These sensors use high-precision "weight measuring devices" to measure weight changes in real time and transmit the data to the server. The server receives this data and determines whether it falls below a set threshold. 【0605】 If the level falls below the baseline, the server automatically generates a replenishment order using the order generation mechanism. This replenishment order includes details such as the type of product, the quantity to be replenished, and the desired delivery date and time. Next, the server uses the information transmission mechanism to send the generated replenishment order to the supplier. A general communication module and communication device are used for this information transmission. 【0606】 Furthermore, the terminal is equipped with voice recognition functionality, allowing users to check inventory status via voice commands. The terminal uses voice recognition software to instantly retrieve the latest inventory information from the server in response to user inquiries. For example, if a user says "check inventory" to the terminal, it communicates with the server and provides the most recent inventory count. 【0607】 In addition, the server collects and analyzes user feedback using data analysis tools and utilizes it to formulate future inventory strategies. This analysis allows the server to identify popular products and colors and reflect them in future ordering plans. This enables inventory adjustments that reflect customer preferences. 【0608】 As a concrete example, if the stock of orange juice on a store's beverage shelf decreases, the server automatically instructs the supplier to order an additional 20 bottles. Users can instantly check the stock status by simply saying "Check orange juice stock" to their terminal. 【0609】 An example of a prompt statement input to a generated AI model is, "Describe the process by which a server analyzes product data and sends an inventory replenishment instruction to a supplier." This prompt statement allows the model to output the exact steps. 【0610】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0611】 Step 1: 【0612】 The server receives product weight data from weighing devices installed on store shelves. It receives real-time weight data from the sensors as input and stores it in a database for comparison with a baseline. For data processing, it converts the received data to integers and calculates trends over time. As output, it retrieves the latest weight data for each product. 【0613】 Step 2: 【0614】 The server evaluates whether the weight has fallen below a certain threshold based on the stored weight data. Specifically, it compares the current weight of the product with a predetermined threshold and determines whether the decrease is within the threshold. The input is the weight data stored in step 1, and the output is the result of the determination of whether it has fallen below the threshold. 【0615】 Step 3: 【0616】 If the level falls below the threshold, the server generates a replenishment order. Specifically, it calculates the required quantity and creates an order form to send to the supplier. Using the evaluation results and order template data within the server as input, it generates an order form specifying the type and quantity of goods required as output. 【0617】 Step 4: 【0618】 The server transmits the generated order instructions to the supplier using an information transmission method. Specifically, data communication takes place via email or a dedicated EDI system. The input is replenishment instruction data, and the output is the completion of sending the instructions to the supplier's inbox. 【0619】 Step 5: 【0620】 The user uses the terminal's voice recognition function to inquire about inventory status. Specifically, the user speaks "Check inventory" into the terminal. The system receives the user's voice signal as input and generates an inventory information confirmation request as output. 【0621】 Step 6: 【0622】 The server retrieves the latest inventory data based on inventory check requests from terminals and sends the results to the terminals. Specifically, it extracts necessary information from the inventory database, converts it into a voice response format, and sends it back to the terminal. It receives inventory check request data as input and generates information including inventory levels and replenishment status as output, which is then output as voice. 【0623】 Step 7: 【0624】 The server collects user feedback through data analysis and uses it to inform future inventory strategies. Specifically, it analyzes feedback data to identify popular products and areas for improvement. It receives user feedback data as input and generates analysis results as materials for strategic meetings as output. 【0625】 (Application Example 1) 【0626】 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". 【0627】 Traditional inventory management systems did not update store inventory information in real time, sometimes leading to stock shortages or excesses. Furthermore, the difficulty in using voice commands for inventory inquiries made efficient inventory management challenging. Additionally, there was insufficient mechanism for utilizing customer feedback to inform future ordering strategies. 【0628】 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. 【0629】 In this invention, the server includes measuring means for measuring product weight, receiving means for receiving data from the measuring means, order instruction generation means for generating order instructions based on the data received by the receiving means, transmitting means for sending the order instructions generated by the order instruction generation means to suppliers, voice recognition means for querying inventory information based on voice instructions and providing the information in real time, feedback analysis means for collecting customer feedback and reflecting it in the next inventory strategy, and user interface means for managing inventory status using a smart device. This enables real-time updating of inventory information and efficient inventory inquiry by voice instructions, and further enables the construction of an effective ordering strategy that utilizes customer feedback. 【0630】 "Measuring means" refers to devices or methods for continuously measuring the weight of a product. 【0631】 A "receiving means" is a device or system that receives weight data sent from a measuring means. 【0632】 The "order instruction generation means" is a system that generates data to instruct the ordering of new inventory based on the received weight data. 【0633】 "Transmission means" refers to devices or communication methods used to transmit generated order instructions to suppliers. 【0634】 "Voice recognition means" refers to technology that interprets user voice commands and provides inventory information in real time. 【0635】 A "feedback analysis system" is a mechanism that collects customer opinions and comments and analyzes them to reflect them in future inventory strategies. 【0636】 A "user interface means" refers to a screen or system that manages inventory status via a smart device and allows users to operate it intuitively. 【0637】 The system for implementing this invention provides integrated technology for measuring product weight in real time and efficiently managing store inventory. A server receives product weight data from measuring devices installed on store shelves and continuously monitors this data. When the product weight falls below a set threshold, the server automatically uses an order instruction generation device to send an order instruction for new inventory to the supplier. This enables automatic replenishment before inventory runs out. 【0638】 The terminal is equipped with voice recognition capabilities. When a user gives a voice command such as "check inventory" to the terminal, the terminal analyzes the voice data and communicates with a server to immediately provide the latest inventory status. Furthermore, customer feedback is collected through feedback analysis capabilities and used to inform future inventory strategies. In this way, inventory management that reflects customer needs is realized. 【0639】 As a concrete example, consider its use in a supermarket. Sensors installed on the shelves measure the weight of products, and terminals provide inventory information via voice commands from staff. Simultaneously, customer feedback after purchase is analyzed to identify popular products and incorporate this information into future orders. This ensures that the necessary products are always available on the shelves. 【0640】 An example of a prompt message could be: "Please describe a smartphone app that streamlines inventory management in retail stores. Please specifically show how to utilize features such as automatic inventory data acquisition and voice recognition." This is how the input to the generating AI model might look. 【0641】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0642】 Step 1: 【0643】 The server receives product weight data from measuring devices placed on shelves. The input is the product weight measured by the measuring device, and the output is continuously updated weight data. By receiving this weight data, the server can instantly grasp the current inventory status. 【0644】 Step 2: 【0645】 The server compares the received weight data to a set baseline value. The input is the weight data obtained in step 1, and the output is the result of determining whether it falls below the baseline value. Based on this determination, the server determines whether there is a shortage of inventory. 【0646】 Step 3: 【0647】 If the server determines that the threshold value has fallen below a certain level, it automatically generates a new order using the order order generation mechanism. The input is the result of the determination in step 2, and the output is the generated order order data. The order order data is used to place orders with suppliers. 【0648】 Step 4: 【0649】 The user gives voice commands to the terminal to check the inventory status. The input is the user's voice commands, and the output is voice data. The terminal analyzes the voice data and converts the voice commands into text data. 【0650】 Step 5: 【0651】 The terminal sends text data to the server and retrieves the latest inventory information. The input is the text data converted in step 4, and the output is the latest inventory information sent from the server. The terminal displays the latest inventory information to the user. 【0652】 Step 6: 【0653】 The user inputs feedback from their purchase at the store into a terminal. The input is customer feedback data, and the output is a feedback record for analysis by the server. The server uses feedback analysis tools to utilize this data for future inventory strategies. 【0654】 The specific implementation and input considerations for the generated AI model and prompt statements are utilized in the data processing related to each of the steps described above. This processing flow enables servers and terminals to perform inventory management quickly and accurately. 【0655】 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. 【0656】 This invention aims to optimize inventory management based on user emotions by incorporating an emotion engine into a conventional inventory management system. The inventory management system includes a measuring means for measuring product weight, a receiving means for receiving data from the measuring means, an order instruction generating means for generating order instructions, and a transmitting means for sending the generated order instructions to suppliers. Furthermore, it includes a voice recognition means for querying inventory information via voice commands and an emotion engine for recognizing and analyzing user emotions. 【0657】 Specifically, the device receives voice commands from the user using voice recognition, and simultaneously analyzes the user's emotional state using an emotion engine. If the user is, for example, feeling stressed, the emotion engine sends this information to the server in real time. Based on this emotional data, the server adjusts how inventory information is provided and controls the device to present more user-friendly information. 【0658】 For example, when a user gives a voice command such as "I need to check this urgently!", the emotion engine recognizes the urgency and communicates this to the server. The server then displays the inventory information on the terminal in a quick and easy-to-use format and, if necessary, also presents immediate support options. 【0659】 Furthermore, the emotion engine is also used to analyze user feedback. For example, if a user expresses a positive emotion towards a product, the server analyzes that information and incorporates it into the strategy as a product that should be increased in inventory. In this way, incorporating an emotion engine can significantly improve the accuracy of inventory management and the quality of user support. 【0660】 The following describes the processing flow. 【0661】 Step 1: 【0662】 The device uses voice recognition to receive voice commands from the user. At this time, the voice data is transmitted to the emotion engine in real time. 【0663】 Step 2: 【0664】 The emotion engine analyzes the user's emotional state from the received audio data, determining levels of tension, excitement, calmness, etc. The analysis results are then sent to the server as numerical data. 【0665】 Step 3: 【0666】 Based on the emotional data received from the emotion engine, the server determines how to provide inventory information while considering the user's emotional state. If the situation is urgent, the information is made concise, and reassuring information is added. 【0667】 Step 4: 【0668】 The server sends the adjusted inventory information to the terminal. The terminal receives this information and presents it to the user visually or through audio announcements. 【0669】 Step 5: 【0670】 The user checks the inventory information provided via the terminal and gives additional voice instructions if further action is required. The terminal receives these instructions again using voice recognition and sends a new request to the server. 【0671】 Step 6: 【0672】 User feedback is sent to the server via the device. The server uses feedback analysis tools to analyze the benefits and challenges, including sentiment data. 【0673】 Step 7: 【0674】 Based on the analysis results, the server formulates the next inventory strategy and adjusts inventory according to demand. This process is performed regularly to maintain the efficient operation of the system. 【0675】 (Example 2) 【0676】 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". 【0677】 Traditional inventory management systems lacked the ability to provide information and adjust inventory levels based on user emotions, making it difficult to improve user satisfaction and inventory efficiency. Furthermore, they were insufficient in providing real-time inventory information that considered user urgency and emotional states, highlighting the need for improved user experience. 【0678】 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. 【0679】 In this invention, the server includes a voice recognition means for querying inventory information via voice commands, an emotion analysis means for analyzing the emotions associated with the voice commands, and an information provision adjustment means for adjusting the information provision method based on the analyzed emotion data. This enables flexible information provision and inventory adjustment based on the user's emotions. 【0680】 "Measuring means" refers to a device or function for accurately measuring the weight of a product. 【0681】 A "receiving device" refers to a device or function that plays the role of receiving data acquired from a measuring device. 【0682】 A "order instruction generation device" refers to a device or function that automatically creates necessary order instructions based on data from a receiving device. 【0683】 "Transmission device" refers to a device or function for transmitting generated order instructions to the supplier. 【0684】 A "voice recognition device" refers to a device or function that analyzes a user's voice commands and processes them as text data. 【0685】 "Emotional analysis means" refers to a device or function for identifying and analyzing the user's emotions associated with voice commands. 【0686】 "Information provision adjustment means" refers to a device or function for adjusting the format and content of information provided to users based on analyzed sentiment data. 【0687】 A "feedback analysis device" refers to a device or function that analyzes feedback collected from customers and uses it to formulate future inventory policies. 【0688】 This invention aims to realize an advanced inventory management system that utilizes user emotions. Specific embodiments of this system are described below. 【0689】 The terminal uses a speech recognition device to receive voice commands from the user. This device uses a common voice input device (e.g., a voice assistant device) to convert speech into text data. A "speech recognition API" is used as the technology supporting this process. This text data is then sent to a sentiment analysis system. 【0690】 The emotion analysis method uses emotion analysis software (e.g., an emotion analysis SDK) to analyze the emotions associated with the text. The emotion data obtained from this analysis is transmitted from the terminal to the server. 【0691】 Based on the received emotional data and voice instructions, the server utilizes information provision adjustment mechanisms to determine the optimal method for providing inventory information to the user. Specifically, it retrieves inventory information using a database management system and provides information tailored to urgency and emotional needs. In some cases, it provides information quickly and offers additional support options as needed. 【0692】 Furthermore, user feedback is collected through a feedback analysis device. This device analyzes the feedback to incorporate it into the next inventory policy, and the server modifies the inventory strategy based on the results. 【0693】 For example, if a user gives a voice command saying, "I want to quickly check inventory information," the sentiment analysis tool will identify that the command is urgent. The server will then present detailed and immediate inventory information and promptly provide the necessary support. 【0694】 An example of a prompt message is, "How should inventory data be provided when a user expresses an urgent need?" This system enables efficient inventory management while improving the user experience. 【0695】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0696】 Step 1: 【0697】 The terminal accepts voice commands from the user. When the user says, "I want to know the stock status of product A," the voice recognition system captures the voice and converts it into digital voice data. From this input data, text data is generated using the voice recognition system (e.g., a voice recognition API). The resulting output is text data related to the voice command. 【0698】 Step 2: 【0699】 The terminal processes text data using sentiment analysis tools. Here, sentiment analysis software (e.g., sentiment analysis SDK) is used to analyze the user's emotional state. The input is the text data obtained in step 1, and by analyzing the urgency and emotional nuances of the voice, the output is generated as user sentiment data. 【0700】 Step 3: 【0701】 The terminal sends analyzed sentiment data to the server. The server receives text data and sentiment data based on the user's voice instructions as input. Based on this data, the server determines the user's state and decides how to provide inventory information. The output is an instruction to provide appropriate inventory information. 【0702】 Step 4: 【0703】 The server retrieves inventory information from the database according to the determined delivery method. The input consists of information about the product requested by the user (e.g., product ID) and an assessment of its urgency. The server uses a database management system to extract the necessary inventory information, processes and formats it, and sends it to the terminal as output. 【0704】 Step 5: 【0705】 The terminal displays the acquired inventory information to the user. The input is inventory information sent from the server. The terminal provides this to the user via screen display or audio. The output is the display of inventory information to the user, and additional support is provided as needed. 【0706】 Step 6: 【0707】 The user provides feedback on the provided inventory information. The terminal captures this feedback and sends it back as text data to a sentiment analysis system. The input is text data about the user's feedback, and the output is data for analysis to inform future strategies. 【0708】 Step 7: 【0709】 The server analyzes feedback data and incorporates it into the next inventory strategy. The input is text data of user feedback. A feedback analysis device is used to identify positive or negative sentiment and adjust the inventory plan accordingly. The output is updated inventory strategy information. 【0710】 (Application Example 2) 【0711】 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". 【0712】 Modern inventory management systems require information that takes customer emotions into account to improve the customer experience. However, traditional systems fail to grasp customer emotions, making it difficult to respond effectively according to customer urgency and satisfaction levels, potentially leading to decreased customer satisfaction. Solving this problem requires flexible inventory information provision tailored to customer needs and strategic inventory adjustments. 【0713】 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. 【0714】 In this invention, the server includes means for evaluating products, emotion analysis means for analyzing customer emotions and optimizing the method of providing information, and speech recognition means for querying information based on voice instructions from customers. This makes it possible to provide information and optimize inventory strategies according to the customer's emotional state. 【0715】 "Means of evaluating products" refers to functions for inspecting and evaluating the condition and characteristics of a product. 【0716】 A "receiving means" is a function for receiving data sent from an external source. 【0717】 The "order instruction generation means" is a function that creates instructions for ordering goods and supplies based on received data. 【0718】 "Transmission means" refers to the function for sending the generated order instructions to external suppliers. 【0719】 "Voice recognition means" refers to a function that recognizes voice commands from the user and converts them into digital data. 【0720】 "Emotional analysis tools" are functions that analyze the user's emotions and optimize the way information is provided based on the results. 【0721】 This invention is a system that enables inventory management based on user sentiment information through data processing between a server and a terminal. The server evaluates the condition of the products and generates order instructions as needed. The server also receives instructions from the user via speech recognition and uses speech recognition software to convert them into text format. Here, the Google Cloud Speech-to-Text API is used. The received speech data is subjected to sentiment analysis using IBM Watson Tone Analyzer. 【0722】 The terminal provides inventory information based on user input and offers real-time information tailored to the user's emotional state. When a user searches for products by voice, the terminal displays quick and appropriate information based on the received data. 【0723】 For example, if a user says, "I need to quickly check the stock of this product," the terminal converts this instruction into text and sends it to the server. The server performs sentiment analysis and quickly sends concise stock information to the terminal, which then immediately provides that information to the user. 【0724】 To utilize a generative AI model, a prompt is required. An example of a prompt is, "Please tell me how to determine if this customer's voice instruction is urgent and how to provide appropriate inventory information and support options." 【0725】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0726】 Step 1: 【0727】 The user requests product availability via voice command to the terminal. This voice input is collected by the microphone built into the terminal. The terminal receives this voice data and sends it to the server in digital format. 【0728】 Step 2: 【0729】 The device uses the Google Cloud Speech-to-Text API to convert received audio data into text data. This text data is then preprocessed and parsed to make it easier to understand the user's intent. 【0730】 Step 3: 【0731】 Based on the text data received by the server, IBM Watson Tone Analyzer is used to analyze the user's emotions. The input for this emotion analysis is text data, and the output is an analysis result indicating the user's emotions. Based on the analysis result, it is determined whether urgency or positive emotions are present. 【0732】 Step 4: 【0733】 The server uses sentiment analysis results to determine how to provide inventory information. If the analysis results indicate urgency, the server quickly extracts the necessary information from the inventory database. If necessary, it also provides support information that can be used immediately. 【0734】 Step 5: 【0735】 The server sends extracted inventory and support information to the terminal. This data is presented in the most user-friendly and organized format. The terminal receives this information and displays it visually to the user. 【0736】 Step 6: 【0737】 The user checks the inventory information displayed on the terminal. They can request further assistance if needed. The terminal receives these requests and prepares to communicate with the server again. 【0738】 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. 【0739】 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. 【0740】 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. 【0741】 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. 【0742】 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. 【0743】 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. 【0744】 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. 【0745】 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. 【0746】 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." 【0747】 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. 【0748】 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. 【0749】 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. 【0750】 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. 【0751】 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. 【0752】 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. 【0753】 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. 【0754】 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. 【0755】 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. 【0756】 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. 【0757】 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. 【0758】 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 as being incorporated by reference. 【0759】 The following is further disclosed regarding the embodiments described above. 【0760】 (Claim 1) 【0761】 A measuring means for measuring the weight of a product, 【0762】 A receiving means for receiving data from the measuring means, 【0763】 An order instruction generation means that generates an order instruction based on the data received by the receiving means, 【0764】 A transmission means for transmitting the order instructions generated from the order instruction generation means to a supplier, 【0765】 A voice recognition means for inquiring about inventory information via voice commands, 【0766】 A feedback analysis tool that collects customer feedback and formulates the next inventory strategy, 【0767】 A system that includes this. 【0768】 (Claim 2) 【0769】 The system according to claim 1, which is generated only when the order instruction falls below a set threshold value. 【0770】 (Claim 3) 【0771】 The system according to claim 1, wherein the voice recognition means provides inventory information in real time based on voice instructions from the user. 【0772】 "Example 1" 【0773】 (Claim 1) 【0774】 A measuring means for measuring the weight of a product, 【0775】 Information processing means that receives data from the measurement means, 【0776】 An instruction generation means that generates supplementary instructions based on data received by the information processing means, 【0777】 Information transmission means for transmitting the replenishment instructions generated from the instruction generation means to the supplier, 【0778】 A voice recognition means for obtaining inventory information via voice commands, 【0779】 A data analysis tool that analyzes user feedback to plan the next inventory strategy, 【0780】 A system that includes this. 【0781】 (Claim 2) 【0782】 The system according to claim 1, which generates a replenishment instruction only when the replenishment instruction falls below a set reference value. 【0783】 (Claim 3) 【0784】 The system according to claim 1, wherein the voice recognition means provides inventory information immediately based on voice instructions from the user. 【0785】 "Application Example 1" 【0786】 (Claim 1) 【0787】 A measuring means for measuring the weight of a product, 【0788】 A receiving means for receiving data from the measuring means, 【0789】 An order instruction generation means that generates an order instruction based on the data received by the receiving means, 【0790】 A transmission means for transmitting the order instructions generated from the order instruction generation means to a supplier, 【0791】 A voice recognition system that queries inventory information based on voice commands and provides the information in real time, 【0792】 A feedback analysis method that collects customer feedback and incorporates it into the next inventory strategy, 【0793】 A user interface means for managing inventory status using a smart device, 【0794】 A system that includes this. 【0795】 (Claim 2) 【0796】 The system according to claim 1, which is generated only when the order instruction falls below a set threshold value. 【0797】 (Claim 3) 【0798】 The system according to claim 1, wherein a voice recognition means provides inventory information in real time based on voice instructions from a user, and the information is displayed on a smart device. 【0799】 "Example 2 of combining an emotion engine" 【0800】 (Claim 1) 【0801】 A measuring device for measuring the weight of a product, 【0802】 A receiving device that receives data from the measuring means, 【0803】 An order instruction generation device that generates order instructions based on data received by the receiving device, 【0804】 A transmitting device that transmits the order instructions generated from the order instruction generation device to the supplier, 【0805】 A voice recognition device that allows users to inquire about inventory information using voice commands, 【0806】 An emotion analysis method for analyzing the emotions associated with voice instructions, 【0807】 Information provision adjustment means that adjusts the information provision method based on analyzed sentiment data, 【0808】 A feedback analysis device that collects customer feedback and formulates the next inventory policy, 【0809】 A system that includes this. 【0810】 (Claim 2) 【0811】 The system according to claim 1, which is generated only when the order instruction falls below a set threshold value. 【0812】 (Claim 3) 【0813】 The system according to claim 1, wherein a voice recognition device provides inventory information in real time based on voice instructions from a user. 【0814】 "Application example 2 when combining with an emotional engine" 【0815】 (Claim 1) 【0816】 Methods for evaluating products, 【0817】 A receiving means for receiving data from the evaluation means, 【0818】 An order instruction generation means that generates an order instruction based on the data received by the receiving means, 【0819】 A transmission means for transmitting the order instructions generated from the order instruction generation means to the supplier, 【0820】 A voice recognition means that performs information retrieval based on voice commands from the user, 【0821】 A sentiment analysis tool that analyzes customer emotions and optimizes the method of providing information, 【0822】 A system that includes this. 【0823】 (Claim 2) 【0824】 It is generated only when the order instruction falls below the set threshold value. 【0825】 The system according to claim 1, which is further adjusted according to the customer's emotional state. 【0826】 (Claim 3) 【0827】 The system according to claim 1, wherein the voice recognition means provides information that takes into account the emotional state in real time based on voice instructions from the customer. [Explanation of symbols] 【0828】 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

[Claim 1] A measuring means for measuring the weight of a product, A receiving means for receiving data from the measuring means, An order instruction generation means that generates an order instruction based on the data received by the receiving means, A transmission means for transmitting the order instructions generated from the order instruction generation means to a supplier, A voice recognition means for inquiring about inventory information via voice commands, A feedback analysis tool that collects customer feedback and formulates the next inventory strategy, A system that includes this. [Claim 2] The system according to claim 1, which is generated only when the order instruction falls below a set threshold value. [Claim 3] The system according to claim 1, wherein the voice recognition means provides inventory information in real time based on voice instructions from the user.

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