system

Abstract

Provide a system. 【Solution means】 As an information acquisition means, means for acquiring environmental information and regional event information via a communication network, Means for acquiring past transaction records from a recording medium, As an analysis means, means for executing an intelligent algorithm for prediction based on the acquired environmental information, regional event information, and past transaction records, As an optimization means, means for creating an optimal supply plan based on the prediction result and the held information, As a notification means, means for reporting the optimized supply plan to the administrator, Means for transmitting information to the administrator using a real-time notification function, Equipped with an adjustable user interface, means for the administrator to modify the supply plan, A system including.

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, the method 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In stores such as convenience stores, effective order management considering the impact of weather information and local events on sales is required. However, currently, it is difficult to respond immediately to sudden changes in the weather or the occurrence of local events, and there may be losses in sales opportunities and food losses due to excessive inventory. In order to solve such problems and improve the operational efficiency of stores, appropriate sales forecasting and inventory management are essential.

Means for Solving the Problems

[0005] This invention comprises information acquisition means for acquiring weather information and local event information, information acquisition means for acquiring past sales performance, analysis means for performing sales forecasts using an artificial intelligence algorithm based on the acquired information, optimization means for creating an optimal ordering plan based on the forecast results and inventory information, and notification means for reporting the optimized ordering plan to the administrator. By aggregating various data related to inventory and ordering, and through analysis and forecasting utilizing artificial intelligence, it becomes possible to maximize sales opportunities while reducing food waste.

[0006] "Information acquisition means" refers to a device or program that has the function of acquiring weather information and local event information via a communication network.

[0007] "Analysis means" refers to a device or program that has the function of making sales forecasts by using acquired weather information, local event information, and past sales performance to execute an artificial intelligence algorithm.

[0008] "Optimization means" refers to a device or program that has the function of creating an optimal ordering plan based on prediction results from analysis means and inventory information.

[0009] "Notification means" refers to a device or program that has the function of reporting the order plan created by the optimization means to the administrator.

[0010] An "artificial intelligence algorithm" is a computational method used to predict future sales trends by analyzing the impact of weather information and local event information on past sales performance.

[0011] "Manager" refers to a person or position responsible for receiving proposals for procurement plans and making final decisions and adjustments.

[0012] "Sales forecasting" is the process of evaluating and predicting future product sales based on weather conditions, local event data, and past sales performance.

[0013] An "optimal ordering plan" is a plan that outlines the order quantities for goods determined to meet predicted demand while minimizing inventory waste. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

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

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

[0017] In the following embodiments, a 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.

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

[0019] In the following embodiments, a 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, etc.

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0035] This invention provides an efficient ordering management system for retail stores such as convenience stores. The system is primarily composed of three elements: a server, terminals, and users.

[0036] The server has multiple functions. First, it periodically collects weather and local event information via the internet through its information acquisition function. This allows users to understand environmental changes around their stores in real time. The server also collects past sales performance data from stores and stores it in a database.

[0037] The analysis is performed by an artificial intelligence algorithm on a server. It analyzes collected weather and event information along with historical sales data to assess the impact of these factors on product sales. Based on this assessment, future sales forecasts are generated.

[0038] Based on the generated sales forecast, the server uses its optimization function to calculate the appropriate order quantity for each product. Inventory status is also taken into consideration to create an ordering plan that avoids excess inventory and stockouts.

[0039] The terminal receives an optimized order plan provided by the server. The store manager, who is the user, reviews this plan on their terminal and makes adjustments as needed. For example, if a promotional campaign for a particular product is running concurrently, the user can fine-tune the plan and increase the order quantity.

[0040] As a concrete example, let's assume a large music event is being held at a certain store over the weekend. The server retrieves this information and analyzes sales data from similar events held in the past. As a result, it predicts that certain beverages and snacks will sell well, and therefore plans to place larger orders than usual. The user receives this plan and can place an order to prepare for the demand during the event.

[0041] This system flexibly responds to fluctuating consumer demand and enables efficient order management. Through this system, users can maximize sales opportunities while minimizing inventory losses.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server accesses weather information services and local event information services to retrieve the latest data. It uses APIs to extract the information and saves it to an internal database.

[0045] Step 2:

[0046] The server retrieves historical sales data from the store management system. Based on this data, it integrates it into a database in a format that allows for analysis of its relationship with weather and event information.

[0047] Step 3:

[0048] The server uses collected weather, event, and sales data to run artificial intelligence algorithms. The algorithms analyze past patterns and make future sales predictions. For example, they might use historical data to determine that certain products tend to sell less during rainy weather.

[0049] Step 4:

[0050] The server calculates the optimal order quantity based on predicted sales data and current inventory information. The server creates an order plan and adjusts it to minimize the risk of stockouts or excess inventory.

[0051] Step 5:

[0052] The server sends the generated order plan to the terminal. The terminal notifies the user of this information and indicates that the order details can be confirmed.

[0053] Step 6:

[0054] Users review the proposed order plan via their device and make changes as needed. For example, they might adjust the order quantity to increase during a promotional period for a particular product.

[0055] Step 7:

[0056] Once the user confirms the order, the terminal sends that information to the server. The server processes the final order and accurately transmits it to the supplier.

[0057] (Example 1)

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

[0059] In retail stores, demand is prone to fluctuations due to weather conditions and local events. This makes it easy for excess inventory and stockouts to occur, posing a challenge to efficient order management. Furthermore, many conventional ordering systems rely on static predictive models based on historical data, failing to adequately respond to real-time environmental changes. Solving these problems is essential.

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

[0061] In this invention, the server includes means for acquiring weather data and local event information via a communication network as an information gathering means, means for acquiring past sales history from a database, and means for performing estimations using an intelligent algorithm. This makes it possible to create an optimal ordering plan for fluctuating demand and to improve the efficiency of inventory management.

[0062] "Information gathering means" refers to functions for acquiring weather data and local event information from external sources via a communication network.

[0063] "Sales history" refers to records showing what products a store has sold in the past, when, and in what quantities.

[0064] A "database" is an information management system that structures and stores collected data, and allows for efficient searching and extraction as needed.

[0065] An "intelligent algorithm" is a computational method that analyzes acquired data to predict future demand and sales patterns.

[0066] An "optimization tool" is a function that creates an optimal ordering plan based on estimated demand and taking inventory data into consideration.

[0067] "Notification methods" refer to methods of providing administrators with optimized ordering plans and communicating information for reviewing and modifying ordering details.

[0068] "Interface means" refers to means for realizing a user interface that allows administrators to modify quantities based on the order plan.

[0069] This invention embodies a system that supports efficient order management in retail stores. The system consists of three elements: a server, a terminal, and a user.

[0070] The server's role is to acquire weather data and local event information via the communication network as a means of information gathering. Specifically, it periodically collects data from weather information services and publicly available local event databases. This information is obtained through web scraping techniques using Python and APIs. The server also collects past sales history from the database and integrates the information. MySQL® and PostgreSQL are used for managing this database.

[0071] The collected data is analyzed on the server using intelligent algorithms. First, the data is preprocessed using libraries such as Pandas and NumPy in Python. Next, time series analysis and predictions using machine learning models are performed using Scikit-learn and StatsModels. This makes it possible to forecast demand while taking into account the impact of weather and events.

[0072] The server performs mathematical optimization using GuRoBi and ORTools as optimization tools, generating an optimal ordering plan based on inventory data and forecast results. This enables effective ordering management that prevents excess inventory and stockouts.

[0073] The terminal provides the administrator user with an optimized order plan sent from the server. The terminal has a dedicated application installed, allowing the user to check the displayed plan on the spot and adjust order quantities using touch controls or keyboard input.

[0074] The user makes modifications to the optimized plan through this interface and returns the finalized ordering plan to the server. The server then automatically places orders with suppliers based on this confirmed information.

[0075] As a concrete example, if a large-scale music festival is held nearby on a weekend, the server collects event information in advance and analyzes it in combination with historical data. The analysis results predict an increase in demand for a specific product and determine the optimal order quantity accordingly. The user can review the plan, make any necessary adjustments, and finalize it, thus avoiding missing sales opportunities on the day of the event.

[0076] An example of a prompt might be: "Based on a weekend event in a specific region, analyze expected consumer behavior and purchasing trends, and develop a product ordering plan accordingly. Please also consider past data from similar events and the weather forecast for the day."

[0077] This system allows users to respond appropriately to changing circumstances, maximizing sales while minimizing inventory waste.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server uses a communication network to acquire external weather data and local event information as a means of information gathering. This process involves periodically collecting data through API calls. Specifically, the server calls the weather information provider's API and performs web scraping of event calendars. The input data consists of weather conditions (e.g., temperature, precipitation) and event information (e.g., date, time, and location), which are converted into an internal format and stored in the database.

[0081] Step 2:

[0082] The server retrieves past sales history from the database. This gathers the basic data necessary for analyzing sales patterns. Specifically, it extracts sales data using SQL queries. The input for this process is sales transaction data for a specific period. The processed data is aggregated into sales figures and revenue data by category and used in the next analysis phase.

[0083] Step 3:

[0084] The server uses intelligent algorithms to analyze the collected data. This analysis is used to predict demand. Specifically, it preprocesses the data using Python's Pandas library and builds a machine learning model using Scikit-learn. The inputs are weather data, event information, and sales history. This data is combined and input into the model to output future sales forecasts. This output quantifies the demand for the product.

[0085] Step 4:

[0086] The server uses optimization techniques to generate an order plan based on sales forecasts. Specifically, it uses mathematical optimization tools (GuRoBi or ORTools) to perform optimization considering inventory levels and forecasted demand. The inputs to this process are sales forecast data and current inventory data. The output is an order plan that includes recommended order quantities for each product.

[0087] Step 5:

[0088] The terminal receives an optimized order plan from the server and notifies the administrator user. Specifically, a dedicated application on the terminal displays the outputted order plan. The input for this process is the optimized order plan data. The user visually confirms this information and adjusts the order quantities via the interface as needed.

[0089] Step 6:

[0090] Users modify and finalize their order plans via their terminals and provide feedback to the server. Specifically, they adjust quantities using the user interface and send the revised data to the server. The input to this process is the order quantity adjusted by the user. The output is the final order information confirmed by the server.

[0091] Step 7:

[0092] The server automatically places orders with the supplier system based on the final order information. Specifically, it sends the necessary order information to the supplier's system. The input to this process is the confirmed order information, and the output is the order confirmation sent to the supplier.

[0093] (Application Example 1)

[0094] 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."

[0095] Traditional order management systems in retail stores suffered from low demand forecasting accuracy, leading to frequent inventory oversupply and stockouts. Furthermore, it was difficult to reflect the impact of environmental changes and local events on sales in a timely manner, hindering managers from responding quickly. There is a need for a more efficient and responsive order management system that addresses these challenges.

[0096] 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.

[0097] In this invention, the server includes, as an information acquisition means, means for acquiring environmental information and local event information via a communication network, and means for acquiring past transaction records from a recording medium, and as an analysis means, means for executing an intelligent algorithm for making predictions based on the acquired environmental information, local event information, and past transaction records. This enables highly accurate demand forecasting and the creation of optimal ordering plans in real time.

[0098] "Information acquisition means" refers to means of collecting environmental information and local event information through communication networks.

[0099] "Analysis means" refers to the means of executing intelligent algorithms for making predictions based on acquired environmental information, local event information, and past transaction records.

[0100] "Optimization means" refers to the means of creating an optimal supply plan based on prediction results and available information.

[0101] "Notification means" refers to a means of reporting the optimized supply plan to the administrator.

[0102] The "real-time notification function" is a feature that instantly sends information to the administrator.

[0103] A "customizable user interface" is an interface that provides the operational capabilities for administrators to modify supply plans.

[0104] The system implementing this invention mainly consists of a server, a terminal, and a user. The server uses information acquisition means to acquire environmental information and local event information via a communication network, and obtains this information, along with past transaction records, from a recording medium for analysis. This information is processed by a schematic analysis means equipped with an intelligent algorithm to make future demand forecasts.

[0105] Based on this prediction, the server uses optimization techniques to build a supply plan and calculate the appropriate supply quantities for all products. This result is notified to the terminal in real time and presented to the administrator (user). The administrator can then use this information to review and modify the specific and strategic supply plan through an adjustable user interface. This design prevents oversupply and shortages, improving the efficiency of transactions.

[0106] For example, based on past event data, it can be predicted that demand for a particular product will increase on the day of a local festival. In this case, the server can create a plan to increase the supply quantity beyond the usual amount and notify the administrator. The administrator can then fine-tune the supply plan to adapt to the increased demand based on the information displayed on the terminal. By operating such a system, the operational efficiency of the store improves, and timely responses become possible.

[0107] The following is an example of a specific prompt using a generative AI model: "Predict the sales demand for products at convenience stores and generate an optimal supply plan based on historical sales data, weather forecasts, and local event information."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server acquires environmental information and local event information via a communication network using information acquisition means. This involves using data obtained from weather APIs and local event APIs. The acquired data is stored directly on the recording medium. The input is environmental information and local event information, and the output is the acquired information.

[0111] Step 2:

[0112] The server retrieves past transaction records from the storage medium. This data consists of past sales records extracted from the sales management system. The input is sales data obtained from the sales management system, and the output is past transaction records.

[0113] Step 3:

[0114] The server processes environmental information, local event information, and historical transaction data acquired using intelligent algorithms as analytical tools, and predicts future demand. This prediction uses a generative AI model to analyze data correlations and generate sales forecasts. The inputs are environmental information, local event information, and transaction data, and the output is demand forecast data.

[0115] Step 4:

[0116] The server constructs a supply plan based on predicted demand using optimization techniques. This process integrates inventory status and demand forecasts using Monte Carlo simulation to generate an optimal supply plan. The inputs are demand forecast data and inventory information, and the output is the supply plan.

[0117] Step 5:

[0118] The server uses a real-time notification function to notify terminals of the generated supply plan. The terminals display the detailed supply plan, which administrators can immediately review. The input is the supply plan, and the output is a notification to the administrator.

[0119] Step 6:

[0120] The user (administrator) reviews the supply plan on the terminal's adjustable user interface and makes modifications as needed. This process can take into account special store information, such as promotional campaigns. The input is the supply plan, and the output is the final, modified supply plan.

[0121] 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.

[0122] This invention combines a system for streamlining order management in retail stores such as convenience stores with a function that recognizes user emotions and adjusts the order plan accordingly. This system consists mainly of a server, terminals, and users, and achieves flexible order management using an emotion engine.

[0123] The server is equipped with information acquisition capabilities and collects weather and local event information via the internet. This allows for real-time monitoring of environmental changes around the store. Furthermore, the server retrieves the store's past sales performance from a database and uses artificial intelligence algorithms to make sales forecasts based on this information.

[0124] Based on predicted sales data and inventory information, the server develops an optimal ordering plan. It calculates the required order quantity for each product and adjusts it to prevent inventory shortages or surpluses. The generated ordering plan is sent to the terminal and notified to the user.

[0125] A distinctive feature of this invention is the incorporation of an emotion engine. The emotion engine analyzes the user's facial expressions and voice via the terminal and evaluates their emotions regarding the order plan. For example, if the user feels that the order quantity is too high, anxiety or doubt may be detected. Based on this data, the server automatically readjusts the order plan and proposes a plan that is closer to the user's wishes.

[0126] As a concrete example, consider a store that is considering ordering a new product line. The server uses a predictive model to analyze sales potential and proposes a certain quantity. However, when the user reviews this proposal through their terminal, if the emotion engine detects emotions such as weak interest or dissatisfaction, the server takes this emotional information into account and creates an alternative proposal. In this way, users can communicate their emotions to the system as feedback, which in turn better supports their actual decision-making.

[0127] This system evolves retail store ordering processes into more effective and user-driven operations through a multifaceted approach that combines information acquisition, analysis, prediction, optimization, notification, and sentiment recognition.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server collects weather and local event information via the internet. This data is retrieved using an API or web scraping and stored in a database.

[0131] Step 2:

[0132] The server retrieves historical sales data from the store management system. This data, along with weather and event information, is integrated into a database in an analyzable format.

[0133] Step 3:

[0134] The server executes an artificial intelligence algorithm and creates future sales forecasts based on the collected data. The algorithm delves into past patterns and demand trends to predict sales figures for each product.

[0135] Step 4:

[0136] The server creates an optimal ordering plan based on forecast results and inventory information. The plan is adjusted to prevent excess inventory and stockouts, and the required order quantities are calculated.

[0137] Step 5:

[0138] The server sends the order plan it has created to the terminal. The terminal notifies the user and displays the details.

[0139] Step 6:

[0140] The user reviews the proposed order plan through the device. The device detects the user's emotions through facial recognition and voice data, and an emotion engine analyzes their response.

[0141] Step 7:

[0142] Based on the user's emotions, the server readjusts the order plan if there are any dissatisfactions or doubts. The emotion engine then generates and proposes a new plan that better suits the user's preferences.

[0143] Step 8:

[0144] Once the user is satisfied with the final order plan, they approve it on their device. The device then sends this information to the server, and the final order is placed.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Order management in retail stores requires improved accuracy in demand forecasting based on environmental changes and past sales history, as well as adjustments that reflect the operator's emotions. However, currently, no system exists that takes emotions into account, so order plans sometimes deviate from the operator's wishes. The challenge is to solve this problem and realize more flexible and appropriate order management.

[0148] 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.

[0149] In this invention, the server includes, as an information acquisition means, means for acquiring environmental data via a communication network and means for acquiring past sales history from a storage device, and as an analysis means, means for executing a generative AI model for making predictions based on the acquired environmental data and past sales history. This enables appropriate order management that takes into account the emotions of the operator.

[0150] "Information acquisition means" refers to a system for acquiring environmental data and past sales history via a communication network.

[0151] The "analysis method" is a mechanism that executes a generative AI model to forecast demand based on acquired environmental data and past sales history.

[0152] An "optimization method" is a mechanism for constructing an appropriate replenishment plan based on forecast results and inventory information.

[0153] "Emotion recognition means" refers to a system that analyzes the operator's facial expressions and voice to evaluate their emotions regarding the replenishment plan.

[0154] A "determination method" is a mechanism for readjusting the supplementation plan based on evaluations obtained through emotion recognition.

[0155] A "notification mechanism" is a system for communicating the adjusted replenishment plan to the operator.

[0156] This invention is a system for streamlining order management in retail stores. It not only proposes an optimal ordering plan but also has the function of recognizing user emotions and fine-tuning the plan accordingly. This system primarily includes a server, terminals, and users.

[0157] The server collects environmental data via a communication network using information acquisition means. This data includes weather information and local event information, which are factors that influence sales around the store. It also retrieves past sales history from storage devices and processes this data through analysis means.

[0158] Specifically, the server uses a generative AI model to make predictions based on environmental data and sales history. This estimates product demand. The generative AI model used may include libraries such as TENSORFLOW® and PyTorch. This is used to calculate sales potential and order quantities.

[0159] The server utilizes optimization techniques to formulate replenishment plans based on prediction results and inventory information. Replenishment quantities are optimized for each product to prevent excess inventory and stockouts.

[0160] The device is equipped with emotion recognition capabilities, which use the camera and microphone to analyze the user's facial expressions and voice during operation. This analysis detects the user's emotions regarding the order plan, such as anxiety or dissatisfaction, and sends this information to the server.

[0161] The server receives user sentiment information through a judgment mechanism and readjusts the replenishment plan. This readjustment provides an order plan that meets the user's expectations. The adjusted plan is then sent to the terminal via a notification mechanism and presented to the user.

[0162] As a concrete example, consider a store planning to order a new product line. The server uses an AI model to analyze the sales potential of this product and suggests a standard quantity. When the user reviews this suggestion via their device, if the emotion engine detects low interest or dissatisfaction in the user's emotions, the server then presents alternative options based on that.

[0163] For example, by inputting a prompt such as, "When introducing a new product, how do you determine the optimal order quantity while considering user emotional feedback?" into the generative AI model, more sophisticated predictions and adjustments can be made.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server collects environmental data from the communication network via information acquisition methods. Specifically, it uses APIs to obtain weather information and local event information from the internet and stores it in storage. The input consists of weather data and event data, which are converted into the required format through data processing. The output is formatted environmental data.

[0167] Step 2:

[0168] The server retrieves past sales history from its storage device. It references sales data for a specific product as input and uses database queries to retrieve the necessary data. The output is sales performance data for the target product. This data is used for subsequent analysis.

[0169] Step 3:

[0170] The server utilizes analytical tools and generates AI models to perform sales forecasts. The input consists of environmental data and sales performance data, which are fed into the AI ​​model. Specifically, it uses neural networks, such as TensorFlow, to perform data calculations and predict sales trends. The output is the predicted sales volume.

[0171] Step 4:

[0172] The server uses optimization techniques to develop an optimal replenishment plan based on predicted sales volume and current inventory information. The input consists of predicted data and inventory data, and the algorithm is executed to calculate replenishment quantities. The output is a replenishment plan for each product.

[0173] Step 5:

[0174] The terminal activates emotion recognition when the user receives a replenishment plan through the application. Input consists of the user's facial expressions and voice data, collected using the camera and microphone. Data processing involves facial recognition and voice analysis to determine the user's emotions. The output is emotion evaluation information.

[0175] Step 6:

[0176] The server readjusts the supplementation plan based on the emotional evaluation information using a judgment mechanism. The input is evaluation data, which is used to perform recalculations, and the generating AI model may be reapplied. The output is the adjusted supplementation plan.

[0177] Step 7:

[0178] The server sends the adjusted replenishment plan to the terminal via a notification system and presents it to the user. The terminal displays the notification through its user interface so that the user can confirm it. The output is the notification of the final replenishment plan.

[0179] (Application Example 2)

[0180] 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".

[0181] In retail store order management, uncertainty in demand forecasting and inefficiency in inventory management are often cited as problems. Furthermore, there is a need to create flexible ordering plans that reflect the intentions and feelings of managers, but conventional systems have struggled to effectively address these issues. Moreover, it has been difficult to appropriately analyze the impact of fluctuations in environmental information and local event information on sales and reflect this in ordering plans. This invention aims to solve these problems.

[0182] 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.

[0183] In this invention, the server includes means for acquiring environmental information and local event information as an information acquisition device, means for executing machine learning algorithms to make predictions as an analysis device, and means for analyzing and evaluating the user's facial expressions and voice as an emotion recognition device. This makes it possible to create highly accurate ordering plans that reflect environmental changes and local event information, and further optimize the plans by taking the user's emotions into consideration.

[0184] An "information acquisition device" is a device that has the function of acquiring environmental information and local event information via a communication network and collecting data in real time.

[0185] A "data storage device" is a device that stores past transaction history and related data, and has the function of quickly retrieving information as needed.

[0186] An "analytical device" is a device that has the function of making predictions using machine learning algorithms based on acquired data, and plays a role in analyzing sales trends.

[0187] An "emotion recognition device" is a device equipped with the function of analyzing the user's facial expressions and voice and evaluating their emotions, making it possible to incorporate user feedback into the ordering plan.

[0188] An "optimization device" is a device that creates an optimal ordering plan based on prediction results, inventory data, and sentiment evaluations, and readjusts the plan as needed.

[0189] A "notification device" is a device that has the function of quickly and accurately reporting optimized ordering plans to administrators.

[0190] A "user interface" is a system that provides operation screens and interactive functions used by administrators when making adjustments based on factors such as order quantities and sentiment ratings.

[0191] The server acquires environmental and local event information via a communication network using an information acquisition device. This data is collected in real time using services such as Google Cloud API. Past transaction history is stored in a data storage device, and this data is analyzed by machine learning algorithms such as TensorFlow. The analysis device predicts sales trends based on the acquired data and creates the most suitable order plan based on this prediction.

[0192] The user operates the emotion recognition device using a smartphone or similar device. The device utilizes the camera and microphone to analyze the user's facial expressions and voice, and evaluates their emotions. The evaluated emotion data is sent to a server and reflected in the order planning by an optimization device.

[0193] The optimization system integrates prediction results, inventory data, and sentiment evaluations to automatically adjust the ordering plan. This allows user feedback to be reflected in the system, resulting in a more realistic and precise plan. The created plan is quickly reported to administrators via a notification system, who can then review order quantities and the plan through the user interface.

[0194] For example, if a large-scale festival is being held in the area on a holiday with a forecast of sunny weather, the server will suggest ordering confectionery suitable for the event. However, if the user expresses concern, the server can readjust the order quantity based on that sentiment. An example of a prompt might be, "Adjust the order quantity of confectionery based on emotional feedback for a weekday morning on a sunny holiday with a nearby music event."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server uses an information acquisition device to obtain environmental information and local event information via a communication network. It collects weather data and local event information from the internet as input, and stores the acquired data in a data storage device as output. Specifically, it periodically acquires data via an API, formats it, and stores it in a database.

[0198] Step 2:

[0199] The server retrieves historical transaction history stored in the data storage device and uses a machine learning algorithm in the analysis device to perform sales forecasts. It uses historical sales history and environmental information obtained in step 1 as input and generates forecast data regarding future sales trends as output. This forecasting involves processing the data and running a forecasting model using tools such as TensorFlow.

[0200] Step 3:

[0201] The user uses a device to input their facial expressions and voice through an emotion recognition device. The device uses camera images and microphone audio data as input and generates user emotion evaluation data as output. Specifically, the device's camera analyzes facial features in real time, and a machine learning model is applied to determine emotions from the audio data.

[0202] Step 4:

[0203] Sentiment evaluation data and sales forecasts are sent to the server, and the optimization device integrates this data to create the most suitable order plan. The forecast data from step 2 and the sentiment evaluation data from step 3 are used as input, and the optimized order plan is derived as output. Here, an optimization algorithm based on the forecast results and sentiment data is used to calculate and adjust the planned quantities.

[0204] Step 5:

[0205] The server reports the optimized ordering plan to the administrator via a notification device. It uses the ordering plan created in step 4 as input and sends the draft plan to the administration screen or mobile device as output. Specifically, it exports the plan for administrator review and provides timely information using push notifications, etc.

[0206] Step 6:

[0207] Users make adjustments based on the provided order plan via the user interface. The notified order plan and its background information are used as input, and the modified order quantity is determined as output. Specific actions include manipulating the interface to modify the plan and sending feedback back to the server.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention provides an efficient ordering management system for retail stores such as convenience stores. The system is primarily composed of three elements: a server, terminals, and users.

[0225] The server has multiple functions. First, it periodically collects weather and local event information via the internet through its information acquisition function. This allows users to understand environmental changes around their stores in real time. The server also collects past sales performance data from stores and stores it in a database.

[0226] The analysis is performed by an artificial intelligence algorithm on a server. It analyzes collected weather and event information along with historical sales data to assess the impact of these factors on product sales. Based on this assessment, future sales forecasts are generated.

[0227] Based on the generated sales forecast, the server uses its optimization function to calculate the appropriate order quantity for each product. Inventory status is also taken into consideration to create an ordering plan that avoids excess inventory and stockouts.

[0228] The terminal receives an optimized order plan provided by the server. The store manager, who is the user, reviews this plan on their terminal and makes adjustments as needed. For example, if a promotional campaign for a particular product is running concurrently, the user can fine-tune the plan and increase the order quantity.

[0229] As a concrete example, let's assume a large music event is being held at a certain store over the weekend. The server retrieves this information and analyzes sales data from similar events held in the past. As a result, it predicts that certain beverages and snacks will sell well, and therefore plans to place larger orders than usual. The user receives this plan and can place an order to prepare for the demand during the event.

[0230] This system flexibly responds to fluctuating consumer demand and enables efficient order management. Through this system, users can maximize sales opportunities while minimizing inventory losses.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server accesses weather information services and local event information services to retrieve the latest data. It uses APIs to extract the information and saves it to an internal database.

[0234] Step 2:

[0235] The server retrieves historical sales data from the store management system. Based on this data, it integrates it into a database in a format that allows for analysis of its relationship with weather and event information.

[0236] Step 3:

[0237] The server uses collected weather, event, and sales data to run artificial intelligence algorithms. The algorithms analyze past patterns and make future sales predictions. For example, they might use historical data to determine that certain products tend to sell less during rainy weather.

[0238] Step 4:

[0239] The server calculates the optimal order quantity based on predicted sales data and current inventory information. The server creates an order plan and adjusts it to minimize the risk of stockouts or excess inventory.

[0240] Step 5:

[0241] The server sends the generated order plan to the terminal. The terminal notifies the user of this information and indicates that the order details can be confirmed.

[0242] Step 6:

[0243] Users review the proposed order plan via their device and make changes as needed. For example, they might adjust the order quantity to increase during a promotional period for a particular product.

[0244] Step 7:

[0245] Once the user confirms the order, the terminal sends that information to the server. The server processes the final order and accurately transmits it to the supplier.

[0246] (Example 1)

[0247] 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."

[0248] In retail stores, demand is prone to fluctuations due to weather conditions and local events. This makes it easy for excess inventory and stockouts to occur, posing a challenge to efficient order management. Furthermore, many conventional ordering systems rely on static predictive models based on historical data, failing to adequately respond to real-time environmental changes. Solving these problems is essential.

[0249] 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.

[0250] In this invention, the server includes means for acquiring weather data and local event information via a communication network as an information gathering means, means for acquiring past sales history from a database, and means for performing estimations using an intelligent algorithm. This makes it possible to create an optimal ordering plan for fluctuating demand and to improve the efficiency of inventory management.

[0251] "Information gathering means" refers to functions for acquiring weather data and local event information from external sources via a communication network.

[0252] "Sales history" refers to records showing what products a store has sold in the past, when, and in what quantities.

[0253] A "database" is an information management system that structures and stores collected data, and allows for efficient searching and extraction as needed.

[0254] An "intelligent algorithm" is a computational method that analyzes acquired data to predict future demand and sales patterns.

[0255] An "optimization tool" is a function that creates an optimal ordering plan based on estimated demand and taking inventory data into consideration.

[0256] "Notification methods" refer to methods of providing administrators with optimized ordering plans and communicating information for reviewing and modifying ordering details.

[0257] "Interface means" refers to means for realizing a user interface that allows administrators to modify quantities based on the order plan.

[0258] This invention embodies a system that supports efficient order management in retail stores. The system consists of three elements: a server, a terminal, and a user.

[0259] The server's role is to acquire weather data and local event information via the communication network as a means of information gathering. Specifically, it periodically collects data from weather information services and publicly available local event databases. This information is obtained through web scraping techniques using Python and APIs. The server also collects past sales history from the database and integrates the information. MySQL and PostgreSQL are used for managing this database.

[0260] The collected data is analyzed on the server using intelligent algorithms. First, the data is preprocessed using libraries such as Pandas and NumPy in Python. Next, time series analysis and predictions using machine learning models are performed using Scikit-learn and StatsModels. This makes it possible to forecast demand while taking into account the impact of weather and events.

[0261] The server performs mathematical optimization using GuRoBi and ORTools as optimization tools, generating an optimal ordering plan based on inventory data and forecast results. This enables effective ordering management that prevents excess inventory and stockouts.

[0262] The terminal provides the administrator user with an optimized order plan sent from the server. The terminal has a dedicated application installed, allowing the user to check the displayed plan on the spot and adjust order quantities using touch controls or keyboard input.

[0263] The user makes modifications to the optimized plan through this interface and returns the finalized ordering plan to the server. The server then automatically places orders with suppliers based on this confirmed information.

[0264] As a concrete example, if a large-scale music festival is held nearby on a weekend, the server collects event information in advance and analyzes it in combination with historical data. The analysis results predict an increase in demand for a specific product and determine the optimal order quantity accordingly. The user can review the plan, make any necessary adjustments, and finalize it, thus avoiding missing sales opportunities on the day of the event.

[0265] An example of a prompt might be: "Based on a weekend event in a specific region, analyze expected consumer behavior and purchasing trends, and develop a product ordering plan accordingly. Please also consider past data from similar events and the weather forecast for the day."

[0266] This system allows users to respond appropriately to changing circumstances, maximizing sales while minimizing inventory waste.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server uses a communication network to acquire external weather data and local event information as a means of information gathering. This process involves periodically collecting data through API calls. Specifically, the server calls the weather information provider's API and performs web scraping of event calendars. The input data consists of weather conditions (e.g., temperature, precipitation) and event information (e.g., date, time, and location), which are converted into an internal format and stored in the database.

[0270] Step 2:

[0271] The server retrieves past sales history from the database. This gathers the basic data necessary for analyzing sales patterns. Specifically, it extracts sales data using SQL queries. The input for this process is sales transaction data for a specific period. The processed data is aggregated into sales figures and revenue data by category and used in the next analysis phase.

[0272] Step 3:

[0273] The server uses intelligent algorithms to analyze the collected data. This analysis is used to predict demand. Specifically, it preprocesses the data using Python's Pandas library and builds a machine learning model using Scikit-learn. The inputs are weather data, event information, and sales history. This data is combined and input into the model to output future sales forecasts. This output quantifies the demand for the product.

[0274] Step 4:

[0275] The server uses optimization techniques to generate an order plan based on sales forecasts. Specifically, it uses mathematical optimization tools (GuRoBi or ORTools) to perform optimization considering inventory levels and forecasted demand. The inputs to this process are sales forecast data and current inventory data. The output is an order plan that includes recommended order quantities for each product.

[0276] Step 5:

[0277] The terminal receives the optimized order plan from the server and notifies the user who is the administrator. As a specific operation, the dedicated application on the terminal displays the output order plan. The input for this process is the optimized order plan data. The user visually checks this information and adjusts the order quantity via the interface if necessary.

[0278] Step 6:

[0279] The user modifies and finalizes the order plan via the terminal and provides feedback to the server. As a specific operation, the user adjusts the quantity in the user interface and transmits the modified data to the server. The input for this process is the order quantity adjusted by the user. The output is the order information finally finalized by the server.

[0280] Step 7:

[0281] The server automatically places an order with the supplier system based on the final order information. As a specific operation, the necessary order information is transmitted to the supplier's system. The input for this process is the finalized order information, and the output is the order confirmation transmitted to the supplier.

[0282] (Application Example 1)

[0283] Next, Application Example 1 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".

[0284] Order management in conventional retail stores has problems such as low accuracy in demand forecasting and a high likelihood of overstocking or out-of-stock situations. Also, it has been difficult to promptly reflect the impact of environmental changes and local events on sales, and there has been an issue that administrators cannot respond quickly. There is a need for a more efficient and responsive order management system to solve such problems.

[0285] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0286] In this invention, the server includes, as information acquisition means, means for acquiring environmental information and regional event information via a communication network, and means for acquiring past transaction records from a recording medium, and, as analysis means, means for executing an intelligent algorithm for making a prediction based on the acquired environmental information, regional event information, and past transaction records. Thereby, highly accurate demand prediction becomes possible, and creation of an optimal ordering plan in real time becomes possible.

[0287] The "information acquisition means" is means for collecting environmental information and regional event information through a communication network.

[0288] The "analysis means" is means for executing an intelligent algorithm for making a prediction based on the acquired environmental information, regional event information, and past transaction records.

[0289] The "optimization means" is means for creating an optimal supply plan based on the prediction result and the held information.

[0290] The "notification means" is means for reporting the optimized supply plan to the administrator.

[0291] The "real-time notification function" is a function for immediately transmitting information to the administrator.

[0292] The "adjustable user interface" is an interface having operability for the administrator to modify the supply plan.

[0293] The system implementing this invention mainly consists of a server, a terminal, and a user. The server uses information acquisition means to acquire environmental information and local event information via a communication network, and obtains this information, along with past transaction records, from a recording medium for analysis. This information is processed by a schematic analysis means equipped with an intelligent algorithm to make future demand forecasts.

[0294] Based on this prediction, the server uses optimization techniques to build a supply plan and calculate the appropriate supply quantities for all products. This result is notified to the terminal in real time and presented to the administrator (user). The administrator can then use this information to review and modify the specific and strategic supply plan through an adjustable user interface. This design prevents oversupply and shortages, improving the efficiency of transactions.

[0295] For example, based on past event data, it can be predicted that demand for a particular product will increase on the day of a local festival. In this case, the server can create a plan to increase the supply quantity beyond the usual amount and notify the administrator. The administrator can then fine-tune the supply plan to adapt to the increased demand based on the information displayed on the terminal. By operating such a system, the operational efficiency of the store improves, and timely responses become possible.

[0296] The following is an example of a specific prompt using a generative AI model: "Predict the sales demand for products at convenience stores and generate an optimal supply plan based on historical sales data, weather forecasts, and local event information."

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server uses information acquisition means to obtain environmental information and regional event information via a communication network. In this process, the data obtained from weather APIs and regional event APIs is used. The acquired data is stored in the recording medium as it is. The input is environmental information and regional event information, and the output is the acquired corresponding information.

[0300] Step 2:

[0301] The server obtains past transaction records from the recording medium. This data is the past sales performance extracted from the sales management system. The input is the sales data obtained from the sales management system, and the output is the past transaction records.

[0302] Step 3:

[0303] The server processes the obtained environmental information, regional event information, and past transaction records using intelligent algorithms as analysis means to predict future demand. In this prediction, a generative AI model is used to analyze the correlation of data and generate a sales prediction. The input is environmental information, regional event information, and transaction records, and the output is demand prediction data.

[0304] Step 4:

[0305] The server constructs a supply plan based on the predicted demand using optimization means. In this process, Monte Carlo simulation is used to integrate the inventory situation and demand prediction to generate an optimal supply plan. The input is demand prediction data and inventory information, and the output is the supply plan.

[0306] Step 5:

[0307] The server uses the real-time notification function to notify the generated supply plan to the terminal. A detailed supply plan is displayed on the terminal for the administrator to view immediately. The input is the supply plan, and the output is the notification to the administrator.

[0308] Step 6:

[0309] The user (administrator) reviews the supply plan on the terminal's adjustable user interface and makes modifications as needed. This process can take into account special store information, such as promotional campaigns. The input is the supply plan, and the output is the final, modified supply plan.

[0310] 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.

[0311] This invention combines a system for streamlining order management in retail stores such as convenience stores with a function that recognizes user emotions and adjusts the order plan accordingly. This system consists mainly of a server, terminals, and users, and achieves flexible order management using an emotion engine.

[0312] The server is equipped with information acquisition capabilities and collects weather and local event information via the internet. This allows for real-time monitoring of environmental changes around the store. Furthermore, the server retrieves the store's past sales performance from a database and uses artificial intelligence algorithms to make sales forecasts based on this information.

[0313] Based on predicted sales data and inventory information, the server develops an optimal ordering plan. It calculates the required order quantity for each product and adjusts it to prevent inventory shortages or surpluses. The generated ordering plan is sent to the terminal and notified to the user.

[0314] A distinctive feature of this invention is the incorporation of an emotion engine. The emotion engine analyzes the user's facial expressions and voice via the terminal and evaluates their emotions regarding the order plan. For example, if the user feels that the order quantity is too high, anxiety or doubt may be detected. Based on this data, the server automatically readjusts the order plan and proposes a plan that is closer to the user's wishes.

[0315] As a concrete example, consider a store that is considering ordering a new product line. The server uses a predictive model to analyze sales potential and proposes a certain quantity. However, when the user reviews this proposal through their terminal, if the emotion engine detects emotions such as weak interest or dissatisfaction, the server takes this emotional information into account and creates an alternative proposal. In this way, users can communicate their emotions to the system as feedback, which in turn better supports their actual decision-making.

[0316] This system evolves retail store ordering processes into more effective and user-driven operations through a multifaceted approach that combines information acquisition, analysis, prediction, optimization, notification, and sentiment recognition.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server collects weather and local event information via the internet. This data is retrieved using an API or web scraping and stored in a database.

[0320] Step 2:

[0321] The server retrieves historical sales data from the store management system. This data, along with weather and event information, is integrated into a database in an analyzable format.

[0322] Step 3:

[0323] The server executes an artificial intelligence algorithm and creates future sales forecasts based on the collected data. The algorithm delves into past patterns and demand trends to predict sales figures for each product.

[0324] Step 4:

[0325] The server creates an optimal ordering plan based on forecast results and inventory information. The plan is adjusted to prevent excess inventory and stockouts, and the required order quantities are calculated.

[0326] Step 5:

[0327] The server sends the order plan it has created to the terminal. The terminal notifies the user and displays the details.

[0328] Step 6:

[0329] The user reviews the proposed order plan through the device. The device detects the user's emotions through facial recognition and voice data, and an emotion engine analyzes their response.

[0330] Step 7:

[0331] Based on the user's emotions, the server readjusts the order plan if there are any dissatisfactions or doubts. The emotion engine then generates and proposes a new plan that better suits the user's preferences.

[0332] Step 8:

[0333] Once the user is satisfied with the final order plan, they approve it on their device. The device then sends this information to the server, and the final order is placed.

[0334] (Example 2)

[0335] 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 glasses 214 will be referred to as the "terminal".

[0336] Order management in retail stores requires improved accuracy in demand forecasting based on environmental changes and past sales history, as well as adjustments that reflect the operator's emotions. However, currently, no system exists that takes emotions into account, so order plans sometimes deviate from the operator's wishes. The challenge is to solve this problem and realize more flexible and appropriate order management.

[0337] 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.

[0338] In this invention, the server includes, as an information acquisition means, means for acquiring environmental data via a communication network and means for acquiring past sales history from a storage device, and as an analysis means, means for executing a generative AI model for making predictions based on the acquired environmental data and past sales history. This enables appropriate order management that takes into account the emotions of the operator.

[0339] "Information acquisition means" refers to a system for acquiring environmental data and past sales history via a communication network.

[0340] The "analysis method" is a mechanism that executes a generative AI model to forecast demand based on acquired environmental data and past sales history.

[0341] An "optimization method" is a mechanism for constructing an appropriate replenishment plan based on forecast results and inventory information.

[0342] "Emotion recognition means" refers to a system that analyzes the operator's facial expressions and voice to evaluate their emotions regarding the replenishment plan.

[0343] A "determination method" is a mechanism for readjusting the supplementation plan based on evaluations obtained through emotion recognition.

[0344] A "notification mechanism" is a system for communicating the adjusted replenishment plan to the operator.

[0345] This invention is a system for streamlining order management in retail stores. It not only proposes an optimal ordering plan but also has the function of recognizing user emotions and fine-tuning the plan accordingly. This system primarily includes a server, terminals, and users.

[0346] The server collects environmental data via a communication network using information acquisition means. This data includes weather information and local event information, which are factors that influence sales around the store. It also retrieves past sales history from storage devices and processes this data through analysis means.

[0347] Specifically, the server uses a generative AI model to make predictions based on environmental data and sales history. This estimates product demand. The generative AI model used may include libraries such as TensorFlow and PyTorch. This is used to calculate sales potential and order quantities.

[0348] The server utilizes optimization techniques to formulate replenishment plans based on prediction results and inventory information. Replenishment quantities are optimized for each product to prevent excess inventory and stockouts.

[0349] The device is equipped with emotion recognition capabilities, which use the camera and microphone to analyze the user's facial expressions and voice during operation. This analysis detects the user's emotions regarding the order plan, such as anxiety or dissatisfaction, and sends this information to the server.

[0350] The server receives user sentiment information through a judgment mechanism and readjusts the replenishment plan. This readjustment provides an order plan that meets the user's expectations. The adjusted plan is then sent to the terminal via a notification mechanism and presented to the user.

[0351] As a concrete example, consider a store planning to order a new product line. The server uses an AI model to analyze the sales potential of this product and suggests a standard quantity. When the user reviews this suggestion via their device, if the emotion engine detects low interest or dissatisfaction in the user's emotions, the server then presents alternative options based on that.

[0352] For example, by inputting a prompt such as, "When introducing a new product, how do you determine the optimal order quantity while considering user emotional feedback?" into the generative AI model, more sophisticated predictions and adjustments can be made.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The server collects environmental data from the communication network via information acquisition methods. Specifically, it uses APIs to obtain weather information and local event information from the internet and stores it in storage. The input consists of weather data and event data, which are converted into the required format through data processing. The output is formatted environmental data.

[0356] Step 2:

[0357] The server retrieves past sales history from its storage device. It references sales data for a specific product as input and uses database queries to retrieve the necessary data. The output is sales performance data for the target product. This data is used for subsequent analysis.

[0358] Step 3:

[0359] The server utilizes analytical tools and generates AI models to perform sales forecasts. The input consists of environmental data and sales performance data, which are fed into the AI ​​model. Specifically, it uses neural networks, such as TensorFlow, to perform data calculations and predict sales trends. The output is the predicted sales volume.

[0360] Step 4:

[0361] The server uses optimization techniques to develop an optimal replenishment plan based on predicted sales volume and current inventory information. The input consists of predicted data and inventory data, and the algorithm is executed to calculate replenishment quantities. The output is a replenishment plan for each product.

[0362] Step 5:

[0363] The terminal activates emotion recognition when the user receives a replenishment plan through the application. Input consists of the user's facial expressions and voice data, collected using the camera and microphone. Data processing involves facial recognition and voice analysis to determine the user's emotions. The output is emotion evaluation information.

[0364] Step 6:

[0365] The server readjusts the supplementation plan based on the emotional evaluation information using a judgment mechanism. The input is evaluation data, which is used to perform recalculations, and the generating AI model may be reapplied. The output is the adjusted supplementation plan.

[0366] Step 7:

[0367] The server sends the adjusted replenishment plan to the terminal via a notification system and presents it to the user. The terminal displays the notification through its user interface so that the user can confirm it. The output is the notification of the final replenishment plan.

[0368] (Application Example 2)

[0369] 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 as the "terminal".

[0370] In retail store order management, uncertainty in demand forecasting and inefficiency in inventory management are often cited as problems. Furthermore, there is a need to create flexible ordering plans that reflect the intentions and feelings of managers, but conventional systems have struggled to effectively address these issues. Moreover, it has been difficult to appropriately analyze the impact of fluctuations in environmental information and local event information on sales and reflect this in ordering plans. This invention aims to solve these problems.

[0371] 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.

[0372] In this invention, the server includes means for acquiring environmental information and local event information as an information acquisition device, means for executing machine learning algorithms to make predictions as an analysis device, and means for analyzing and evaluating the user's facial expressions and voice as an emotion recognition device. This makes it possible to create highly accurate ordering plans that reflect environmental changes and local event information, and further optimize the plans by taking the user's emotions into consideration.

[0373] An "information acquisition device" is a device that has the function of acquiring environmental information and local event information via a communication network and collecting data in real time.

[0374] A "data storage device" is a device that stores past transaction history and related data, and has the function of quickly retrieving information as needed.

[0375] An "analytical device" is a device that has the function of making predictions using machine learning algorithms based on acquired data, and plays a role in analyzing sales trends.

[0376] An "emotion recognition device" is a device equipped with the function of analyzing the user's facial expressions and voice and evaluating their emotions, making it possible to incorporate user feedback into the ordering plan.

[0377] An "optimization device" is a device that creates an optimal ordering plan based on prediction results, inventory data, and sentiment evaluations, and readjusts the plan as needed.

[0378] A "notification device" is a device that has the function of quickly and accurately reporting optimized ordering plans to administrators.

[0379] A "user interface" is a system that provides operation screens and interactive functions used by administrators when making adjustments based on factors such as order quantities and sentiment ratings.

[0380] The server acquires environmental and local event information via a communication network using an information acquisition device. This data is collected in real time using services such as the Google Cloud API. The data storage device stores past transaction history, and this data is analyzed by machine learning algorithms such as TensorFlow. The analysis device predicts sales trends based on the acquired data and creates the most suitable order plan based on this prediction.

[0381] The user operates the emotion recognition device using a smartphone or similar device. The device utilizes the camera and microphone to analyze the user's facial expressions and voice, and evaluates their emotions. The evaluated emotion data is sent to a server and reflected in the order planning by an optimization device.

[0382] The optimization system integrates prediction results, inventory data, and sentiment evaluations to automatically adjust the ordering plan. This allows user feedback to be reflected in the system, resulting in a more realistic and precise plan. The created plan is quickly reported to administrators via a notification system, who can then review order quantities and the plan through the user interface.

[0383] For example, if a large-scale festival is being held in the area on a holiday with a forecast of sunny weather, the server will suggest ordering confectionery suitable for the event. However, if the user expresses concern, the server can readjust the order quantity based on that sentiment. An example of a prompt might be, "Adjust the order quantity of confectionery based on emotional feedback for a weekday morning on a sunny holiday with a nearby music event."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server uses an information acquisition device to obtain environmental information and local event information via a communication network. It collects weather data and local event information from the internet as input, and stores the acquired data in a data storage device as output. Specifically, it periodically acquires data via an API, formats it, and stores it in a database.

[0387] Step 2:

[0388] The server retrieves historical transaction history stored in the data storage device and uses a machine learning algorithm in the analysis device to perform sales forecasts. It uses historical sales history and environmental information obtained in step 1 as input and generates forecast data regarding future sales trends as output. This forecasting involves processing the data and running a forecasting model using tools such as TensorFlow.

[0389] Step 3:

[0390] The user uses a device to input their facial expressions and voice through an emotion recognition device. The device uses camera images and microphone audio data as input and generates user emotion evaluation data as output. Specifically, the device's camera analyzes facial features in real time, and a machine learning model is applied to determine emotions from the audio data.

[0391] Step 4:

[0392] Sentiment evaluation data and sales forecasts are sent to the server, and the optimization device integrates this data to create the most suitable order plan. The forecast data from step 2 and the sentiment evaluation data from step 3 are used as input, and the optimized order plan is derived as output. Here, an optimization algorithm based on the forecast results and sentiment data is used to calculate and adjust the planned quantities.

[0393] Step 5:

[0394] The server reports the optimized ordering plan to the administrator via a notification device. It uses the ordering plan created in step 4 as input and sends the draft plan to the administration screen or mobile device as output. Specifically, it exports the plan for administrator review and provides timely information using push notifications, etc.

[0395] Step 6:

[0396] Users make adjustments based on the provided order plan via the user interface. The notified order plan and its background information are used as input, and the modified order quantity is determined as output. Specific actions include manipulating the interface to modify the plan and sending feedback back to the server.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention provides an efficient ordering management system for retail stores such as convenience stores. The system is primarily composed of three elements: a server, terminals, and users.

[0414] The server has multiple functions. First, it periodically collects weather and local event information via the internet through its information acquisition function. This allows users to understand environmental changes around their stores in real time. The server also collects past sales performance data from stores and stores it in a database.

[0415] The analysis is performed by an artificial intelligence algorithm on a server. It analyzes collected weather and event information along with historical sales data to assess the impact of these factors on product sales. Based on this assessment, future sales forecasts are generated.

[0416] Based on the generated sales forecast, the server uses its optimization function to calculate the appropriate order quantity for each product. Inventory status is also taken into consideration to create an ordering plan that avoids excess inventory and stockouts.

[0417] The terminal receives an optimized order plan provided by the server. The store manager, who is the user, reviews this plan on their terminal and makes adjustments as needed. For example, if a promotional campaign for a particular product is running concurrently, the user can fine-tune the plan and increase the order quantity.

[0418] As a concrete example, let's assume a large music event is being held at a certain store over the weekend. The server retrieves this information and analyzes sales data from similar events held in the past. As a result, it predicts that certain beverages and snacks will sell well, and therefore plans to place larger orders than usual. The user receives this plan and can place an order to prepare for the demand during the event.

[0419] This system flexibly responds to fluctuating consumer demand and enables efficient order management. Through this system, users can maximize sales opportunities while minimizing inventory losses.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The server accesses weather information services and local event information services to retrieve the latest data. It uses APIs to extract the information and saves it to an internal database.

[0423] Step 2:

[0424] The server retrieves historical sales data from the store management system. Based on this data, it integrates it into a database in a format that allows for analysis of its relationship with weather and event information.

[0425] Step 3:

[0426] The server uses collected weather, event, and sales data to run artificial intelligence algorithms. The algorithms analyze past patterns and make future sales predictions. For example, they might use historical data to determine that certain products tend to sell less during rainy weather.

[0427] Step 4:

[0428] The server calculates the optimal order quantity based on predicted sales data and current inventory information. The server creates an order plan and adjusts it to minimize the risk of stockouts or excess inventory.

[0429] Step 5:

[0430] The server sends the generated order plan to the terminal. The terminal notifies the user of this information and indicates that the order details can be confirmed.

[0431] Step 6:

[0432] Users review the proposed order plan via their device and make changes as needed. For example, they might adjust the order quantity to increase during a promotional period for a particular product.

[0433] Step 7:

[0434] Once the user confirms the order, the terminal sends that information to the server. The server processes the final order and accurately transmits it to the supplier.

[0435] (Example 1)

[0436] 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."

[0437] In retail stores, demand is prone to fluctuations due to weather conditions and local events. This makes it easy for excess inventory and stockouts to occur, posing a challenge to efficient order management. Furthermore, many conventional ordering systems rely on static predictive models based on historical data, failing to adequately respond to real-time environmental changes. Solving these problems is essential.

[0438] 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.

[0439] In this invention, the server includes means for acquiring weather data and local event information via a communication network as an information gathering means, means for acquiring past sales history from a database, and means for performing estimations using an intelligent algorithm. This makes it possible to create an optimal ordering plan for fluctuating demand and to improve the efficiency of inventory management.

[0440] "Information gathering means" refers to functions for acquiring weather data and local event information from external sources via a communication network.

[0441] "Sales history" refers to records showing what products a store has sold in the past, when, and in what quantities.

[0442] A "database" is an information management system that structures and stores collected data, and allows for efficient searching and extraction as needed.

[0443] An "intelligent algorithm" is a computational method that analyzes acquired data to predict future demand and sales patterns.

[0444] An "optimization tool" is a function that creates an optimal ordering plan based on estimated demand and taking inventory data into consideration.

[0445] "Notification methods" refer to methods of providing administrators with optimized ordering plans and communicating information for reviewing and modifying ordering details.

[0446] "Interface means" refers to means for realizing a user interface that allows administrators to modify quantities based on the order plan.

[0447] This invention embodies a system that supports efficient order management in retail stores. The system consists of three elements: a server, a terminal, and a user.

[0448] The server's role is to acquire weather data and local event information via the communication network as a means of information gathering. Specifically, it periodically collects data from weather information services and publicly available local event databases. This information is obtained through web scraping techniques using Python and APIs. The server also collects past sales history from the database and integrates the information. MySQL and PostgreSQL are used for managing this database.

[0449] The collected data is analyzed on the server using intelligent algorithms. First, the data is preprocessed using libraries such as Pandas and NumPy in Python. Next, time series analysis and predictions using machine learning models are performed using Scikit-learn and StatsModels. This makes it possible to forecast demand while taking into account the impact of weather and events.

[0450] The server performs mathematical optimization using GuRoBi and ORTools as optimization tools, generating an optimal ordering plan based on inventory data and forecast results. This enables effective ordering management that prevents excess inventory and stockouts.

[0451] The terminal provides the administrator user with an optimized order plan sent from the server. The terminal has a dedicated application installed, allowing the user to check the displayed plan on the spot and adjust order quantities using touch controls or keyboard input.

[0452] The user makes modifications to the optimized plan through this interface and returns the finalized ordering plan to the server. The server then automatically places orders with suppliers based on this confirmed information.

[0453] As a concrete example, if a large-scale music festival is held nearby on a weekend, the server collects event information in advance and analyzes it in combination with historical data. The analysis results predict an increase in demand for a specific product and determine the optimal order quantity accordingly. The user can review the plan, make any necessary adjustments, and finalize it, thus avoiding missing sales opportunities on the day of the event.

[0454] An example of a prompt might be: "Based on a weekend event in a specific region, analyze expected consumer behavior and purchasing trends, and develop a product ordering plan accordingly. Please also consider past data from similar events and the weather forecast for the day."

[0455] This system allows users to respond appropriately to changing circumstances, maximizing sales while minimizing inventory waste.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The server uses a communication network to acquire external weather data and local event information as a means of information gathering. This process involves periodically collecting data through API calls. Specifically, the server calls the weather information provider's API and performs web scraping of event calendars. The input data consists of weather conditions (e.g., temperature, precipitation) and event information (e.g., date, time, and location), which are converted into an internal format and stored in the database.

[0459] Step 2:

[0460] The server retrieves past sales history from the database. This gathers the basic data necessary for analyzing sales patterns. Specifically, it extracts sales data using SQL queries. The input for this process is sales transaction data for a specific period. The processed data is aggregated into sales figures and revenue data by category and used in the next analysis phase.

[0461] Step 3:

[0462] The server uses intelligent algorithms to analyze the collected data. This analysis is used to predict demand. Specifically, it preprocesses the data using Python's Pandas library and builds a machine learning model using Scikit-learn. The inputs are weather data, event information, and sales history. This data is combined and input into the model to output future sales forecasts. This output quantifies the demand for the product.

[0463] Step 4:

[0464] The server uses optimization techniques to generate an order plan based on sales forecasts. Specifically, it uses mathematical optimization tools (GuRoBi or ORTools) to perform optimization considering inventory levels and forecasted demand. The inputs to this process are sales forecast data and current inventory data. The output is an order plan that includes recommended order quantities for each product.

[0465] Step 5:

[0466] The terminal receives an optimized order plan from the server and notifies the administrator user. Specifically, a dedicated application on the terminal displays the outputted order plan. The input for this process is the optimized order plan data. The user visually confirms this information and adjusts the order quantities via the interface as needed.

[0467] Step 6:

[0468] Users modify and finalize their order plans via their terminals and provide feedback to the server. Specifically, they adjust quantities using the user interface and send the revised data to the server. The input to this process is the order quantity adjusted by the user. The output is the final order information confirmed by the server.

[0469] Step 7:

[0470] The server automatically places orders with the supplier system based on the final order information. Specifically, it sends the necessary order information to the supplier's system. The input to this process is the confirmed order information, and the output is the order confirmation sent to the supplier.

[0471] (Application Example 1)

[0472] 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."

[0473] Traditional order management systems in retail stores suffered from low demand forecasting accuracy, leading to frequent inventory oversupply and stockouts. Furthermore, it was difficult to reflect the impact of environmental changes and local events on sales in a timely manner, hindering managers from responding quickly. There is a need for a more efficient and responsive order management system that addresses these challenges.

[0474] 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.

[0475] In this invention, the server includes, as an information acquisition means, means for acquiring environmental information and local event information via a communication network, and means for acquiring past transaction records from a recording medium, and as an analysis means, means for executing an intelligent algorithm for making predictions based on the acquired environmental information, local event information, and past transaction records. This enables highly accurate demand forecasting and the creation of optimal ordering plans in real time.

[0476] "Information acquisition means" refers to means of collecting environmental information and local event information through communication networks.

[0477] "Analysis means" refers to the means of executing intelligent algorithms for making predictions based on acquired environmental information, local event information, and past transaction records.

[0478] "Optimization means" refers to the means of creating an optimal supply plan based on prediction results and available information.

[0479] "Notification means" refers to a means of reporting the optimized supply plan to the administrator.

[0480] The "real-time notification function" is a feature that instantly sends information to the administrator.

[0481] A "customizable user interface" is an interface that provides the operational capabilities for administrators to modify supply plans.

[0482] The system implementing this invention mainly consists of a server, a terminal, and a user. The server uses information acquisition means to acquire environmental information and local event information via a communication network, and obtains this information, along with past transaction records, from a recording medium for analysis. This information is processed by a schematic analysis means equipped with an intelligent algorithm to make future demand forecasts.

[0483] Based on this prediction, the server uses optimization techniques to build a supply plan and calculate the appropriate supply quantities for all products. This result is notified to the terminal in real time and presented to the administrator (user). The administrator can then use this information to review and modify the specific and strategic supply plan through an adjustable user interface. This design prevents oversupply and shortages, improving the efficiency of transactions.

[0484] For example, based on past event data, it can be predicted that demand for a particular product will increase on the day of a local festival. In this case, the server can create a plan to increase the supply quantity beyond the usual amount and notify the administrator. The administrator can then fine-tune the supply plan to adapt to the increased demand based on the information displayed on the terminal. By operating such a system, the operational efficiency of the store improves, and timely responses become possible.

[0485] The following is an example of a specific prompt using a generative AI model: "Predict the sales demand for products at convenience stores and generate an optimal supply plan based on historical sales data, weather forecasts, and local event information."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server acquires environmental information and local event information via a communication network using information acquisition means. This involves using data obtained from weather APIs and local event APIs. The acquired data is stored directly on the recording medium. The input is environmental information and local event information, and the output is the acquired information.

[0489] Step 2:

[0490] The server retrieves past transaction records from the storage medium. This data consists of past sales records extracted from the sales management system. The input is sales data obtained from the sales management system, and the output is past transaction records.

[0491] Step 3:

[0492] The server processes environmental information, local event information, and historical transaction data acquired using intelligent algorithms as analytical tools, and predicts future demand. This prediction uses a generative AI model to analyze data correlations and generate sales forecasts. The inputs are environmental information, local event information, and transaction data, and the output is demand forecast data.

[0493] Step 4:

[0494] The server constructs a supply plan based on predicted demand using optimization techniques. This process integrates inventory status and demand forecasts using Monte Carlo simulation to generate an optimal supply plan. The inputs are demand forecast data and inventory information, and the output is the supply plan.

[0495] Step 5:

[0496] The server uses a real-time notification function to notify terminals of the generated supply plan. The terminals display the detailed supply plan, which administrators can immediately review. The input is the supply plan, and the output is a notification to the administrator.

[0497] Step 6:

[0498] The user (administrator) reviews the supply plan on the terminal's adjustable user interface and makes modifications as needed. This process can take into account special store information, such as promotional campaigns. The input is the supply plan, and the output is the final, modified supply plan.

[0499] 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.

[0500] This invention combines a system for streamlining order management in retail stores such as convenience stores with a function that recognizes user emotions and adjusts the order plan accordingly. This system consists mainly of a server, terminals, and users, and achieves flexible order management using an emotion engine.

[0501] The server is equipped with information acquisition capabilities and collects weather and local event information via the internet. This allows for real-time monitoring of environmental changes around the store. Furthermore, the server retrieves the store's past sales performance from a database and uses artificial intelligence algorithms to make sales forecasts based on this information.

[0502] Based on predicted sales data and inventory information, the server develops an optimal ordering plan. It calculates the required order quantity for each product and adjusts it to prevent inventory shortages or surpluses. The generated ordering plan is sent to the terminal and notified to the user.

[0503] A distinctive feature of this invention is the incorporation of an emotion engine. The emotion engine analyzes the user's facial expressions and voice via the terminal and evaluates their emotions regarding the order plan. For example, if the user feels that the order quantity is too high, anxiety or doubt may be detected. Based on this data, the server automatically readjusts the order plan and proposes a plan that is closer to the user's wishes.

[0504] As a concrete example, consider a store that is considering ordering a new product line. The server uses a predictive model to analyze sales potential and proposes a certain quantity. However, when the user reviews this proposal through their terminal, if the emotion engine detects emotions such as weak interest or dissatisfaction, the server takes this emotional information into account and creates an alternative proposal. In this way, users can communicate their emotions to the system as feedback, which in turn better supports their actual decision-making.

[0505] This system evolves retail store ordering processes into more effective and user-driven operations through a multifaceted approach that combines information acquisition, analysis, prediction, optimization, notification, and sentiment recognition.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server collects weather and local event information via the internet. This data is retrieved using an API or web scraping and stored in a database.

[0509] Step 2:

[0510] The server retrieves historical sales data from the store management system. This data, along with weather and event information, is integrated into a database in an analyzable format.

[0511] Step 3:

[0512] The server executes an artificial intelligence algorithm and creates future sales forecasts based on the collected data. The algorithm delves into past patterns and demand trends to predict sales figures for each product.

[0513] Step 4:

[0514] The server creates an optimal ordering plan based on forecast results and inventory information. The plan is adjusted to prevent excess inventory and stockouts, and the required order quantities are calculated.

[0515] Step 5:

[0516] The server sends the order plan it has created to the terminal. The terminal notifies the user and displays the details.

[0517] Step 6:

[0518] The user reviews the proposed order plan through the device. The device detects the user's emotions through facial recognition and voice data, and an emotion engine analyzes their response.

[0519] Step 7:

[0520] Based on the user's emotions, the server readjusts the order plan if there are any dissatisfactions or doubts. The emotion engine then generates and proposes a new plan that better suits the user's preferences.

[0521] Step 8:

[0522] Once the user is satisfied with the final order plan, they approve it on their device. The device then sends this information to the server, and the final order is placed.

[0523] (Example 2)

[0524] 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."

[0525] Order management in retail stores requires improved accuracy in demand forecasting based on environmental changes and past sales history, as well as adjustments that reflect the operator's emotions. However, currently, no system exists that takes emotions into account, so order plans sometimes deviate from the operator's wishes. The challenge is to solve this problem and realize more flexible and appropriate order management.

[0526] 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.

[0527] In this invention, the server includes, as an information acquisition means, means for acquiring environmental data via a communication network and means for acquiring past sales history from a storage device, and as an analysis means, means for executing a generative AI model for making predictions based on the acquired environmental data and past sales history. This enables appropriate order management that takes into account the emotions of the operator.

[0528] "Information acquisition means" refers to a system for acquiring environmental data and past sales history via a communication network.

[0529] The "analysis method" is a mechanism that executes a generative AI model to forecast demand based on acquired environmental data and past sales history.

[0530] An "optimization method" is a mechanism for constructing an appropriate replenishment plan based on forecast results and inventory information.

[0531] "Emotion recognition means" refers to a system that analyzes the operator's facial expressions and voice to evaluate their emotions regarding the replenishment plan.

[0532] A "determination method" is a mechanism for readjusting the supplementation plan based on evaluations obtained through emotion recognition.

[0533] A "notification mechanism" is a system for communicating the adjusted replenishment plan to the operator.

[0534] This invention is a system for streamlining order management in retail stores. It not only proposes an optimal ordering plan but also has the function of recognizing user emotions and fine-tuning the plan accordingly. This system primarily includes a server, terminals, and users.

[0535] The server collects environmental data via a communication network using information acquisition means. This data includes weather information and local event information, which are factors that influence sales around the store. It also retrieves past sales history from storage devices and processes this data through analysis means.

[0536] Specifically, the server uses a generative AI model to make predictions based on environmental data and sales history. This estimates product demand. The generative AI model used may include libraries such as TensorFlow and PyTorch. This is used to calculate sales potential and order quantities.

[0537] The server utilizes optimization techniques to formulate replenishment plans based on prediction results and inventory information. Replenishment quantities are optimized for each product to prevent excess inventory and stockouts.

[0538] The device is equipped with emotion recognition capabilities, which use the camera and microphone to analyze the user's facial expressions and voice during operation. This analysis detects the user's emotions regarding the order plan, such as anxiety or dissatisfaction, and sends this information to the server.

[0539] The server receives user sentiment information through a judgment mechanism and readjusts the replenishment plan. This readjustment provides an order plan that meets the user's expectations. The adjusted plan is then sent to the terminal via a notification mechanism and presented to the user.

[0540] As a concrete example, consider a store planning to order a new product line. The server uses an AI model to analyze the sales potential of this product and suggests a standard quantity. When the user reviews this suggestion via their device, if the emotion engine detects low interest or dissatisfaction in the user's emotions, the server then presents alternative options based on that.

[0541] For example, by inputting a prompt such as, "When introducing a new product, how do you determine the optimal order quantity while considering user emotional feedback?" into the generative AI model, more sophisticated predictions and adjustments can be made.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The server collects environmental data from the communication network via information acquisition methods. Specifically, it uses APIs to obtain weather information and local event information from the internet and stores it in storage. The input consists of weather data and event data, which are converted into the required format through data processing. The output is formatted environmental data.

[0545] Step 2:

[0546] The server retrieves past sales history from its storage device. It references sales data for a specific product as input and uses database queries to retrieve the necessary data. The output is sales performance data for the target product. This data is used for subsequent analysis.

[0547] Step 3:

[0548] The server utilizes analytical tools and generates AI models to perform sales forecasts. The input consists of environmental data and sales performance data, which are fed into the AI ​​model. Specifically, it uses neural networks, such as TensorFlow, to perform data calculations and predict sales trends. The output is the predicted sales volume.

[0549] Step 4:

[0550] The server uses optimization techniques to develop an optimal replenishment plan based on predicted sales volume and current inventory information. The input consists of predicted data and inventory data, and the algorithm is executed to calculate replenishment quantities. The output is a replenishment plan for each product.

[0551] Step 5:

[0552] The terminal activates emotion recognition when the user receives a replenishment plan through the application. Input consists of the user's facial expressions and voice data, collected using the camera and microphone. Data processing involves facial recognition and voice analysis to determine the user's emotions. The output is emotion evaluation information.

[0553] Step 6:

[0554] The server readjusts the supplementation plan based on the emotional evaluation information using a judgment mechanism. The input is evaluation data, which is used to perform recalculations, and the generating AI model may be reapplied. The output is the adjusted supplementation plan.

[0555] Step 7:

[0556] The server sends the adjusted replenishment plan to the terminal via a notification system and presents it to the user. The terminal displays the notification through its user interface so that the user can confirm it. The output is the notification of the final replenishment plan.

[0557] (Application Example 2)

[0558] 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."

[0559] In retail store order management, uncertainty in demand forecasting and inefficiency in inventory management are often cited as problems. Furthermore, there is a need to create flexible ordering plans that reflect the intentions and feelings of managers, but conventional systems have struggled to effectively address these issues. Moreover, it has been difficult to appropriately analyze the impact of fluctuations in environmental information and local event information on sales and reflect this in ordering plans. This invention aims to solve these problems.

[0560] 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.

[0561] In this invention, the server includes means for acquiring environmental information and local event information as an information acquisition device, means for executing machine learning algorithms to make predictions as an analysis device, and means for analyzing and evaluating the user's facial expressions and voice as an emotion recognition device. This makes it possible to create highly accurate ordering plans that reflect environmental changes and local event information, and further optimize the plans by taking the user's emotions into consideration.

[0562] An "information acquisition device" is a device that has the function of acquiring environmental information and local event information via a communication network and collecting data in real time.

[0563] A "data storage device" is a device that stores past transaction history and related data, and has the function of quickly retrieving information as needed.

[0564] An "analytical device" is a device that has the function of making predictions using machine learning algorithms based on acquired data, and plays a role in analyzing sales trends.

[0565] An "emotion recognition device" is a device equipped with the function of analyzing the user's facial expressions and voice and evaluating their emotions, making it possible to incorporate user feedback into the ordering plan.

[0566] An "optimization device" is a device that creates an optimal ordering plan based on prediction results, inventory data, and sentiment evaluations, and readjusts the plan as needed.

[0567] A "notification device" is a device that has the function of quickly and accurately reporting optimized ordering plans to administrators.

[0568] A "user interface" is a system that provides operation screens and interactive functions used by administrators when making adjustments based on factors such as order quantities and sentiment ratings.

[0569] The server acquires environmental and local event information via a communication network using an information acquisition device. This data is collected in real time using services such as the Google Cloud API. The data storage device stores past transaction history, and this data is analyzed by machine learning algorithms such as TensorFlow. The analysis device predicts sales trends based on the acquired data and creates the most suitable order plan based on this prediction.

[0570] The user operates the emotion recognition device using a smartphone or similar device. The device utilizes the camera and microphone to analyze the user's facial expressions and voice, and evaluates their emotions. The evaluated emotion data is sent to a server and reflected in the order planning by an optimization device.

[0571] The optimization system integrates prediction results, inventory data, and sentiment evaluations to automatically adjust the ordering plan. This allows user feedback to be reflected in the system, resulting in a more realistic and precise plan. The created plan is quickly reported to administrators via a notification system, who can then review order quantities and the plan through the user interface.

[0572] For example, if a large-scale festival is being held in the area on a holiday with a forecast of sunny weather, the server will suggest ordering confectionery suitable for the event. However, if the user expresses concern, the server can readjust the order quantity based on that sentiment. An example of a prompt might be, "Adjust the order quantity of confectionery based on emotional feedback for a weekday morning on a sunny holiday with a nearby music event."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The server uses an information acquisition device to obtain environmental information and local event information via a communication network. It collects weather data and local event information from the internet as input, and stores the acquired data in a data storage device as output. Specifically, it periodically acquires data via an API, formats it, and stores it in a database.

[0576] Step 2:

[0577] The server retrieves historical transaction history stored in the data storage device and uses a machine learning algorithm in the analysis device to perform sales forecasts. It uses historical sales history and environmental information obtained in step 1 as input and generates forecast data regarding future sales trends as output. This forecasting involves processing the data and running a forecasting model using tools such as TensorFlow.

[0578] Step 3:

[0579] The user uses a device to input their facial expressions and voice through an emotion recognition device. The device uses camera images and microphone audio data as input and generates user emotion evaluation data as output. Specifically, the device's camera analyzes facial features in real time, and a machine learning model is applied to determine emotions from the audio data.

[0580] Step 4:

[0581] Sentiment evaluation data and sales forecasts are sent to the server, and the optimization device integrates this data to create the most suitable order plan. The forecast data from step 2 and the sentiment evaluation data from step 3 are used as input, and the optimized order plan is derived as output. Here, an optimization algorithm based on the forecast results and sentiment data is used to calculate and adjust the planned quantities.

[0582] Step 5:

[0583] The server reports the optimized ordering plan to the administrator via a notification device. It uses the ordering plan created in step 4 as input and sends the draft plan to the administration screen or mobile device as output. Specifically, it exports the plan for administrator review and provides timely information using push notifications, etc.

[0584] Step 6:

[0585] Users make adjustments based on the provided order plan via the user interface. The notified order plan and its background information are used as input, and the modified order quantity is determined as output. Specific actions include manipulating the interface to modify the plan and sending feedback back to the server.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention provides an efficient ordering management system for retail stores such as convenience stores. The system is primarily composed of three elements: a server, terminals, and users.

[0604] The server has multiple functions. First, it periodically collects weather and local event information via the internet through its information acquisition function. This allows users to understand environmental changes around their stores in real time. The server also collects past sales performance data from stores and stores it in a database.

[0605] The analysis is performed by an artificial intelligence algorithm on a server. It analyzes collected weather and event information along with historical sales data to assess the impact of these factors on product sales. Based on this assessment, future sales forecasts are generated.

[0606] Based on the generated sales forecast, the server uses its optimization function to calculate the appropriate order quantity for each product. Inventory status is also taken into consideration to create an ordering plan that avoids excess inventory and stockouts.

[0607] The terminal receives an optimized order plan provided by the server. The store manager, who is the user, reviews this plan on their terminal and makes adjustments as needed. For example, if a promotional campaign for a particular product is running concurrently, the user can fine-tune the plan and increase the order quantity.

[0608] As a concrete example, let's assume a large music event is being held at a certain store over the weekend. The server retrieves this information and analyzes sales data from similar events held in the past. As a result, it predicts that certain beverages and snacks will sell well, and therefore plans to place larger orders than usual. The user receives this plan and can place an order to prepare for the demand during the event.

[0609] This system flexibly responds to fluctuating consumer demand and enables efficient order management. Through this system, users can maximize sales opportunities while minimizing inventory losses.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server accesses weather information services and local event information services to retrieve the latest data. It uses APIs to extract the information and saves it to an internal database.

[0613] Step 2:

[0614] The server retrieves historical sales data from the store management system. Based on this data, it integrates it into a database in a format that allows for analysis of its relationship with weather and event information.

[0615] Step 3:

[0616] The server uses collected weather, event, and sales data to run artificial intelligence algorithms. The algorithms analyze past patterns and make future sales predictions. For example, they might use historical data to determine that certain products tend to sell less during rainy weather.

[0617] Step 4:

[0618] The server calculates the optimal order quantity based on predicted sales data and current inventory information. The server creates an order plan and adjusts it to minimize the risk of stockouts or excess inventory.

[0619] Step 5:

[0620] The server sends the generated order plan to the terminal. The terminal notifies the user of this information and indicates that the order details can be confirmed.

[0621] Step 6:

[0622] Users review the proposed order plan via their device and make changes as needed. For example, they might adjust the order quantity to increase during a promotional period for a particular product.

[0623] Step 7:

[0624] Once the user confirms the order, the terminal sends that information to the server. The server processes the final order and accurately transmits it to the supplier.

[0625] (Example 1)

[0626] 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".

[0627] In retail stores, demand is prone to fluctuations due to weather conditions and local events. This makes it easy for excess inventory and stockouts to occur, posing a challenge to efficient order management. Furthermore, many conventional ordering systems rely on static predictive models based on historical data, failing to adequately respond to real-time environmental changes. Solving these problems is essential.

[0628] 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.

[0629] In this invention, the server includes means for acquiring weather data and local event information via a communication network as an information gathering means, means for acquiring past sales history from a database, and means for performing estimations using an intelligent algorithm. This makes it possible to create an optimal ordering plan for fluctuating demand and to improve the efficiency of inventory management.

[0630] "Information gathering means" refers to functions for acquiring weather data and local event information from external sources via a communication network.

[0631] "Sales history" refers to records showing what products a store has sold in the past, when, and in what quantities.

[0632] A "database" is an information management system that structures and stores collected data, and allows for efficient searching and extraction as needed.

[0633] An "intelligent algorithm" is a computational method that analyzes acquired data to predict future demand and sales patterns.

[0634] An "optimization tool" is a function that creates an optimal ordering plan based on estimated demand and taking inventory data into consideration.

[0635] "Notification methods" refer to methods of providing administrators with optimized ordering plans and communicating information for reviewing and modifying ordering details.

[0636] "Interface means" refers to means for realizing a user interface that allows administrators to modify quantities based on the order plan.

[0637] This invention embodies a system that supports efficient order management in retail stores. The system consists of three elements: a server, a terminal, and a user.

[0638] The server's role is to acquire weather data and local event information via the communication network as a means of information gathering. Specifically, it periodically collects data from weather information services and publicly available local event databases. This information is obtained through web scraping techniques using Python and APIs. The server also collects past sales history from the database and integrates the information. MySQL and PostgreSQL are used for managing this database.

[0639] The collected data is analyzed on the server using intelligent algorithms. First, the data is preprocessed using libraries such as Pandas and NumPy in Python. Next, time series analysis and predictions using machine learning models are performed using Scikit-learn and StatsModels. This makes it possible to forecast demand while taking into account the impact of weather and events.

[0640] The server performs mathematical optimization using GuRoBi and ORTools as optimization tools, generating an optimal ordering plan based on inventory data and forecast results. This enables effective ordering management that prevents excess inventory and stockouts.

[0641] The terminal provides the administrator user with an optimized order plan sent from the server. The terminal has a dedicated application installed, allowing the user to check the displayed plan on the spot and adjust order quantities using touch controls or keyboard input.

[0642] The user makes modifications to the optimized plan through this interface and returns the finalized ordering plan to the server. The server then automatically places orders with suppliers based on this confirmed information.

[0643] As a concrete example, if a large-scale music festival is held nearby on a weekend, the server collects event information in advance and analyzes it in combination with historical data. The analysis results predict an increase in demand for a specific product and determine the optimal order quantity accordingly. The user can review the plan, make any necessary adjustments, and finalize it, thus avoiding missing sales opportunities on the day of the event.

[0644] An example of a prompt might be: "Based on a weekend event in a specific region, analyze expected consumer behavior and purchasing trends, and develop a product ordering plan accordingly. Please also consider past data from similar events and the weather forecast for the day."

[0645] This system allows users to respond appropriately to changing circumstances, maximizing sales while minimizing inventory waste.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The server uses a communication network to acquire external weather data and local event information as a means of information gathering. This process involves periodically collecting data through API calls. Specifically, the server calls the weather information provider's API and performs web scraping of event calendars. The input data consists of weather conditions (e.g., temperature, precipitation) and event information (e.g., date, time, and location), which are converted into an internal format and stored in the database.

[0649] Step 2:

[0650] The server retrieves past sales history from the database. This gathers the basic data necessary for analyzing sales patterns. Specifically, it extracts sales data using SQL queries. The input for this process is sales transaction data for a specific period. The processed data is aggregated into sales figures and revenue data by category and used in the next analysis phase.

[0651] Step 3:

[0652] The server uses intelligent algorithms to analyze the collected data. This analysis is used to predict demand. Specifically, it preprocesses the data using Python's Pandas library and builds a machine learning model using Scikit-learn. The inputs are weather data, event information, and sales history. This data is combined and input into the model to output future sales forecasts. This output quantifies the demand for the product.

[0653] Step 4:

[0654] The server uses optimization techniques to generate an order plan based on sales forecasts. Specifically, it uses mathematical optimization tools (GuRoBi or ORTools) to perform optimization considering inventory levels and forecasted demand. The inputs to this process are sales forecast data and current inventory data. The output is an order plan that includes recommended order quantities for each product.

[0655] Step 5:

[0656] The terminal receives an optimized order plan from the server and notifies the administrator user. Specifically, a dedicated application on the terminal displays the outputted order plan. The input for this process is the optimized order plan data. The user visually confirms this information and adjusts the order quantities via the interface as needed.

[0657] Step 6:

[0658] Users modify and finalize their order plans via their terminals and provide feedback to the server. Specifically, they adjust quantities using the user interface and send the revised data to the server. The input to this process is the order quantity adjusted by the user. The output is the final order information confirmed by the server.

[0659] Step 7:

[0660] The server automatically places orders with the supplier system based on the final order information. Specifically, it sends the necessary order information to the supplier's system. The input to this process is the confirmed order information, and the output is the order confirmation sent to the supplier.

[0661] (Application Example 1)

[0662] 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".

[0663] Traditional order management systems in retail stores suffered from low demand forecasting accuracy, leading to frequent inventory oversupply and stockouts. Furthermore, it was difficult to reflect the impact of environmental changes and local events on sales in a timely manner, hindering managers from responding quickly. There is a need for a more efficient and responsive order management system that addresses these challenges.

[0664] 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.

[0665] In this invention, the server includes, as an information acquisition means, means for acquiring environmental information and local event information via a communication network, and means for acquiring past transaction records from a recording medium, and as an analysis means, means for executing an intelligent algorithm for making predictions based on the acquired environmental information, local event information, and past transaction records. This enables highly accurate demand forecasting and the creation of optimal ordering plans in real time.

[0666] "Information acquisition means" refers to means of collecting environmental information and local event information through communication networks.

[0667] "Analysis means" refers to the means of executing intelligent algorithms for making predictions based on acquired environmental information, local event information, and past transaction records.

[0668] "Optimization means" refers to the means of creating an optimal supply plan based on prediction results and available information.

[0669] "Notification means" refers to a means of reporting the optimized supply plan to the administrator.

[0670] The "real-time notification function" is a feature that instantly sends information to the administrator.

[0671] A "customizable user interface" is an interface that provides the operational capabilities for administrators to modify supply plans.

[0672] The system implementing this invention mainly consists of a server, a terminal, and a user. The server uses information acquisition means to acquire environmental information and local event information via a communication network, and obtains this information, along with past transaction records, from a recording medium for analysis. This information is processed by a schematic analysis means equipped with an intelligent algorithm to make future demand forecasts.

[0673] Based on this prediction, the server uses optimization techniques to build a supply plan and calculate the appropriate supply quantities for all products. This result is notified to the terminal in real time and presented to the administrator (user). The administrator can then use this information to review and modify the specific and strategic supply plan through an adjustable user interface. This design prevents oversupply and shortages, improving the efficiency of transactions.

[0674] For example, based on past event data, it can be predicted that demand for a particular product will increase on the day of a local festival. In this case, the server can create a plan to increase the supply quantity beyond the usual amount and notify the administrator. The administrator can then fine-tune the supply plan to adapt to the increased demand based on the information displayed on the terminal. By operating such a system, the operational efficiency of the store improves, and timely responses become possible.

[0675] The following is an example of a specific prompt using a generative AI model: "Predict the sales demand for products at convenience stores and generate an optimal supply plan based on historical sales data, weather forecasts, and local event information."

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server acquires environmental information and local event information via a communication network using information acquisition means. This involves using data obtained from weather APIs and local event APIs. The acquired data is stored directly on the recording medium. The input is environmental information and local event information, and the output is the acquired information.

[0679] Step 2:

[0680] The server retrieves past transaction records from the storage medium. This data consists of past sales records extracted from the sales management system. The input is sales data obtained from the sales management system, and the output is past transaction records.

[0681] Step 3:

[0682] The server processes environmental information, local event information, and historical transaction data acquired using intelligent algorithms as analytical tools, and predicts future demand. This prediction uses a generative AI model to analyze data correlations and generate sales forecasts. The inputs are environmental information, local event information, and transaction data, and the output is demand forecast data.

[0683] Step 4:

[0684] The server constructs a supply plan based on predicted demand using optimization techniques. This process integrates inventory status and demand forecasts using Monte Carlo simulation to generate an optimal supply plan. The inputs are demand forecast data and inventory information, and the output is the supply plan.

[0685] Step 5:

[0686] The server uses a real-time notification function to notify terminals of the generated supply plan. The terminals display the detailed supply plan, which administrators can immediately review. The input is the supply plan, and the output is a notification to the administrator.

[0687] Step 6:

[0688] The user (administrator) reviews the supply plan on the terminal's adjustable user interface and makes modifications as needed. This process can take into account special store information, such as promotional campaigns. The input is the supply plan, and the output is the final, modified supply plan.

[0689] 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.

[0690] This invention combines a system for streamlining order management in retail stores such as convenience stores with a function that recognizes user emotions and adjusts the order plan accordingly. This system consists mainly of a server, terminals, and users, and achieves flexible order management using an emotion engine.

[0691] The server is equipped with information acquisition capabilities and collects weather and local event information via the internet. This allows for real-time monitoring of environmental changes around the store. Furthermore, the server retrieves the store's past sales performance from a database and uses artificial intelligence algorithms to make sales forecasts based on this information.

[0692] Based on predicted sales data and inventory information, the server develops an optimal ordering plan. It calculates the required order quantity for each product and adjusts it to prevent inventory shortages or surpluses. The generated ordering plan is sent to the terminal and notified to the user.

[0693] A distinctive feature of this invention is the incorporation of an emotion engine. The emotion engine analyzes the user's facial expressions and voice via the terminal and evaluates their emotions regarding the order plan. For example, if the user feels that the order quantity is too high, anxiety or doubt may be detected. Based on this data, the server automatically readjusts the order plan and proposes a plan that is closer to the user's wishes.

[0694] As a concrete example, consider a store that is considering ordering a new product line. The server uses a predictive model to analyze sales potential and proposes a certain quantity. However, when the user reviews this proposal through their terminal, if the emotion engine detects emotions such as weak interest or dissatisfaction, the server takes this emotional information into account and creates an alternative proposal. In this way, users can communicate their emotions to the system as feedback, which in turn better supports their actual decision-making.

[0695] This system evolves retail store ordering processes into more effective and user-driven operations through a multifaceted approach that combines information acquisition, analysis, prediction, optimization, notification, and sentiment recognition.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server collects weather and local event information via the internet. This data is retrieved using an API or web scraping and stored in a database.

[0699] Step 2:

[0700] The server retrieves historical sales data from the store management system. This data, along with weather and event information, is integrated into a database in an analyzable format.

[0701] Step 3:

[0702] The server executes an artificial intelligence algorithm and creates future sales forecasts based on the collected data. The algorithm delves into past patterns and demand trends to predict sales figures for each product.

[0703] Step 4:

[0704] The server creates an optimal ordering plan based on forecast results and inventory information. The plan is adjusted to prevent excess inventory and stockouts, and the required order quantities are calculated.

[0705] Step 5:

[0706] The server sends the order plan it has created to the terminal. The terminal notifies the user and displays the details.

[0707] Step 6:

[0708] The user reviews the proposed order plan through the device. The device detects the user's emotions through facial recognition and voice data, and an emotion engine analyzes their response.

[0709] Step 7:

[0710] Based on the user's emotions, the server readjusts the order plan if there are any dissatisfactions or doubts. The emotion engine then generates and proposes a new plan that better suits the user's preferences.

[0711] Step 8:

[0712] Once the user is satisfied with the final order plan, they approve it on their device. The device then sends this information to the server, and the final order is placed.

[0713] (Example 2)

[0714] 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".

[0715] Order management in retail stores requires improved accuracy in demand forecasting based on environmental changes and past sales history, as well as adjustments that reflect the operator's emotions. However, currently, no system exists that takes emotions into account, so order plans sometimes deviate from the operator's wishes. The challenge is to solve this problem and realize more flexible and appropriate order management.

[0716] 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.

[0717] In this invention, the server includes, as an information acquisition means, means for acquiring environmental data via a communication network and means for acquiring past sales history from a storage device, and as an analysis means, means for executing a generative AI model for making predictions based on the acquired environmental data and past sales history. This enables appropriate order management that takes into account the emotions of the operator.

[0718] "Information acquisition means" refers to a system for acquiring environmental data and past sales history via a communication network.

[0719] The "analysis method" is a mechanism that executes a generative AI model to forecast demand based on acquired environmental data and past sales history.

[0720] An "optimization method" is a mechanism for constructing an appropriate replenishment plan based on forecast results and inventory information.

[0721] "Emotion recognition means" refers to a system that analyzes the operator's facial expressions and voice to evaluate their emotions regarding the replenishment plan.

[0722] A "determination method" is a mechanism for readjusting the supplementation plan based on evaluations obtained through emotion recognition.

[0723] A "notification mechanism" is a system for communicating the adjusted replenishment plan to the operator.

[0724] This invention is a system for streamlining order management in retail stores. It not only proposes an optimal ordering plan but also has the function of recognizing user emotions and fine-tuning the plan accordingly. This system primarily includes a server, terminals, and users.

[0725] The server collects environmental data via a communication network using information acquisition means. This data includes weather information and local event information, which are factors that influence sales around the store. It also retrieves past sales history from storage devices and processes this data through analysis means.

[0726] Specifically, the server uses a generative AI model to make predictions based on environmental data and sales history. This estimates product demand. The generative AI model used may include libraries such as TensorFlow and PyTorch. This is used to calculate sales potential and order quantities.

[0727] The server utilizes optimization techniques to formulate replenishment plans based on prediction results and inventory information. Replenishment quantities are optimized for each product to prevent excess inventory and stockouts.

[0728] The device is equipped with emotion recognition capabilities, which use the camera and microphone to analyze the user's facial expressions and voice during operation. This analysis detects the user's emotions regarding the order plan, such as anxiety or dissatisfaction, and sends this information to the server.

[0729] The server receives user sentiment information through a judgment mechanism and readjusts the replenishment plan. This readjustment provides an order plan that meets the user's expectations. The adjusted plan is then sent to the terminal via a notification mechanism and presented to the user.

[0730] As a concrete example, consider a store planning to order a new product line. The server uses an AI model to analyze the sales potential of this product and suggests a standard quantity. When the user reviews this suggestion via their device, if the emotion engine detects low interest or dissatisfaction in the user's emotions, the server then presents alternative options based on that.

[0731] For example, by inputting a prompt such as, "When introducing a new product, how do you determine the optimal order quantity while considering user emotional feedback?" into the generative AI model, more sophisticated predictions and adjustments can be made.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The server collects environmental data from the communication network via information acquisition methods. Specifically, it uses APIs to obtain weather information and local event information from the internet and stores it in storage. The input consists of weather data and event data, which are converted into the required format through data processing. The output is formatted environmental data.

[0735] Step 2:

[0736] The server retrieves past sales history from its storage device. It references sales data for a specific product as input and uses database queries to retrieve the necessary data. The output is sales performance data for the target product. This data is used for subsequent analysis.

[0737] Step 3:

[0738] The server utilizes analytical tools and generates AI models to perform sales forecasts. The input consists of environmental data and sales performance data, which are fed into the AI ​​model. Specifically, it uses neural networks, such as TensorFlow, to perform data calculations and predict sales trends. The output is the predicted sales volume.

[0739] Step 4:

[0740] The server uses optimization techniques to develop an optimal replenishment plan based on predicted sales volume and current inventory information. The input consists of predicted data and inventory data, and the algorithm is executed to calculate replenishment quantities. The output is a replenishment plan for each product.

[0741] Step 5:

[0742] The terminal activates emotion recognition when the user receives a replenishment plan through the application. Input consists of the user's facial expressions and voice data, collected using the camera and microphone. Data processing involves facial recognition and voice analysis to determine the user's emotions. The output is emotion evaluation information.

[0743] Step 6:

[0744] The server readjusts the supplementation plan based on the emotional evaluation information using a judgment mechanism. The input is evaluation data, which is used to perform recalculations, and the generating AI model may be reapplied. The output is the adjusted supplementation plan.

[0745] Step 7:

[0746] The server sends the adjusted replenishment plan to the terminal via a notification system and presents it to the user. The terminal displays the notification through its user interface so that the user can confirm it. The output is the notification of the final replenishment plan.

[0747] (Application Example 2)

[0748] 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".

[0749] In retail store order management, uncertainty in demand forecasting and inefficiency in inventory management are often cited as problems. Furthermore, there is a need to create flexible ordering plans that reflect the intentions and feelings of managers, but conventional systems have struggled to effectively address these issues. Moreover, it has been difficult to appropriately analyze the impact of fluctuations in environmental information and local event information on sales and reflect this in ordering plans. This invention aims to solve these problems.

[0750] 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.

[0751] In this invention, the server includes means for acquiring environmental information and local event information as an information acquisition device, means for executing machine learning algorithms to make predictions as an analysis device, and means for analyzing and evaluating the user's facial expressions and voice as an emotion recognition device. This makes it possible to create highly accurate ordering plans that reflect environmental changes and local event information, and further optimize the plans by taking the user's emotions into consideration.

[0752] An "information acquisition device" is a device that has the function of acquiring environmental information and local event information via a communication network and collecting data in real time.

[0753] A "data storage device" is a device that stores past transaction history and related data, and has the function of quickly retrieving information as needed.

[0754] An "analytical device" is a device that has the function of making predictions using machine learning algorithms based on acquired data, and plays a role in analyzing sales trends.

[0755] An "emotion recognition device" is a device equipped with the function of analyzing the user's facial expressions and voice and evaluating their emotions, making it possible to incorporate user feedback into the ordering plan.

[0756] An "optimization device" is a device that creates an optimal ordering plan based on prediction results, inventory data, and sentiment evaluations, and readjusts the plan as needed.

[0757] A "notification device" is a device that has the function of quickly and accurately reporting optimized ordering plans to administrators.

[0758] A "user interface" is a system that provides operation screens and interactive functions used by administrators when making adjustments based on factors such as order quantities and sentiment ratings.

[0759] The server acquires environmental and local event information via a communication network using an information acquisition device. This data is collected in real time using services such as the Google Cloud API. The data storage device stores past transaction history, and this data is analyzed by machine learning algorithms such as TensorFlow. The analysis device predicts sales trends based on the acquired data and creates the most suitable order plan based on this prediction.

[0760] The user operates the emotion recognition device using a smartphone or similar device. The device utilizes the camera and microphone to analyze the user's facial expressions and voice, and evaluates their emotions. The evaluated emotion data is sent to a server and reflected in the order planning by an optimization device.

[0761] The optimization system integrates prediction results, inventory data, and sentiment evaluations to automatically adjust the ordering plan. This allows user feedback to be reflected in the system, resulting in a more realistic and precise plan. The created plan is quickly reported to administrators via a notification system, who can then review order quantities and the plan through the user interface.

[0762] For example, if a large-scale festival is being held in the area on a holiday with a forecast of sunny weather, the server will suggest ordering confectionery suitable for the event. However, if the user expresses concern, the server can readjust the order quantity based on that sentiment. An example of a prompt might be, "Adjust the order quantity of confectionery based on emotional feedback for a weekday morning on a sunny holiday with a nearby music event."

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server uses an information acquisition device to obtain environmental information and local event information via a communication network. It collects weather data and local event information from the internet as input, and stores the acquired data in a data storage device as output. Specifically, it periodically acquires data via an API, formats it, and stores it in a database.

[0766] Step 2:

[0767] The server retrieves historical transaction history stored in the data storage device and uses a machine learning algorithm in the analysis device to perform sales forecasts. It uses historical sales history and environmental information obtained in step 1 as input and generates forecast data regarding future sales trends as output. This forecasting involves processing the data and running a forecasting model using tools such as TensorFlow.

[0768] Step 3:

[0769] The user uses a device to input their facial expressions and voice through an emotion recognition device. The device uses camera images and microphone audio data as input and generates user emotion evaluation data as output. Specifically, the device's camera analyzes facial features in real time, and a machine learning model is applied to determine emotions from the audio data.

[0770] Step 4:

[0771] Sentiment evaluation data and sales forecasts are sent to the server, and the optimization device integrates this data to create the most suitable order plan. The forecast data from step 2 and the sentiment evaluation data from step 3 are used as input, and the optimized order plan is derived as output. Here, an optimization algorithm based on the forecast results and sentiment data is used to calculate and adjust the planned quantities.

[0772] Step 5:

[0773] The server reports the optimized ordering plan to the administrator via a notification device. It uses the ordering plan created in step 4 as input and sends the draft plan to the administration screen or mobile device as output. Specifically, it exports the plan for administrator review and provides timely information using push notifications, etc.

[0774] Step 6:

[0775] Users make adjustments based on the provided order plan via the user interface. The notified order plan and its background information are used as input, and the modified order quantity is determined as output. Specific actions include manipulating the interface to modify the plan and sending feedback back to the server.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] As means of acquiring information, there are means of acquiring weather information and local event information via a communication network,

[0800] Methods for obtaining past sales performance from a database,

[0801] The analytical means includes executing an artificial intelligence algorithm for making predictions based on acquired weather information, local event information, and past sales performance.

[0802] As an optimization means, a means for creating an optimal ordering plan based on forecast results and inventory information,

[0803] As a means of notification, there is a means of reporting the optimized ordering plan to the administrator,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the artificial intelligence algorithm has a learning function for analyzing the impact of changes in weather information or local event information on sales performance.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising a user interface that allows the order quantity to be adjusted based on the order plan provided to the administrator.

[0809] "Example 1"

[0810] (Claim 1)

[0811] As a means of gathering information, a means of acquiring weather data and local event information via a communication network,

[0812] Methods for retrieving past sales history from a database,

[0813] The analytical means includes a means for executing an intelligent algorithm to perform estimations based on acquired weather data, local event information, and past sales history.

[0814] As an optimization means, a means for constructing an optimal ordering plan based on estimation results and inventory data,

[0815] As a means of notification, a means of providing the administrator with an optimized ordering plan,

[0816] As an interface means, there is a means by which the administrator can modify the order quantity based on the order plan,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the intelligent algorithm has a learning function for analyzing the impact of changes in weather data or local event information on sales history.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising a user interface that allows the order quantity to be adjusted based on the order plan provided to the administrator.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] As a means of acquiring information, there is a means of acquiring environmental information and local event information via a communication network,

[0825] A means of obtaining past transaction records from a recording medium,

[0826] The analytical means includes executing an intelligent algorithm for making predictions based on acquired environmental information, regional event information, and past transaction history.

[0827] As an optimization means, a means for creating an optimal supply plan based on prediction results and possessed information,

[0828] As a means of notification, a means of reporting the optimized supply plan to the administrator,

[0829] A means of sending information to the administrator using a real-time notification function,

[0830] It features an adjustable user interface and means for administrators to modify supply plans,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the intelligent algorithm has a learning function for analyzing the impact of changes in environmental information or local event information on transaction performance.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising an operating interface that allows adjustment of the supply quantity based on the supply plan provided to the administrator.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] As a means of acquiring information, there is a means of acquiring environmental data via a communication network,

[0839] A means of obtaining past sales history from storage devices,

[0840] The analytical means includes a means for executing a generative AI model to make predictions based on acquired environmental data and past sales history,

[0841] As an optimization means, means for creating an appropriate replenishment plan based on forecast results and inventory information,

[0842] As a means of emotion recognition, it includes a means for analyzing the operator's facial expressions and voice and evaluating their emotions towards the generated replenishment plan,

[0843] As a means of determination, a means of readjusting the supplementation plan based on the evaluated emotions,

[0844] As a means of notification, a means of reporting the adjusted replenishment plan to the operator,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the generating AI model has a learning function for analyzing the impact of changes in environmental data on sales history.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising an operating device that allows adjustment of the replenishment quantity based on a replenishment plan provided to the operator.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] As an information acquisition device, it includes means for acquiring environmental information and local event information via a communication network,

[0853] A means of obtaining past transaction history from a data storage device,

[0854] The analytical device includes means for executing a machine learning algorithm to make predictions based on acquired environmental information, regional event information, and past transaction history.

[0855] As an emotion recognition device, it analyzes the user's facial expressions and voice, and provides means for evaluating their emotions regarding the order proposal.

[0856] As an optimization device, it includes means for creating and readjusting the most suitable ordering plan based on prediction results, inventory data, and sentiment evaluation,

[0857] As a notification device, it provides a means to report the optimized ordering plan to the administrator,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the machine learning algorithm has a learning function for analyzing the impact of changes in environmental information or regional event information on transaction history.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising a user interface that allows adjustment of the order quantity and user sentiment evaluation based on the order plan provided to the administrator. [Explanation of Symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. As a means of acquiring information, there is a means of acquiring environmental information and local event information via a communication network, A means of obtaining past transaction records from a recording medium, The analytical means includes executing an intelligent algorithm for making predictions based on acquired environmental information, regional event information, and past transaction history. As an optimization means, a means for creating an optimal supply plan based on prediction results and possessed information, As a means of notification, a means of reporting the optimized supply plan to the administrator, A means of sending information to the administrator using a real-time notification function, It features an adjustable user interface and means for administrators to modify supply plans, A system that includes this.

2. The system according to claim 1, wherein the intelligent algorithm has a learning function for analyzing the impact of changes in environmental information or local event information on transaction performance.

3. The system according to claim 1, comprising an operating interface that allows the supply quantity to be adjusted based on the supply plan provided to the administrator.

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