system

Abstract

We provide the system. [Solution] A means for collecting input data including sales performance forecast information obtained from multiple business facilities, equipment allocation information for desired personnel, and external factor information, Based on the aforementioned input data, a means for optimizing the operational plan of food service support equipment and the appropriate allocation of personnel at each business facility, A means for creating an appropriate sharing plan for food service support equipment between business facilities based on the results of the optimization described above, A means for presenting the optimized operational plan and personnel allocation to the operations manager and providing an interface for confirmation and modification, A system including means for updating the operation plan and sharing plan of the food service support equipment using confirmation and correction information from the aforementioned administrator.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In recent years, in business facilities, in order to address labor shortages and reduce labor costs, meal delivery support equipment has been introduced, but its operational efficiency has become an issue. Although certain effects are expected from the introduction of meal delivery support equipment, it itself increases fixed costs and may impose an economic burden. In addition, since it is impossible to fully respond to different peak and slack periods for each business facility, there is over-operation of equipment and overstaffing, making it difficult to optimize the overall cost. The present invention intends to improve the efficiency of the entire business facility through shared operation of meal delivery support equipment to address these issues. 【Means for Solving the Problems】 【0005】 This invention collects sales performance forecast information, personnel preference information, and external factor information from multiple business facilities, and uses this data as a basis to provide a system that optimizes the operation of food service support equipment and the allocation of personnel at each business facility through AI-powered analysis. Furthermore, based on the optimization results, it creates an inter-facility sharing plan for food service support equipment tailored to the specific situation, and enables confirmation and modification through an interface visualized and presented to business managers. This achieves efficient equipment utilization, cost reduction, and qualitative improvement of operations across all business facilities. 【0006】 "Business facilities" refer to places where services are provided, and in this context, they specifically refer to restaurants and hospitality-related facilities. 【0007】 "Sales performance forecast information" refers to information used to predict future sales and customer numbers based on past data and trends. 【0008】 "Personnel and Equipment Placement Information" refers to information regarding the desired placement of staff and food service support equipment. 【0009】 "External factor information" refers to information about external factors that affect the operation of business facilities, such as weather and events. 【0010】 "Food delivery support equipment" refers to automated machinery used to deliver food and products to customers. 【0011】 A "sharing plan" is a plan that determines how and when to share food service support equipment between different service facilities. 【0012】 A "business manager" refers to a person responsible for the operation of a business facility and the management of staff. 【0013】 An "interface" refers to the means or screen configuration through which a system and a user exchange information. 【0014】 A "visual dashboard" is a user interface for visually organizing and displaying data and information. [Brief explanation of the drawing] 【0015】 [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 the data processing device and 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. 【Embodiments for Carrying Out the Invention】 【0016】 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. 【0017】 First, the terms used in the following description will be explained. 【0018】 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. 【0019】 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. 【0020】 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. 【0021】 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). 【0022】 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." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 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. 【0026】 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). 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 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". 【0036】 This invention provides a system for optimizing the shared operation of food service support equipment in multiple service facilities. This system includes functions for data collection, data analysis, optimization calculation, suggestion display, feedback, and adjustment. Its specific operation is described below. 【0037】 First, users register sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into the system via a dedicated input interface. The terminal aggregates this information and sends it to the server. The server stores this data in a database for processing and builds a sales forecast model based on past performance and trends. 【0038】 Next, the server predicts peak hours for each service facility based on external factors such as weather and event information. Based on this, it generates an operational plan for food service support equipment and a proposed staffing allocation. By using an optimization algorithm, it plans the movement of food service support equipment between facilities, enabling effective sharing. 【0039】 The server then notifies terminals of this optimized plan and presents it to business administrators on a visual dashboard. This allows administrators to intuitively review the proposed working hours and staffing and make adjustments as needed. 【0040】 Ultimately, users make corrections to shifts and equipment placement based on the information provided through the management screen, and send these changes back to the server via their terminal. The server then uses this feedback to continuously train its AI model, aiming to improve the accuracy of future suggestions. 【0041】 For example, if a large sporting event is scheduled nearby, the server will take that information into account, predict an increase in the number of visitors to nearby business facilities, and create a plan to deploy additional food service equipment as needed. In this way, the system responds to events and external factors, thereby improving the efficiency of business operations. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 Users register information such as projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into the input interface. Users input this information in an orderly manner while verifying the accuracy of the data. 【0045】 Step 2: 【0046】 The terminal collects the entered data and sends it to the server. The terminal transfers the data in real time and provides feedback to the user to confirm that it has reached the server. 【0047】 Step 3: 【0048】 The server stores the received data in a database and uses this data to predict the sales performance of each business facility. The server uses a machine learning model to perform sales forecasts while referring to past data. 【0049】 Step 4: 【0050】 The server incorporates information on external factors, such as weather and nearby events, to calculate predicted peak hours for each facility. The server integrates these factors into its analysis to provide more accurate predictions. 【0051】 Step 5: 【0052】 The server optimizes the operational schedule for food service support equipment and the proposed staffing arrangements based on predictions. The server uses algorithms to create an efficient equipment sharing plan and formulates that plan. 【0053】 Step 6: 【0054】 The server sends an optimized plan to the terminal and presents the information to the user using a visual dashboard. The terminal displays the visualized data in a format that is easily understandable to administrators. 【0055】 Step 7: 【0056】 Users review the dashboard and provide feedback or suggestions for revisions to the proposed plan. Users propose changes to the plan based on their own judgment and send feedback based on those changes back to the server via their device. 【0057】 Step 8: 【0058】 The server receives user feedback and updates the plan. The server uses this data to improve its AI model and learn to increase the accuracy of future suggestions. 【0059】 (Example 1) 【0060】 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." 【0061】 Efficient operation of support equipment across multiple business facilities is challenging because it requires appropriate operational planning and staffing based on business indicators influenced by diverse factors. In particular, adjustments are needed to achieve efficient equipment sharing while considering environmental factors and anticipated peak hours. However, achieving this requires rapid and appropriate planning, verification, and revision, which is inefficient when done manually. 【0062】 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. 【0063】 In this invention, the server includes means for collecting input data including business indicator forecast information, personnel preference allocation information, and environmental factor information obtained from multiple business facilities; means for optimizing the operation plan of support equipment and appropriate personnel allocation at each business facility based on the input data; and means for predicting peak hours and generating a plan to improve business indicators using the environmental factor information. This enables efficient operation and sharing of equipment across multiple business facilities. 【0064】 "Business performance indicator forecast information" refers to information used to predict future business performance and activity levels of business facilities, and is estimated based on past data and current conditions. 【0065】 "Personnel Placement Request Information" refers to information indicating the desired placement of personnel necessary to efficiently carry out operations at a work facility. 【0066】 "Environmental factor information" refers to information about external conditions that affect the activities of the business facility, and includes weather conditions and scheduled events. 【0067】 "Support equipment" refers to machinery, devices, and equipment used in workplaces to assist in the performance of tasks and improve efficiency. 【0068】 An "operational plan" is a plan that determines the operational schedule for support equipment and personnel to function most effectively. 【0069】 "Optimization" is the process of adjusting plans and arrangements to achieve maximum efficiency in order to obtain the best results under specific conditions. 【0070】 A "sharing plan" is an operational plan for sharing limited resources among multiple business facilities, with the aim of efficient utilization. 【0071】 An "operation screen" is a visual interface that allows users to operate, check, and modify a system. 【0072】 A "generated model" is a hypothetical data structure or computational model formed through algorithms used in the prediction and optimization process. 【0073】 The following describes embodiments for carrying out the invention. 【0074】 This system facilitates communication between servers, terminals, and users to optimize the operation of support equipment in business facilities. Users input business performance indicator forecasts, personnel allocation preferences, and environmental factors using a dedicated interface. Terminals collect this input data and transmit it to the server. 【0075】 The server stores the received data in a "database system" and uses a generated AI model based on past performance and current trends to build a sales forecasting model. Machine learning libraries such as "scikit-learn" and "TENSORFLOW®" are utilized here. In addition, environmental factor information is obtained using the "weather API" and "event information API," and this data is used to predict peak hours. 【0076】 Based on the predicted data, the server uses an "optimization engine" to optimize the operational plan and personnel allocation of support equipment. Furthermore, it uses a "visualization tool" to visually present the generated plan and send it to the terminal. This allows administrators to review the proposed operational plan through the operation screen and make modifications as needed. 【0077】 As a concrete example, when a large-scale sporting event is held near the facility, the server takes event information into account to make predictions and creates a sharing plan for support equipment to accommodate the increased number of visitors. This prediction and optimization improves the efficiency of operational tasks. 【0078】 An example of a specific prompt for a generated AI model is, "Please develop the optimal support equipment deployment and personnel plan to handle this weekend's large-scale event." 【0079】 This system enables efficient equipment sharing and operation between business facilities, thereby promoting the optimization of business operations. 【0080】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0081】 Step 1: 【0082】 Users input sales performance forecasts, staffing preferences, and environmental factors into a dedicated interface. This data is aggregated by the terminal and sent to the server. Inputs include sales forecasts for each business facility, staffing preferences, and expected weather and event information. This forms the basis of the data. 【0083】 Step 2: 【0084】 The server stores the data received from the terminal in a database system. Based on the input information, the server refers to past performance data and builds a sales forecasting model using a generative AI model. Specifically, it uses "scikit-learn" and "TensorFlow" to execute a prediction algorithm and estimate future sales. The output of this process is the predicted sales data. 【0085】 Step 3: 【0086】 The server collects environmental factor information using weather APIs and event information APIs, and uses this information to predict peak hours. It analyzes the input external information to estimate the increase in the number of visitors during specific time periods. As a result of this analysis, data for the predicted peak hours is output. 【0087】 Step 4: 【0088】 The server uses sales forecast data and peak time information to optimize the operation plan and staffing of support equipment with its optimization engine. Optimization software is used to calculate a plan for efficiently utilizing limited resources. The output of this step is the optimized operation plan. 【0089】 Step 5: 【0090】 The server visualizes the optimized operational plan using a visualization tool, sends it to the terminal, and presents it to the administrator. The visual display allows the administrator to intuitively understand the proposed plan. This step outputs verifiable and modifiable visualization data. 【0091】 Step 6: 【0092】 Users review the plan through the operation screen and make modifications as needed. The modified information is sent back to the server via the terminal. Feedback on the plan is generated based on the user's input. 【0093】 Step 7: 【0094】 The server improves the generated AI model based on user feedback, enhancing the accuracy of future suggestions. Through a continuous learning process, the model becomes more accurate and efficient in its suggestions. The output of this step is the improved predictive model. 【0095】 (Application Example 1) 【0096】 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." 【0097】 Inefficient use of support equipment and personnel in business facilities leads to increased costs and a decline in service quality. In particular, optimally allocating and utilizing equipment and personnel across multiple business facilities is extremely difficult. Furthermore, efficient operational planning and optimized personnel allocation are required to respond quickly to fluctuations in demand due to external factors. The development of systems to address these challenges is necessary. 【0098】 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. 【0099】 In this invention, the server includes means for collecting predictive information, placement information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan and personnel allocation of support equipment based on this input data; and means for performing demand forecasting based on external factor information and generating a pre-arrangement plan for equipment and personnel. This enables improved operational efficiency and a rapid response to demand fluctuations. 【0100】 "Sales performance forecast information" refers to data used to estimate future sales and demand based on past sales performance and current market trends at business facilities. 【0101】 "Personnel and Equipment Placement Request Information" is a compilation of information detailing the desired placement of equipment and personnel within a business facility. 【0102】 "External factor information" refers to data about environmental conditions and circumstances outside the facility that affect business operations, such as weather and local events. 【0103】 An "operational plan" is a plan that outlines in advance how support equipment and personnel will be used within a business facility. 【0104】 "Optimization" is the process of adjusting means and methods to obtain the best possible results under given conditions. 【0105】 A "sharing plan" is a plan for efficiently sharing assistive devices among multiple work facilities. 【0106】 An "interface" is a user interface or communication method used to exchange information between a user and a system. 【0107】 A "mobile device" is a portable computer device with communication capabilities, such as a mobile phone or tablet. 【0108】 "Demand forecasting" refers to the process of predicting future market demand based on past data and external factors. 【0109】 A "pre-arrangement plan" is a plan to prepare necessary equipment and personnel in advance, based on future demand and operating conditions. 【0110】 The system for implementing this invention consists primarily of a server, terminals, and users. The server uses Python and the Django framework to aggregate sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities and store them in a database. This server processes this data and analyzes it using machine learning models to create an optimal operational plan for support equipment and personnel. 【0111】 The server considers external factors such as weather and event information, and based on this, forecasts the demand for support equipment and personnel. It then uses this information to build a support equipment sharing plan. Furthermore, the optimized operational plan is transmitted in real time to a mobile device application and provided as a visualized dashboard. 【0112】 The device visually presents the optimal operational plan to the business administrator via a mobile application developed using React Native. This application allows the business administrator to review and modify the plan and send feedback back to the server. 【0113】 Users apply this application to their daily operations and use it to adjust future plans as needed. For example, if a large music festival is scheduled to take place nearby on the weekend, the server will use that information to predict an increase in visitor numbers and plan for additional support equipment and delivery personnel. 【0114】 An example of a prompt generated using AI is: "For the market event scheduled for the beginning of next month, please suggest the optimal assistive device sharing plan and delivery route based on the expected increase in demand." In this way, the system supports efficient operations even in complex business environments. 【0115】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0116】 Step 1: 【0117】 The server collects sales performance forecast information, personnel preference information, equipment allocation information, and external factor information from multiple business facilities. This data is stored in a database. Based on this input data, the server uses a predictive model to analyze demand and forecast future business patterns. The inputs are production data and external factor information for each facility, and the outputs are demand forecasts and optimization parameters. 【0118】 Step 2: 【0119】 The server generates an optimal plan for support equipment and personnel allocation based on demand forecasts. Here, an optimization algorithm is applied to create an efficient resource allocation plan. The input is the demand forecast and available resource information, and the output is the optimized allocation plan. 【0120】 Step 3: 【0121】 The server sends optimized operational and sharing plans to the terminal. On the terminal, a React Native application displays a visual dashboard for business administrators. The input is the optimized plan sent from the server, and the output is the visualized data displayed on the terminal's screen. 【0122】 Step 4: 【0123】 The user reviews and modifies the plan presented via the terminal interface, and sends feedback back to the server as needed. This allows for fine-tuning of the plan. The input is the optimized plan on the terminal and the user's modifications, and the output is the modified plan. 【0124】 Step 5: 【0125】 The server updates its operational plan using feedback returned by users and utilizes this data to train its generating AI model. This improves the accuracy of future suggestions. The input is the revised plan and user feedback, and the output is the updated optimized model. 【0126】 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. 【0127】 This invention aims to improve overall efficiency by combining an emotion engine with a system that optimizes the operational planning of food service support equipment and the allocation of staff in a business facility. Specifically, the system uses the emotion engine to detect and analyze the emotional states of business managers and staff, and reflects the results in the operational plan. 【0128】 First, the user inputs sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into a dedicated interface. After the terminal sends this data to the server, the server processes the data and builds a sales forecast model for each business facility. 【0129】 In addition, the emotion engine analyzes the user's facial expressions and voice data to quantify their emotional state. For example, if an employee is in a high-stress state, this information is reflected in the analysis results via the server. The server then takes in this emotional data and recalculates the optimal work plan to contribute to improving work efficiency and the working environment. 【0130】 The server sends optimized operational plans for food service support equipment and proposed staffing arrangements to the terminal, providing users with an intuitive dashboard. Furthermore, the dashboard visualizes the results of the emotion engine's analysis, allowing users to view emotion data in real time. 【0131】 As a concrete example, in a certain business facility, if a staff member experiences a high level of stress while a food service support device is in operation, the emotion engine detects this, and the server automatically adjusts the operation plan. This allows for a shift to a work arrangement that reduces stress, thereby easing the burden on staff while maintaining the operational efficiency of the facility. 【0132】 In this way, by using an emotional engine in conjunction with other technologies, it becomes possible to achieve a high level of optimization in business operations and balance the well-being of personnel with the economic performance of facilities. 【0133】 The following describes the processing flow. 【0134】 Step 1: 【0135】 Users input information on projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into a dedicated interface. Users review the specific data and consider its accuracy when entering it. 【0136】 Step 2: 【0137】 The terminal collects the entered data and sends it to the server. The terminal quickly sends the collected data to the server and verifies that there are no errors. 【0138】 Step 3: 【0139】 The server stores the received data in a database and builds sales forecasting models for each business facility. The server then uses historical data and machine learning models to perform highly accurate sales forecasts. 【0140】 Step 4: 【0141】 The emotion engine analyzes the facial expressions and voices of the workforce and quantifies their emotional state. The server collects this emotional data and uses it to understand the state of the workforce. 【0142】 Step 5: 【0143】 The server optimizes the operation plan for food service support equipment and the allocation of staff based on predictive models and sentiment data. If the sentiment data indicates anomalies, the server adjusts the plan to take into account reducing the workload. 【0144】 Step 6: 【0145】 The server sends an optimized operational plan and staffing proposal to the terminal. The terminal visualizes the information on a dashboard, presenting it in a way that is easy for the user to understand. 【0146】 Step 7: 【0147】 Users review the dashboard and provide instructions through the interface for necessary adjustments to the operational plan and staffing. Users then review the validity of the proposed plan and provide feedback based on their own experience. 【0148】 Step 8: 【0149】 The server updates the plan using feedback received from the user. The server uses this data to improve the AI ​​model for future updates and to continuously improve operational efficiency. 【0150】 (Example 2) 【0151】 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". 【0152】 In today's business environment, there is a need for efficient management of work support devices and worker allocation across multiple business locations. However, there is a lack of means to optimize work plans while considering employees' emotions and stress levels, resulting in decreased work efficiency and a deterioration of the working environment. This invention aims to solve these problems and improve the efficiency of business locations and the working environment. 【0153】 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. 【0154】 In this invention, the server includes means for acquiring business forecast information, personnel allocation preference information, and external environment information obtained from multiple business locations; means for optimizing the operation plan of work support devices and worker allocation at each business location based on the input data; and means for detecting the emotional state of employees using an emotion analysis device and reflecting it in the operation plan. This enables efficient operation of work support devices across business locations and improves work efficiency while taking into account the emotional state of workers. 【0155】 A "business base" is a location where business activities are conducted, and it is a general term for bases where multiple facilities and personnel are located. 【0156】 "Work support equipment" is a general term for devices, including machines and tools, used to improve the efficiency of work. 【0157】 "Worker allocation" refers to assigning workers to appropriate locations and roles in order to efficiently perform necessary tasks. 【0158】 "Business forecast information" refers to information that includes data and forecast results used to estimate future business and sales performance. 【0159】 "Personnel assignment preference information" refers to information regarding the roles and work locations that workers desire. 【0160】 "External environmental information" refers to information about external conditions and circumstances that affect the operation of business locations, and includes weather information and event information. 【0161】 An "emotion analysis device" is a device that includes machine learning models and AI tools for detecting and analyzing an individual's emotional state from their facial expressions and voice data. 【0162】 "Optimization" refers to the process of making plans and arrangements the best possible state in order to make efficient use of limited resources. 【0163】 A "resource allocation plan" refers to a plan that determines how to distribute available resources among multiple business locations. 【0164】 An "interface" refers to the screen or protocol used by a system and a user to exchange information. 【0165】 "Visual information display means" refers to methods and devices for providing information in a graphical format so that users can intuitively understand the information. 【0166】 The system of the present invention optimizes work support devices and worker assignments while taking into account the emotional state of employees. A specific embodiment of this system will be described in detail below. 【0167】 First, users input projected business information, staffing requests, and external environmental information for each business location. This information is entered through a dedicated user interface and transmitted to a terminal. The terminal then formats this data appropriately and sends it to the server. 【0168】 The server analyzes the received data and builds predictive models using machine learning algorithms. This process includes predicting sales performance based on historical data and calculating efficient staffing levels. The hardware used includes a server computer, while the software includes data analysis algorithms and a sentiment analysis engine. 【0169】 The emotion analysis engine collects user facial expressions and voice data and quantifies their emotional state. Specifically, it uses cameras and microphones to acquire data in real time and analyzes it using an AI model for emotion analysis. 【0170】 For example, if a worker at a particular business location is detected to be experiencing high stress levels, the server uses that information to recalculate the work schedule. As a result, an optimal staffing plan is generated to reduce the burden on the workers. 【0171】 Furthermore, the optimized plan and sentiment analysis results are provided to the user via a visual dashboard on their device. This dashboard allows the user to view sentiment data in real time and modify the plan as needed. 【0172】 An example of a prompt to input into the generating AI model is, "Optimize business forecasting and worker allocation at business site A, and address employee stress levels optimally." This prompt allows the system to quickly execute optimization processes. 【0173】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0174】 Step 1: 【0175】 Users input project forecast information, staffing preference information, and external environment information into a dedicated interface. The entered data is formatted on the terminal and prepared for transmission to the server. Specifically, the data is structured in JSON or XML format to prepare for the next step. 【0176】 Step 2: 【0177】 The terminal sends the input data to the server. Secure protocols such as HTTPS are used for transmission. When the server receives the data, it converts it into a format suitable for analysis. This conversion prepares the server for processing. 【0178】 Step 3: 【0179】 The server uses machine learning algorithms to build a sales performance prediction model based on the converted input data. Here, it compares past performance data with the current input data to calculate the most suitable prediction. This process generates prediction results for estimating future business performance. 【0180】 Step 4: 【0181】 The emotion analysis engine detects facial expressions and voice from the user's actions and collects data. The device uses its camera and microphone to acquire data in real time and inputs it into an AI model that quantifies the emotional state. This model analyzes the emotional state using various emotional indicators. 【0182】 Step 5: 【0183】 The server takes in emotional data obtained from the emotion analysis engine and recalculates the worker assignment plan based on the analysis results. Here, it considers the impact of emotional data on work efficiency and adjusts worker assignments and work schedules as needed. 【0184】 Step 6: 【0185】 The server sends optimized operational plans and worker allocations to the terminal. The terminal builds and presents a graphical dashboard to make the information easy for the user to understand. The user can view the data in real time through the dashboard and make corrections as needed. 【0186】 (Application Example 2) 【0187】 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". 【0188】 In commercial facilities, there is a need for efficient operational planning of food service support equipment and appropriate allocation of staff. However, conventional methods make it difficult to formulate optimal plans while considering the emotional state of staff, resulting in an insufficient balance between operational efficiency and staff health. This invention aims to achieve a more advanced optimization of operational management using emotional analysis, thereby achieving both operational efficiency and reduction of staff stress. 【0189】 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. 【0190】 In this invention, the server includes means for collecting sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan of food service support equipment and personnel allocation at business facilities; and means for analyzing the emotional state of personnel using an emotion engine and reflecting the results in the operation plan. This makes it possible to formulate an optimal operation plan that takes emotional states into account at business facilities. 【0191】 "Sales performance forecast information" refers to data related to forecasts of future sales and sales volume at business facilities. 【0192】 "Personnel-requested equipment placement information" refers to information regarding the food service support equipment and its placement as requested by staff members. 【0193】 "External factor information" refers to data about the external environment that may affect the operation of business facilities, including weather conditions and event information. 【0194】 "Optimization" is the process of allocating and utilizing resources in the most efficient way to achieve a specific objective. 【0195】 An "emotional engine" is a system that analyzes the emotional state of work personnel and quantifies or classifies it. 【0196】 "Food service support equipment" refers to devices and equipment used in commercial facilities to streamline food service operations. 【0197】 A "resource sharing plan" is a plan for efficiently sharing equipment and personnel among multiple business facilities. 【0198】 This invention is a system designed to optimize the management of public facilities in smart cities. The system consists of numerous components, including servers, smart devices, and an emotion engine. 【0199】 The server collects sales performance forecast information, equipment allocation information for personnel requests, and external factor information from multiple business facilities. Smart devices such as smartphones and smart glasses provide this information via an interface, creating a platform that allows on-site managers to review and correct the data. The smart devices also collect facial data of personnel using their built-in cameras. 【0200】 The emotion engine analyzes collected facial data to quantify the emotional state of the staff. This analyzed data is sent to a server for integration. The server then uses this emotional data to develop a new plan that optimizes the operation of food service equipment and the allocation of staff within the facility. 【0201】 A concrete example is the development of operational plans to improve the efficiency of library operations while reducing staff stress. Based on an analysis of emotional states during specific time periods, staffing levels and break times are adjusted. This improves the operational efficiency of public facilities and the well-being of staff. 【0202】 An example of a prompt to input into the generating AI model might be: "Optimize the library's operational plan based on the emotional state of the staff. Propose a plan that improves visitor service while providing a more relaxing environment for the staff." 【0203】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0204】 Step 1: 【0205】 The server collects sales performance forecast information, personnel preference information, and external factor information from multiple business facilities. This information is stored in a database as input data. Storing this data in the database provides a foundation for subsequent data analysis. 【0206】 Step 2: 【0207】 The smart device collects facial data of work personnel using its built-in camera. The user takes a picture of the work personnel using the device, acquiring a facial image as input data. The smart device then prepares the image for transmission to the emotion engine. 【0208】 Step 3: 【0209】 The emotion engine analyzes received facial data and quantifies the emotional state of the work personnel. The input data is a facial image, which is analyzed by an emotion recognition algorithm and outputs an emotion score. This score numerically represents the emotional state of each individual. 【0210】 Step 4: 【0211】 The server integrates the sentiment score obtained from the sentiment engine with collected sales performance forecast information and external factor information. It receives this information as input data and runs an algorithm to optimize equipment operation plans and personnel allocation. The output is the optimized operation plan. 【0212】 Step 5: 【0213】 The terminal presents the administrator with an optimized operational plan and staffing allocation, and provides an interface for review and modification. It uses the optimization results sent from the server as input data. The administrator can review the presented plan and make modifications as needed. 【0214】 Step 6: 【0215】 Upon receiving confirmation and correction information from users, the server updates the operational plan and resource sharing plan for the food service support equipment. It incorporates the administrator's correction information as input data, reconstructs the new plan, and outputs it. This update enables more efficient business operations. 【0216】 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. 【0217】 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. 【0218】 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. 【0219】 [Second Embodiment] 【0220】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0221】 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. 【0222】 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). 【0223】 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. 【0224】 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. 【0225】 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). 【0226】 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. 【0227】 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. 【0228】 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. 【0229】 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. 【0230】 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. 【0231】 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". 【0232】 This invention provides a system for optimizing the shared operation of food service support equipment in multiple service facilities. This system includes functions for data collection, data analysis, optimization calculation, suggestion display, feedback, and adjustment. Its specific operation is described below. 【0233】 First, users register sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into the system via a dedicated input interface. The terminal aggregates this information and sends it to the server. The server stores this data in a database for processing and builds a sales forecast model based on past performance and trends. 【0234】 Next, the server predicts peak hours for each service facility based on external factors such as weather and event information. Based on this, it generates an operational plan for food service support equipment and a proposed staffing allocation. By using an optimization algorithm, it plans the movement of food service support equipment between facilities, enabling effective sharing. 【0235】 The server then notifies the terminals of this optimized plan and presents it to the operations manager on a visual dashboard. This allows the manager to intuitively review the proposed working hours and staffing and make adjustments as needed. 【0236】 Ultimately, users make adjustments to shifts and equipment placement based on the information provided through the management screen, and send these changes back to the server via their terminal. The server then uses this feedback to continuously train its AI model, aiming to improve the accuracy of future suggestions. 【0237】 For example, if a large sporting event is scheduled nearby, the server will take that information into account, predict an increase in the number of visitors to nearby business facilities, and create a plan to deploy additional food service equipment as needed. In this way, the system responds to events and external factors, thereby improving the efficiency of business operations. 【0238】 The following describes the processing flow. 【0239】 Step 1: 【0240】 Users register information such as projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into the input interface. Users input this information in an orderly manner while verifying the accuracy of the data. 【0241】 Step 2: 【0242】 The terminal collects the entered data and sends it to the server. The terminal transfers the data in real time and provides feedback to the user to confirm that it has reached the server. 【0243】 Step 3: 【0244】 The server stores the received data in a database and uses this data to predict the sales performance of each business facility. The server uses a machine learning model to perform sales forecasts while referring to past data. 【0245】 Step 4: 【0246】 The server incorporates information on external factors, such as weather and nearby events, to calculate predicted peak hours for each facility. The server integrates these factors into its analysis to provide more accurate predictions. 【0247】 Step 5: 【0248】 The server optimizes the operational schedule for food service support equipment and the proposed staffing arrangements based on predictions. The server uses algorithms to create an efficient equipment sharing plan and formulates that plan. 【0249】 Step 6: 【0250】 The server sends an optimized plan to the terminal and presents the information to the user using a visual dashboard. The terminal displays the visualized data in a format that is easily understandable to administrators. 【0251】 Step 7: 【0252】 Users review the dashboard and provide feedback or suggestions for revisions to the proposed plan. Users propose changes to the plan based on their own judgment and send feedback based on those changes back to the server via their device. 【0253】 Step 8: 【0254】 The server receives user feedback and updates the plan. The server uses this data to improve its AI model and learn to increase the accuracy of future suggestions. 【0255】 (Example 1) 【0256】 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." 【0257】 Efficient operation of support equipment across multiple business facilities is challenging because it requires appropriate operational planning and staffing based on business indicators influenced by diverse factors. In particular, adjustments are needed to achieve efficient equipment sharing while considering environmental factors and anticipated peak hours. However, achieving this requires rapid and appropriate planning, verification, and revision, which is inefficient when done manually. 【0258】 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. 【0259】 In this invention, the server includes means for collecting input data including business indicator forecast information, personnel preference allocation information, and environmental factor information obtained from multiple business facilities; means for optimizing the operation plan of support equipment and appropriate personnel allocation at each business facility based on the input data; and means for predicting peak hours and generating a plan to improve business indicators using the environmental factor information. This enables efficient operation and sharing of equipment across multiple business facilities. 【0260】 "Business performance indicator forecast information" refers to information used to predict future business performance and activity levels of business facilities, and is estimated based on past data and current conditions. 【0261】 "Personnel Placement Request Information" refers to information indicating the desired placement of personnel necessary to efficiently carry out operations at a work facility. 【0262】 "Environmental factor information" refers to information about external conditions that affect the activities of the business facility, and includes weather conditions and scheduled events. 【0263】 "Support equipment" refers to machinery, devices, and equipment used in workplaces to assist in the performance of tasks and improve efficiency. 【0264】 An "operational plan" is a plan that determines the operational schedule for support equipment and personnel to function most effectively. 【0265】 "Optimization" is the process of adjusting plans and arrangements to achieve maximum efficiency in order to obtain the best results under specific conditions. 【0266】 A "sharing plan" is an operational plan for sharing limited resources among multiple business facilities, with the aim of efficient utilization. 【0267】 An "operation screen" is a visual interface that allows users to operate, check, and modify a system. 【0268】 A "generated model" is a hypothetical data structure or computational model formed through algorithms used in the prediction and optimization process. 【0269】 The following describes embodiments for carrying out the invention. 【0270】 This system facilitates communication between servers, terminals, and users to optimize the operation of support equipment in business facilities. Users input business performance indicator forecasts, personnel allocation preferences, and environmental factors using a dedicated interface. Terminals collect this input data and transmit it to the server. 【0271】 The server stores the received data in a "database system" and uses a generated AI model based on past performance and current trends to build a sales forecasting model. Machine learning libraries such as "scikit-learn" and "TensorFlow" are utilized here. In addition, environmental factor information is obtained using the "weather API" and "event information API," and this data is used to predict peak hours. 【0272】 Based on the predicted data, the server uses an "optimization engine" to optimize the operational plan and personnel allocation of support equipment. Furthermore, it uses a "visualization tool" to visually present the generated plan and send it to the terminal. This allows administrators to review the proposed operational plan through the operation screen and make modifications as needed. 【0273】 As a concrete example, when a large-scale sporting event is held near the facility, the server takes event information into account to make predictions and creates a sharing plan for support equipment to accommodate the increased number of visitors. This prediction and optimization improves the efficiency of operational tasks. 【0274】 An example of a specific prompt for a generated AI model is, "Please develop the optimal support equipment deployment and personnel plan to handle this weekend's large-scale event." 【0275】 This system enables efficient equipment sharing and operation between business facilities, thereby promoting the optimization of business operations. 【0276】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0277】 Step 1: 【0278】 The user inputs sales performance prediction information, personnel placement preference information, and environmental factor information into the dedicated interface. This data is aggregated by the terminal and sent to the server. The input includes sales predictions for each business facility, staff placement preferences, and expected weather and event information. This forms the basis of the data. 【0279】 Step 2: 【0280】 The server stores the data received from the terminal in the database system. Based on the input information, the server refers to past performance data and constructs a sales prediction model using a generated AI model. Specifically, "scikit-learn" and "TensorFlow" are used to execute the prediction algorithm and estimate future sales. The output of this process is the predicted sales data. 【0281】 Step 3: 【0282】 The server collects environmental factor information using the weather API and event information API, and predicts peak hours based on this information. It analyzes the input external information and estimates the increase in the number of visitors during specific time periods. As a result of this analysis, data on the predicted peak hours is output. 【0283】 Step 4: 【0284】 The server uses the sales prediction data and peak hour information to optimize the operation plan and personnel placement of support equipment with an optimization engine. It utilizes optimization software to calculate a plan for efficiently utilizing limited resources. The output of this step is the optimized operation plan. 【0285】 Step 5: 【0286】 The server visualizes the optimized operation plan using a visualization tool, sends it to the terminal, and presents it to the administrator. Using the visual display, the administrator can intuitively understand the proposed plan. In this step, visual data that can be confirmed and corrected is output. 【0287】 Step 6: 【0288】 The user checks the plan through the operation screen and makes corrections if necessary. The corrected information is sent back to the server via the terminal. Based on the user's input, feedback on the plan is generated. 【0289】 Step 7: 【0290】 Based on the feedback from the user, the server improves the generated AI model and enhances the accuracy of future proposals. Through the continuous learning process, the model will be able to make more accurate and efficient proposals. The output of this step is an improved prediction model. 【0291】 (Application Example 1) 【0292】 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". 【0293】 Inefficient operation of support equipment and personnel in business facilities causes increased costs and reduced service quality. In particular, it is very difficult to optimally allocate and utilize equipment and personnel across multiple business facilities. Also, in order to quickly respond to fluctuations in demand due to external factors, efficient operation planning and optimization of personnel allocation are required. Development of a system to solve these problems is needed. 【0294】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0295】 In this invention, the server includes means for collecting predictive information, placement information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan and personnel allocation of support equipment based on this input data; and means for performing demand forecasting based on external factor information and generating a pre-arrangement plan for equipment and personnel. This enables improved operational efficiency and a rapid response to demand fluctuations. 【0296】 "Sales performance forecast information" refers to data used to estimate future sales and demand based on past sales performance and current market trends at business facilities. 【0297】 "Personnel and Equipment Placement Request Information" is a compilation of information detailing the desired placement of equipment and personnel within a business facility. 【0298】 "External factor information" refers to data about environmental conditions and circumstances outside the facility that affect business operations, such as weather and local events. 【0299】 An "operational plan" is a plan that outlines in advance how support equipment and personnel will be used within a business facility. 【0300】 "Optimization" is the process of adjusting means and methods to obtain the best possible results under given conditions. 【0301】 A "sharing plan" is a plan for efficiently sharing assistive devices among multiple work facilities. 【0302】 An "interface" is a user interface or communication method used to exchange information between a user and a system. 【0303】 A "mobile device" is a portable computer device with communication capabilities, such as a mobile phone or tablet. 【0304】 "Demand forecasting" refers to the process of predicting future market demand based on past data and external factors. 【0305】 The "advance arrangement plan" is a plan to prepare the necessary equipment and personnel in advance in anticipation of future demand and operating conditions. 【0306】 The system for implementing this invention is centered around a server, terminals, and users. The server uses the Python and Django frameworks to aggregate sales performance prediction information, business staff preferred device placement information, and external factor information obtained from multiple business facilities, and stores it in a database. This server processes this data and analyzes the data using a machine learning model in order to create an optimal operation plan for support equipment and personnel. 【0307】 The server considers external factors such as weather information and event information, and based on this, predicts the demand for support equipment and personnel. And based on this information, it constructs a sharing plan for support equipment. Furthermore, the optimized operation plan is sent in real time to the application on the mobile terminal and provided as a visualized dashboard. 【0308】 The terminal visually presents the optimal operation plan to the business manager through a mobile application developed using React Native. Through this application, the business manager can view and modify the plan and send feedback to the server. 【0309】 The user applies this to daily business operations using this application and utilizes it for adjusting future plans as needed. For example, if a large-scale music festival is scheduled to be held in the neighborhood near the weekend, the server predicts an increase in the number of visitors based on this information and plans additional arrangements for support equipment and delivery staff. 【0310】 Examples of prompt texts using generative AI include "Please propose an optimal support equipment sharing plan and delivery route in response to the predicted increase in demand for the market event scheduled at the beginning of next month." In this way, the system supports efficient operation even in a complex business environment. 【0311】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0312】 Step 1: 【0313】 The server collects sales performance forecast information, personnel preference information, equipment allocation information, and external factor information from multiple business facilities. This data is stored in a database. Based on this input data, the server uses a predictive model to analyze demand and forecast future business patterns. The inputs are production data and external factor information for each facility, and the outputs are demand forecasts and optimization parameters. 【0314】 Step 2: 【0315】 The server generates an optimal plan for support equipment and personnel allocation based on demand forecasts. Here, an optimization algorithm is applied to create an efficient resource allocation plan. The input is the demand forecast and available resource information, and the output is the optimized allocation plan. 【0316】 Step 3: 【0317】 The server sends optimized operational and sharing plans to the terminal. On the terminal, a React Native application displays a visual dashboard for business administrators. The input is the optimized plan sent from the server, and the output is the visualized data displayed on the terminal's screen. 【0318】 Step 4: 【0319】 The user reviews and modifies the plan presented via the terminal interface, and sends feedback back to the server as needed. This allows for fine-tuning of the plan. The input is the optimized plan on the terminal and the user's modifications, and the output is the modified plan. 【0320】 Step 5: 【0321】 The server updates its operational plan using feedback returned by users and utilizes this data to train its generating AI model. This improves the accuracy of future suggestions. The input is the revised plan and user feedback, and the output is the updated optimized model. 【0322】 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. 【0323】 This invention aims to improve overall efficiency by combining an emotion engine with a system that optimizes the operational planning of food service support equipment and the allocation of staff in a business facility. Specifically, the system uses the emotion engine to detect and analyze the emotional states of business managers and staff, and reflects the results in the operational plan. 【0324】 First, the user inputs sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into a dedicated interface. After the terminal sends this data to the server, the server processes the data and builds a sales forecast model for each business facility. 【0325】 In addition, the emotion engine analyzes the user's facial expressions and voice data to quantify their emotional state. For example, if an employee is in a high-stress state, this information is reflected in the analysis results via the server. The server then takes in this emotional data and recalculates the optimal work plan to contribute to improving work efficiency and the working environment. 【0326】 The server sends optimized operational plans for food service support equipment and proposed staffing arrangements to the terminal, providing users with an intuitive dashboard. Furthermore, the dashboard visualizes the results of the emotion engine's analysis, allowing users to view emotion data in real time. 【0327】 As a concrete example, in a certain business facility, if a staff member experiences a high level of stress while a food service support device is in operation, the emotion engine detects this, and the server automatically adjusts the operation plan. This allows for a shift to a work arrangement that reduces stress, thereby easing the burden on staff while maintaining the operational efficiency of the facility. 【0328】 In this way, by using an emotional engine in conjunction with other technologies, it becomes possible to achieve a high level of optimization in business operations and balance the well-being of personnel with the economic performance of facilities. 【0329】 The following describes the processing flow. 【0330】 Step 1: 【0331】 Users input information on projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into a dedicated interface. Users review the specific data and consider its accuracy when entering it. 【0332】 Step 2: 【0333】 The terminal collects the entered data and sends it to the server. The terminal quickly sends the collected data to the server and verifies that there are no errors. 【0334】 Step 3: 【0335】 The server stores the received data in a database and builds sales forecasting models for each business facility. The server then uses historical data and machine learning models to perform highly accurate sales forecasts. 【0336】 Step 4: 【0337】 The emotion engine analyzes the facial expressions and voices of the workforce and quantifies their emotional state. The server collects this emotional data and uses it to understand the state of the workforce. 【0338】 Step 5: 【0339】 The server optimizes the operation plan for food service support equipment and the allocation of staff based on predictive models and sentiment data. If the sentiment data indicates anomalies, the server adjusts the plan to take into account reducing the workload. 【0340】 Step 6: 【0341】 The server sends an optimized operational plan and staffing proposal to the terminal. The terminal visualizes the information on a dashboard, presenting it in a way that is easy for the user to understand. 【0342】 Step 7: 【0343】 Users review the dashboard and provide instructions through the interface for necessary adjustments to the operational plan and staffing. Users then review the validity of the proposed plan and provide feedback based on their own experience. 【0344】 Step 8: 【0345】 The server updates the plan using feedback received from the user. The server uses this data to improve the AI ​​model for future updates and to continuously improve operational efficiency. 【0346】 (Example 2) 【0347】 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". 【0348】 In today's business environment, there is a need for efficient management of work support devices and worker allocation across multiple business locations. However, there is a lack of means to optimize work plans while considering employees' emotions and stress levels, resulting in decreased work efficiency and a deterioration of the working environment. This invention aims to solve these problems and improve the efficiency of business locations and the working environment. 【0349】 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. 【0350】 In this invention, the server includes means for acquiring business forecast information, personnel allocation preference information, and external environment information obtained from multiple business locations; means for optimizing the operation plan of work support devices and worker allocation at each business location based on the input data; and means for detecting the emotional state of employees using an emotion analysis device and reflecting it in the operation plan. This enables efficient operation of work support devices across business locations and improves work efficiency while taking into account the emotional state of workers. 【0351】 A "business base" is a location where business activities are conducted, and it is a general term for bases where multiple facilities and personnel are located. 【0352】 "Work support equipment" is a general term for devices, including machines and tools, used to improve the efficiency of work. 【0353】 "Worker allocation" refers to assigning workers to appropriate locations and roles in order to efficiently perform necessary tasks. 【0354】 "Business forecast information" refers to information that includes data and forecast results used to estimate future business and sales performance. 【0355】 "Personnel assignment preference information" refers to information regarding the roles and work locations that workers desire. 【0356】 "External environmental information" refers to information about external conditions and circumstances that affect the operation of business locations, and includes weather information and event information. 【0357】 An "emotion analysis device" is a device that includes machine learning models and AI tools for detecting and analyzing an individual's emotional state from their facial expressions and voice data. 【0358】 "Optimization" refers to the process of making plans and arrangements the best possible state in order to make efficient use of limited resources. 【0359】 A "resource allocation plan" refers to a plan that determines how to distribute available resources among multiple business locations. 【0360】 An "interface" refers to the screen or protocol used by a system and a user to exchange information. 【0361】 "Visual information display means" refers to methods and devices for providing information in a graphical format so that users can intuitively understand the information. 【0362】 The system of the present invention optimizes work support devices and worker assignments while taking into account the emotional state of employees. A specific embodiment of this system will be described in detail below. 【0363】 First, users input projected business information, staffing requests, and external environmental information for each business location. This information is entered through a dedicated user interface and transmitted to a terminal. The terminal then formats this data appropriately and sends it to the server. 【0364】 The server analyzes the received data and builds predictive models using machine learning algorithms. This process includes predicting sales performance based on historical data and calculating efficient staffing levels. The hardware used includes a server computer, while the software includes data analysis algorithms and a sentiment analysis engine. 【0365】 The emotion analysis engine collects user facial expressions and voice data and quantifies their emotional state. Specifically, it uses cameras and microphones to acquire data in real time and analyzes it using an AI model for emotion analysis. 【0366】 For example, if a worker at a particular business location is detected to be experiencing high stress levels, the server uses that information to recalculate the work schedule. As a result, an optimal staffing plan is generated to reduce the burden on the workers. 【0367】 Furthermore, the optimized plan and sentiment analysis results are provided to the user via a visual dashboard on their device. This dashboard allows the user to view sentiment data in real time and modify the plan as needed. 【0368】 An example of a prompt to input into the generating AI model is, "Optimize business forecasting and worker allocation at business site A, and address employee stress levels optimally." This prompt allows the system to quickly execute optimization processes. 【0369】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0370】 Step 1: 【0371】 Users input project forecast information, staffing preference information, and external environment information into a dedicated interface. The entered data is formatted on the terminal and prepared for transmission to the server. Specifically, the data is structured in JSON or XML format to prepare for the next step. 【0372】 Step 2: 【0373】 The terminal sends the input data to the server. Secure protocols such as HTTPS are used for transmission. When the server receives the data, it converts it into a format suitable for analysis. This conversion prepares the server for processing. 【0374】 Step 3: 【0375】 The server uses machine learning algorithms to build a sales performance prediction model based on the converted input data. Here, it compares past performance data with the current input data to calculate the most suitable prediction. This process generates prediction results for estimating future business performance. 【0376】 Step 4: 【0377】 The emotion analysis engine detects facial expressions and voice from the user's actions and collects data. The device uses its camera and microphone to acquire data in real time and inputs it into an AI model that quantifies the emotional state. This model analyzes the emotional state using various emotional indicators. 【0378】 Step 5: 【0379】 The server takes in emotional data obtained from the emotion analysis engine and recalculates the worker assignment plan based on the analysis results. Here, it considers the impact of emotional data on work efficiency and adjusts worker assignments and work schedules as needed. 【0380】 Step 6: 【0381】 The server sends optimized operational plans and worker allocations to the terminal. The terminal builds and presents a graphical dashboard to make the information easy for the user to understand. The user can view the data in real time through the dashboard and make corrections as needed. 【0382】 (Application Example 2) 【0383】 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". 【0384】 In commercial facilities, there is a need for efficient operational planning of food service support equipment and appropriate allocation of staff. However, conventional methods make it difficult to formulate optimal plans while considering the emotional state of staff, resulting in an insufficient balance between operational efficiency and staff health. This invention aims to achieve a more advanced optimization of operational management using emotional analysis, thereby achieving both operational efficiency and reduction of staff stress. 【0385】 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. 【0386】 In this invention, the server includes means for collecting sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan of food service support equipment and personnel allocation at business facilities; and means for analyzing the emotional state of personnel using an emotion engine and reflecting the results in the operation plan. This makes it possible to formulate an optimal operation plan that takes emotional states into account at business facilities. 【0387】 "Sales performance forecast information" refers to data related to forecasts of future sales and sales volume at business facilities. 【0388】 "Personnel-requested equipment placement information" refers to information regarding the food service support equipment and its placement as requested by staff members. 【0389】 "External factor information" refers to data about the external environment that may affect the operation of business facilities, including weather conditions and event information. 【0390】 "Optimization" is the process of allocating and utilizing resources in the most efficient way to achieve a specific objective. 【0391】 An "emotional engine" is a system that analyzes the emotional state of work personnel and quantifies or classifies it. 【0392】 "Food service support equipment" refers to devices and equipment used in commercial facilities to streamline food service operations. 【0393】 A "resource sharing plan" is a plan for efficiently sharing equipment and personnel among multiple business facilities. 【0394】 This invention is a system designed to optimize the management of public facilities in smart cities. The system consists of numerous components, including servers, smart devices, and an emotion engine. 【0395】 The server collects sales performance forecast information, equipment allocation information for personnel requests, and external factor information from multiple business facilities. Smart devices such as smartphones and smart glasses provide this information via an interface, creating a platform that allows on-site managers to review and correct the data. The smart devices also collect facial data of personnel using their built-in cameras. 【0396】 The emotion engine analyzes collected facial data to quantify the emotional state of the staff. This analyzed data is sent to a server for integration. The server then uses this emotional data to develop a new plan that optimizes the operation of food service equipment and the allocation of staff within the facility. 【0397】 A concrete example is the development of operational plans to improve the efficiency of library operations while reducing staff stress. Based on an analysis of emotional states during specific time periods, staffing levels and break times are adjusted. This improves the operational efficiency of public facilities and the well-being of staff. 【0398】 An example of a prompt to input into the generating AI model might be: "Optimize the library's operational plan based on the emotional state of the staff. Propose a plan that improves visitor service while providing a more relaxing environment for the staff." 【0399】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0400】 Step 1: 【0401】 The server collects sales performance forecast information, personnel preference information, and external factor information from multiple business facilities. This information is stored in a database as input data. Storing this data in the database provides a foundation for subsequent data analysis. 【0402】 Step 2: 【0403】 The smart device collects facial data of work personnel using its built-in camera. The user takes a picture of the work personnel using the device, acquiring a facial image as input data. The smart device then prepares the image for transmission to the emotion engine. 【0404】 Step 3: 【0405】 The emotion engine analyzes received facial data and quantifies the emotional state of the work personnel. The input data is a facial image, which is analyzed by an emotion recognition algorithm and outputs an emotion score. This score numerically represents the emotional state of each individual. 【0406】 Step 4: 【0407】 The server integrates the sentiment score obtained from the sentiment engine with collected sales performance forecast information and external factor information. It receives this information as input data and runs an algorithm to optimize equipment operation plans and personnel allocation. The output is the optimized operation plan. 【0408】 Step 5: 【0409】 The terminal presents the administrator with an optimized operational plan and staffing allocation, and provides an interface for review and modification. It uses the optimization results sent from the server as input data. The administrator can review the presented plan and make modifications as needed. 【0410】 Step 6: 【0411】 Upon receiving confirmation and correction information from users, the server updates the operational plan and resource sharing plan for the food service support equipment. It incorporates the administrator's correction information as input data, reconstructs the new plan, and outputs it. This update enables more efficient business operations. 【0412】 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. 【0413】 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. 【0414】 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. 【0415】 [Third Embodiment] 【0416】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0417】 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. 【0418】 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). 【0419】 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. 【0420】 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. 【0421】 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). 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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. 【0426】 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. 【0427】 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". 【0428】 This invention provides a system for optimizing the shared operation of food service support equipment in multiple service facilities. This system includes functions for data collection, data analysis, optimization calculation, suggestion display, feedback, and adjustment. Its specific operation is described below. 【0429】 First, users register sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into the system via a dedicated input interface. The terminal aggregates this information and sends it to the server. The server stores this data in a database for processing and builds a sales forecast model based on past performance and trends. 【0430】 Next, the server predicts peak hours for each service facility based on external factors such as weather and event information. Based on this, it generates an operational plan for food service support equipment and a proposed staffing allocation. By using an optimization algorithm, it plans the movement of food service support equipment between facilities, enabling effective sharing. 【0431】 The server then notifies the terminals of this optimized plan and presents it to the operations manager on a visual dashboard. This allows the manager to intuitively review the proposed working hours and staffing and make adjustments as needed. 【0432】 Ultimately, users make adjustments to shifts and equipment placement based on the information provided through the management screen, and send these changes back to the server via their terminal. The server then uses this feedback to continuously train its AI model, aiming to improve the accuracy of future suggestions. 【0433】 For example, if a large sporting event is scheduled nearby, the server will take that information into account, predict an increase in the number of visitors to nearby business facilities, and create a plan to deploy additional food service equipment as needed. In this way, the system responds to events and external factors, thereby improving the efficiency of business operations. 【0434】 The following describes the processing flow. 【0435】 Step 1: 【0436】 Users register information such as projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into the input interface. Users input this information in an orderly manner while verifying the accuracy of the data. 【0437】 Step 2: 【0438】 The terminal collects the entered data and sends it to the server. The terminal transfers the data in real time and provides feedback to the user to confirm that it has reached the server. 【0439】 Step 3: 【0440】 The server stores the received data in a database and uses this data to predict the sales performance of each business facility. The server uses a machine learning model to perform sales forecasts while referring to past data. 【0441】 Step 4: 【0442】 The server incorporates information on external factors, such as weather and nearby events, to calculate predicted peak hours for each facility. The server integrates these factors into its analysis to provide more accurate predictions. 【0443】 Step 5: 【0444】 The server optimizes the operational schedule for food service support equipment and the proposed staffing arrangements based on predictions. The server uses algorithms to create an efficient equipment sharing plan and formulates that plan. 【0445】 Step 6: 【0446】 The server sends an optimized plan to the terminal and presents the information to the user using a visual dashboard. The terminal displays the visualized data in a format that is easily understandable to administrators. 【0447】 Step 7: 【0448】 Users review the dashboard and provide feedback or suggestions for revisions to the proposed plan. Users propose changes to the plan based on their own judgment and send feedback based on those changes back to the server via their device. 【0449】 Step 8: 【0450】 The server receives user feedback and updates the plan. The server uses this data to improve its AI model and learn to increase the accuracy of future suggestions. 【0451】 (Example 1) 【0452】 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." 【0453】 Efficient operation of support equipment across multiple business facilities is challenging because it requires appropriate operational planning and staffing based on business indicators influenced by diverse factors. In particular, adjustments are needed to achieve efficient equipment sharing while considering environmental factors and anticipated peak hours. However, achieving this requires rapid and appropriate planning, verification, and revision, which is inefficient when done manually. 【0454】 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. 【0455】 In this invention, the server includes means for collecting input data including business indicator forecast information, personnel preference allocation information, and environmental factor information obtained from multiple business facilities; means for optimizing the operation plan of support equipment and appropriate personnel allocation at each business facility based on the input data; and means for predicting peak hours and generating a plan to improve business indicators using the environmental factor information. This enables efficient operation and sharing of equipment across multiple business facilities. 【0456】 "Business performance indicator forecast information" refers to information used to predict future business performance and activity levels of business facilities, and is estimated based on past data and current conditions. 【0457】 "Personnel Placement Request Information" refers to information indicating the desired placement of personnel necessary to efficiently carry out operations at a work facility. 【0458】 "Environmental factor information" refers to information about external conditions that affect the activities of the business facility, and includes weather conditions and scheduled events. 【0459】 "Support equipment" refers to machinery, devices, and equipment used in workplaces to assist in the performance of tasks and improve efficiency. 【0460】 An "operational plan" is a plan that determines the operational schedule for support equipment and personnel to function most effectively. 【0461】 "Optimization" is the process of adjusting plans and arrangements to achieve maximum efficiency in order to obtain the best results under specific conditions. 【0462】 A "sharing plan" is an operational plan for sharing limited resources among multiple business facilities, with the aim of efficient utilization. 【0463】 An "operation screen" is a visual interface that allows users to operate, check, and modify a system. 【0464】 A "generated model" is a hypothetical data structure or computational model formed through algorithms used in the prediction and optimization process. 【0465】 The following describes embodiments for carrying out the invention. 【0466】 This system facilitates communication between servers, terminals, and users to optimize the operation of support equipment in business facilities. Users input business performance indicator forecasts, personnel allocation preferences, and environmental factors using a dedicated interface. Terminals collect this input data and transmit it to the server. 【0467】 The server stores the received data in a "database system" and uses a generated AI model based on past performance and current trends to build a sales forecasting model. Machine learning libraries such as "scikit-learn" and "TensorFlow" are utilized here. In addition, environmental factor information is obtained using the "weather API" and "event information API," and this data is used to predict peak hours. 【0468】 Based on the predicted data, the server uses an "optimization engine" to optimize the operational plan and personnel allocation of support equipment. Furthermore, it uses a "visualization tool" to visually present the generated plan and send it to the terminal. This allows administrators to review the proposed operational plan through the operation screen and make modifications as needed. 【0469】 As a concrete example, when a large-scale sporting event is held near the facility, the server takes event information into account to make predictions and creates a sharing plan for support equipment to accommodate the increased number of visitors. This prediction and optimization improves the efficiency of operational tasks. 【0470】 An example of a specific prompt for a generated AI model is, "Please develop the optimal support equipment deployment and personnel plan to handle this weekend's large-scale event." 【0471】 This system enables efficient equipment sharing and operation between business facilities, thereby promoting the optimization of business operations. 【0472】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0473】 Step 1: 【0474】 Users input sales performance forecasts, staffing preferences, and environmental factors into a dedicated interface. This data is aggregated by the terminal and sent to the server. Inputs include sales forecasts for each business facility, staffing preferences, and expected weather and event information. This forms the basis of the data. 【0475】 Step 2: 【0476】 The server stores the data received from the terminal in a database system. Based on the input information, the server refers to past performance data and builds a sales forecasting model using a generative AI model. Specifically, it uses "scikit-learn" and "TensorFlow" to execute a prediction algorithm and estimate future sales. The output of this process is the predicted sales data. 【0477】 Step 3: 【0478】 The server collects environmental factor information using weather APIs and event information APIs, and uses this information to predict peak hours. It analyzes the input external information to estimate the increase in the number of visitors during specific time periods. As a result of this analysis, data for the predicted peak hours is output. 【0479】 Step 4: 【0480】 The server uses sales forecast data and peak time information to optimize the operation plan and staffing of support equipment with its optimization engine. Optimization software is used to calculate a plan for efficiently utilizing limited resources. The output of this step is the optimized operation plan. 【0481】 Step 5: 【0482】 The server visualizes the optimized operational plan using a visualization tool, sends it to the terminal, and presents it to the administrator. The visual display allows the administrator to intuitively understand the proposed plan. This step outputs verifiable and modifiable visualization data. 【0483】 Step 6: 【0484】 Users review the plan through the operation screen and make modifications as needed. The modified information is sent back to the server via the terminal. Feedback on the plan is generated based on the user's input. 【0485】 Step 7: 【0486】 The server improves the generated AI model based on user feedback, enhancing the accuracy of future suggestions. Through a continuous learning process, the model becomes more accurate and efficient in its suggestions. The output of this step is the improved predictive model. 【0487】 (Application Example 1) 【0488】 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." 【0489】 Inefficient use of support equipment and personnel in business facilities leads to increased costs and a decline in service quality. In particular, optimally allocating and utilizing equipment and personnel across multiple business facilities is extremely difficult. Furthermore, efficient operational planning and optimized personnel allocation are required to respond quickly to fluctuations in demand due to external factors. The development of systems to address these challenges is necessary. 【0490】 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. 【0491】 In this invention, the server includes means for collecting predictive information, placement information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan and personnel allocation of support equipment based on this input data; and means for performing demand forecasting based on external factor information and generating a pre-arrangement plan for equipment and personnel. This enables improved operational efficiency and a rapid response to demand fluctuations. 【0492】 "Sales performance forecast information" refers to data used to estimate future sales and demand based on past sales performance and current market trends at business facilities. 【0493】 "Personnel and Equipment Placement Request Information" is a compilation of information detailing the desired placement of equipment and personnel within a business facility. 【0494】 "External factor information" refers to data about environmental conditions and circumstances outside the facility that affect business operations, such as weather and local events. 【0495】 An "operational plan" is a plan that outlines in advance how support equipment and personnel will be used within a business facility. 【0496】 "Optimization" is the process of adjusting means and methods to obtain the best possible results under given conditions. 【0497】 A "sharing plan" is a plan for efficiently sharing assistive devices among multiple work facilities. 【0498】 An "interface" is a user interface or communication method used to exchange information between a user and a system. 【0499】 A "mobile device" is a portable computer device with communication capabilities, such as a mobile phone or tablet. 【0500】 "Demand forecasting" refers to the process of predicting future market demand based on past data and external factors. 【0501】 A "pre-arrangement plan" is a plan to prepare necessary equipment and personnel in advance, based on future demand and operating conditions. 【0502】 The system for implementing this invention consists primarily of a server, terminals, and users. The server uses Python and the Django framework to aggregate sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities and store them in a database. This server processes this data and analyzes it using machine learning models to create an optimal operational plan for support equipment and personnel. 【0503】 The server considers external factors such as weather and event information, and based on this, forecasts the demand for support equipment and personnel. It then uses this information to build a support equipment sharing plan. Furthermore, the optimized operational plan is transmitted in real time to a mobile device application and provided as a visualized dashboard. 【0504】 The device visually presents the optimal operational plan to the business administrator via a mobile application developed using React Native. This application allows the business administrator to review and modify the plan and send feedback back to the server. 【0505】 Users apply this application to their daily operations and use it to adjust future plans as needed. For example, if a large music festival is scheduled to take place nearby on the weekend, the server will use that information to predict an increase in visitor numbers and plan for additional support equipment and delivery personnel. 【0506】 An example of a prompt generated using AI is: "For the market event scheduled for the beginning of next month, please suggest the optimal assistive device sharing plan and delivery route based on the expected increase in demand." In this way, the system supports efficient operations even in complex business environments. 【0507】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0508】 Step 1: 【0509】 The server collects sales performance forecast information, personnel preference information, equipment allocation information, and external factor information from multiple business facilities. This data is stored in a database. Based on this input data, the server uses a predictive model to analyze demand and forecast future business patterns. The inputs are production data and external factor information for each facility, and the outputs are demand forecasts and optimization parameters. 【0510】 Step 2: 【0511】 The server generates an optimal plan for support equipment and personnel allocation based on demand forecasts. Here, an optimization algorithm is applied to create an efficient resource allocation plan. The input is the demand forecast and available resource information, and the output is the optimized allocation plan. 【0512】 Step 3: 【0513】 The server sends optimized operational and sharing plans to the terminal. On the terminal, a React Native application displays a visual dashboard for business administrators. The input is the optimized plan sent from the server, and the output is the visualized data displayed on the terminal's screen. 【0514】 Step 4: 【0515】 The user reviews and modifies the plan presented via the terminal interface, and sends feedback back to the server as needed. This allows for fine-tuning of the plan. The input is the optimized plan on the terminal and the user's modifications, and the output is the modified plan. 【0516】 Step 5: 【0517】 The server updates its operational plan using feedback returned by users and utilizes this data to train its generating AI model. This improves the accuracy of future suggestions. The input is the revised plan and user feedback, and the output is the updated optimized model. 【0518】 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. 【0519】 This invention aims to improve overall efficiency by combining an emotion engine with a system that optimizes the operational planning of food service support equipment and the allocation of staff in a business facility. Specifically, the system uses the emotion engine to detect and analyze the emotional states of business managers and staff, and reflects the results in the operational plan. 【0520】 First, the user inputs sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into a dedicated interface. After the terminal sends this data to the server, the server processes the data and builds a sales forecast model for each business facility. 【0521】 In addition, the emotion engine analyzes the user's facial expressions and voice data to quantify their emotional state. For example, if an employee is in a high-stress state, this information is reflected in the analysis results via the server. The server then takes in this emotional data and recalculates the optimal work plan to contribute to improving work efficiency and the working environment. 【0522】 The server sends optimized operational plans for food service support equipment and proposed staffing arrangements to the terminal, providing users with an intuitive dashboard. Furthermore, the dashboard visualizes the results of the emotion engine's analysis, allowing users to view emotion data in real time. 【0523】 As a concrete example, in a certain business facility, if a staff member experiences a high level of stress while a food service support device is in operation, the emotion engine detects this, and the server automatically adjusts the operation plan. This allows for a shift to a work arrangement that reduces stress, thereby easing the burden on staff while maintaining the operational efficiency of the facility. 【0524】 In this way, by using an emotional engine in conjunction with other technologies, it becomes possible to achieve a high level of optimization in business operations and balance the well-being of personnel with the economic performance of facilities. 【0525】 The following describes the processing flow. 【0526】 Step 1: 【0527】 Users input information on projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into a dedicated interface. Users review the specific data and consider its accuracy when entering it. 【0528】 Step 2: 【0529】 The terminal collects the entered data and sends it to the server. The terminal quickly sends the collected data to the server and verifies that there are no errors. 【0530】 Step 3: 【0531】 The server stores the received data in a database and builds sales forecasting models for each business facility. The server then uses historical data and machine learning models to perform highly accurate sales forecasts. 【0532】 Step 4: 【0533】 The emotion engine analyzes the facial expressions and voices of the workforce and quantifies their emotional state. The server collects this emotional data and uses it to understand the state of the workforce. 【0534】 Step 5: 【0535】 The server optimizes the operation plan for food service support equipment and the allocation of staff based on predictive models and sentiment data. If the sentiment data indicates anomalies, the server adjusts the plan to take into account reducing the workload. 【0536】 Step 6: 【0537】 The server sends an optimized operational plan and staffing proposal to the terminal. The terminal visualizes the information on a dashboard, presenting it in a way that is easy for the user to understand. 【0538】 Step 7: 【0539】 Users review the dashboard and provide instructions through the interface for necessary adjustments to the operational plan and staffing. Users then review the validity of the proposed plan and provide feedback based on their own experience. 【0540】 Step 8: 【0541】 The server updates the plan using feedback received from the user. The server uses this data to improve the AI ​​model for future updates and to continuously improve operational efficiency. 【0542】 (Example 2) 【0543】 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." 【0544】 In today's business environment, there is a need for efficient management of work support devices and worker allocation across multiple business locations. However, there is a lack of means to optimize work plans while considering employees' emotions and stress levels, resulting in decreased work efficiency and a deterioration of the working environment. This invention aims to solve these problems and improve the efficiency of business locations and the working environment. 【0545】 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. 【0546】 In this invention, the server includes means for acquiring business forecast information, personnel allocation preference information, and external environment information obtained from multiple business locations; means for optimizing the operation plan of work support devices and worker allocation at each business location based on the input data; and means for detecting the emotional state of employees using an emotion analysis device and reflecting it in the operation plan. This enables efficient operation of work support devices across business locations and improves work efficiency while taking into account the emotional state of workers. 【0547】 A "business base" is a location where business activities are conducted, and it is a general term for bases where multiple facilities and personnel are located. 【0548】 "Work support equipment" is a general term for devices, including machines and tools, used to improve the efficiency of work. 【0549】 "Worker allocation" refers to assigning workers to appropriate locations and roles in order to efficiently perform necessary tasks. 【0550】 "Business forecast information" refers to information that includes data and forecast results used to estimate future business and sales performance. 【0551】 "Personnel assignment preference information" refers to information regarding the roles and work locations that workers desire. 【0552】 "External environmental information" refers to information about external conditions and circumstances that affect the operation of business locations, and includes weather information and event information. 【0553】 An "emotion analysis device" is a device that includes machine learning models and AI tools for detecting and analyzing an individual's emotional state from their facial expressions and voice data. 【0554】 "Optimization" refers to the process of making plans and arrangements the best possible state in order to make efficient use of limited resources. 【0555】 A "resource allocation plan" refers to a plan that determines how to distribute available resources among multiple business locations. 【0556】 An "interface" refers to the screen or protocol used by a system and a user to exchange information. 【0557】 "Visual information display means" refers to methods and devices for providing information in a graphical format so that users can intuitively understand the information. 【0558】 The system of the present invention optimizes work support devices and worker assignments while taking into account the emotional state of employees. A specific embodiment of this system will be described in detail below. 【0559】 First, users input projected business information, staffing requests, and external environmental information for each business location. This information is entered through a dedicated user interface and transmitted to a terminal. The terminal then formats this data appropriately and sends it to the server. 【0560】 The server analyzes the received data and builds predictive models using machine learning algorithms. This process includes predicting sales performance based on historical data and calculating efficient staffing levels. The hardware used includes a server computer, while the software includes data analysis algorithms and a sentiment analysis engine. 【0561】 The emotion analysis engine collects user facial expressions and voice data and quantifies their emotional state. Specifically, it uses cameras and microphones to acquire data in real time and analyzes it using an AI model for emotion analysis. 【0562】 For example, if a worker at a particular business location is detected to be experiencing high stress levels, the server uses that information to recalculate the work schedule. As a result, an optimal staffing plan is generated to reduce the burden on the workers. 【0563】 Furthermore, the optimized plan and sentiment analysis results are provided to the user via a visual dashboard on their device. This dashboard allows the user to view sentiment data in real time and modify the plan as needed. 【0564】 An example of a prompt to input into the generating AI model is, "Optimize business forecasting and worker allocation at business site A, and address employee stress levels optimally." This prompt allows the system to quickly execute optimization processes. 【0565】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0566】 Step 1: 【0567】 Users input project forecast information, staffing preference information, and external environment information into a dedicated interface. The entered data is formatted on the terminal and prepared for transmission to the server. Specifically, the data is structured in JSON or XML format to prepare for the next step. 【0568】 Step 2: 【0569】 The terminal sends the input data to the server. Secure protocols such as HTTPS are used for transmission. When the server receives the data, it converts it into a format suitable for analysis. This conversion prepares the server for processing. 【0570】 Step 3: 【0571】 The server uses machine learning algorithms to build a sales performance prediction model based on the converted input data. Here, it compares past performance data with the current input data to calculate the most suitable prediction. This process generates prediction results for estimating future business performance. 【0572】 Step 4: 【0573】 The emotion analysis engine detects facial expressions and voice from the user's actions and collects data. The device uses its camera and microphone to acquire data in real time and inputs it into an AI model that quantifies the emotional state. This model analyzes the emotional state using various emotional indicators. 【0574】 Step 5: 【0575】 The server takes in emotional data obtained from the emotion analysis engine and recalculates the worker assignment plan based on the analysis results. Here, it considers the impact of emotional data on work efficiency and adjusts worker assignments and work schedules as needed. 【0576】 Step 6: 【0577】 The server sends optimized operational plans and worker allocations to the terminal. The terminal builds and presents a graphical dashboard to make the information easy for the user to understand. The user can view the data in real time through the dashboard and make corrections as needed. 【0578】 (Application Example 2) 【0579】 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." 【0580】 In commercial facilities, there is a need for efficient operational planning of food service support equipment and appropriate allocation of staff. However, conventional methods make it difficult to formulate optimal plans while considering the emotional state of staff, resulting in an insufficient balance between operational efficiency and staff health. This invention aims to achieve a more advanced optimization of operational management using emotional analysis, thereby achieving both operational efficiency and reduction of staff stress. 【0581】 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. 【0582】 In this invention, the server includes means for collecting sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan of food service support equipment and personnel allocation at business facilities; and means for analyzing the emotional state of personnel using an emotion engine and reflecting the results in the operation plan. This makes it possible to formulate an optimal operation plan that takes emotional states into account at business facilities. 【0583】 "Sales performance forecast information" refers to data related to forecasts of future sales and sales volume at business facilities. 【0584】 "Personnel-requested equipment placement information" refers to information regarding the food service support equipment and its placement as requested by staff members. 【0585】 "External factor information" refers to data about the external environment that may affect the operation of business facilities, including weather conditions and event information. 【0586】 "Optimization" is the process of allocating and utilizing resources in the most efficient way to achieve a specific objective. 【0587】 An "emotional engine" is a system that analyzes the emotional state of work personnel and quantifies or classifies it. 【0588】 "Food service support equipment" refers to devices and equipment used in commercial facilities to streamline food service operations. 【0589】 A "resource sharing plan" is a plan for efficiently sharing equipment and personnel among multiple business facilities. 【0590】 This invention is a system designed to optimize the management of public facilities in smart cities. The system consists of numerous components, including servers, smart devices, and an emotion engine. 【0591】 The server collects sales performance forecast information, equipment allocation information for personnel requests, and external factor information from multiple business facilities. Smart devices such as smartphones and smart glasses provide this information via an interface, creating a platform that allows on-site managers to review and correct the data. The smart devices also collect facial data of personnel using their built-in cameras. 【0592】 The emotion engine analyzes collected facial data to quantify the emotional state of the staff. This analyzed data is sent to a server for integration. The server then uses this emotional data to develop a new plan that optimizes the operation of food service equipment and the allocation of staff within the facility. 【0593】 A concrete example is the development of operational plans to improve the efficiency of library operations while reducing staff stress. Based on an analysis of emotional states during specific time periods, staffing levels and break times are adjusted. This improves the operational efficiency of public facilities and the well-being of staff. 【0594】 An example of a prompt to input into the generating AI model might be: "Optimize the library's operational plan based on the emotional state of the staff. Propose a plan that improves visitor service while providing a more relaxing environment for the staff." 【0595】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0596】 Step 1: 【0597】 The server collects sales performance forecast information, personnel preference information, and external factor information from multiple business facilities. This information is stored in a database as input data. Storing this data in the database provides a foundation for subsequent data analysis. 【0598】 Step 2: 【0599】 The smart device collects facial data of work personnel using its built-in camera. The user takes a picture of the work personnel using the device, acquiring a facial image as input data. The smart device then prepares the image for transmission to the emotion engine. 【0600】 Step 3: 【0601】 The emotion engine analyzes received facial data and quantifies the emotional state of the work personnel. The input data is a facial image, which is analyzed by an emotion recognition algorithm and outputs an emotion score. This score numerically represents the emotional state of each individual. 【0602】 Step 4: 【0603】 The server integrates the sentiment score obtained from the sentiment engine with collected sales performance forecast information and external factor information. It receives this information as input data and runs an algorithm to optimize equipment operation plans and personnel allocation. The output is the optimized operation plan. 【0604】 Step 5: 【0605】 The terminal presents the administrator with an optimized operational plan and staffing allocation, and provides an interface for review and modification. It uses the optimization results sent from the server as input data. The administrator can review the presented plan and make modifications as needed. 【0606】 Step 6: 【0607】 Upon receiving confirmation and correction information from users, the server updates the operational plan and resource sharing plan for the food service support equipment. It incorporates the administrator's correction information as input data, reconstructs the new plan, and outputs it. This update enables more efficient business operations. 【0608】 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. 【0609】 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. 【0610】 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. 【0611】 [Fourth Embodiment] 【0612】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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). 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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. 【0622】 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. 【0623】 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. 【0624】 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". 【0625】 This invention provides a system for optimizing the shared operation of food service support equipment in multiple service facilities. This system includes functions for data collection, data analysis, optimization calculation, suggestion display, feedback, and adjustment. Its specific operation is described below. 【0626】 First, users register sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into the system via a dedicated input interface. The terminal aggregates this information and sends it to the server. The server stores this data in a database for processing and builds a sales forecast model based on past performance and trends. 【0627】 Next, the server predicts peak hours for each service facility based on external factors such as weather and event information. Based on this, it generates an operational plan for food service support equipment and a proposed staffing allocation. By using an optimization algorithm, it plans the movement of food service support equipment between facilities, enabling effective sharing. 【0628】 The server then notifies the terminals of this optimized plan and presents it to the operations manager on a visual dashboard. This allows the manager to intuitively review the proposed working hours and staffing and make adjustments as needed. 【0629】 Ultimately, users make adjustments to shifts and equipment placement based on the information provided through the management screen, and send these changes back to the server via their terminal. The server then uses this feedback to continuously train its AI model, aiming to improve the accuracy of future suggestions. 【0630】 For example, if a large sporting event is scheduled nearby, the server will take that information into account, predict an increase in the number of visitors to nearby business facilities, and create a plan to deploy additional food service equipment as needed. In this way, the system responds to events and external factors, thereby improving the efficiency of business operations. 【0631】 The following describes the processing flow. 【0632】 Step 1: 【0633】 Users register information such as projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into the input interface. Users input this information in an orderly manner while verifying the accuracy of the data. 【0634】 Step 2: 【0635】 The terminal collects the entered data and sends it to the server. The terminal transfers the data in real time and provides feedback to the user to confirm that it has reached the server. 【0636】 Step 3: 【0637】 The server stores the received data in a database and uses this data to predict the sales performance of each business facility. The server uses a machine learning model to perform sales forecasts while referring to past data. 【0638】 Step 4: 【0639】 The server incorporates information on external factors, such as weather and nearby events, to calculate predicted peak hours for each facility. The server integrates these factors into its analysis to provide more accurate predictions. 【0640】 Step 5: 【0641】 The server optimizes the operational schedule for food service support equipment and the proposed staffing arrangements based on predictions. The server uses algorithms to create an efficient equipment sharing plan and formulates that plan. 【0642】 Step 6: 【0643】 The server sends an optimized plan to the terminal and presents the information to the user using a visual dashboard. The terminal displays the visualized data in a format that is easily understandable to administrators. 【0644】 Step 7: 【0645】 Users review the dashboard and provide feedback or suggestions for revisions to the proposed plan. Users propose changes to the plan based on their own judgment and send feedback based on those changes back to the server via their device. 【0646】 Step 8: 【0647】 The server receives user feedback and updates the plan. The server uses this data to improve its AI model and learn to increase the accuracy of future suggestions. 【0648】 (Example 1) 【0649】 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". 【0650】 Efficient operation of support equipment across multiple business facilities is challenging because it requires appropriate operational planning and staffing based on business indicators influenced by diverse factors. In particular, adjustments are needed to achieve efficient equipment sharing while considering environmental factors and anticipated peak hours. However, achieving this requires rapid and appropriate planning, verification, and revision, which is inefficient when done manually. 【0651】 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. 【0652】 In this invention, the server includes means for collecting input data including business indicator forecast information, personnel preference allocation information, and environmental factor information obtained from multiple business facilities; means for optimizing the operation plan of support equipment and appropriate personnel allocation at each business facility based on the input data; and means for predicting peak hours and generating a plan to improve business indicators using the environmental factor information. This enables efficient operation and sharing of equipment across multiple business facilities. 【0653】 "Business performance indicator forecast information" refers to information used to predict future business performance and activity levels of business facilities, and is estimated based on past data and current conditions. 【0654】 "Personnel Placement Request Information" refers to information indicating the desired placement of personnel necessary to efficiently carry out operations at a work facility. 【0655】 "Environmental factor information" refers to information about external conditions that affect the activities of the business facility, and includes weather conditions and scheduled events. 【0656】 "Support equipment" refers to machinery, devices, and equipment used in workplaces to assist in the performance of tasks and improve efficiency. 【0657】 An "operational plan" is a plan that determines the operational schedule for support equipment and personnel to function most effectively. 【0658】 "Optimization" is the process of adjusting plans and arrangements to achieve maximum efficiency in order to obtain the best results under specific conditions. 【0659】 A "sharing plan" is an operational plan for sharing limited resources among multiple business facilities, with the aim of efficient utilization. 【0660】 An "operation screen" is a visual interface that allows users to operate, check, and modify a system. 【0661】 A "generated model" is a hypothetical data structure or computational model formed through algorithms used in the prediction and optimization process. 【0662】 The following describes embodiments for carrying out the invention. 【0663】 This system facilitates communication between servers, terminals, and users to optimize the operation of support equipment in business facilities. Users input business performance indicator forecasts, personnel allocation preferences, and environmental factors using a dedicated interface. Terminals collect this input data and transmit it to the server. 【0664】 The server stores the received data in a "database system" and uses a generated AI model based on past performance and current trends to build a sales forecasting model. Machine learning libraries such as "scikit-learn" and "TensorFlow" are utilized here. In addition, environmental factor information is obtained using the "weather API" and "event information API," and this data is used to predict peak hours. 【0665】 Based on the predicted data, the server uses an "optimization engine" to optimize the operational plan and personnel allocation of support equipment. Furthermore, it uses a "visualization tool" to visually present the generated plan and send it to the terminal. This allows administrators to review the proposed operational plan through the operation screen and make modifications as needed. 【0666】 As a concrete example, when a large-scale sporting event is held near the facility, the server takes event information into account to make predictions and creates a sharing plan for support equipment to accommodate the increased number of visitors. This prediction and optimization improves the efficiency of operational tasks. 【0667】 An example of a specific prompt for a generated AI model is, "Please develop the optimal support equipment deployment and personnel plan to handle this weekend's large-scale event." 【0668】 This system enables efficient equipment sharing and operation between business facilities, thereby promoting the optimization of business operations. 【0669】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0670】 Step 1: 【0671】 Users input sales performance forecasts, staffing preferences, and environmental factors into a dedicated interface. This data is aggregated by the terminal and sent to the server. Inputs include sales forecasts for each business facility, staffing preferences, and expected weather and event information. This forms the basis of the data. 【0672】 Step 2: 【0673】 The server stores the data received from the terminal in a database system. Based on the input information, the server refers to past performance data and builds a sales forecasting model using a generative AI model. Specifically, it uses "scikit-learn" and "TensorFlow" to execute a prediction algorithm and estimate future sales. The output of this process is the predicted sales data. 【0674】 Step 3: 【0675】 The server collects environmental factor information using weather APIs and event information APIs, and uses this information to predict peak hours. It analyzes the input external information to estimate the increase in the number of visitors during specific time periods. As a result of this analysis, data for the predicted peak hours is output. 【0676】 Step 4: 【0677】 The server uses sales forecast data and peak time information to optimize the operation plan and staffing of support equipment with its optimization engine. Optimization software is used to calculate a plan for efficiently utilizing limited resources. The output of this step is the optimized operation plan. 【0678】 Step 5: 【0679】 The server visualizes the optimized operational plan using a visualization tool, sends it to the terminal, and presents it to the administrator. The visual display allows the administrator to intuitively understand the proposed plan. This step outputs verifiable and modifiable visualization data. 【0680】 Step 6: 【0681】 Users review the plan through the operation screen and make modifications as needed. The modified information is sent back to the server via the terminal. Feedback on the plan is generated based on the user's input. 【0682】 Step 7: 【0683】 The server improves the generated AI model based on user feedback, enhancing the accuracy of future suggestions. Through a continuous learning process, the model becomes more accurate and efficient in its suggestions. The output of this step is the improved predictive model. 【0684】 (Application Example 1) 【0685】 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". 【0686】 Inefficient use of support equipment and personnel in business facilities leads to increased costs and a decline in service quality. In particular, optimally allocating and utilizing equipment and personnel across multiple business facilities is extremely difficult. Furthermore, efficient operational planning and optimized personnel allocation are required to respond quickly to fluctuations in demand due to external factors. The development of systems to address these challenges is necessary. 【0687】 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. 【0688】 In this invention, the server includes means for collecting predictive information, placement information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan and personnel allocation of support equipment based on this input data; and means for performing demand forecasting based on external factor information and generating a pre-arrangement plan for equipment and personnel. This enables improved operational efficiency and a rapid response to demand fluctuations. 【0689】 "Sales performance forecast information" refers to data used to estimate future sales and demand based on past sales performance and current market trends at business facilities. 【0690】 "Personnel and Equipment Placement Request Information" is a compilation of information detailing the desired placement of equipment and personnel within a business facility. 【0691】 "External factor information" refers to data about environmental conditions and circumstances outside the facility that affect business operations, such as weather and local events. 【0692】 An "operational plan" is a plan that outlines in advance how support equipment and personnel will be used within a business facility. 【0693】 "Optimization" is the process of adjusting means and methods to obtain the best possible results under given conditions. 【0694】 A "sharing plan" is a plan for efficiently sharing assistive devices among multiple work facilities. 【0695】 An "interface" is a user interface or communication method used to exchange information between a user and a system. 【0696】 A "mobile device" is a portable computer device with communication capabilities, such as a mobile phone or tablet. 【0697】 "Demand forecasting" refers to the process of predicting future market demand based on past data and external factors. 【0698】 A "pre-arrangement plan" is a plan to prepare necessary equipment and personnel in advance, based on future demand and operating conditions. 【0699】 The system for implementing this invention consists primarily of a server, terminals, and users. The server uses Python and the Django framework to aggregate sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities and store them in a database. This server processes this data and analyzes it using machine learning models to create an optimal operational plan for support equipment and personnel. 【0700】 The server considers external factors such as weather and event information, and based on this, forecasts the demand for support equipment and personnel. It then uses this information to build a support equipment sharing plan. Furthermore, the optimized operational plan is transmitted in real time to a mobile device application and provided as a visualized dashboard. 【0701】 The device visually presents the optimal operational plan to the business administrator via a mobile application developed using React Native. This application allows the business administrator to review and modify the plan and send feedback back to the server. 【0702】 Users apply this application to their daily operations and use it to adjust future plans as needed. For example, if a large music festival is scheduled to take place nearby on the weekend, the server will use that information to predict an increase in visitor numbers and plan for additional support equipment and delivery personnel. 【0703】 An example of a prompt generated using AI is: "For the market event scheduled for the beginning of next month, please suggest the optimal assistive device sharing plan and delivery route based on the expected increase in demand." In this way, the system supports efficient operations even in complex business environments. 【0704】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0705】 Step 1: 【0706】 The server collects sales performance forecast information, personnel preference information, equipment allocation information, and external factor information from multiple business facilities. This data is stored in a database. Based on this input data, the server uses a predictive model to analyze demand and forecast future business patterns. The inputs are production data and external factor information for each facility, and the outputs are demand forecasts and optimization parameters. 【0707】 Step 2: 【0708】 The server generates an optimal plan for support equipment and personnel allocation based on demand forecasts. Here, an optimization algorithm is applied to create an efficient resource allocation plan. The input is the demand forecast and available resource information, and the output is the optimized allocation plan. 【0709】 Step 3: 【0710】 The server sends optimized operational and sharing plans to the terminal. On the terminal, a React Native application displays a visual dashboard for business administrators. The input is the optimized plan sent from the server, and the output is the visualized data displayed on the terminal's screen. 【0711】 Step 4: 【0712】 The user reviews and modifies the plan presented via the terminal interface, and sends feedback back to the server as needed. This allows for fine-tuning of the plan. The input is the optimized plan on the terminal and the user's modifications, and the output is the modified plan. 【0713】 Step 5: 【0714】 The server updates its operational plan using feedback returned by users and utilizes this data to train its generating AI model. This improves the accuracy of future suggestions. The input is the revised plan and user feedback, and the output is the updated optimized model. 【0715】 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. 【0716】 This invention aims to improve overall efficiency by combining an emotion engine with a system that optimizes the operational planning of food service support equipment and the allocation of staff in a business facility. Specifically, the system uses the emotion engine to detect and analyze the emotional states of business managers and staff, and reflects the results in the operational plan. 【0717】 First, the user inputs sales performance forecast information for each business facility, equipment allocation information for desired personnel, and external factor information into a dedicated interface. After the terminal sends this data to the server, the server processes the data and builds a sales forecast model for each business facility. 【0718】 In addition, the emotion engine analyzes the user's facial expressions and voice data to quantify their emotional state. For example, if an employee is in a high-stress state, this information is reflected in the analysis results via the server. The server then takes in this emotional data and recalculates the optimal work plan to contribute to improving work efficiency and the working environment. 【0719】 The server sends optimized operational plans for food service support equipment and proposed staffing arrangements to the terminal, providing users with an intuitive dashboard. Furthermore, the dashboard visualizes the results of the emotion engine's analysis, allowing users to view emotion data in real time. 【0720】 As a concrete example, in a certain business facility, if a staff member experiences a high level of stress while a food service support device is in operation, the emotion engine detects this, and the server automatically adjusts the operation plan. This allows for a shift to a work arrangement that reduces stress, thereby easing the burden on staff while maintaining the operational efficiency of the facility. 【0721】 In this way, by using an emotional engine in conjunction with other technologies, it becomes possible to achieve a high level of optimization in business operations and balance the well-being of personnel with the economic performance of facilities. 【0722】 The following describes the processing flow. 【0723】 Step 1: 【0724】 Users input information on projected sales performance of business facilities, desired equipment placement for business personnel, and external factors into a dedicated interface. Users review the specific data and consider its accuracy when entering it. 【0725】 Step 2: 【0726】 The terminal collects the entered data and sends it to the server. The terminal quickly sends the collected data to the server and verifies that there are no errors. 【0727】 Step 3: 【0728】 The server stores the received data in a database and builds sales forecasting models for each business facility. The server then uses historical data and machine learning models to perform highly accurate sales forecasts. 【0729】 Step 4: 【0730】 The emotion engine analyzes the facial expressions and voices of the workforce and quantifies their emotional state. The server collects this emotional data and uses it to understand the state of the workforce. 【0731】 Step 5: 【0732】 The server optimizes the operation plan for food service support equipment and the allocation of staff based on predictive models and sentiment data. If the sentiment data indicates anomalies, the server adjusts the plan to take into account reducing the workload. 【0733】 Step 6: 【0734】 The server sends an optimized operational plan and staffing proposal to the terminal. The terminal visualizes the information on a dashboard, presenting it in a way that is easy for the user to understand. 【0735】 Step 7: 【0736】 Users review the dashboard and provide instructions through the interface for necessary adjustments to the operational plan and staffing. Users then review the validity of the proposed plan and provide feedback based on their own experience. 【0737】 Step 8: 【0738】 The server updates the plan using feedback received from the user. The server uses this data to improve the AI ​​model for future updates and to continuously improve operational efficiency. 【0739】 (Example 2) 【0740】 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". 【0741】 In today's business environment, there is a need for efficient management of work support devices and worker allocation across multiple business locations. However, there is a lack of means to optimize work plans while considering employees' emotions and stress levels, resulting in decreased work efficiency and a deterioration of the working environment. This invention aims to solve these problems and improve the efficiency of business locations and the working environment. 【0742】 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. 【0743】 In this invention, the server includes means for acquiring business forecast information, personnel allocation preference information, and external environment information obtained from multiple business locations; means for optimizing the operation plan of work support devices and worker allocation at each business location based on the input data; and means for detecting the emotional state of employees using an emotion analysis device and reflecting it in the operation plan. This enables efficient operation of work support devices across business locations and improves work efficiency while taking into account the emotional state of workers. 【0744】 A "business base" is a location where business activities are conducted, and it is a general term for bases where multiple facilities and personnel are located. 【0745】 "Work support equipment" is a general term for devices, including machines and tools, used to improve the efficiency of work. 【0746】 "Worker allocation" refers to assigning workers to appropriate locations and roles in order to efficiently perform necessary tasks. 【0747】 "Business forecast information" refers to information that includes data and forecast results used to estimate future business and sales performance. 【0748】 "Personnel assignment preference information" refers to information regarding the roles and work locations that workers desire. 【0749】 "External environmental information" refers to information about external conditions and circumstances that affect the operation of business locations, and includes weather information and event information. 【0750】 An "emotion analysis device" is a device that includes machine learning models and AI tools for detecting and analyzing an individual's emotional state from their facial expressions and voice data. 【0751】 "Optimization" refers to the process of making plans and arrangements the best possible state in order to make efficient use of limited resources. 【0752】 A "resource allocation plan" refers to a plan that determines how to distribute available resources among multiple business locations. 【0753】 An "interface" refers to the screen or protocol used by a system and a user to exchange information. 【0754】 "Visual information display means" refers to methods and devices for providing information in a graphical format so that users can intuitively understand the information. 【0755】 The system of the present invention optimizes work support devices and worker assignments while taking into account the emotional state of employees. A specific embodiment of this system will be described in detail below. 【0756】 First, users input projected business information, staffing requests, and external environmental information for each business location. This information is entered through a dedicated user interface and transmitted to a terminal. The terminal then formats this data appropriately and sends it to the server. 【0757】 The server analyzes the received data and builds predictive models using machine learning algorithms. This process includes predicting sales performance based on historical data and calculating efficient staffing levels. The hardware used includes a server computer, while the software includes data analysis algorithms and a sentiment analysis engine. 【0758】 The emotion analysis engine collects user facial expressions and voice data and quantifies their emotional state. Specifically, it uses cameras and microphones to acquire data in real time and analyzes it using an AI model for emotion analysis. 【0759】 For example, if a worker at a particular business location is detected to be experiencing high stress levels, the server uses that information to recalculate the work schedule. As a result, an optimal staffing plan is generated to reduce the burden on the workers. 【0760】 Furthermore, the optimized plan and sentiment analysis results are provided to the user via a visual dashboard on their device. This dashboard allows the user to view sentiment data in real time and modify the plan as needed. 【0761】 An example of a prompt to input into the generating AI model is, "Optimize business forecasting and worker allocation at business site A, and address employee stress levels optimally." This prompt allows the system to quickly execute optimization processes. 【0762】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0763】 Step 1: 【0764】 Users input project forecast information, staffing preference information, and external environment information into a dedicated interface. The entered data is formatted on the terminal and prepared for transmission to the server. Specifically, the data is structured in JSON or XML format to prepare for the next step. 【0765】 Step 2: 【0766】 The terminal sends the input data to the server. Secure protocols such as HTTPS are used for transmission. When the server receives the data, it converts it into a format suitable for analysis. This conversion prepares the server for processing. 【0767】 Step 3: 【0768】 The server uses machine learning algorithms to build a sales performance prediction model based on the converted input data. Here, it compares past performance data with the current input data to calculate the most suitable prediction. This process generates prediction results for estimating future business performance. 【0769】 Step 4: 【0770】 The emotion analysis engine detects facial expressions and voice from the user's actions and collects data. The device uses its camera and microphone to acquire data in real time and inputs it into an AI model that quantifies the emotional state. This model analyzes the emotional state using various emotional indicators. 【0771】 Step 5: 【0772】 The server takes in emotional data obtained from the emotion analysis engine and recalculates the worker assignment plan based on the analysis results. Here, it considers the impact of emotional data on work efficiency and adjusts worker assignments and work schedules as needed. 【0773】 Step 6: 【0774】 The server sends optimized operational plans and worker allocations to the terminal. The terminal builds and presents a graphical dashboard to make the information easy for the user to understand. The user can view the data in real time through the dashboard and make corrections as needed. 【0775】 (Application Example 2) 【0776】 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". 【0777】 In commercial facilities, there is a need for efficient operational planning of food service support equipment and appropriate allocation of staff. However, conventional methods make it difficult to formulate optimal plans while considering the emotional state of staff, resulting in an insufficient balance between operational efficiency and staff health. This invention aims to achieve a more advanced optimization of operational management using emotional analysis, thereby achieving both operational efficiency and reduction of staff stress. 【0778】 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. 【0779】 In this invention, the server includes means for collecting sales performance forecast information, personnel preference equipment allocation information, and external factor information obtained from multiple business facilities; means for optimizing the operation plan of food service support equipment and personnel allocation at business facilities; and means for analyzing the emotional state of personnel using an emotion engine and reflecting the results in the operation plan. This makes it possible to formulate an optimal operation plan that takes emotional states into account at business facilities. 【0780】 "Sales performance forecast information" refers to data related to forecasts of future sales and sales volume at business facilities. 【0781】 "Personnel-requested equipment placement information" refers to information regarding the food service support equipment and its placement as requested by staff members. 【0782】 "External factor information" refers to data about the external environment that may affect the operation of business facilities, including weather conditions and event information. 【0783】 "Optimization" is the process of allocating and utilizing resources in the most efficient way to achieve a specific objective. 【0784】 An "emotional engine" is a system that analyzes the emotional state of work personnel and quantifies or classifies it. 【0785】 "Food service support equipment" refers to devices and equipment used in commercial facilities to streamline food service operations. 【0786】 A "resource sharing plan" is a plan for efficiently sharing equipment and personnel among multiple business facilities. 【0787】 This invention is a system designed to optimize the management of public facilities in smart cities. The system consists of numerous components, including servers, smart devices, and an emotion engine. 【0788】 The server collects sales performance forecast information, equipment allocation information for personnel requests, and external factor information from multiple business facilities. Smart devices such as smartphones and smart glasses provide this information via an interface, creating a platform that allows on-site managers to review and correct the data. The smart devices also collect facial data of personnel using their built-in cameras. 【0789】 The emotion engine analyzes collected facial data to quantify the emotional state of the staff. This analyzed data is sent to a server for integration. The server then uses this emotional data to develop a new plan that optimizes the operation of food service equipment and the allocation of staff within the facility. 【0790】 A concrete example is the development of operational plans to improve the efficiency of library operations while reducing staff stress. Based on an analysis of emotional states during specific time periods, staffing levels and break times are adjusted. This improves the operational efficiency of public facilities and the well-being of staff. 【0791】 An example of a prompt to input into the generating AI model might be: "Optimize the library's operational plan based on the emotional state of the staff. Propose a plan that improves visitor service while providing a more relaxing environment for the staff." 【0792】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0793】 Step 1: 【0794】 The server collects sales performance forecast information, personnel preference information, and external factor information from multiple business facilities. This information is stored in a database as input data. Storing this data in the database provides a foundation for subsequent data analysis. 【0795】 Step 2: 【0796】 The smart device collects facial data of work personnel using its built-in camera. The user takes a picture of the work personnel using the device, acquiring a facial image as input data. The smart device then prepares the image for transmission to the emotion engine. 【0797】 Step 3: 【0798】 The emotion engine analyzes received facial data and quantifies the emotional state of the work personnel. The input data is a facial image, which is analyzed by an emotion recognition algorithm and outputs an emotion score. This score numerically represents the emotional state of each individual. 【0799】 Step 4: 【0800】 The server integrates the sentiment score obtained from the sentiment engine with collected sales performance forecast information and external factor information. It receives this information as input data and runs an algorithm to optimize equipment operation plans and personnel allocation. The output is the optimized operation plan. 【0801】 Step 5: 【0802】 The terminal presents the administrator with an optimized operational plan and staffing allocation, and provides an interface for review and modification. It uses the optimization results sent from the server as input data. The administrator can review the presented plan and make modifications as needed. 【0803】 Step 6: 【0804】 Upon receiving confirmation and correction information from users, the server updates the operational plan and resource sharing plan for the food service support equipment. It incorporates the administrator's correction information as input data, reconstructs the new plan, and outputs it. This update enables more efficient business operations. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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." 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0826】 The following is further disclosed regarding the embodiments described above. 【0827】 (Claim 1) 【0828】 A means for collecting input data including sales performance forecast information obtained from multiple business facilities, equipment allocation information for desired personnel, and external factor information, 【0829】 Based on the aforementioned input data, a means for optimizing the operational plan of food service support equipment and the appropriate allocation of personnel at each business facility, 【0830】 A means for creating an appropriate sharing plan for food service support equipment between business facilities based on the results of the optimization described above, 【0831】 A means for presenting the optimized operational plan and personnel allocation to the operations manager and providing an interface for confirmation and modification, 【0832】 A system including means for updating the operation plan and sharing plan of the food service support equipment using confirmation and correction information from the aforementioned administrator. 【0833】 (Claim 2) 【0834】 The system according to claim 1, wherein the external factor information includes weather conditions and information regarding scheduled events. 【0835】 (Claim 3) 【0836】 The system according to claim 1, which provides the business manager with a visual dashboard via the interface. 【0837】 "Example 1" 【0838】 (Claim 1) 【0839】 A means for collecting input data including business indicator forecast information, personnel preference allocation information, and environmental factor information obtained from multiple business facilities, 【0840】 Based on the aforementioned input data, a means for optimizing the operational plan of support equipment and appropriate staffing at each business facility, 【0841】 A means for predicting peak hours and generating a plan to improve business indicators using the aforementioned environmental factor information, 【0842】 A means for presenting the optimized operational plan and personnel allocation proposal to the manager and providing an operation screen for confirmation and modification, 【0843】 A system that includes means for updating operational plans and equipment sharing plans and improving the performance of the generated models, using confirmation and correction information from the aforementioned administrator. 【0844】 (Claim 2) 【0845】 The system according to claim 1, wherein the environmental factor information includes information on weather conditions and planned activities. 【0846】 (Claim 3) 【0847】 The system according to claim 1, which provides visual information to the administrator via the operation screen. 【0848】 "Application Example 1" 【0849】 (Claim 1) 【0850】 A means for collecting input data including sales performance forecast information obtained from multiple business facilities, equipment allocation information for desired personnel, and external factor information, 【0851】 Based on the aforementioned input data, a means for optimizing the operation plan of support equipment and the appropriate allocation of personnel at each work facility, 【0852】 A means for creating an appropriate sharing plan for assistive devices between business facilities based on the results of the optimization described above, 【0853】 A means for presenting the optimized operational plan and personnel allocation to the operations manager and providing an interface for confirmation and modification, 【0854】 A means for updating the operation plan and sharing plan of the support equipment using the confirmation and correction information from the administrator, 【0855】 Based on the aforementioned operational plan, a means is provided to perform personnel allocation planning and delivery route optimization, and to present relevant information in real time. 【0856】 A means of generating a pre-arrangement plan for support equipment and personnel based on external factor information to forecast demand, 【0857】 A system that includes means for providing an operator-operable interface using visual information presented via a mobile device. 【0858】 (Claim 2) 【0859】 The system according to claim 1, wherein the external factors information includes information on natural environmental conditions and scheduled events. 【0860】 (Claim 3) 【0861】 The system according to claim 1, which provides the aforementioned business manager with a visual dashboard via the interface and dynamically issues instructions to delivery personnel. 【0862】 "Example 2 of combining an emotion engine" 【0863】 (Claim 1) 【0864】 A means for acquiring input data including business forecast information, personnel allocation preference information, and external environment information obtained from multiple business locations, 【0865】 Based on the aforementioned input data, a means for optimizing the operation plan of work support devices and worker allocation at each business site, 【0866】 A means of detecting the emotional state of employees using an emotion analysis device and reflecting it in the work plan, 【0867】 A means for creating a resource allocation plan between business locations of work support devices based on the results of the optimization, 【0868】 A means for presenting the optimized work plan and worker allocation to the manager and providing an interface for confirmation and modification, 【0869】 A system including means for updating the operation plan and resource allocation plan of the work support device using confirmation and correction information from the aforementioned administrator. 【0870】 (Claim 2) 【0871】 The system according to claim 1, wherein the external environmental information includes information on weather conditions and scheduled events. 【0872】 (Claim 3) 【0873】 The system according to claim 1, which provides the administrator with a means for displaying visual information via the interface. 【0874】 "Application example 2 when combining with an emotional engine" 【0875】 (Claim 1) 【0876】 A means for collecting data including sales performance forecast information obtained from multiple business facilities, equipment allocation information for business personnel, and external factor information, 【0877】 Based on the aforementioned data, a means to optimize the operational plan for food service support equipment and the appropriate allocation of personnel at each service facility, 【0878】 A means of analyzing the emotional state of work personnel using an emotion engine and reflecting the results in the operational plan, 【0879】 Based on the results of the optimization described above, a means for formulating an appropriate resource sharing plan for food service support equipment between operational facilities, 【0880】 A means for presenting the optimized operational plan and personnel allocation to the manager and providing an interface for confirmation and modification, 【0881】 A system including means for updating the operation plan and resource sharing plan of the food service support equipment using confirmation and correction information from the aforementioned administrator. 【0882】 (Claim 2) 【0883】 The system according to claim 1, wherein the external factor information includes information on weather conditions and planned activities. 【0884】 (Claim 3) 【0885】 The system according to claim 1, which provides the administrator with a visual dashboard via an interface and visualizes the emotional state analyzed by the emotion engine in real time. [Explanation of symbols] 【0886】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for collecting input data including sales performance forecast information obtained from multiple business facilities, equipment allocation information for desired personnel, and external factor information, Based on the aforementioned input data, a means for optimizing the operational plan of food service support equipment and the appropriate allocation of personnel at each business facility, A means for creating an appropriate sharing plan for food service support equipment between business facilities based on the results of the optimization described above, A means for presenting the optimized operational plan and personnel allocation to the operations manager and providing an interface for confirmation and modification, A system including means for updating the operation plan and sharing plan of the food service support equipment using confirmation and correction information from the aforementioned administrator. [Claim 2] The system according to claim 1, wherein the external factor information includes information on weather conditions and scheduled events. [Claim 3] The system according to claim 1, which provides the business manager with a visual dashboard via the interface.

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