system

Abstract

Provide a system. 【Solution means】 Circuit means consisting of an information processing device, Means for a data analysis device to determine an abnormality based on the image data acquired by the information processing device, Means for notifying the user when there is an abnormality, Means for a task management device to generate a business list, Means for monitoring the progress of the business list, Means for reminding the user of unfinished tasks, Means for aggregating performance data and analyzing the sales strategy to be focused on next, Means for acquiring basic information of visitors, calculating the waiting time, and guiding them, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern commercial facilities, employees are engaged in a wide variety of tasks, and some simple tasks have become a burden on employees. As a result, it is difficult to concentrate on important tasks, leading to a decline in work efficiency. In particular, routinized tasks such as in-store patrols, creation and management of work lists, and customer service have room for automation, and their efficiency needs to be improved. It is necessary to solve such problems.

Means for Solving the Problems

[0005] This invention provides a store operations efficiency system using an information processing device. The information processing device patrols the store and, in cooperation with a data analysis device, detects anomalies from image data, thereby automating the monitoring of cleaning status and display conditions. Furthermore, a task management device generates and distributes task lists, monitors progress, and reminds users of incomplete tasks, thereby improving the efficiency of task management. In addition, by acquiring basic information of customers, calculating waiting times, and providing guidance, the system automates customer service. Through this series of measures, employees can concentrate on more valuable tasks.

[0006] An "information processing device" is a device that includes hardware and software for acquiring and processing data, and is a device that collects and processes data necessary to automate operations within a store.

[0007] "Patrolling method" refers to a system or part of a device that uses information processing equipment to periodically patrol the store and collect necessary data.

[0008] A "data analysis device" is a device that analyzes data obtained from an information processing device to identify anomalies and problems in store operations.

[0009] A "task list" is a list of tasks that need to be performed within a store, and is used to efficiently manage operations.

[0010] A "task management device" is a device or system for creating, distributing, and managing the progress of task lists, and is intended to support the efficiency of task management.

[0011] A "user" is a person or organization that operates information processing equipment and related systems and receives instructions and information from the system in the course of performing their duties.

[0012] "Customers" refer to individuals or groups who visit a store seeking services or products, and are the people who receive service and guidance within the store. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system for improving operational efficiency in commercial facilities. The system comprises an information processing device, a data analysis device, a task management device, and a user interface. The system aims to automate routine tasks within stores, providing an environment where employees can focus on more important tasks.

[0035] The information processing unit of this system automatically patrols the store, acquiring information on cleanliness and product display status using cameras and sensors. The terminal transmits the captured data to a data analysis device. The server analyzes the transmitted image data and determines any abnormalities by comparing it to standards. For example, if a price tag is not properly installed, the server detects the abnormality and notifies the user. This notification allows employees to take appropriate action quickly.

[0036] The task management system automatically generates a task list based on daily or weekly tasks. The generated task list is distributed to terminals by the server, and the terminals display the list to the user. Users can check the displayed task list and manage their progress in real time. The terminals also support reliable task completion by reminding users of incomplete tasks and prompting them to take action.

[0037] Furthermore, this system aggregates performance data daily and generates the next sales strategy to focus on. The server delivers the analysis results to users via terminals. As a result, users can quickly implement sales strategies, further improving operational efficiency.

[0038] In serving customers, the terminal retrieves basic information about the customer, calculates the current waiting time, and provides guidance. This allows customers to receive service at the appropriate time. It also provides guidance for future visit reservations, further improving customer convenience. For example, by centrally managing the calculation of waiting times and guidance from the start of reception, the burden on employees is reduced and the quality of service is improved.

[0039] This system will streamline store operations, allowing employees to focus on more creative and important tasks.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal begins patrolling the store according to a schedule. It uses cameras and sensors to photograph the cleanliness and the condition of product displays, and collects data.

[0043] Step 2:

[0044] The device sends the acquired image data to the server. A secure communication protocol is used for transmission.

[0045] Step 3:

[0046] The server uses AI to analyze the received image data and determine if there are any anomalies by comparing it against a set of criteria. For example, it checks whether price tags are properly placed.

[0047] Step 4:

[0048] Based on the analysis results, the server generates an alert and sends a notification to the user if any anomalies are found. The notification will include points that need improvement.

[0049] Step 5:

[0050] The server generates a daily or weekly task list. This list is created based on historical task data and current sales targets.

[0051] Step 6:

[0052] The server distributes a generated list of tasks to the terminal, which then displays it to the user. The user then performs the tasks based on the displayed list.

[0053] Step 7:

[0054] The device monitors the progress and sends a reminder notification to the user if there are any incomplete tasks.

[0055] Step 8:

[0056] The server aggregates daily performance data and analyzes sales strategies. Generative AI is used to identify the next areas of focus.

[0057] Step 9:

[0058] The server delivers analysis results to the terminal, and the terminal presents sales strategy information to the user.

[0059] Step 10:

[0060] The terminal prompts customers to input their basic information and calculates the current waiting time. It then provides customers with appropriate waiting time information, streamlining the service delivery process.

[0061] (Example 1)

[0062] 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."

[0063] Inefficiencies and human errors in commercial facilities and other similar establishments are becoming a major problem, compounded by labor shortages and increased workloads. In particular, errors in product placement and pricing, as well as delays in promptly assisting visitors, negatively impact customer satisfaction. While efficient task management and rapid deployment of sales strategies are needed, traditional methods have their limitations. To address these challenges, automation of operations and visualization of information are essential.

[0064] 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.

[0065] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine an anomaly based on visual data acquired by the information processing unit, and a means for notifying the user if an anomaly occurs. This enables the automation of operations in commercial facilities and a rapid response in the event of an anomaly.

[0066] An "information processing unit" is a device that patrols within a commercial facility and acquires visual data and environmental information.

[0067] "Visual data" refers to image and video information acquired by the information processing unit, and it serves as the basic data for analyzing the situation inside the store.

[0068] A "data analysis unit" refers to a device or software equipped with algorithms and functions for analyzing acquired visual data and determining whether or not anomalies are present.

[0069] "Means for determining abnormalities" refers to a function that detects deviations from standards or misalignments in placement from visual data and issues warnings or corrective instructions as needed.

[0070] "Means of notifying the user" refers to an interface that has the function of providing warnings and corrective instructions via display or audio when an abnormality is detected.

[0071] A "task management unit" refers to a device or software that has the function of listing tasks within a facility and managing their progress in real time.

[0072] A "task list" refers to a list of necessary tasks generated by the business management unit, which makes it easier to plan and prioritize work.

[0073] "Performance information" refers to past sales data and records of business operations, and is used as data for analyzing sales strategies and other related matters.

[0074] "Visitors" refers to customers and client representatives who visit commercial facilities, and services are provided by measuring their basic information and waiting times.

[0075] A "price display card" refers to printed or digital displays that inform consumers of the price of a product and other information, and it is important to ensure that these are placed appropriately.

[0076] This invention relates to a system for streamlining the operation of commercial facilities and includes an information processing unit, a data analysis unit, a business management unit, terminal devices, and server devices. This system automates routine tasks within the facility, providing an environment where employees can focus on more important tasks.

[0077] The server analyzes the visual data acquired by the information processing unit using an image recognition algorithm. Specifically, the information processing unit constantly patrols the commercial facility, collecting data using high-resolution surveillance cameras and light sensors. This allows the server to determine whether the placement of products and price tags is accurate.

[0078] The terminal transmits data from the information processing unit to the data analysis unit, and the server uses this data to determine whether or not there are any anomalies. In this process, it can detect issues such as price tags being upside down or misaligned. After detecting an anomaly, the server immediately notifies the user and prompts them to take appropriate action. The user receives the notification on the terminal's display and can quickly verify and correct the issue on-site.

[0079] The task management unit automatically generates daily or weekly tasks and distributes them to terminals via the server. Users can use their terminals to view task lists and manage progress, thereby clarifying task priorities. The terminals periodically remind users of incomplete tasks to support task completion.

[0080] Furthermore, the server aggregates performance data to generate the next sales strategy and proposes it to the user. For example, if sales of a particular product are increasing, it will suggest marketing measures and inventory management adjustments related to that product.

[0081] For visitors, the terminal retrieves basic information, calculates waiting times, and provides guidance. For example, once a visitor completes check-in, a message such as "The waiting time is approximately 10 minutes" is displayed, allowing them to wait efficiently until their turn. This entire process reduces the burden on employees and leads to improved service quality.

[0082] Examples of prompts include, "Please describe a system that analyzes images acquired from in-store surveillance cameras and notifies of abnormal product placement." This allows the system to monitor various operational conditions within a commercial facility in real time, supporting efficient business operations.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server receives visual data sent from the information processing unit. High-resolution image data is provided as input, and this data is stored on the server as output. Specifically, the information processing unit automatically patrols the store and periodically captures images using connected cameras.

[0086] Step 2:

[0087] The server sends the received data to the data analysis unit for analysis. The input is visual data stored on the server, and the output is the result of anomaly detection. Specifically, the data analysis unit uses an AI model to perform image recognition and check the placement of products and the status of price tags.

[0088] Step 3:

[0089] When the server detects an anomaly from the analyzed data, it immediately notifies the user via the terminal. The input is information about the detected anomaly, and the output is a notification message displayed on the terminal. Specifically, a message such as "The price tag on shelf A is not in the correct position" will be displayed on the terminal's screen.

[0090] Step 4:

[0091] The terminal displays a list of tasks generated by the business management unit to the user, allowing them to check the progress. The input is a business list updated daily or weekly, and the output is this list displayed on the terminal. In practice, the user can select a task to view detailed information and its progress status.

[0092] Step 5:

[0093] Users update the progress of each task using their terminal and send feedback to the server. The input is the updated progress information, and the output is the data recorded on the server. Specifically, when a user marks a task as "completed," that information is immediately reflected on the server.

[0094] Step 6:

[0095] The server aggregates performance data, generates the next sales strategy, and distributes it to the terminal. Past sales data and business logs are used as input, and new sales strategies and adjustment proposals are provided as output. Specifically, the user may be presented with a suggestion such as "increase inventory of a specific product."

[0096] Step 7:

[0097] The terminal retrieves basic information about visitors, calculates waiting times, and provides guidance. Input is the registration information provided by the visitor, and output includes waiting times and guidance messages. For example, the terminal might display a message such as, "The waiting time until your next guidance is approximately 10 minutes."

[0098] (Application Example 1)

[0099] 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."

[0100] In commercial facilities, improving operational efficiency requires automating daily tasks and reducing the burden on employees. However, current systems struggle to monitor store conditions and respond quickly to anomalies, and employees are unable to focus on critical tasks. In particular, managing product displays, waiting times, and the rapid execution of sales strategies are problematic. Therefore, there is a need to solve these problems and improve the efficiency of store operations.

[0101] 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.

[0102] In this invention, the server includes a patrol means consisting of an information processing device, a means for a data analysis device to determine anomalies based on image data acquired by the information processing device, a means for notifying the user if an anomaly occurs, a means for a task management device to generate a list of tasks, a means for monitoring the progress of the list of tasks, a means for reminding the user of incomplete tasks, a means for aggregating performance data to analyze the next sales strategy to focus on, a means for acquiring basic information of customers, calculating waiting times, and providing guidance, a means for displaying work progress information in real time using a visual device, and a means for detecting anomalies in the display state using a motion analysis model. This makes it possible to constantly understand the situation inside the store, respond quickly to anomalies, and improve operational efficiency.

[0103] An "information processing device" is a combination of hardware and software that patrols within a commercial facility and collects specified data.

[0104] A "data analysis device" is a digital device that analyzes data acquired by an information processing device and compares it to a standard to determine if there are any anomalies.

[0105] "Means for determining anomalies" refers to methods for detecting anomalies based on collected data and against established criteria.

[0106] "Means of notification" refers to the means used to communicate abnormalities or important information to users.

[0107] A "task management device" is a device that can automatically organize and list tasks.

[0108] A "task list" is a list used to organize daily tasks and display them in an actionable format.

[0109] "Means of monitoring progress" refers to functions for tracking and managing how far each task has progressed.

[0110] A "reminder method" is a way of notifying users about incomplete tasks and prompting them to take action.

[0111] "Performance data" refers to data showing the results of past completed projects and sales.

[0112] A "visual device" is a digital device used to present visual information to a user.

[0113] A "motion analysis model" is an analytical model used to analyze the state in which products are displayed and to determine whether it is normal or abnormal.

[0114] A "sales strategy" is a set of measures or strategies planned to promote effective sales.

[0115] "Basic customer information" refers to basic customer data necessary for providing services, such as name and time of visit.

[0116] "Methods for calculating and guiding customers to wait times" refers to methods for calculating how long customers will have to wait and then informing them of the result.

[0117] The system for implementing the present invention is configured to link multiple devices in order to achieve efficient business operations within a commercial facility. A server controls an information processing device and patrols the store to acquire image data. This image data is transmitted to a data analysis device and analyzed using image processing software such as OpenCV. If an anomaly is detected as a result of the analysis, the server immediately notifies the user. The notification is made via a smartphone or smart glasses, enabling a quick response.

[0118] The task management system organizes daily tasks and generates a task list. This list is displayed in real time on the terminal, allowing the user to manage their progress. For incomplete tasks, the user is notified via a reminder function, prompting appropriate action.

[0119] Furthermore, the data analysis system formulates the next sales strategy to focus on based on performance data. Data analysis tools such as Pandas and Scikit-learn are used for the analysis, and the results are quickly delivered to the user through the user interface.

[0120] In obtaining basic customer information, the server receives the information from the customer via a terminal and calculates the waiting time. This allows customers to receive efficient service and makes it easier to book their next visit.

[0121] As a concrete example, when a supermarket manager wears smart glasses, they can perform their duties while checking for any abnormalities in product displays. If an abnormality is detected, a notification is displayed on the glasses, allowing for immediate action. Furthermore, they can visually check customer waiting times, enabling quicker customer service.

[0122] An example of a prompt message to input into a generative AI model might be, "Create the following task list. Which products should be prioritized for display today?"

[0123] This system significantly improves operational efficiency within commercial facilities, providing employees with an environment where they can focus on more creative tasks.

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The server controls the information processing unit to patrol the commercial facility. The camera on the information processing unit acquires image data from inside the store and transmits it to the server. The input is live video from inside the store, and the output is the digital image data received by the server.

[0127] Step 2:

[0128] The server sends the received image data to the data analysis device. The data analysis device uses OpenCV to analyze the image data and detect anomalies. In this process, the input is image data, and the output is an analysis result indicating whether or not anomalies are present. Specifically, it compares the product display state in the image with a reference image and detects differences.

[0129] Step 3:

[0130] The server receives anomaly results from the data analysis device. If an anomaly is detected, it notifies the user. The notification is sent via smart glasses or a smartphone, allowing the user to take immediate action. The input is the analysis result indicating the presence or absence of an anomaly, and the output is the notification to the user. Specific operations include generating and sending notification messages.

[0131] Step 4:

[0132] The task management system automatically generates a task list based on instructions from the server. The task list includes the tasks to be performed that day and is provided to the terminal in real time. Input is store operation schedule data, and output is the task list displayed on the user's screen.

[0133] Step 5:

[0134] The terminal monitors the progress of the task list generated by the task management device and reminds the user of incomplete tasks. The input is progress information of the task list, and the output is a reminder notification to the user.

[0135] Step 6:

[0136] The server aggregates performance data and generates the next sales strategy to focus on. This involves data analysis using Pandas and Scikit-learn. The input is historical sales data, and the output is a proposed sales strategy. The generated sales strategy is delivered to the user via a terminal.

[0137] Step 7:

[0138] The server retrieves customer information from the terminal, calculates the waiting time, and provides guidance. The input is the customer's basic information, and the output is the calculated waiting time. Guidance messages are conveyed to customers via terminals or smart devices. Specific operations include calculating the waiting time using an algorithm and displaying the result.

[0139] 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.

[0140] This invention is a system that improves operational efficiency in commercial facilities and enhances the customer and employee experience. The system comprises an information processing device, a data analysis device, a task management device, an emotion engine, and a user interface. The system aims to automate in-store operations while understanding the emotional state of individual users and providing appropriate responses accordingly.

[0141] The information processing unit in this system patrols the store, acquiring information on cleanliness and product display conditions using cameras and sensors. The terminal transmits the captured data to a data analysis device, where a server analyzes the image data. The server compares the data against standards to determine if there are any abnormalities and, if so, provides a notification to the user.

[0142] The task management system automatically generates a task list based on daily or weekly tasks and monitors progress. The task list is displayed to the user via a terminal, and a reminder function notifies them of incomplete tasks, supporting reliable task completion.

[0143] The emotion engine analyzes user voice and facial expression data in real time to estimate their emotional state. For example, if a customer is dissatisfied during a store visit, the emotion engine detects this and provides the user with instructions to improve customer service and attitude. It also dynamically changes the priority of task lists based on emotional data, adjusting them so that employees can perform their tasks with reduced stress.

[0144] The server manages these functions and analyzes the next sales strategy as needed. Using generative AI, it delivers the results to the user via their terminal and advises them on appropriate actions.

[0145] Furthermore, the terminal acquires basic information from customers, calculates waiting times, and provides guidance. The emotion engine analyzes customers' emotions and improves the experience through waiting time guidance and feedback during service provision. For example, it can detect dissatisfaction with long waiting times early and offer special countermeasures to provide a better consumer experience.

[0146] In this way, by incorporating an emotion engine, a system is built that not only improves the efficiency of existing operations but also enhances the experience for both customers and employees.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The terminal patrols the store, using cameras and sensors to collect data for inspecting cleanliness and product displays.

[0150] Step 2:

[0151] The terminal transmits acquired image data and sensor data to the server. The server then uses this data to understand the store's status in real time.

[0152] Step 3:

[0153] The server analyzes the transmitted data and determines anomalies based on the configured criteria. If an anomaly is detected, an alert is generated.

[0154] Step 4:

[0155] The server generates alerts and notifies the user via their terminal. This allows the user to respond quickly.

[0156] Step 5:

[0157] The task management system automatically generates a list of tasks periodically and distributes it to terminals via a server. The terminals display the list to the user, assisting with task planning.

[0158] Step 6:

[0159] The device monitors task progress and sends reminder notifications to the user if there are any incomplete tasks. This ensures smooth progress in work.

[0160] Step 7:

[0161] The emotion engine analyzes the user's voice input and facial expressions to determine their emotional state in real time.

[0162] Step 8:

[0163] The server uses the results of the emotion engine analysis to determine the appropriate action based on the user's state and notifies them via the terminal. For example, if a customer is feeling stressed, it will suggest countermeasures.

[0164] Step 9:

[0165] Customers operate a terminal and input their basic information, which is then used to acquire data. The terminal calculates the waiting time based on this data and provides guidance to the customer.

[0166] Step 10:

[0167] The server aggregates performance data and analyzes the next sales strategy, taking into account feedback from the emotion engine. The analysis results are provided to the user via their terminal, optimizing sales activities.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0170] Modern commercial facilities are required to simultaneously improve operational efficiency and enhance the customer and employee experience. However, traditional systems often struggle to achieve these elements in a single system, resulting in ineffective operation. Consequently, there is a problem of not being able to balance improved operational efficiency with maintaining customer satisfaction.

[0171] 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.

[0172] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine anomalies based on image information acquired by the information processing unit, and a means for analyzing the emotional state of users using an emotion estimation unit. This makes it possible not only to efficiently automate operations within commercial facilities but also to simultaneously improve the experience based on the emotional states of customers and employees.

[0173] An "information processing unit" is a general term for devices and functions that patrol and acquire information within a commercial facility, particularly those that use cameras and sensors to check cleaning status and display status.

[0174] A "data analysis unit" is a general term for devices and functions used to determine anomalies based on image information transmitted from the information processing unit.

[0175] A "task management unit" is a general term for devices and systems used to generate and manage daily or weekly task lists.

[0176] An "emotion estimation unit" is a general term for devices and functions that analyze a user's voice and facial expression data to estimate their emotional state.

[0177] A "generative AI system" refers to an entire system that uses artificial intelligence technology to provide guidance and recommend actions based on analyzed data.

[0178] A "terminal" refers to a device or equipment that allows a user to interact with a system through an interface.

[0179] A description of the embodiment for carrying out the invention will be provided.

[0180] This system, designed to improve operational efficiency and customer experience in commercial facilities, consists of an information processing unit, a data analysis unit, a work management unit, an emotion estimation unit, and terminals.

[0181] The server controls the information processing unit, which patrols the commercial facility and acquires information on cleaning status and product display conditions using cameras and sensors. This data is acquired as image information and then transmitted to the data analysis unit via a terminal. The data analysis unit determines anomalies based on the acquired image information. For example, it can automatically detect disarray on product shelves or areas where cleaning has not been completed.

[0182] The terminal has the function of displaying a list of tasks sent from the server to the user. The task list generated by this work management unit is structured based on daily and weekly tasks, and its progress is monitored in real time. The user is notified of incomplete tasks by the terminal's reminder function, which supports efficient work execution.

[0183] A server equipped with an emotion estimation unit analyzes voice and facial expression data acquired from the terminal to estimate the user's emotional state. This unit can, for example, detect if a customer is dissatisfied and, accordingly, provide instructions for improving customer service or offer special treatment. Furthermore, this system uses a generative AI model to analyze the next sales strategy and provides recommended actions to the user through the terminal.

[0184] As a concrete example, an example of a prompt message for a generative AI model is shown below.

[0185] "Please suggest ways to improve customer satisfaction in commercial facilities. For example, what measures can be taken to address customer dissatisfaction when waiting for long periods of time?"

[0186] In this way, by having each unit cooperate to efficiently manage operations and provide feedback that responds to users' emotions and behaviors, it is possible to simultaneously improve operational efficiency within commercial facilities and enhance customer satisfaction.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The server controls the information processing unit to patrol the commercial facility, using cameras and sensors to acquire image data of cleaning conditions and product display status. At this stage, the input is raw data obtained from sensors and cameras, and the output is image data that can be processed.

[0190] Step 2:

[0191] The terminal transmits image data acquired by the server to the data analysis unit. The server uses the data analysis unit based on the transmitted image data to determine anomalies. For example, it may perform specific actions such as detecting product defects. The input is image data, and the output is the anomaly detection result.

[0192] Step 3:

[0193] Based on the data analysis results, the server immediately notifies the user via the terminal if an anomaly occurs. The user then confirms the anomaly and takes specific actions to implement corrective procedures. The input is the detection result, and the output is the transmission of the anomaly notification.

[0194] Step 4:

[0195] The server uses a task management unit to generate daily and weekly task lists and displays them to the user via a terminal. The user then carries out tasks based on the displayed task list. The input is store schedule information, and the output is the task list.

[0196] Step 5:

[0197] The terminal monitors work progress based on user actions. For incomplete tasks, it triggers reminders and sends notifications to the user prompting them to complete the tasks. Input is user action information, and output is reminder notifications.

[0198] Step 6:

[0199] The server uses an emotion estimation unit to analyze the user's voice and facial expression data and estimate the user's emotional state. If a specific emotional state, such as dissatisfaction, is detected, the server provides feedback to the user to improve customer service or work processes. The input is voice and facial expression data, and the output is the emotion analysis result.

[0200] Step 7:

[0201] The server utilizes a generative AI model to analyze the next sales strategy to focus on and provides recommended actions to the user through the terminal. This process includes the analysis of sales data and consumer behavior patterns. The input is historical performance data, and the output is a proposed sales strategy.

[0202] Step 8:

[0203] The terminal acquires basic visitor information, sends it to the server to calculate waiting times, and provides appropriate guidance. An emotion estimation unit analyzes the visitor's emotional state and implements actions to provide a better customer experience. The input is visitor information, and the output is waiting time guidance.

[0204] In this way, the system aims to improve operational efficiency and enhance the user experience by appropriately processing input data at each step and obtaining output results.

[0205] (Application Example 2)

[0206] 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 device 14 will be referred to as the "terminal."

[0207] The challenge lies in solving two problems: improving customer satisfaction in stores and improving employee work efficiency. While conventional systems allow for automated task management, they lack the means to grasp customers' emotional states in real time and respond appropriately accordingly. As a result, it was difficult to achieve both improved customer experience and operational efficiency.

[0208] 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.

[0209] In this invention, the server includes an analysis means for estimating the emotional state of store users based on image and audio data, a means for notifying users of appropriate countermeasures based on the results of the analysis means, and a means for monitoring the situation within the store using a mobile information terminal carried by staff and optimizing the customer experience. This makes it possible to grasp the emotional state of customers in real time and automatically send appropriate countermeasures, thereby achieving both improved customer satisfaction and operational efficiency.

[0210] "Image and audio data" refers to video and audio information of customers and staff, and this data is used as the basis for detecting emotional states and abnormal situations by analyzing it.

[0211] "Analysis means" refers to technical elements that process and analyze image and audio data to determine and estimate the user's emotional state.

[0212] "Notification methods" refer to functions that convey appropriate information and instructions to staff and system users based on the analysis results.

[0213] A "personal information terminal" is a mobile device carried by staff to monitor the situation within a store and to acquire and utilize information.

[0214] "Means of optimizing the customer experience" refers to a collection of processes and functions that adjust services according to the user's emotional state and the situation within the store, thereby improving customer satisfaction.

[0215] "Means for determining abnormalities" refers to a function that detects unusual events by comparing information acquired by a data analysis device with established criteria.

[0216] "Means for generating task lists" refers to the process by which a task management system automatically lists daily or weekly tasks and provides instructions to staff.

[0217] "Means of monitoring progress" refers to a function that allows staff to track the status of their ongoing tasks based on a task list and update information as needed.

[0218] A "reminder mechanism" is a function that notifies staff about incomplete tasks and encourages them to complete those tasks.

[0219] "Means of analyzing sales strategies" refer to the processes and techniques for formulating the next sales strategy to focus on, based on performance data.

[0220] "Methods for calculating and providing information about waiting times" refers to a function that acquires customer information, calculates the waiting time until service is provided, and notifies the user.

[0221] The system implemented in this invention aims to improve operational efficiency and customer experience in commercial facilities. The main components of the system are a personal digital assistant (PDTA), a server, analysis means, and notification means. The PDTA is carried by staff and acquires images and audio data of the situation inside the store. The PDTA transmits this data to the server in real time.

[0222] The server is built using Python and Java (registered trademark) and features an emotion analysis engine utilizing TENSORFLOW (registered trademark). The server uses acquired image and audio data to estimate the customer's emotional state. Based on the analysis results, the server generates appropriate countermeasures and notifies staff members via their mobile devices. This process is implemented using an API based on Flask.

[0223] For example, if a customer shows signs of dissatisfaction in the store, the emotion analysis engine will immediately detect this and send a notification to the staff saying, "The customer is showing dissatisfaction. Please address this immediately."

[0224] An example of a prompt for a generative AI model is, "Please suggest some appropriate actions to take when a customer is feeling stressed in a crowded store."

[0225] In this way, the system analyzes customer emotions in real time and enables the provision of appropriate services, thereby improving customer satisfaction.

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The terminal collects image and audio data acquired within the store. A user (staff member) uses a mobile device to patrol the store, activating the camera and microphone to record customer and store conditions. The input for this step is actual video and audio of customers and merchandise shelves, while the output is digital image and audio files.

[0229] Step 2:

[0230] The device transmits the collected digital data to the server. The device uploads the acquired image and audio data to the server in real time via Wi-Fi or a mobile network. The input for this step is the image and audio files acquired in step 1, and the output is a file stored in the server-side database.

[0231] Step 3:

[0232] The server analyzes the transmitted image and audio data to estimate the customer's emotional state. The server uses TensorFlow to run an emotion analysis model and analyze the input data. The input is digital data, and the output is the estimated emotional state (e.g., satisfied, dissatisfied, angry). Specifically, the analysis results are output as a digital signal for use in the next step.

[0233] Step 4:

[0234] Based on the analysis results, the server generates and notifies the employee of a course of action. The AI ​​model on the server uses prompts to generate actions that the employee should take. The input is the emotional state obtained in step 3, and the output is specific service instructions (e.g., improve customer service attitude, offer a discount, etc.). At this time, the server sends a notification to the staff member's terminal via a Flask-based API.

[0235] Step 5:

[0236] The terminal receives notifications from the server and displays them to the staff. The user (staff) checks the notification on the mobile device and modifies their actions according to the instructions displayed on the screen. The input is the notification information from the server, and the output is the specific action that the staff will take. This operation is a crucial step in improving the customer experience within the store.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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".

[0253] This invention relates to a system for improving operational efficiency in commercial facilities. The system comprises an information processing device, a data analysis device, a task management device, and a user interface. The system aims to automate routine tasks within stores, providing an environment where employees can focus on more important tasks.

[0254] The information processing unit of this system automatically patrols the store, acquiring information on cleanliness and product display status using cameras and sensors. The terminal transmits the captured data to a data analysis device. The server analyzes the transmitted image data and determines any abnormalities by comparing it to standards. For example, if a price tag is not properly installed, the server detects the abnormality and notifies the user. This notification allows employees to take appropriate action quickly.

[0255] The task management system automatically generates a task list based on daily or weekly tasks. The generated task list is distributed to terminals by the server, and the terminals display the list to the user. Users can check the displayed task list and manage their progress in real time. The terminals also support reliable task completion by reminding users of incomplete tasks and prompting them to take action.

[0256] Furthermore, this system aggregates performance data daily and generates the next sales strategy to focus on. The server delivers the analysis results to users via terminals. As a result, users can quickly implement sales strategies, further improving operational efficiency.

[0257] In serving customers, the terminal retrieves basic information about the customer, calculates the current waiting time, and provides guidance. This allows customers to receive service at the appropriate time. It also provides guidance for future visit reservations, further improving customer convenience. For example, by centrally managing the calculation of waiting times and guidance from the start of reception, the burden on employees is reduced and the quality of service is improved.

[0258] This system will streamline store operations, allowing employees to focus on more creative and important tasks.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The terminal begins patrolling the store according to a schedule. It uses cameras and sensors to photograph the cleanliness and the condition of product displays, and collects data.

[0262] Step 2:

[0263] The device sends the acquired image data to the server. A secure communication protocol is used for transmission.

[0264] Step 3:

[0265] The server uses AI to analyze the received image data and determine if there are any anomalies by comparing it against a set of criteria. For example, it checks whether price tags are properly placed.

[0266] Step 4:

[0267] Based on the analysis results, the server generates an alert and sends a notification to the user if any anomalies are found. The notification will include points that need improvement.

[0268] Step 5:

[0269] The server generates a daily or weekly task list. This list is created based on historical task data and current sales targets.

[0270] Step 6:

[0271] The server distributes a generated list of tasks to the terminal, which then displays it to the user. The user then performs the tasks based on the displayed list.

[0272] Step 7:

[0273] The device monitors the progress and sends a reminder notification to the user if there are any incomplete tasks.

[0274] Step 8:

[0275] The server aggregates daily performance data and analyzes the sales strategy. Using generative AI, it identifies the points to focus on next.

[0276] Step 9:

[0277] The server distributes the analysis results to the terminal, and the terminal presents the sales strategy information to the user.

[0278] Step 10:

[0279] The terminal makes the incoming customer input their basic information and calculates the current waiting time. It provides appropriate waiting time guidance to the incoming customers to improve the efficiency of service delivery.

[0280] (Example 1)

[0281] Next, 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".

[0282] The decline in business efficiency and human errors in commercial facilities and the like have become a major problem in combination with a shortage of manpower and an increase in the volume of business. In particular, mistakes in product placement and price display, and delays in promptly responding to visitors are having an adverse impact on customer satisfaction. Also, efficient task management and rapid deployment of sales strategies are required, but there are limitations with conventional methods. To solve these problems, automation of operations and visualization of information are necessary.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0284] In this invention, the server includes a patrol means composed of an information processing unit, a means for the data analysis unit to judge an abnormality based on the visual data acquired by the information processing unit, and a means for notifying the user when there is an abnormality. Thereby, automation of operations in commercial facilities and prompt response at the time of occurrence of an abnormality become possible.

[0285] The "information processing unit" is a device that patrols inside a commercial facility and acquires visual data and environmental information.

[0286] "Visual data" refers to information of images and videos acquired by the information processing unit, and is basic data for analyzing the situation inside the store based on this.

[0287] The "data analysis unit" refers to a device or software equipped with algorithms and functions for analyzing the acquired visual data and determining the presence or absence of abnormalities.

[0288] The "means for determining an abnormality" refers to a function that detects a state deviating from a standard or a misalignment in arrangement from the visual data and issues a warning or a correction instruction as necessary.

[0289] The "means for notifying a user" refers to an interface equipped with a function for providing a warning or amendment content by display or voice when an abnormality is detected.

[0290] The "business management unit" refers to a device or software equipped with a function for listing tasks inside the facility and managing the progress in real time.

[0291] The "task list" refers to a list compiled by the business management unit that enumerates necessary tasks, which facilitates work planning and prioritization.

[0292] "Performance information" refers to past sales data and records of business performance, and is data used for analyzing sales strategies and the like based on this.

[0293] "Visitors" refer to customers and client representatives who visit a commercial facility, and services are provided by measuring their basic information and waiting time.

[0294] A "price display card" refers to printed or digital displays that inform consumers of the price of a product and other information, and it is important to ensure that these are placed appropriately.

[0295] This invention relates to a system for streamlining the operation of commercial facilities and includes an information processing unit, a data analysis unit, a business management unit, terminal devices, and server devices. This system automates routine tasks within the facility, providing an environment where employees can focus on more important tasks.

[0296] The server analyzes the visual data acquired by the information processing unit using an image recognition algorithm. Specifically, the information processing unit constantly patrols the commercial facility, collecting data using high-resolution surveillance cameras and light sensors. This allows the server to determine whether the placement of products and price tags is accurate.

[0297] The terminal transmits data from the information processing unit to the data analysis unit, and the server uses this data to determine whether or not there are any anomalies. In this process, it can detect issues such as price tags being upside down or misaligned. After detecting an anomaly, the server immediately notifies the user and prompts them to take appropriate action. The user receives the notification on the terminal's display and can quickly verify and correct the issue on-site.

[0298] The task management unit automatically generates daily or weekly tasks and distributes them to terminals via the server. Users can use their terminals to view task lists and manage progress, thereby clarifying task priorities. The terminals periodically remind users of incomplete tasks to support task completion.

[0299] Furthermore, the server aggregates performance data to generate the next sales strategy and proposes it to the user. For example, if sales of a particular product are increasing, it will suggest marketing measures and inventory management adjustments related to that product.

[0300] For visitors, the terminal acquires basic information, calculates the waiting time, and guides them. For example, when a visitor completes the reception, a message such as "The waiting time is about 10 minutes" is displayed on the spot, enabling efficient waiting until their turn. The entire process reduces the burden on employees and leads to an improvement in service quality.

[0301] Examples of prompt sentences include "Please explain the system that analyzes the images acquired by the in-store surveillance cameras and notifies of abnormal product arrangements." This enables the system to monitor various operating situations within the commercial facility in real time and support efficient business operations.

[0302] The flow of the specific process in Example 1 will be described using FIG. 11.

[0303] Step 1:

[0304] The server receives the visual data sent from the information processing unit. As input, high-resolution image data is provided, and as output, this data is stored in the server. As a specific operation, the information processing unit automatically patrols the store and periodically captures images with the connected cameras.

[0305] Step 2:

[0306] The server sends the received data to the data analysis unit for analysis. The input is the visual data stored in the server, and as output, the results of anomaly detection are generated. In a specific operation, the data analysis unit performs image recognition using an AI model and checks the product arrangement and the status of price display cards.

[0307] Step 3:

[0308] When the server detects an anomaly from the analyzed data, it immediately notifies the user via the terminal. The input is information about the detected anomaly, and the output is a notification message displayed on the terminal. Specifically, a message such as "The price tag on shelf A is not in the correct position" will be displayed on the terminal's screen.

[0309] Step 4:

[0310] The terminal displays a list of tasks generated by the business management unit to the user, allowing them to check the progress. The input is a business list updated daily or weekly, and the output is this list displayed on the terminal. In practice, the user can select a task to view detailed information and its progress status.

[0311] Step 5:

[0312] Users update the progress of each task using their terminal and send feedback to the server. The input is the updated progress information, and the output is the data recorded on the server. Specifically, when a user marks a task as "completed," that information is immediately reflected on the server.

[0313] Step 6:

[0314] The server aggregates performance data, generates the next sales strategy, and distributes it to the terminal. Past sales data and business logs are used as input, and new sales strategies and adjustment proposals are provided as output. Specifically, the user may be presented with a suggestion such as "increase inventory of a specific product."

[0315] Step 7:

[0316] The terminal retrieves basic information about visitors, calculates waiting times, and provides guidance. Input is the registration information provided by the visitor, and output includes waiting times and guidance messages. For example, the terminal might display a message such as, "The waiting time until your next guidance is approximately 10 minutes."

[0317] (Application Example 1)

[0318] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0319] In commercial facilities, improving operational efficiency requires automating daily tasks and reducing the burden on employees. However, current systems struggle to monitor store conditions and respond quickly to anomalies, and employees are unable to focus on critical tasks. In particular, managing product displays, waiting times, and the rapid execution of sales strategies are problematic. Therefore, there is a need to solve these problems and improve the efficiency of store operations.

[0320] 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.

[0321] In this invention, the server includes a patrol means consisting of an information processing device, a means for a data analysis device to determine anomalies based on image data acquired by the information processing device, a means for notifying the user if an anomaly occurs, a means for a task management device to generate a list of tasks, a means for monitoring the progress of the list of tasks, a means for reminding the user of incomplete tasks, a means for aggregating performance data to analyze the next sales strategy to focus on, a means for acquiring basic information of customers, calculating waiting times, and providing guidance, a means for displaying work progress information in real time using a visual device, and a means for detecting anomalies in the display state using a motion analysis model. This makes it possible to constantly understand the situation inside the store, respond quickly to anomalies, and improve operational efficiency.

[0322] An "information processing device" is a combination of hardware and software that patrols within a commercial facility and collects specified data.

[0323] A "data analysis device" is a digital device that analyzes data acquired by an information processing device and compares it to a standard to determine if there are any anomalies.

[0324] "Means for determining anomalies" refers to methods for detecting anomalies based on collected data and against established criteria.

[0325] "Means of notification" refers to the means used to communicate abnormalities or important information to users.

[0326] A "task management device" is a device that can automatically organize and list tasks.

[0327] A "task list" is a list used to organize daily tasks and display them in an actionable format.

[0328] "Means of monitoring progress" refers to functions for tracking and managing how far each task has progressed.

[0329] A "reminder method" is a way of notifying users about incomplete tasks and prompting them to take action.

[0330] "Performance data" refers to data showing the results of past completed projects and sales.

[0331] A "visual device" is a digital device used to present visual information to a user.

[0332] A "motion analysis model" is an analytical model used to analyze the state in which products are displayed and to determine whether it is normal or abnormal.

[0333] A "sales strategy" is a set of measures or strategies planned to promote effective sales.

[0334] "Basic customer information" refers to basic customer data necessary for providing services, such as name and time of visit.

[0335] "Methods for calculating and guiding customers to wait times" refers to methods for calculating how long customers will have to wait and then informing them of the result.

[0336] The system for implementing the present invention is configured to link multiple devices in order to achieve efficient business operations within a commercial facility. A server controls an information processing device and patrols the store to acquire image data. This image data is transmitted to a data analysis device and analyzed using image processing software such as OpenCV. If an anomaly is detected as a result of the analysis, the server immediately notifies the user. The notification is made via a smartphone or smart glasses, enabling a quick response.

[0337] The task management system organizes daily tasks and generates a task list. This list is displayed in real time on the terminal, allowing the user to manage their progress. For incomplete tasks, the user is notified via a reminder function, prompting appropriate action.

[0338] Furthermore, the data analysis system formulates the next sales strategy to focus on based on performance data. Data analysis tools such as Pandas and Scikit-learn are used for the analysis, and the results are quickly delivered to the user through the user interface.

[0339] In obtaining basic customer information, the server receives the information from the customer via a terminal and calculates the waiting time. This allows customers to receive efficient service and makes it easier to book their next visit.

[0340] As a concrete example, when a supermarket manager wears smart glasses, they can perform their duties while checking for any abnormalities in product displays. If an abnormality is detected, a notification is displayed on the glasses, allowing for immediate action. Furthermore, they can visually check customer waiting times, enabling quicker customer service.

[0341] An example of a prompt message to input into a generative AI model might be, "Create the following task list. Which products should be prioritized for display today?"

[0342] This system significantly improves operational efficiency within commercial facilities, providing employees with an environment where they can focus on more creative tasks.

[0343] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0344] Step 1:

[0345] The server controls the information processing unit to patrol the commercial facility. The camera on the information processing unit acquires image data from inside the store and transmits it to the server. The input is live video from inside the store, and the output is the digital image data received by the server.

[0346] Step 2:

[0347] The server sends the received image data to the data analysis device. The data analysis device uses OpenCV to analyze the image data and detect anomalies. In this process, the input is image data, and the output is an analysis result indicating whether or not anomalies are present. Specifically, it compares the product display state in the image with a reference image and detects differences.

[0348] Step 3:

[0349] The server receives anomaly results from the data analysis device. If an anomaly is detected, it notifies the user. The notification is sent via smart glasses or a smartphone, allowing the user to take immediate action. The input is the analysis result indicating the presence or absence of an anomaly, and the output is the notification to the user. Specific operations include generating and sending notification messages.

[0350] Step 4:

[0351] The task management system automatically generates a task list based on instructions from the server. The task list includes the tasks to be performed that day and is provided to the terminal in real time. Input is store operation schedule data, and output is the task list displayed on the user's screen.

[0352] Step 5:

[0353] The terminal monitors the progress of the task list generated by the task management device and reminds the user of incomplete tasks. The input is progress information of the task list, and the output is a reminder notification to the user.

[0354] Step 6:

[0355] The server aggregates performance data and generates the next sales strategy to focus on. This involves data analysis using Pandas and Scikit-learn. The input is historical sales data, and the output is a proposed sales strategy. The generated sales strategy is delivered to the user via a terminal.

[0356] Step 7:

[0357] The server retrieves customer information from the terminal, calculates the waiting time, and provides guidance. The input is the customer's basic information, and the output is the calculated waiting time. Guidance messages are conveyed to customers via terminals or smart devices. Specific operations include calculating the waiting time using an algorithm and displaying the result.

[0358] 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.

[0359] This invention is a system that improves operational efficiency in commercial facilities and enhances the customer and employee experience. The system comprises an information processing device, a data analysis device, a task management device, an emotion engine, and a user interface. The system aims to automate in-store operations while understanding the emotional state of individual users and providing appropriate responses accordingly.

[0360] The information processing unit in this system patrols the store, acquiring information on cleanliness and product display conditions using cameras and sensors. The terminal transmits the captured data to a data analysis device, where a server analyzes the image data. The server compares the data against standards to determine if there are any abnormalities and, if so, provides a notification to the user.

[0361] The task management system automatically generates a task list based on daily or weekly tasks and monitors progress. The task list is displayed to the user via a terminal, and a reminder function notifies them of incomplete tasks, supporting reliable task completion.

[0362] The emotion engine analyzes user voice and facial expression data in real time to estimate their emotional state. For example, if a customer is dissatisfied during a store visit, the emotion engine detects this and provides the user with instructions to improve customer service and attitude. It also dynamically changes the priority of task lists based on emotional data, adjusting them so that employees can perform their tasks with reduced stress.

[0363] The server manages these functions and analyzes the next sales strategy as needed. Using generative AI, it delivers the results to the user via their terminal and advises them on appropriate actions.

[0364] Furthermore, the terminal acquires basic information from customers, calculates waiting times, and provides guidance. The emotion engine analyzes customers' emotions and improves the experience through waiting time guidance and feedback during service provision. For example, it can detect dissatisfaction with long waiting times early and offer special countermeasures to provide a better consumer experience.

[0365] In this way, by incorporating an emotion engine, a system is built that not only improves the efficiency of existing operations but also enhances the experience for both customers and employees.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The terminal patrols the store, using cameras and sensors to collect data for inspecting cleanliness and product displays.

[0369] Step 2:

[0370] The terminal transmits acquired image data and sensor data to the server. The server then uses this data to understand the store's status in real time.

[0371] Step 3:

[0372] The server analyzes the transmitted data and determines anomalies based on the configured criteria. If an anomaly is detected, an alert is generated.

[0373] Step 4:

[0374] The server generates alerts and notifies the user via their terminal. This allows the user to respond quickly.

[0375] Step 5:

[0376] The task management system automatically generates a list of tasks periodically and distributes it to terminals via a server. The terminals display the list to the user, assisting with task planning.

[0377] Step 6:

[0378] The device monitors task progress and sends reminder notifications to the user if there are any incomplete tasks. This ensures smooth progress in work.

[0379] Step 7:

[0380] The emotion engine analyzes the user's voice input and facial expressions to determine their emotional state in real time.

[0381] Step 8:

[0382] The server uses the results of the emotion engine analysis to determine the appropriate action based on the user's state and notifies them via the terminal. For example, if a customer is feeling stressed, it will suggest countermeasures.

[0383] Step 9:

[0384] Customers operate a terminal and input their basic information, which is then used to acquire data. The terminal calculates the waiting time based on this data and provides guidance to the customer.

[0385] Step 10:

[0386] The server aggregates performance data and analyzes the next sales strategy, taking into account feedback from the emotion engine. The analysis results are provided to the user via their terminal, optimizing sales activities.

[0387] (Example 2)

[0388] 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".

[0389] Modern commercial facilities are required to simultaneously improve operational efficiency and enhance the customer and employee experience. However, traditional systems often struggle to achieve these elements in a single system, resulting in ineffective operation. Consequently, there is a problem of not being able to balance improved operational efficiency with maintaining customer satisfaction.

[0390] 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.

[0391] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine anomalies based on image information acquired by the information processing unit, and a means for analyzing the emotional state of users using an emotion estimation unit. This makes it possible not only to efficiently automate operations within commercial facilities but also to simultaneously improve the experience based on the emotional states of customers and employees.

[0392] An "information processing unit" is a general term for devices and functions that patrol and acquire information within a commercial facility, particularly those that use cameras and sensors to check cleaning status and display status.

[0393] A "data analysis unit" is a general term for devices and functions used to determine anomalies based on image information transmitted from the information processing unit.

[0394] A "task management unit" is a general term for devices and systems used to generate and manage daily or weekly task lists.

[0395] An "emotion estimation unit" is a general term for devices and functions that analyze a user's voice and facial expression data to estimate their emotional state.

[0396] A "generative AI system" refers to an entire system that uses artificial intelligence technology to provide guidance and recommend actions based on analyzed data.

[0397] A "terminal" refers to a device or equipment that allows a user to interact with a system through an interface.

[0398] A description of the embodiment for carrying out the invention will be provided.

[0399] This system, designed to improve operational efficiency and customer experience in commercial facilities, consists of an information processing unit, a data analysis unit, a work management unit, an emotion estimation unit, and terminals.

[0400] The server controls the information processing unit, which patrols the commercial facility and acquires information on cleaning status and product display conditions using cameras and sensors. This data is acquired as image information and then transmitted to the data analysis unit via a terminal. The data analysis unit determines anomalies based on the acquired image information. For example, it can automatically detect disarray on product shelves or areas where cleaning has not been completed.

[0401] The terminal has the function of displaying a list of tasks sent from the server to the user. The task list generated by this work management unit is structured based on daily and weekly tasks, and its progress is monitored in real time. The user is notified of incomplete tasks by the terminal's reminder function, which supports efficient work execution.

[0402] A server equipped with an emotion estimation unit analyzes voice and facial expression data acquired from the terminal to estimate the user's emotional state. This unit can, for example, detect if a customer is dissatisfied and, accordingly, provide instructions for improving customer service or offer special treatment. Furthermore, this system uses a generative AI model to analyze the next sales strategy and provides recommended actions to the user through the terminal.

[0403] As a concrete example, an example of a prompt message for a generative AI model is shown below.

[0404] "Please suggest ways to improve customer satisfaction in commercial facilities. For example, what measures can be taken to address customer dissatisfaction when waiting for long periods of time?"

[0405] In this way, by having each unit cooperate to efficiently manage operations and provide feedback that responds to users' emotions and behaviors, it is possible to simultaneously improve operational efficiency within commercial facilities and enhance customer satisfaction.

[0406] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0407] Step 1:

[0408] The server controls the information processing unit to patrol the commercial facility, using cameras and sensors to acquire image data of cleaning conditions and product display status. At this stage, the input is raw data obtained from sensors and cameras, and the output is image data that can be processed.

[0409] Step 2:

[0410] The terminal transmits image data acquired by the server to the data analysis unit. The server uses the data analysis unit based on the transmitted image data to determine anomalies. For example, it may perform specific actions such as detecting product defects. The input is image data, and the output is the anomaly detection result.

[0411] Step 3:

[0412] Based on the data analysis results, the server immediately notifies the user via the terminal if an anomaly occurs. The user then confirms the anomaly and takes specific actions to implement corrective procedures. The input is the detection result, and the output is the transmission of the anomaly notification.

[0413] Step 4:

[0414] The server uses a task management unit to generate daily and weekly task lists and displays them to the user via a terminal. The user then carries out tasks based on the displayed task list. The input is store schedule information, and the output is the task list.

[0415] Step 5:

[0416] The terminal monitors work progress based on user actions. For incomplete tasks, it triggers reminders and sends notifications to the user prompting them to complete the tasks. Input is user action information, and output is reminder notifications.

[0417] Step 6:

[0418] The server uses an emotion estimation unit to analyze the user's voice and facial expression data and estimate the user's emotional state. If a specific emotional state, such as dissatisfaction, is detected, the server provides feedback to the user to improve customer service or work processes. The input is voice and facial expression data, and the output is the emotion analysis result.

[0419] Step 7:

[0420] The server utilizes a generative AI model to analyze the next sales strategy to focus on and provides recommended actions to the user through the terminal. This process includes the analysis of sales data and consumer behavior patterns. The input is historical performance data, and the output is a proposed sales strategy.

[0421] Step 8:

[0422] The terminal acquires basic visitor information, sends it to the server to calculate waiting times, and provides appropriate guidance. An emotion estimation unit analyzes the visitor's emotional state and implements actions to provide a better customer experience. The input is visitor information, and the output is waiting time guidance.

[0423] In this way, the system aims to improve operational efficiency and enhance the user experience by appropriately processing input data at each step and obtaining output results.

[0424] (Application Example 2)

[0425] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0426] The challenge lies in solving two problems: improving customer satisfaction in stores and improving employee work efficiency. While conventional systems allow for automated task management, they lack the means to grasp customers' emotional states in real time and respond appropriately accordingly. As a result, it was difficult to achieve both improved customer experience and operational efficiency.

[0427] 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.

[0428] In this invention, the server includes an analysis means for estimating the emotional state of store users based on image and audio data, a means for notifying users of appropriate countermeasures based on the results of the analysis means, and a means for monitoring the situation within the store using a mobile information terminal carried by staff and optimizing the customer experience. This makes it possible to grasp the emotional state of customers in real time and automatically send appropriate countermeasures, thereby achieving both improved customer satisfaction and operational efficiency.

[0429] "Image and audio data" refers to video and audio information of customers and staff, and this data is used as the basis for detecting emotional states and abnormal situations by analyzing it.

[0430] "Analysis means" refers to technical elements that process and analyze image and audio data to determine and estimate the user's emotional state.

[0431] "Notification methods" refer to functions that convey appropriate information and instructions to staff and system users based on the analysis results.

[0432] A "personal information terminal" is a mobile device carried by staff to monitor the situation within a store and to acquire and utilize information.

[0433] "Means of optimizing the customer experience" refers to a collection of processes and functions that adjust services according to the user's emotional state and the situation within the store, thereby improving customer satisfaction.

[0434] "Means for determining abnormalities" refers to a function that detects unusual events by comparing information acquired by a data analysis device with established criteria.

[0435] "Means for generating task lists" refers to the process by which a task management system automatically lists daily or weekly tasks and provides instructions to staff.

[0436] "Means of monitoring progress" refers to a function that allows staff to track the status of their ongoing tasks based on a task list and update information as needed.

[0437] A "reminder mechanism" is a function that notifies staff about incomplete tasks and encourages them to complete those tasks.

[0438] "Means of analyzing sales strategies" refer to the processes and techniques for formulating the next sales strategy to focus on, based on performance data.

[0439] "Methods for calculating and providing information about waiting times" refers to a function that acquires customer information, calculates the waiting time until service is provided, and notifies the user.

[0440] The system implemented in this invention aims to improve operational efficiency and customer experience in commercial facilities. The main components of the system are a personal digital assistant (PDTA), a server, analysis means, and notification means. The PDTA is carried by staff and acquires images and audio data of the situation inside the store. The PDTA transmits this data to the server in real time.

[0441] The server is built using Python or Java and features an emotion analysis engine powered by TensorFlow. The server uses acquired image and audio data to estimate the customer's emotional state. Based on the analysis results, the server generates appropriate countermeasures and notifies staff members via their mobile devices. This process is implemented using an API based on Flask.

[0442] For example, if a customer shows signs of dissatisfaction in the store, the emotion analysis engine will immediately detect this and send a notification to the staff saying, "The customer is showing dissatisfaction. Please address this immediately."

[0443] An example of a prompt for a generative AI model is, "Please suggest some appropriate actions to take when a customer is feeling stressed in a crowded store."

[0444] In this way, the system analyzes customer emotions in real time and enables the provision of appropriate services, thereby improving customer satisfaction.

[0445] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0446] Step 1:

[0447] The terminal collects image and audio data acquired within the store. A user (staff member) uses a mobile device to patrol the store, activating the camera and microphone to record customer and store conditions. The input for this step is actual video and audio of customers and merchandise shelves, while the output is digital image and audio files.

[0448] Step 2:

[0449] The device transmits the collected digital data to the server. The device uploads the acquired image and audio data to the server in real time via Wi-Fi or a mobile network. The input for this step is the image and audio files acquired in step 1, and the output is a file stored in the server-side database.

[0450] Step 3:

[0451] The server analyzes the transmitted image and audio data to estimate the customer's emotional state. The server uses TensorFlow to run an emotion analysis model and analyze the input data. The input is digital data, and the output is the estimated emotional state (e.g., satisfied, dissatisfied, angry). Specifically, the analysis results are output as a digital signal for use in the next step.

[0452] Step 4:

[0453] Based on the analysis results, the server generates and notifies the employee of a course of action. The AI ​​model on the server uses prompts to generate actions that the employee should take. The input is the emotional state obtained in step 3, and the output is specific service instructions (e.g., improve customer service attitude, offer a discount, etc.). At this time, the server sends a notification to the staff member's terminal via a Flask-based API.

[0454] Step 5:

[0455] The terminal receives notifications from the server and displays them to the staff. The user (staff) checks the notification on the mobile device and modifies their actions according to the instructions displayed on the screen. The input is the notification information from the server, and the output is the specific action that the staff will take. This operation is a crucial step in improving the customer experience within the store.

[0456] 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.

[0457] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0458] 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.

[0459] [Third Embodiment]

[0460] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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".

[0472] This invention relates to a system for improving operational efficiency in commercial facilities. The system comprises an information processing device, a data analysis device, a task management device, and a user interface. The system aims to automate routine tasks within stores, providing an environment where employees can focus on more important tasks.

[0473] The information processing unit of this system automatically patrols the store, acquiring information on cleanliness and product display status using cameras and sensors. The terminal transmits the captured data to a data analysis device. The server analyzes the transmitted image data and determines any abnormalities by comparing it to standards. For example, if a price tag is not properly installed, the server detects the abnormality and notifies the user. This notification allows employees to take appropriate action quickly.

[0474] The task management system automatically generates a task list based on daily or weekly tasks. The generated task list is distributed to terminals by the server, and the terminals display the list to the user. Users can check the displayed task list and manage their progress in real time. The terminals also support reliable task completion by reminding users of incomplete tasks and prompting them to take action.

[0475] Furthermore, this system aggregates performance data daily and generates the next sales strategy to focus on. The server delivers the analysis results to users via terminals. As a result, users can quickly implement sales strategies, further improving operational efficiency.

[0476] In serving customers, the terminal retrieves basic information about the customer, calculates the current waiting time, and provides guidance. This allows customers to receive service at the appropriate time. It also provides guidance for future visit reservations, further improving customer convenience. For example, by centrally managing the calculation of waiting times and guidance from the start of reception, the burden on employees is reduced and the quality of service is improved.

[0477] This system will streamline store operations, allowing employees to focus on more creative and important tasks.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The terminal begins patrolling the store according to a schedule. It uses cameras and sensors to photograph the cleanliness and the condition of product displays, and collects data.

[0481] Step 2:

[0482] The device sends the acquired image data to the server. A secure communication protocol is used for transmission.

[0483] Step 3:

[0484] The server uses AI to analyze the received image data and determine if there are any anomalies by comparing it against a set of criteria. For example, it checks whether price tags are properly placed.

[0485] Step 4:

[0486] Based on the analysis results, the server generates an alert and sends a notification to the user if any anomalies are found. The notification will include points that need improvement.

[0487] Step 5:

[0488] The server generates a daily or weekly task list. This list is created based on historical task data and current sales targets.

[0489] Step 6:

[0490] The server distributes a generated list of tasks to the terminal, which then displays it to the user. The user then performs the tasks based on the displayed list.

[0491] Step 7:

[0492] The device monitors the progress and sends a reminder notification to the user if there are any incomplete tasks.

[0493] Step 8:

[0494] The server aggregates daily performance data and analyzes sales strategies. Generative AI is used to identify the next areas of focus.

[0495] Step 9:

[0496] The server delivers analysis results to the terminal, and the terminal presents sales strategy information to the user.

[0497] Step 10:

[0498] The terminal prompts customers to input their basic information and calculates the current waiting time. It then provides customers with appropriate waiting time information, streamlining the service delivery process.

[0499] (Example 1)

[0500] 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."

[0501] Inefficiencies and human errors in commercial facilities and other similar establishments are becoming a major problem, compounded by labor shortages and increased workloads. In particular, errors in product placement and pricing, as well as delays in promptly assisting visitors, negatively impact customer satisfaction. While efficient task management and rapid deployment of sales strategies are needed, traditional methods have their limitations. To address these challenges, automation of operations and visualization of information are essential.

[0502] 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.

[0503] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine an anomaly based on visual data acquired by the information processing unit, and a means for notifying the user if an anomaly occurs. This enables the automation of operations in commercial facilities and a rapid response in the event of an anomaly.

[0504] An "information processing unit" is a device that patrols within a commercial facility and acquires visual data and environmental information.

[0505] "Visual data" refers to image and video information acquired by the information processing unit, and it serves as the basic data for analyzing the situation inside the store.

[0506] A "data analysis unit" refers to a device or software equipped with algorithms and functions for analyzing acquired visual data and determining whether or not anomalies are present.

[0507] "Means for determining abnormalities" refers to a function that detects deviations from standards or misalignments in placement from visual data and issues warnings or corrective instructions as needed.

[0508] "Means of notifying the user" refers to an interface that has the function of providing warnings and corrective instructions via display or audio when an abnormality is detected.

[0509] A "task management unit" refers to a device or software that has the function of listing tasks within a facility and managing their progress in real time.

[0510] A "task list" refers to a list of necessary tasks generated by the business management unit, which makes it easier to plan and prioritize work.

[0511] "Performance information" refers to past sales data and records of business operations, and is used as data for analyzing sales strategies and other related matters.

[0512] "Visitors" refers to customers and client representatives who visit commercial facilities, and services are provided by measuring their basic information and waiting times.

[0513] A "price display card" refers to printed or digital displays that inform consumers of the price of a product and other information, and it is important to ensure that these are placed appropriately.

[0514] This invention relates to a system for streamlining the operation of commercial facilities and includes an information processing unit, a data analysis unit, a business management unit, terminal devices, and server devices. This system automates routine tasks within the facility, providing an environment where employees can focus on more important tasks.

[0515] The server analyzes the visual data acquired by the information processing unit using an image recognition algorithm. Specifically, the information processing unit constantly patrols the commercial facility, collecting data using high-resolution surveillance cameras and light sensors. This allows the server to determine whether the placement of products and price tags is accurate.

[0516] The terminal transmits data from the information processing unit to the data analysis unit, and the server uses this data to determine whether or not there are any anomalies. In this process, it can detect issues such as price tags being upside down or misaligned. After detecting an anomaly, the server immediately notifies the user and prompts them to take appropriate action. The user receives the notification on the terminal's display and can quickly verify and correct the issue on-site.

[0517] The task management unit automatically generates daily or weekly tasks and distributes them to terminals via the server. Users can use their terminals to view task lists and manage progress, thereby clarifying task priorities. The terminals periodically remind users of incomplete tasks to support task completion.

[0518] Furthermore, the server aggregates performance data to generate the next sales strategy and proposes it to the user. For example, if sales of a particular product are increasing, it will suggest marketing measures and inventory management adjustments related to that product.

[0519] For visitors, the terminal retrieves basic information, calculates waiting times, and provides guidance. For example, once a visitor completes check-in, a message such as "The waiting time is approximately 10 minutes" is displayed, allowing them to wait efficiently until their turn. This entire process reduces the burden on employees and leads to improved service quality.

[0520] Examples of prompts include, "Please describe a system that analyzes images acquired from in-store surveillance cameras and notifies of abnormal product placement." This allows the system to monitor various operational conditions within a commercial facility in real time, supporting efficient business operations.

[0521] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0522] Step 1:

[0523] The server receives visual data sent from the information processing unit. High-resolution image data is provided as input, and this data is stored on the server as output. Specifically, the information processing unit automatically patrols the store and periodically captures images using connected cameras.

[0524] Step 2:

[0525] The server sends the received data to the data analysis unit for analysis. The input is visual data stored on the server, and the output is the result of anomaly detection. Specifically, the data analysis unit uses an AI model to perform image recognition and check the placement of products and the status of price tags.

[0526] Step 3:

[0527] When the server detects an anomaly from the analyzed data, it immediately notifies the user via the terminal. The input is information about the detected anomaly, and the output is a notification message displayed on the terminal. Specifically, a message such as "The price tag on shelf A is not in the correct position" will be displayed on the terminal's screen.

[0528] Step 4:

[0529] The terminal displays a list of tasks generated by the business management unit to the user, allowing them to check the progress. The input is a business list updated daily or weekly, and the output is this list displayed on the terminal. In practice, the user can select a task to view detailed information and its progress status.

[0530] Step 5:

[0531] Users update the progress of each task using their terminal and send feedback to the server. The input is the updated progress information, and the output is the data recorded on the server. Specifically, when a user marks a task as "completed," that information is immediately reflected on the server.

[0532] Step 6:

[0533] The server aggregates performance data, generates the next sales strategy, and distributes it to the terminal. Past sales data and business logs are used as input, and new sales strategies and adjustment proposals are provided as output. Specifically, the user may be presented with a suggestion such as "increase inventory of a specific product."

[0534] Step 7:

[0535] The terminal retrieves basic information about visitors, calculates waiting times, and provides guidance. Input is the registration information provided by the visitor, and output includes waiting times and guidance messages. For example, the terminal might display a message such as, "The waiting time until your next guidance is approximately 10 minutes."

[0536] (Application Example 1)

[0537] 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."

[0538] In commercial facilities, improving operational efficiency requires automating daily tasks and reducing the burden on employees. However, current systems struggle to monitor store conditions and respond quickly to anomalies, and employees are unable to focus on critical tasks. In particular, managing product displays, waiting times, and the rapid execution of sales strategies are problematic. Therefore, there is a need to solve these problems and improve the efficiency of store operations.

[0539] 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.

[0540] In this invention, the server includes a patrol means consisting of an information processing device, a means for a data analysis device to determine anomalies based on image data acquired by the information processing device, a means for notifying the user if an anomaly occurs, a means for a task management device to generate a list of tasks, a means for monitoring the progress of the list of tasks, a means for reminding the user of incomplete tasks, a means for aggregating performance data to analyze the next sales strategy to focus on, a means for acquiring basic information of customers, calculating waiting times, and providing guidance, a means for displaying work progress information in real time using a visual device, and a means for detecting anomalies in the display state using a motion analysis model. This makes it possible to constantly understand the situation inside the store, respond quickly to anomalies, and improve operational efficiency.

[0541] An "information processing device" is a combination of hardware and software that patrols within a commercial facility and collects specified data.

[0542] A "data analysis device" is a digital device that analyzes data acquired by an information processing device and compares it to a standard to determine if there are any anomalies.

[0543] "Means for determining anomalies" refers to methods for detecting anomalies based on collected data and against established criteria.

[0544] "Means of notification" refers to the means used to communicate abnormalities or important information to users.

[0545] A "task management device" is a device that can automatically organize and list tasks.

[0546] A "task list" is a list used to organize daily tasks and display them in an actionable format.

[0547] "Means of monitoring progress" refers to functions for tracking and managing how far each task has progressed.

[0548] A "reminder method" is a way of notifying users about incomplete tasks and prompting them to take action.

[0549] "Performance data" refers to data showing the results of past completed projects and sales.

[0550] A "visual device" is a digital device used to present visual information to a user.

[0551] A "motion analysis model" is an analytical model used to analyze the state in which products are displayed and to determine whether it is normal or abnormal.

[0552] A "sales strategy" is a set of measures or strategies planned to promote effective sales.

[0553] "Basic customer information" refers to basic customer data necessary for providing services, such as name and time of visit.

[0554] "Methods for calculating and guiding customers to wait times" refers to methods for calculating how long customers will have to wait and then informing them of the result.

[0555] The system for implementing the present invention is configured to link multiple devices in order to achieve efficient business operations within a commercial facility. A server controls an information processing device and patrols the store to acquire image data. This image data is transmitted to a data analysis device and analyzed using image processing software such as OpenCV. If an anomaly is detected as a result of the analysis, the server immediately notifies the user. The notification is made via a smartphone or smart glasses, enabling a quick response.

[0556] The task management system organizes daily tasks and generates a task list. This list is displayed in real time on the terminal, allowing the user to manage their progress. For incomplete tasks, the user is notified via a reminder function, prompting appropriate action.

[0557] Furthermore, the data analysis system formulates the next sales strategy to focus on based on performance data. Data analysis tools such as Pandas and Scikit-learn are used for the analysis, and the results are quickly delivered to the user through the user interface.

[0558] In obtaining basic customer information, the server receives the information from the customer via a terminal and calculates the waiting time. This allows customers to receive efficient service and makes it easier to book their next visit.

[0559] As a concrete example, when a supermarket manager wears smart glasses, they can perform their duties while checking for any abnormalities in product displays. If an abnormality is detected, a notification is displayed on the glasses, allowing for immediate action. Furthermore, they can visually check customer waiting times, enabling quicker customer service.

[0560] An example of a prompt message to input into a generative AI model might be, "Create the following task list. Which products should be prioritized for display today?"

[0561] This system significantly improves operational efficiency within commercial facilities, providing employees with an environment where they can focus on more creative tasks.

[0562] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0563] Step 1:

[0564] The server controls the information processing unit to patrol the commercial facility. The camera on the information processing unit acquires image data from inside the store and transmits it to the server. The input is live video from inside the store, and the output is the digital image data received by the server.

[0565] Step 2:

[0566] The server sends the received image data to the data analysis device. The data analysis device uses OpenCV to analyze the image data and detect anomalies. In this process, the input is image data, and the output is an analysis result indicating whether or not anomalies are present. Specifically, it compares the product display state in the image with a reference image and detects differences.

[0567] Step 3:

[0568] The server receives anomaly results from the data analysis device. If an anomaly is detected, it notifies the user. The notification is sent via smart glasses or a smartphone, allowing the user to take immediate action. The input is the analysis result indicating the presence or absence of an anomaly, and the output is the notification to the user. Specific operations include generating and sending notification messages.

[0569] Step 4:

[0570] The task management system automatically generates a task list based on instructions from the server. The task list includes the tasks to be performed that day and is provided to the terminal in real time. Input is store operation schedule data, and output is the task list displayed on the user's screen.

[0571] Step 5:

[0572] The terminal monitors the progress of the task list generated by the task management device and reminds the user of incomplete tasks. The input is progress information of the task list, and the output is a reminder notification to the user.

[0573] Step 6:

[0574] The server aggregates performance data and generates the next sales strategy to focus on. This involves data analysis using Pandas and Scikit-learn. The input is historical sales data, and the output is a proposed sales strategy. The generated sales strategy is delivered to the user via a terminal.

[0575] Step 7:

[0576] The server retrieves customer information from the terminal, calculates the waiting time, and provides guidance. The input is the customer's basic information, and the output is the calculated waiting time. Guidance messages are conveyed to customers via terminals or smart devices. Specific operations include calculating the waiting time using an algorithm and displaying the result.

[0577] 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.

[0578] This invention is a system that improves operational efficiency in commercial facilities and enhances the customer and employee experience. The system comprises an information processing device, a data analysis device, a task management device, an emotion engine, and a user interface. The system aims to automate in-store operations while understanding the emotional state of individual users and providing appropriate responses accordingly.

[0579] The information processing unit in this system patrols the store, acquiring information on cleanliness and product display conditions using cameras and sensors. The terminal transmits the captured data to a data analysis device, where a server analyzes the image data. The server compares the data against standards to determine if there are any abnormalities and, if so, provides a notification to the user.

[0580] The task management system automatically generates a task list based on daily or weekly tasks and monitors progress. The task list is displayed to the user via a terminal, and a reminder function notifies them of incomplete tasks, supporting reliable task completion.

[0581] The emotion engine analyzes user voice and facial expression data in real time to estimate their emotional state. For example, if a customer is dissatisfied during a store visit, the emotion engine detects this and provides the user with instructions to improve customer service and attitude. It also dynamically changes the priority of task lists based on emotional data, adjusting them so that employees can perform their tasks with reduced stress.

[0582] The server manages these functions and analyzes the next sales strategy as needed. Using generative AI, it delivers the results to the user via their terminal and advises them on appropriate actions.

[0583] Furthermore, the terminal acquires basic information from customers, calculates waiting times, and provides guidance. The emotion engine analyzes customers' emotions and improves the experience through waiting time guidance and feedback during service provision. For example, it can detect dissatisfaction with long waiting times early and offer special countermeasures to provide a better consumer experience.

[0584] In this way, by incorporating an emotion engine, a system is built that not only improves the efficiency of existing operations but also enhances the experience for both customers and employees.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The terminal patrols the store, using cameras and sensors to collect data for inspecting cleanliness and product displays.

[0588] Step 2:

[0589] The terminal transmits acquired image data and sensor data to the server. The server then uses this data to understand the store's status in real time.

[0590] Step 3:

[0591] The server analyzes the transmitted data and determines anomalies based on the configured criteria. If an anomaly is detected, an alert is generated.

[0592] Step 4:

[0593] The server generates alerts and notifies the user via their terminal. This allows the user to respond quickly.

[0594] Step 5:

[0595] The task management system automatically generates a list of tasks periodically and distributes it to terminals via a server. The terminals display the list to the user, assisting with task planning.

[0596] Step 6:

[0597] The device monitors task progress and sends reminder notifications to the user if there are any incomplete tasks. This ensures smooth progress in work.

[0598] Step 7:

[0599] The emotion engine analyzes the user's voice input and facial expressions to determine their emotional state in real time.

[0600] Step 8:

[0601] The server uses the results of the emotion engine analysis to determine the appropriate action based on the user's state and notifies them via the terminal. For example, if a customer is feeling stressed, it will suggest countermeasures.

[0602] Step 9:

[0603] Customers operate a terminal and input their basic information, which is then used to acquire data. The terminal calculates the waiting time based on this data and provides guidance to the customer.

[0604] Step 10:

[0605] The server aggregates performance data and analyzes the next sales strategy, taking into account feedback from the emotion engine. The analysis results are provided to the user via their terminal, optimizing sales activities.

[0606] (Example 2)

[0607] 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."

[0608] Modern commercial facilities are required to simultaneously improve operational efficiency and enhance the customer and employee experience. However, traditional systems often struggle to achieve these elements in a single system, resulting in ineffective operation. Consequently, there is a problem of not being able to balance improved operational efficiency with maintaining customer satisfaction.

[0609] 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.

[0610] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine anomalies based on image information acquired by the information processing unit, and a means for analyzing the emotional state of users using an emotion estimation unit. This makes it possible not only to efficiently automate operations within commercial facilities but also to simultaneously improve the experience based on the emotional states of customers and employees.

[0611] An "information processing unit" is a general term for devices and functions that patrol and acquire information within a commercial facility, particularly those that use cameras and sensors to check cleaning status and display status.

[0612] A "data analysis unit" is a general term for devices and functions used to determine anomalies based on image information transmitted from the information processing unit.

[0613] A "task management unit" is a general term for devices and systems used to generate and manage daily or weekly task lists.

[0614] An "emotion estimation unit" is a general term for devices and functions that analyze a user's voice and facial expression data to estimate their emotional state.

[0615] A "generative AI system" refers to an entire system that uses artificial intelligence technology to provide guidance and recommend actions based on analyzed data.

[0616] A "terminal" refers to a device or equipment that allows a user to interact with a system through an interface.

[0617] A description of the embodiment for carrying out the invention will be provided.

[0618] This system, designed to improve operational efficiency and customer experience in commercial facilities, consists of an information processing unit, a data analysis unit, a work management unit, an emotion estimation unit, and terminals.

[0619] The server controls the information processing unit, which patrols the commercial facility and acquires information on cleaning status and product display conditions using cameras and sensors. This data is acquired as image information and then transmitted to the data analysis unit via a terminal. The data analysis unit determines anomalies based on the acquired image information. For example, it can automatically detect disarray on product shelves or areas where cleaning has not been completed.

[0620] The terminal has the function of displaying a list of tasks sent from the server to the user. The task list generated by this work management unit is structured based on daily and weekly tasks, and its progress is monitored in real time. The user is notified of incomplete tasks by the terminal's reminder function, which supports efficient work execution.

[0621] A server equipped with an emotion estimation unit analyzes voice and facial expression data acquired from the terminal to estimate the user's emotional state. This unit can, for example, detect if a customer is dissatisfied and, accordingly, provide instructions for improving customer service or offer special treatment. Furthermore, this system uses a generative AI model to analyze the next sales strategy and provides recommended actions to the user through the terminal.

[0622] As a concrete example, an example of a prompt message for a generative AI model is shown below.

[0623] "Please suggest ways to improve customer satisfaction in commercial facilities. For example, what measures can be taken to address customer dissatisfaction when waiting for long periods of time?"

[0624] In this way, by having each unit cooperate to efficiently manage operations and provide feedback that responds to users' emotions and behaviors, it is possible to simultaneously improve operational efficiency within commercial facilities and enhance customer satisfaction.

[0625] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0626] Step 1:

[0627] The server controls the information processing unit to patrol the commercial facility, using cameras and sensors to acquire image data of cleaning conditions and product display status. At this stage, the input is raw data obtained from sensors and cameras, and the output is image data that can be processed.

[0628] Step 2:

[0629] The terminal transmits image data acquired by the server to the data analysis unit. The server uses the data analysis unit based on the transmitted image data to determine anomalies. For example, it may perform specific actions such as detecting product defects. The input is image data, and the output is the anomaly detection result.

[0630] Step 3:

[0631] Based on the data analysis results, the server immediately notifies the user via the terminal if an anomaly occurs. The user then confirms the anomaly and takes specific actions to implement corrective procedures. The input is the detection result, and the output is the transmission of the anomaly notification.

[0632] Step 4:

[0633] The server uses a task management unit to generate daily and weekly task lists and displays them to the user via a terminal. The user then carries out tasks based on the displayed task list. The input is store schedule information, and the output is the task list.

[0634] Step 5:

[0635] The terminal monitors work progress based on user actions. For incomplete tasks, it triggers reminders and sends notifications to the user prompting them to complete the tasks. Input is user action information, and output is reminder notifications.

[0636] Step 6:

[0637] The server uses an emotion estimation unit to analyze the user's voice and facial expression data and estimate the user's emotional state. If a specific emotional state, such as dissatisfaction, is detected, the server provides feedback to the user to improve customer service or work processes. The input is voice and facial expression data, and the output is the emotion analysis result.

[0638] Step 7:

[0639] The server utilizes a generative AI model to analyze the next sales strategy to focus on and provides recommended actions to the user through the terminal. This process includes the analysis of sales data and consumer behavior patterns. The input is historical performance data, and the output is a proposed sales strategy.

[0640] Step 8:

[0641] The terminal acquires basic visitor information, sends it to the server to calculate waiting times, and provides appropriate guidance. An emotion estimation unit analyzes the visitor's emotional state and implements actions to provide a better customer experience. The input is visitor information, and the output is waiting time guidance.

[0642] In this way, the system aims to improve operational efficiency and enhance the user experience by appropriately processing input data at each step and obtaining output results.

[0643] (Application Example 2)

[0644] 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."

[0645] The challenge lies in solving two problems: improving customer satisfaction in stores and improving employee work efficiency. While conventional systems allow for automated task management, they lack the means to grasp customers' emotional states in real time and respond appropriately accordingly. As a result, it was difficult to achieve both improved customer experience and operational efficiency.

[0646] 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.

[0647] In this invention, the server includes an analysis means for estimating the emotional state of store users based on image and audio data, a means for notifying users of appropriate countermeasures based on the results of the analysis means, and a means for monitoring the situation within the store using a mobile information terminal carried by staff and optimizing the customer experience. This makes it possible to grasp the emotional state of customers in real time and automatically send appropriate countermeasures, thereby achieving both improved customer satisfaction and operational efficiency.

[0648] "Image and audio data" refers to video and audio information of customers and staff, and this data is used as the basis for detecting emotional states and abnormal situations by analyzing it.

[0649] "Analysis means" refers to technical elements that process and analyze image and audio data to determine and estimate the user's emotional state.

[0650] "Notification methods" refer to functions that convey appropriate information and instructions to staff and system users based on the analysis results.

[0651] A "personal information terminal" is a mobile device carried by staff to monitor the situation within a store and to acquire and utilize information.

[0652] "Means of optimizing the customer experience" refers to a collection of processes and functions that adjust services according to the user's emotional state and the situation within the store, thereby improving customer satisfaction.

[0653] "Means for determining abnormalities" refers to a function that detects unusual events by comparing information acquired by a data analysis device with established criteria.

[0654] "Means for generating task lists" refers to the process by which a task management system automatically lists daily or weekly tasks and provides instructions to staff.

[0655] "Means of monitoring progress" refers to a function that allows staff to track the status of their ongoing tasks based on a task list and update information as needed.

[0656] A "reminder mechanism" is a function that notifies staff about incomplete tasks and encourages them to complete those tasks.

[0657] "Means of analyzing sales strategies" refer to the processes and techniques for formulating the next sales strategy to focus on, based on performance data.

[0658] "Methods for calculating and providing information about waiting times" refers to a function that acquires customer information, calculates the waiting time until service is provided, and notifies the user.

[0659] The system implemented in this invention aims to improve operational efficiency and customer experience in commercial facilities. The main components of the system are a personal digital assistant (PDTA), a server, analysis means, and notification means. The PDTA is carried by staff and acquires images and audio data of the situation inside the store. The PDTA transmits this data to the server in real time.

[0660] The server is built using Python or Java and features an emotion analysis engine powered by TensorFlow. The server uses acquired image and audio data to estimate the customer's emotional state. Based on the analysis results, the server generates appropriate countermeasures and notifies staff members via their mobile devices. This process is implemented using an API based on Flask.

[0661] For example, if a customer shows signs of dissatisfaction in the store, the emotion analysis engine will immediately detect this and send a notification to the staff saying, "The customer is showing dissatisfaction. Please address this immediately."

[0662] An example of a prompt for a generative AI model is, "Please suggest some appropriate actions to take when a customer is feeling stressed in a crowded store."

[0663] In this way, the system analyzes customer emotions in real time and enables the provision of appropriate services, thereby improving customer satisfaction.

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] The terminal collects image and audio data acquired within the store. A user (staff member) uses a mobile device to patrol the store, activating the camera and microphone to record customer and store conditions. The input for this step is actual video and audio of customers and merchandise shelves, while the output is digital image and audio files.

[0667] Step 2:

[0668] The device transmits the collected digital data to the server. The device uploads the acquired image and audio data to the server in real time via Wi-Fi or a mobile network. The input for this step is the image and audio files acquired in step 1, and the output is a file stored in the server-side database.

[0669] Step 3:

[0670] The server analyzes the transmitted image and audio data to estimate the customer's emotional state. The server uses TensorFlow to run an emotion analysis model and analyze the input data. The input is digital data, and the output is the estimated emotional state (e.g., satisfied, dissatisfied, angry). Specifically, the analysis results are output as a digital signal for use in the next step.

[0671] Step 4:

[0672] Based on the analysis results, the server generates and notifies the employee of a course of action. The AI ​​model on the server uses prompts to generate actions that the employee should take. The input is the emotional state obtained in step 3, and the output is specific service instructions (e.g., improve customer service attitude, offer a discount, etc.). At this time, the server sends a notification to the staff member's terminal via a Flask-based API.

[0673] Step 5:

[0674] The terminal receives notifications from the server and displays them to the staff. The user (staff) checks the notification on the mobile device and modifies their actions according to the instructions displayed on the screen. The input is the notification information from the server, and the output is the specific action that the staff will take. This operation is a crucial step in improving the customer experience within the store.

[0675] 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.

[0676] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0677] 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.

[0678] [Fourth Embodiment]

[0679] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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".

[0692] This invention relates to a system for improving operational efficiency in commercial facilities. The system comprises an information processing device, a data analysis device, a task management device, and a user interface. The system aims to automate routine tasks within stores, providing an environment where employees can focus on more important tasks.

[0693] The information processing unit of this system automatically patrols the store, acquiring information on cleanliness and product display status using cameras and sensors. The terminal transmits the captured data to a data analysis device. The server analyzes the transmitted image data and determines any abnormalities by comparing it to standards. For example, if a price tag is not properly installed, the server detects the abnormality and notifies the user. This notification allows employees to take appropriate action quickly.

[0694] The task management system automatically generates a task list based on daily or weekly tasks. The generated task list is distributed to terminals by the server, and the terminals display the list to the user. Users can check the displayed task list and manage their progress in real time. The terminals also support reliable task completion by reminding users of incomplete tasks and prompting them to take action.

[0695] Furthermore, this system aggregates performance data daily and generates the next sales strategy to focus on. The server delivers the analysis results to users via terminals. As a result, users can quickly implement sales strategies, further improving operational efficiency.

[0696] In serving customers, the terminal retrieves basic information about the customer, calculates the current waiting time, and provides guidance. This allows customers to receive service at the appropriate time. It also provides guidance for future visit reservations, further improving customer convenience. For example, by centrally managing the calculation of waiting times and guidance from the start of reception, the burden on employees is reduced and the quality of service is improved.

[0697] This system will streamline store operations, allowing employees to focus on more creative and important tasks.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The terminal begins patrolling the store according to a schedule. It uses cameras and sensors to photograph the cleanliness and the condition of product displays, and collects data.

[0701] Step 2:

[0702] The device sends the acquired image data to the server. A secure communication protocol is used for transmission.

[0703] Step 3:

[0704] The server uses AI to analyze the received image data and determine if there are any anomalies by comparing it against a set of criteria. For example, it checks whether price tags are properly placed.

[0705] Step 4:

[0706] Based on the analysis results, the server generates an alert and sends a notification to the user if any anomalies are found. The notification will include points that need improvement.

[0707] Step 5:

[0708] The server generates a daily or weekly task list. This list is created based on historical task data and current sales targets.

[0709] Step 6:

[0710] The server distributes a generated list of tasks to the terminal, which then displays it to the user. The user then performs the tasks based on the displayed list.

[0711] Step 7:

[0712] The device monitors the progress and sends a reminder notification to the user if there are any incomplete tasks.

[0713] Step 8:

[0714] The server aggregates daily performance data and analyzes sales strategies. Generative AI is used to identify the next areas of focus.

[0715] Step 9:

[0716] The server delivers analysis results to the terminal, and the terminal presents sales strategy information to the user.

[0717] Step 10:

[0718] The terminal prompts customers to input their basic information and calculates the current waiting time. It then provides customers with appropriate waiting time information, streamlining the service delivery process.

[0719] (Example 1)

[0720] 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".

[0721] Inefficiencies and human errors in commercial facilities and other similar establishments are becoming a major problem, compounded by labor shortages and increased workloads. In particular, errors in product placement and pricing, as well as delays in promptly assisting visitors, negatively impact customer satisfaction. While efficient task management and rapid deployment of sales strategies are needed, traditional methods have their limitations. To address these challenges, automation of operations and visualization of information are essential.

[0722] 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.

[0723] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine an anomaly based on visual data acquired by the information processing unit, and a means for notifying the user if an anomaly occurs. This enables the automation of operations in commercial facilities and a rapid response in the event of an anomaly.

[0724] An "information processing unit" is a device that patrols within a commercial facility and acquires visual data and environmental information.

[0725] "Visual data" refers to image and video information acquired by the information processing unit, and it serves as the basic data for analyzing the situation inside the store.

[0726] A "data analysis unit" refers to a device or software equipped with algorithms and functions for analyzing acquired visual data and determining whether or not anomalies are present.

[0727] "Means for determining abnormalities" refers to a function that detects deviations from standards or misalignments in placement from visual data and issues warnings or corrective instructions as needed.

[0728] "Means of notifying the user" refers to an interface that has the function of providing warnings and corrective instructions via display or audio when an abnormality is detected.

[0729] A "task management unit" refers to a device or software that has the function of listing tasks within a facility and managing their progress in real time.

[0730] A "task list" refers to a list of necessary tasks generated by the business management unit, which makes it easier to plan and prioritize work.

[0731] "Performance information" refers to past sales data and records of business operations, and is used as data for analyzing sales strategies and other related matters.

[0732] "Visitors" refers to customers and client representatives who visit commercial facilities, and services are provided by measuring their basic information and waiting times.

[0733] A "price display card" refers to printed or digital displays that inform consumers of the price of a product and other information, and it is important to ensure that these are placed appropriately.

[0734] This invention relates to a system for streamlining the operation of commercial facilities and includes an information processing unit, a data analysis unit, a business management unit, terminal devices, and server devices. This system automates routine tasks within the facility, providing an environment where employees can focus on more important tasks.

[0735] The server analyzes the visual data acquired by the information processing unit using an image recognition algorithm. Specifically, the information processing unit constantly patrols the commercial facility, collecting data using high-resolution surveillance cameras and light sensors. This allows the server to determine whether the placement of products and price tags is accurate.

[0736] The terminal transmits data from the information processing unit to the data analysis unit, and the server uses this data to determine whether or not there are any anomalies. In this process, it can detect issues such as price tags being upside down or misaligned. After detecting an anomaly, the server immediately notifies the user and prompts them to take appropriate action. The user receives the notification on the terminal's display and can quickly verify and correct the issue on-site.

[0737] The task management unit automatically generates daily or weekly tasks and distributes them to terminals via the server. Users can use their terminals to view task lists and manage progress, thereby clarifying task priorities. The terminals periodically remind users of incomplete tasks to support task completion.

[0738] Furthermore, the server aggregates performance data to generate the next sales strategy and proposes it to the user. For example, if sales of a particular product are increasing, it will suggest marketing measures and inventory management adjustments related to that product.

[0739] For visitors, the terminal retrieves basic information, calculates waiting times, and provides guidance. For example, once a visitor completes check-in, a message such as "The waiting time is approximately 10 minutes" is displayed, allowing them to wait efficiently until their turn. This entire process reduces the burden on employees and leads to improved service quality.

[0740] Examples of prompts include, "Please describe a system that analyzes images acquired from in-store surveillance cameras and notifies of abnormal product placement." This allows the system to monitor various operational conditions within a commercial facility in real time, supporting efficient business operations.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] The server receives visual data sent from the information processing unit. High-resolution image data is provided as input, and this data is stored on the server as output. Specifically, the information processing unit automatically patrols the store and periodically captures images using connected cameras.

[0744] Step 2:

[0745] The server sends the received data to the data analysis unit for analysis. The input is visual data stored on the server, and the output is the result of anomaly detection. Specifically, the data analysis unit uses an AI model to perform image recognition and check the placement of products and the status of price tags.

[0746] Step 3:

[0747] When the server detects an anomaly from the analyzed data, it immediately notifies the user via the terminal. The input is information about the detected anomaly, and the output is a notification message displayed on the terminal. Specifically, a message such as "The price tag on shelf A is not in the correct position" will be displayed on the terminal's screen.

[0748] Step 4:

[0749] The terminal displays a list of tasks generated by the business management unit to the user, allowing them to check the progress. The input is a business list updated daily or weekly, and the output is this list displayed on the terminal. In practice, the user can select a task to view detailed information and its progress status.

[0750] Step 5:

[0751] Users update the progress of each task using their terminal and send feedback to the server. The input is the updated progress information, and the output is the data recorded on the server. Specifically, when a user marks a task as "completed," that information is immediately reflected on the server.

[0752] Step 6:

[0753] The server aggregates performance data, generates the next sales strategy, and distributes it to the terminal. Past sales data and business logs are used as input, and new sales strategies and adjustment proposals are provided as output. Specifically, the user may be presented with a suggestion such as "increase inventory of a specific product."

[0754] Step 7:

[0755] The terminal retrieves basic information about visitors, calculates waiting times, and provides guidance. Input is the registration information provided by the visitor, and output includes waiting times and guidance messages. For example, the terminal might display a message such as, "The waiting time until your next guidance is approximately 10 minutes."

[0756] (Application Example 1)

[0757] 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".

[0758] In commercial facilities, improving operational efficiency requires automating daily tasks and reducing the burden on employees. However, current systems struggle to monitor store conditions and respond quickly to anomalies, and employees are unable to focus on critical tasks. In particular, managing product displays, waiting times, and the rapid execution of sales strategies are problematic. Therefore, there is a need to solve these problems and improve the efficiency of store operations.

[0759] 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.

[0760] In this invention, the server includes a patrol means consisting of an information processing device, a means for a data analysis device to determine anomalies based on image data acquired by the information processing device, a means for notifying the user if an anomaly occurs, a means for a task management device to generate a list of tasks, a means for monitoring the progress of the list of tasks, a means for reminding the user of incomplete tasks, a means for aggregating performance data to analyze the next sales strategy to focus on, a means for acquiring basic information of customers, calculating waiting times, and providing guidance, a means for displaying work progress information in real time using a visual device, and a means for detecting anomalies in the display state using a motion analysis model. This makes it possible to constantly understand the situation inside the store, respond quickly to anomalies, and improve operational efficiency.

[0761] An "information processing device" is a combination of hardware and software that patrols within a commercial facility and collects specified data.

[0762] A "data analysis device" is a digital device that analyzes data acquired by an information processing device and compares it to a standard to determine if there are any anomalies.

[0763] "Means for determining anomalies" refers to methods for detecting anomalies based on collected data and against established criteria.

[0764] "Means of notification" refers to the means used to communicate abnormalities or important information to users.

[0765] A "task management device" is a device that can automatically organize and list tasks.

[0766] A "task list" is a list used to organize daily tasks and display them in an actionable format.

[0767] "Means of monitoring progress" refers to functions for tracking and managing how far each task has progressed.

[0768] A "reminder method" is a way of notifying users about incomplete tasks and prompting them to take action.

[0769] "Performance data" refers to data showing the results of past completed projects and sales.

[0770] A "visual device" is a digital device used to present visual information to a user.

[0771] A "motion analysis model" is an analytical model used to analyze the state in which products are displayed and to determine whether it is normal or abnormal.

[0772] A "sales strategy" is a set of measures or strategies planned to promote effective sales.

[0773] "Basic customer information" refers to basic customer data necessary for providing services, such as name and time of visit.

[0774] "Methods for calculating and guiding customers to wait times" refers to methods for calculating how long customers will have to wait and then informing them of the result.

[0775] The system for implementing the present invention is configured to link multiple devices in order to achieve efficient business operations within a commercial facility. A server controls an information processing device and patrols the store to acquire image data. This image data is transmitted to a data analysis device and analyzed using image processing software such as OpenCV. If an anomaly is detected as a result of the analysis, the server immediately notifies the user. The notification is made via a smartphone or smart glasses, enabling a quick response.

[0776] The task management system organizes daily tasks and generates a task list. This list is displayed in real time on the terminal, allowing the user to manage their progress. For incomplete tasks, the user is notified via a reminder function, prompting appropriate action.

[0777] Furthermore, the data analysis system formulates the next sales strategy to focus on based on performance data. Data analysis tools such as Pandas and Scikit-learn are used for the analysis, and the results are quickly delivered to the user through the user interface.

[0778] In obtaining basic customer information, the server receives the information from the customer via a terminal and calculates the waiting time. This allows customers to receive efficient service and makes it easier to book their next visit.

[0779] As a concrete example, when a supermarket manager wears smart glasses, they can perform their duties while checking for any abnormalities in product displays. If an abnormality is detected, a notification is displayed on the glasses, allowing for immediate action. Furthermore, they can visually check customer waiting times, enabling quicker customer service.

[0780] An example of a prompt message to input into a generative AI model might be, "Create the following task list. Which products should be prioritized for display today?"

[0781] This system significantly improves operational efficiency within commercial facilities, providing employees with an environment where they can focus on more creative tasks.

[0782] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0783] Step 1:

[0784] The server controls the information processing unit to patrol the commercial facility. The camera on the information processing unit acquires image data from inside the store and transmits it to the server. The input is live video from inside the store, and the output is the digital image data received by the server.

[0785] Step 2:

[0786] The server sends the received image data to the data analysis device. The data analysis device uses OpenCV to analyze the image data and detect anomalies. In this process, the input is image data, and the output is an analysis result indicating whether or not anomalies are present. Specifically, it compares the product display state in the image with a reference image and detects differences.

[0787] Step 3:

[0788] The server receives anomaly results from the data analysis device. If an anomaly is detected, it notifies the user. The notification is sent via smart glasses or a smartphone, allowing the user to take immediate action. The input is the analysis result indicating the presence or absence of an anomaly, and the output is the notification to the user. Specific operations include generating and sending notification messages.

[0789] Step 4:

[0790] The task management system automatically generates a task list based on instructions from the server. The task list includes the tasks to be performed that day and is provided to the terminal in real time. Input is store operation schedule data, and output is the task list displayed on the user's screen.

[0791] Step 5:

[0792] The terminal monitors the progress of the task list generated by the task management device and reminds the user of incomplete tasks. The input is progress information of the task list, and the output is a reminder notification to the user.

[0793] Step 6:

[0794] The server aggregates performance data and generates the next sales strategy to focus on. This involves data analysis using Pandas and Scikit-learn. The input is historical sales data, and the output is a proposed sales strategy. The generated sales strategy is delivered to the user via a terminal.

[0795] Step 7:

[0796] The server retrieves customer information from the terminal, calculates the waiting time, and provides guidance. The input is the customer's basic information, and the output is the calculated waiting time. Guidance messages are conveyed to customers via terminals or smart devices. Specific operations include calculating the waiting time using an algorithm and displaying the result.

[0797] 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.

[0798] This invention is a system that improves operational efficiency in commercial facilities and enhances the customer and employee experience. The system comprises an information processing device, a data analysis device, a task management device, an emotion engine, and a user interface. The system aims to automate in-store operations while understanding the emotional state of individual users and providing appropriate responses accordingly.

[0799] The information processing unit in this system patrols the store, acquiring information on cleanliness and product display conditions using cameras and sensors. The terminal transmits the captured data to a data analysis device, where a server analyzes the image data. The server compares the data against standards to determine if there are any abnormalities and, if so, provides a notification to the user.

[0800] The task management system automatically generates a task list based on daily or weekly tasks and monitors progress. The task list is displayed to the user via a terminal, and a reminder function notifies them of incomplete tasks, supporting reliable task completion.

[0801] The emotion engine analyzes user voice and facial expression data in real time to estimate their emotional state. For example, if a customer is dissatisfied during a store visit, the emotion engine detects this and provides the user with instructions to improve customer service and attitude. It also dynamically changes the priority of task lists based on emotional data, adjusting them so that employees can perform their tasks with reduced stress.

[0802] The server manages these functions and analyzes the next sales strategy as needed. Using generative AI, it delivers the results to the user via their terminal and advises them on appropriate actions.

[0803] Furthermore, the terminal acquires basic information from customers, calculates waiting times, and provides guidance. The emotion engine analyzes customers' emotions and improves the experience through waiting time guidance and feedback during service provision. For example, it can detect dissatisfaction with long waiting times early and offer special countermeasures to provide a better consumer experience.

[0804] In this way, by incorporating an emotion engine, a system is built that not only improves the efficiency of existing operations but also enhances the experience for both customers and employees.

[0805] The following describes the processing flow.

[0806] Step 1:

[0807] The terminal patrols the store, using cameras and sensors to collect data for inspecting cleanliness and product displays.

[0808] Step 2:

[0809] The terminal transmits acquired image data and sensor data to the server. The server then uses this data to understand the store's status in real time.

[0810] Step 3:

[0811] The server analyzes the transmitted data and determines anomalies based on the configured criteria. If an anomaly is detected, an alert is generated.

[0812] Step 4:

[0813] The server generates alerts and notifies the user via their terminal. This allows the user to respond quickly.

[0814] Step 5:

[0815] The task management system automatically generates a list of tasks periodically and distributes it to terminals via a server. The terminals display the list to the user, assisting with task planning.

[0816] Step 6:

[0817] The device monitors task progress and sends reminder notifications to the user if there are any incomplete tasks. This ensures smooth progress in work.

[0818] Step 7:

[0819] The emotion engine analyzes the user's voice input and facial expressions to determine their emotional state in real time.

[0820] Step 8:

[0821] The server uses the results of the emotion engine analysis to determine the appropriate action based on the user's state and notifies them via the terminal. For example, if a customer is feeling stressed, it will suggest countermeasures.

[0822] Step 9:

[0823] Customers operate a terminal and input their basic information, which is then used to acquire data. The terminal calculates the waiting time based on this data and provides guidance to the customer.

[0824] Step 10:

[0825] The server aggregates performance data and analyzes the next sales strategy, taking into account feedback from the emotion engine. The analysis results are provided to the user via their terminal, optimizing sales activities.

[0826] (Example 2)

[0827] 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".

[0828] Modern commercial facilities are required to simultaneously improve operational efficiency and enhance the customer and employee experience. However, traditional systems often struggle to achieve these elements in a single system, resulting in ineffective operation. Consequently, there is a problem of not being able to balance improved operational efficiency with maintaining customer satisfaction.

[0829] 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.

[0830] In this invention, the server includes a patrol means consisting of an information processing unit, a means for a data analysis unit to determine anomalies based on image information acquired by the information processing unit, and a means for analyzing the emotional state of users using an emotion estimation unit. This makes it possible not only to efficiently automate operations within commercial facilities but also to simultaneously improve the experience based on the emotional states of customers and employees.

[0831] An "information processing unit" is a general term for devices and functions that patrol and acquire information within a commercial facility, particularly those that use cameras and sensors to check cleaning status and display status.

[0832] A "data analysis unit" is a general term for devices and functions used to determine anomalies based on image information transmitted from the information processing unit.

[0833] A "task management unit" is a general term for devices and systems used to generate and manage daily or weekly task lists.

[0834] An "emotion estimation unit" is a general term for devices and functions that analyze a user's voice and facial expression data to estimate their emotional state.

[0835] A "generative AI system" refers to an entire system that uses artificial intelligence technology to provide guidance and recommend actions based on analyzed data.

[0836] A "terminal" refers to a device or equipment that allows a user to interact with a system through an interface.

[0837] A description of the embodiment for carrying out the invention will be provided.

[0838] This system, designed to improve operational efficiency and customer experience in commercial facilities, consists of an information processing unit, a data analysis unit, a work management unit, an emotion estimation unit, and terminals.

[0839] The server controls the information processing unit, which patrols the commercial facility and acquires information on cleaning status and product display conditions using cameras and sensors. This data is acquired as image information and then transmitted to the data analysis unit via a terminal. The data analysis unit determines anomalies based on the acquired image information. For example, it can automatically detect disarray on product shelves or areas where cleaning has not been completed.

[0840] The terminal has the function of displaying a list of tasks sent from the server to the user. The task list generated by this work management unit is structured based on daily and weekly tasks, and its progress is monitored in real time. The user is notified of incomplete tasks by the terminal's reminder function, which supports efficient work execution.

[0841] A server equipped with an emotion estimation unit analyzes voice and facial expression data acquired from the terminal to estimate the user's emotional state. This unit can, for example, detect if a customer is dissatisfied and, accordingly, provide instructions for improving customer service or offer special treatment. Furthermore, this system uses a generative AI model to analyze the next sales strategy and provides recommended actions to the user through the terminal.

[0842] As a concrete example, an example of a prompt message for a generative AI model is shown below.

[0843] "Please suggest ways to improve customer satisfaction in commercial facilities. For example, what measures can be taken to address customer dissatisfaction when waiting for long periods of time?"

[0844] In this way, by having each unit cooperate to efficiently manage operations and provide feedback that responds to users' emotions and behaviors, it is possible to simultaneously improve operational efficiency within commercial facilities and enhance customer satisfaction.

[0845] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0846] Step 1:

[0847] The server controls the information processing unit to patrol the commercial facility, using cameras and sensors to acquire image data of cleaning conditions and product display status. At this stage, the input is raw data obtained from sensors and cameras, and the output is image data that can be processed.

[0848] Step 2:

[0849] The terminal transmits image data acquired by the server to the data analysis unit. The server uses the data analysis unit based on the transmitted image data to determine anomalies. For example, it may perform specific actions such as detecting product defects. The input is image data, and the output is the anomaly detection result.

[0850] Step 3:

[0851] Based on the data analysis results, the server immediately notifies the user via the terminal if an anomaly occurs. The user then confirms the anomaly and takes specific actions to implement corrective procedures. The input is the detection result, and the output is the transmission of the anomaly notification.

[0852] Step 4:

[0853] The server uses a task management unit to generate daily and weekly task lists and displays them to the user via a terminal. The user then carries out tasks based on the displayed task list. The input is store schedule information, and the output is the task list.

[0854] Step 5:

[0855] The terminal monitors work progress based on user actions. For incomplete tasks, it triggers reminders and sends notifications to the user prompting them to complete the tasks. Input is user action information, and output is reminder notifications.

[0856] Step 6:

[0857] The server uses an emotion estimation unit to analyze the user's voice and facial expression data and estimate the user's emotional state. If a specific emotional state, such as dissatisfaction, is detected, the server provides feedback to the user to improve customer service or work processes. The input is voice and facial expression data, and the output is the emotion analysis result.

[0858] Step 7:

[0859] The server utilizes a generative AI model to analyze the next sales strategy to focus on and provides recommended actions to the user through the terminal. This process includes the analysis of sales data and consumer behavior patterns. The input is historical performance data, and the output is a proposed sales strategy.

[0860] Step 8:

[0861] The terminal acquires basic visitor information, sends it to the server to calculate waiting times, and provides appropriate guidance. An emotion estimation unit analyzes the visitor's emotional state and implements actions to provide a better customer experience. The input is visitor information, and the output is waiting time guidance.

[0862] In this way, the system aims to improve operational efficiency and enhance the user experience by appropriately processing input data at each step and obtaining output results.

[0863] (Application Example 2)

[0864] 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".

[0865] The challenge lies in solving two problems: improving customer satisfaction in stores and improving employee work efficiency. While conventional systems allow for automated task management, they lack the means to grasp customers' emotional states in real time and respond appropriately accordingly. As a result, it was difficult to achieve both improved customer experience and operational efficiency.

[0866] 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.

[0867] In this invention, the server includes an analysis means for estimating the emotional state of store users based on image and audio data, a means for notifying users of appropriate countermeasures based on the results of the analysis means, and a means for monitoring the situation within the store using a mobile information terminal carried by staff and optimizing the customer experience. This makes it possible to grasp the emotional state of customers in real time and automatically send appropriate countermeasures, thereby achieving both improved customer satisfaction and operational efficiency.

[0868] "Image and audio data" refers to video and audio information of customers and staff, and this data is used as the basis for detecting emotional states and abnormal situations by analyzing it.

[0869] "Analysis means" refers to technical elements that process and analyze image and audio data to determine and estimate the user's emotional state.

[0870] "Notification methods" refer to functions that convey appropriate information and instructions to staff and system users based on the analysis results.

[0871] A "personal information terminal" is a mobile device carried by staff to monitor the situation within a store and to acquire and utilize information.

[0872] "Means of optimizing the customer experience" refers to a collection of processes and functions that adjust services according to the user's emotional state and the situation within the store, thereby improving customer satisfaction.

[0873] "Means for determining abnormalities" refers to a function that detects unusual events by comparing information acquired by a data analysis device with established criteria.

[0874] "Means for generating task lists" refers to the process by which a task management system automatically lists daily or weekly tasks and provides instructions to staff.

[0875] "Means of monitoring progress" refers to a function that allows staff to track the status of their ongoing tasks based on a task list and update information as needed.

[0876] A "reminder mechanism" is a function that notifies staff about incomplete tasks and encourages them to complete those tasks.

[0877] "Means of analyzing sales strategies" refer to the processes and techniques for formulating the next sales strategy to focus on, based on performance data.

[0878] "Methods for calculating and providing information about waiting times" refers to a function that acquires customer information, calculates the waiting time until service is provided, and notifies the user.

[0879] The system implemented in this invention aims to improve operational efficiency and customer experience in commercial facilities. The main components of the system are a personal digital assistant (PDTA), a server, analysis means, and notification means. The PDTA is carried by staff and acquires images and audio data of the situation inside the store. The PDTA transmits this data to the server in real time.

[0880] The server is built using Python or Java and features an emotion analysis engine powered by TensorFlow. The server uses acquired image and audio data to estimate the customer's emotional state. Based on the analysis results, the server generates appropriate countermeasures and notifies staff members via their mobile devices. This process is implemented using an API based on Flask.

[0881] For example, if a customer shows signs of dissatisfaction in the store, the emotion analysis engine will immediately detect this and send a notification to the staff saying, "The customer is showing dissatisfaction. Please address this immediately."

[0882] An example of a prompt for a generative AI model is, "Please suggest some appropriate actions to take when a customer is feeling stressed in a crowded store."

[0883] In this way, the system analyzes customer emotions in real time and enables the provision of appropriate services, thereby improving customer satisfaction.

[0884] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0885] Step 1:

[0886] The terminal collects image and audio data acquired within the store. A user (staff member) uses a mobile device to patrol the store, activating the camera and microphone to record customer and store conditions. The input for this step is actual video and audio of customers and merchandise shelves, while the output is digital image and audio files.

[0887] Step 2:

[0888] The device transmits the collected digital data to the server. The device uploads the acquired image and audio data to the server in real time via Wi-Fi or a mobile network. The input for this step is the image and audio files acquired in step 1, and the output is a file stored in the server-side database.

[0889] Step 3:

[0890] The server analyzes the transmitted image and audio data to estimate the customer's emotional state. The server uses TensorFlow to run an emotion analysis model and analyze the input data. The input is digital data, and the output is the estimated emotional state (e.g., satisfied, dissatisfied, angry). Specifically, the analysis results are output as a digital signal for use in the next step.

[0891] Step 4:

[0892] Based on the analysis results, the server generates and notifies the employee of a course of action. The AI ​​model on the server uses prompts to generate actions that the employee should take. The input is the emotional state obtained in step 3, and the output is specific service instructions (e.g., improve customer service attitude, offer a discount, etc.). At this time, the server sends a notification to the staff member's terminal via a Flask-based API.

[0893] Step 5:

[0894] The terminal receives notifications from the server and displays them to the staff. The user (staff) checks the notification on the mobile device and modifies their actions according to the instructions displayed on the screen. The input is the notification information from the server, and the output is the specific action that the staff will take. This operation is a crucial step in improving the customer experience within the store.

[0895] 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.

[0896] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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."

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0916] The following is further disclosed regarding the embodiments described above.

[0917] (Claim 1)

[0918] A patrol means consisting of an information processing device,

[0919] The data analysis device has a means for determining an anomaly based on the image data acquired by the aforementioned information processing device,

[0920] A means of notifying the user in case of an anomaly,

[0921] A means by which a task management device generates a list of tasks,

[0922] A means for monitoring the progress of the aforementioned task list,

[0923] A means of reminding users of incomplete tasks,

[0924] A means of compiling performance data to analyze the next sales strategy to focus on,

[0925] A method for obtaining basic information about customers, calculating waiting times, and providing guidance,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising means for an information processing device to detect the presence or absence of a price card during patrol.

[0929] (Claim 3)

[0930] The system according to claim 1, comprising means for initiating data acquisition when a customer operates a terminal.

[0931] "Example 1"

[0932] (Claim 1)

[0933] A patrol means consisting of an information processing unit,

[0934] A means by which a data analysis unit determines an anomaly based on the visual data acquired by the aforementioned information processing unit,

[0935] A means of notifying the user in case of an abnormality,

[0936] The means by which the business management unit creates a task list,

[0937] A means for monitoring the progress of the aforementioned task list,

[0938] A means of reminding users of incomplete tasks,

[0939] A means of aggregating performance data and analyzing the next sales strategy to focus on,

[0940] A method for obtaining basic visitor information, calculating waiting times, and providing guidance,

[0941] A means of transferring data to a server and detecting anomalies using an image recognition algorithm,

[0942] A means by which the user can operate the display device to check the details of the task,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, further comprising means for an information processing unit to detect the presence or absence of price display cards during patrol.

[0946] (Claim 3)

[0947] The system according to claim 1, comprising means by which information collection is initiated when a visitor operates an interface.

[0948] "Application Example 1"

[0949] (Claim 1)

[0950] A patrol means consisting of an information processing device,

[0951] The data analysis device has a means for determining an anomaly based on the image data acquired by the aforementioned information processing device,

[0952] A means of notifying the user in case of an anomaly,

[0953] A means by which a task management device generates a list of tasks,

[0954] A means for monitoring the progress of the aforementioned task list,

[0955] A means of reminding users of incomplete tasks,

[0956] A means of compiling performance data to analyze the next sales strategy to focus on,

[0957] A method for obtaining basic information about customers, calculating waiting times, and providing guidance,

[0958] A means of displaying work progress information in real time using a visual device,

[0959] A means of detecting abnormalities in the display state using a motion analysis model,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, further comprising means for an information processing device to detect the presence or absence of a price display medium during a patrol.

[0963] (Claim 3)

[0964] The system according to claim 1, comprising means by which information acquisition is initiated when a customer operates a terminal.

[0965] "Example 2 of combining an emotion engine"

[0966] (Claim 1)

[0967] A patrol means consisting of an information processing unit,

[0968] The data analysis unit has means for determining an anomaly based on the image information acquired by the aforementioned information processing unit,

[0969] A means of notifying the user in case of an abnormality,

[0970] A means by which the work management unit generates a list of tasks,

[0971] A means for monitoring the progress of the aforementioned list of tasks,

[0972] A means of reminding users of incomplete tasks,

[0973] A means of compiling performance data and analyzing the sales policies that should be focused on next,

[0974] A method for obtaining basic visitor information, calculating waiting times, and providing guidance,

[0975] A means for analyzing a user's emotional state using an emotion estimation unit,

[0976] A means of providing guidance and recommending actions using a generative AI system,

[0977] A system that includes this.

[0978] (Claim 2)

[0979] The system according to claim 1, further comprising means for an information processing unit to detect the presence or absence of a price display during patrol.

[0980] (Claim 3)

[0981] The system according to claim 1, comprising means by which information acquisition is initiated when a visitor operates a terminal.

[0982] "Application example 2 when combining with an emotional engine"

[0983] (Claim 1)

[0984] An analytical means for estimating the emotional state of store users based on image and audio data,

[0985] A means for notifying the user of countermeasures based on the results of the analysis means,

[0986] A means of monitoring the situation within the store using mobile devices carried by staff and optimizing the customer experience,

[0987] A means by which a data analysis device determines an anomaly based on image data acquired by the aforementioned mobile information terminal,

[0988] A means of notifying the user in case of an anomaly,

[0989] A means by which a task management device generates a list of tasks,

[0990] A means for monitoring the progress of the aforementioned task list,

[0991] A means of reminding users of incomplete tasks,

[0992] A means of compiling performance data to analyze the next sales strategy to focus on,

[0993] A method for obtaining basic information about customers, calculating waiting times, and providing guidance,

[0994] A system that includes this.

[0995] (Claim 2)

[0996] The system according to claim 1, further comprising means for detecting the presence or absence of price tags displayed on shelves during patrols using an information processing device.

[0997] (Claim 3)

[0998] The system according to claim 1, comprising means for initiating data acquisition when a customer operates a user interface. [Explanation of symbols]

[0999] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A patrol means consisting of an information processing device, The data analysis device has a means for determining an anomaly based on the image data acquired by the aforementioned information processing device, A means of notifying the user in case of an anomaly, A means by which a task management device generates a list of tasks, A means for monitoring the progress of the aforementioned task list, A means of reminding users of incomplete tasks, A means of compiling performance data to analyze the next sales strategy to focus on, A method for obtaining basic information about customers, calculating waiting times, and providing guidance, A system that includes this.

2. The system according to claim 1, further comprising means for an information processing device to detect the presence or absence of a price card during patrol.

3. The system according to claim 1, comprising means for initiating data acquisition when a customer operates a terminal.

Images