system

Abstract

We provide the system. [Solution] A means for collecting environmental data from multiple monitoring devices within a store and evaluating the cleanliness of the store and the condition of product displays using image recognition technology, A means for generating a task list based on each staff member's role and priority, and transmitting the task list to the staff member's portable device, A means for monitoring the progress of the task list and sending notifications to the staff member's terminal regarding incomplete tasks, A means for analyzing sales data to generate guidelines regarding sales strategies and providing those guidelines to the terminal, A digital reception device that receives customer requests and provides the customer with waiting time and reservation information, and a means for notifying the staff who should be assisting the customer of the information, A means of displaying information to staff in real time through a portable visualization device, A system that includes this.

Description

Technical Field

[0005] ,

[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 conventional store operations, staff spent a lot of time on daily routine tasks, making it difficult to concentrate on tasks that require strategic and high-level decision-making. In addition, these routine tasks were also accompanied by risks of human errors such as incomplete cleaning, disorganized product displays, and unfinished tasks. Furthermore, it was also difficult to efficiently handle the reception of customers visiting the store, which might lead to a decrease in customer satisfaction. New solutions to address these issues are required.

Means for Solving the Problems

[0005] This invention provides a system that uses image recognition technology to analyze environmental data collected from monitoring devices within a store and automatically evaluates cleanliness and the condition of product displays. This system generates task lists based on each staff member's role and priority, and transmits them to the staff member's portable terminal. It also monitors the progress of the task list in real time and sends notifications to the terminal if any tasks are incomplete. Furthermore, it supports decision-making in sales activities by analyzing sales data to generate strategic guidelines and providing them to the terminal. In addition, it streamlines customer service by using a digital reception device to provide customers with waiting times and reservation information, and by notifying staff of this information upon customer request. This improves the efficiency of store operations and customer satisfaction.

[0006] A "monitoring device" is a device installed within a store to collect environmental data, and includes devices such as cameras and sensors.

[0007] "Image recognition technology" is a technique that analyzes collected image data to detect specific patterns or anomalies.

[0008] "Cleanliness" refers to the state of cleanliness and organization within a store, and is an indicator used to evaluate whether the store is well-maintained.

[0009] "Product display" refers to the arrangement and layout of products within a store, and indicates whether the products are displayed appropriately.

[0010] A "task list" is a list that enumerates specific tasks or activities in bullet points, summarizing the tasks that staff members need to complete.

[0011] A "portable device" is a digital device that staff can carry with them, and includes devices such as smartphones and tablets.

[0012] "Sales data" refers to data related to the sales and transactions of goods and services at a store.

[0013] A "digital reception device" is an automated interface device installed to handle customer registration and reception. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention provides a system that utilizes AI technology to support daily operations in order to improve the efficiency of store operations. The system is implemented by linking monitoring devices placed within the store with portable terminals.

[0036] The server first acquires environmental data in real time from monitoring devices within the store. This data includes video footage from cameras and status information from sensors. The server analyzes the acquired data using image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, a notification is immediately sent to the relevant terminal. In this way, users (staff) can take immediate action.

[0037] The server also generates task lists based on staff roles and schedules and sends them to each staff member's mobile device. This allows users to effectively manage their tasks and improve work efficiency. In addition, the server monitors the progress of the task lists and sends reminders to the devices if any tasks remain incomplete.

[0038] Furthermore, the server analyzes sales data to identify key focus points in store sales activities. Based on this, it generates strategic guidelines and sends reports to the user's mobile device so that they can appropriately adjust their sales strategy.

[0039] On the terminal side, a digital reception device efficiently collects customer information and informs customers of waiting times and reservation status. Based on the collected information, the server sends notifications to the appropriate users prompting them to respond to the customer. This enables smooth and effective customer service.

[0040] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server checks the overall status of the store from the monitoring device and detects areas that need cleaning. This information is automatically notified to the terminals of the cleaning staff. Next, the server analyzes sales points to adjust the sales strategy according to the day's sales target and distributes this information to the terminals of the sales staff. After the store opens for the day, the terminals register customers and provide real-time information on waiting times. In this way, the present invention utilizes the system to provide advanced support for store operations, allowing staff to concentrate on more strategic tasks.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects environmental data in real time from monitoring devices installed within the store. This includes video data from cameras and situational information from sensors.

[0044] Step 2:

[0045] The server applies image recognition technology to analyze the collected environmental data and evaluates the cleanliness of the store and the arrangement of merchandise displays.

[0046] Step 3:

[0047] Based on the analysis results, if the server detects any abnormalities or areas requiring improvement, it will send a notification to the relevant user's mobile device.

[0048] Step 4:

[0049] The user checks the notification received on their device and confirms the required actions and content for the specified location. The user takes immediate action if necessary.

[0050] Step 5:

[0051] The server generates a task list based on each staff member's role and priority. This task list is then sent to each user's mobile device via the cloud.

[0052] Step 6:

[0053] Users receive a task list on their device and can create effective work plans by reviewing the task content and priority.

[0054] Step 7:

[0055] The server monitors the progress of each task in real time and sends a notification to the user's device if any tasks in the task list remain incomplete.

[0056] Step 8:

[0057] The terminal provides customers with a digital check-in function, allowing them to input waiting times and reservation information. The server then uses this information to issue instructions to the necessary staff.

[0058] (Example 1)

[0059] 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."

[0060] Modern commercial facilities demand efficient operations and improved customer service. However, manually checking store cleanliness and product placement is time-consuming and prone to human error. Furthermore, it's difficult to provide instructions to individual staff and track progress, hindering the rapid implementation of effective sales strategies. Prompt and accurate responses to visitors also remain a challenge. A solution is needed to address these issues while simultaneously improving overall operational efficiency and customer service quality.

[0061] 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.

[0062] In this invention, the server includes means for acquiring environmental information from monitoring equipment and evaluating the cleanliness of the facility and product placement using video analysis technology; means for generating a list of tasks based on the role and priority of each worker and transmitting the corresponding list of tasks to the workers' mobile devices; and means for sending real-time notifications when an anomaly is detected based on information analysis. This makes it possible to significantly improve the operational efficiency and service quality of the store.

[0063] "Surveillance equipment" refers to devices, including cameras and sensors, installed within stores and facilities to acquire environmental information.

[0064] "Environmental information" refers to all data that indicates the conditions of a store or facility, such as temperature, humidity, and video data.

[0065] "Video analysis technology" is a technique that uses acquired video data to judge and evaluate specific conditions, and it utilizes image processing algorithms.

[0066] "Facility" refers to a space used for providing specific services or operations, including commercial facilities and shops.

[0067] "Cleanliness status" refers to information indicating the degree of cleanliness and tidiness of the floors, shelves, and other fixtures within the facility.

[0068] "Product placement" refers to information regarding the arrangement and layout of products displayed within a facility.

[0069] A "task list" refers to a list of tasks and duties that each worker is responsible for performing.

[0070] "Portable devices" refer to portable electronic devices carried by each worker for receiving and displaying information.

[0071] "Information analysis" refers to the technique of analyzing the meaning and trends associated with acquired environmental and sales information.

[0072] Anomaly detection is a process that automatically identifies situations that deviate from the normal state.

[0073] "Real-time notification" refers to the process of sending relevant information to the device without delay immediately after an anomaly is detected.

[0074] "Sales information" refers to data related to transactions conducted within the facility, including sales data.

[0075] The system of this invention is designed to streamline the operation of commercial facilities and stores and improve customer service. The system consists of the following important elements:

[0076] The server acquires environmental information from multiple monitoring devices installed within the facility. These monitoring devices include network cameras and various sensors, and the data is sent to the server in real time. The acquired video data is analyzed using OpenCV and other image processing libraries to evaluate the cleanliness of the facility and the placement of products. If an anomaly is detected through this data processing and evaluation, a notification is immediately sent to the relevant terminal using Firebase Cloud Messaging (FCM), etc.

[0077] The terminal is an electronic device such as a smartphone or tablet carried by the user. A dedicated application is installed to receive notifications and instructions from the server, providing information in real time. For example, in the event of an anomaly detection, an alert is immediately sent to the worker, enabling them to respond quickly to the problem. The terminal also functions as a digital reception device, efficiently collecting visitor information and providing waiting times and reservation information.

[0078] Users act as facility staff, following instructions from the server via portable devices. They perform daily tasks based on a task list generated by the server, updating the task list's progress in real time as needed. The task list is generated via the Google® Tasks API, enabling smooth updates and sharing through cloud computing.

[0079] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server processes data from monitoring equipment and detects any disarray in the arrangement of merchandise on the shelves. In response, the server immediately sends a notification to the terminal of the relevant staff member, who then quickly tidies up the shelves. This entire process is based on prompts from a generative AI model. For example, a possible prompt might be, "Please describe in detail the mechanism for checking the store's tidiness in real time and reporting any anomalies." In this way, the system effectively supports the operation of commercial facilities and provides an environment where staff can focus on strategic activities.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server acquires environmental information from monitoring equipment within the facility. Its inputs include real-time data from network cameras and environmental sensors. The server collects this data and stores it as video and numerical data. Specifically, it periodically makes HTTP requests to retrieve camera streams and sensor data.

[0083] Step 2:

[0084] The server processes the acquired data using video analysis technology to evaluate the placement and cleanliness of the products. The input is the previously acquired video data, and the server uses image processing libraries such as OpenCV to perform background subtraction and edge detection on the images to detect anomalies in placement and cleanliness. If an anomaly is found as a result of the analysis, it is output as anomaly detection information.

[0085] Step 3:

[0086] The server generates a task list based on each worker's role and schedule and sends it to their portable device. The input includes worker information and schedule data. The server uses the Google Tasks API to generate the task list in JSON format and sends it to the device. Specifically, it sets role-specific work instructions for each worker and distributes them via the cloud.

[0087] Step 4:

[0088] The terminal displays a task list received from the server to the user, who then performs tasks based on this list. The input is the task list received from the server, and the output is progress data of the tasks performed by the user. Specifically, the terminal's application updates the task list in real time and feeds the progress back to the server.

[0089] Step 5:

[0090] The server monitors task completion status based on progress data and sends reminders for incomplete tasks. Input is progress data from each worker, and output is reminder messages. The server analyzes the progress and notifies users of uncompleted tasks via the Slack API or similar.

[0091] Step 6:

[0092] The server analyzes sales information within the facility and creates guidelines for sales strategies. Sales data from the POS system is used as input, and the server processes this data using the Pandas library. It performs trend analysis and generates reports, outputting the results to terminals as strategic guidelines.

[0093] Step 7:

[0094] The terminal provides the user with a generated sales strategy report. Input is strategic guidance data from the server, which the user uses to adjust their sales activities. Specifically, the terminal displays the report in PDF format, making it available for the user to view.

[0095] Step 8:

[0096] The terminal collects visitor information through a digital reception system and displays waiting times and reservation status. Input is data entered by the visitor into the terminal, and output is the displayed information. Specifically, the terminal receives data by scanning a QR code (registered trademark) or manual input, and displays this information on the screen for the visitor.

[0097] (Application Example 1)

[0098] 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."

[0099] In traditional store operations, staff often struggled to work efficiently when cleaning, checking merchandise displays, managing tasks, or assisting customers. This problem stemmed particularly from insufficient real-time information provision and management, reducing both store operational efficiency and staff productivity. Furthermore, there was a lack of systems to enable staff to respond quickly and effectively when customers visited the store.

[0100] 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.

[0101] In this invention, the server includes means for collecting environmental data from multiple monitoring devices within the store and evaluating the cleanliness of the store and the state of product display using image recognition technology; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable terminal; and means for displaying information to the staff member in real time through a portable visualization device. This allows staff members to obtain accurate information in real time, enabling more efficient store operations and faster customer service.

[0102] A "monitoring device" is a device that collects environmental data within a store, and may include cameras and sensors.

[0103] "Image recognition technology" is a technology that analyzes acquired image data to identify and evaluate specific features.

[0104] "Cleanliness" is a standard used to evaluate the cleanliness of a specific area within a store.

[0105] "Product display condition" refers to the criteria used to evaluate how products are arranged and replenished within a store.

[0106] "Staff" refers to the personnel involved in store operations and their role is to complete tasks.

[0107] A "task list" refers to a list of tasks that a staff member needs to complete.

[0108] A "portable terminal" is a device carried by staff members and used to display task lists and notifications.

[0109] "Sales data" refers to information about product sales at stores and is useful for strategic decision-making.

[0110] A "digital reception device" is a device that receives requests from customers and provides them with information such as waiting times and reservation details.

[0111] A "portable visualization device" refers to a visual device that is portable by staff and used to display information in real time.

[0112] The system of this invention utilizes AI technology to streamline in-store operations and consists of the following components.

[0113] The server receives environmental data in real time from multiple monitoring devices placed within the store. This includes video data from surveillance cameras and status data obtained from various sensors. Based on this environmental data, the server uses image recognition software to evaluate the cleanliness of the store and the condition of the merchandise display. It is envisioned that an AI platform such as TENSORFLOW® will be used here.

[0114] The portable devices provided to staff members are responsible for receiving task lists generated by the server. The server dynamically generates task lists based on each staff member's role and priority, and sends them to the portable devices. The progress of the task lists is constantly monitored, and if there are any incomplete tasks, the server sends a reminder to the staff member's device.

[0115] Sales data is analyzed by a server to provide important insights into store operations. The results of this analysis are sent to staff members' portable devices and used to adjust sales strategies.

[0116] The digital reception system has the function of receiving customer requests and transmitting them to a server. This allows customers to be provided with current waiting times and reservation information, enabling staff to respond quickly.

[0117] One of the unique features of this system is that it provides information directly to staff in real time using portable visualization devices. For example, smart glasses are used as visualization devices, displaying store conditions and task lists directly in the staff's field of vision.

[0118] As a concrete example of its use, imagine a staff member preparing a store to open one day. The server detects areas requiring cleaning from monitoring devices and displays them in real time on the cleaning staff member's visualization device. Furthermore, if a popular item runs low on stock, a notification is sent to the relevant staff member prompting them to replenish it quickly. This process improves the efficiency of store operations and enhances customer service.

[0119] An example of a prompt message for a generated AI model is: "We want to develop an application that analyzes current store environment data and notifies staff of inventory shortages and areas that need cleaning. Please suggest a method to link a server with smart glasses and display the situation to store staff in real time."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server receives real-time environmental data from multiple monitoring devices placed within the store. This data includes camera footage and sensor information, and image recognition technology is used to extract detectable features based on this data. The input to this operation is raw data from the monitoring devices, and the output is evaluation data that indicates the specific conditions within the store.

[0123] Step 2:

[0124] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. Specifically, it uses an AI model to analyze image data and detect abnormalities or areas that require improvement. The input to this process is the evaluation data obtained in the previous step, and the output is a list of detected abnormalities and areas that need cleaning.

[0125] Step 3:

[0126] The server generates task lists based on each staff member's role and priority. To do this, it retrieves staff information from the database and, taking into account previously obtained evaluation data, creates an optimal task schedule. The inputs are staff information and evaluation data, and the output is a task list for each staff member.

[0127] Step 4:

[0128] The server sends the generated task list to each portable terminal. The terminal receives this information and uses it as a guide to improve the user's work efficiency. The input for this step is the task list, and the output is the guide task information displayed on the terminal.

[0129] Step 5:

[0130] The server analyzes sales data and generates strategic guidelines. Specifically, it uses an AI model to analyze past sales history and identify key products and timings for increasing sales. The input to this process is sales history data, and the output is strategic guidelines.

[0131] Step 6:

[0132] The server transmits strategic guidelines to each staff member's portable device, prompting immediate tactical adjustments. The device receives this information, supporting the streamlining of sales activities. The input is strategic guidelines, and the output is tactical information displayed on the device.

[0133] Step 7:

[0134] The server receives customer requests via a digital reception device and generates waiting times and reservation information. It receives requests from the digital reception device and compares them with the schedule data registered in the system. The input is customer request data, and the output is waiting time and reservation information.

[0135] Step 8:

[0136] Using a portable visualization device, the server transmits information to staff in real time. The visualization device displays the received information directly in the staff's field of vision, facilitating efficient responses. The input for this step is various notification information from the server, and the output is real-time information displayed on the visualization device.

[0137] 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.

[0138] This invention provides a system using AI technology aimed at improving efficiency in store operations and enhancing the customer experience. The invention incorporates an emotion engine that recognizes user emotions, thereby enabling more personalized services for individual customers.

[0139] The server collects environmental and user data from multiple monitoring devices within the store and uses image recognition technology to evaluate the store's cleanliness and the condition of merchandise displays. In addition, an emotion engine analyzes data such as the user's facial expressions, voice, and body movements to evaluate the customer's emotional state.

[0140] The server sends notifications to store staff's portable devices based on the collected sentiment data. These notifications include hints on the best way to respond to customers and product suggestions. Based on this, staff can dynamically adjust their interactions with customers. For example, if the data suggests that a customer viewing a product is showing interest, the device will display a notification suggesting related products.

[0141] Furthermore, the task list generation system generates tasks based on each staff member's role and priority, and distributes them to their portable devices. The server monitors task progress and sends reminders for incomplete tasks. This process utilizes cloud computing, enabling efficient task management in real time.

[0142] The terminal greets customers with a digital check-in system and, based on the information received, displays waiting times and reservation information to the customer. The server uses this data to notify staff and dynamically adjust the in-store environment, thereby improving the overall quality of service.

[0143] As a concrete example, suppose a customer enters a store, and the customer's emotion engine on the terminal captures their facial expression and detects that they are showing curiosity. The server processes this data and sends promotional information for related products to the staff's terminal. The staff can then use this information to naturally begin explaining recommended products to the customer. This can improve the customer's overall purchasing experience throughout the store.

[0144] These systems streamline daily operations while simultaneously enhancing customer satisfaction through emotion-based interactions.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The server collects environmental data and user sentiment data from multiple monitoring devices placed within the store. This includes camera footage, audio data, and motion sensor data.

[0148] Step 2:

[0149] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, it immediately sends a notification to the terminal.

[0150] Step 3:

[0151] The emotion engine analyzes collected user data to determine the user's emotional state. This allows for real-time understanding of customer emotions such as interest, satisfaction, and dissatisfaction.

[0152] Step 4:

[0153] Based on the analysis results from the emotion engine, the server determines the optimal response method and product recommendations for the customer, and transmits that information to the user's mobile device.

[0154] Step 5:

[0155] The user checks the notifications received from their device, considers how to respond, and then responds to the customer. For example, they might recommend related products to a customer who has shown interest.

[0156] Step 6:

[0157] The server generates task lists based on each staff member's role and priority, and sends these task lists to the staff member's mobile device in real time. This utilizes cloud computing technology.

[0158] Step 7:

[0159] Users check their task list on their mobile devices and work on tasks. They input their progress on their devices and send feedback to the server.

[0160] Step 8:

[0161] The server monitors the progress of the task list and sends reminder notifications to staff members' devices about incomplete tasks.

[0162] Step 9:

[0163] The terminal uses a digital reception system to greet customers, collect necessary information, and display waiting times and reservation information. Based on this information, the server sends instructions to the staff.

[0164] Step 10:

[0165] This allows users (staff) to efficiently manage store operations while appropriately adjusting their actions to enhance customer satisfaction.

[0166] (Example 2)

[0167] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0168] Modern store operations require efficient operational management and personalized service tailored to each customer. However, traditional systems cannot assess customer emotions in real time and provide appropriate guidance to staff based on that assessment. Furthermore, inadequate monitoring of work progress and notifications of incomplete tasks can lead to decreased operational efficiency and reduced customer satisfaction.

[0169] 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.

[0170] In this invention, the server includes means for collecting environmental and customer information from monitoring devices and evaluating the store's condition and customer sentiment using analytical techniques; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable device; and means for monitoring the progress of the task list and transmitting notifications regarding incomplete tasks to the staff member's device. This enables efficient operations within the store, provides optimized service to each individual customer, and improves the customer experience.

[0171] A "monitoring device" is a device installed in a store to collect environmental information and customer information, and includes sensors such as cameras and microphones.

[0172] "Environmental information" refers to information about the physical conditions inside the store, such as temperature, humidity, lighting, and noise levels.

[0173] "Customer information" refers to data that shows a customer's behavior and emotional state, such as their facial expressions, voice, and body movements.

[0174] "Analysis technology" refers to techniques for extracting specific patterns or meanings based on collected information, and includes image recognition technology and speech analysis technology.

[0175] A "task list" is a list of tasks assigned to staff members, organized by priority and role.

[0176] "Portable equipment" refers to electronic devices carried by staff members and used to receive notifications and task lists.

[0177] "Progress" refers to the status indicating the extent to which each task listed in the task list has been completed.

[0178] "Incomplete tasks" refers to tasks listed on the task list that have not yet been completed.

[0179] This invention is a system aimed at improving efficiency in store operations and enhancing the customer experience. In implementing the system, the server collects environmental and customer information from monitoring devices within the store. The monitoring devices include, for example, sensors such as cameras and microphones, and acquire data on temperature, humidity, lighting conditions, and customer facial expressions and voices within the store.

[0180] The server processes the collected data using analytical techniques. Specifically, it analyzes customer facial expressions using image recognition technology and evaluates their emotional state using a generative AI model. For example, it captures a customer's facial expression while they are looking at products in a store, and the AI ​​model evaluates their movements to determine if they are "showing interest."

[0181] Based on the evaluation results, the server sends a notification to the staff's portable devices. These devices are personal information terminals such as smartphones and tablets, which receive notifications from the server and display them to the staff. The notifications include information on customer service guidelines and product suggestions. For example, they might provide specific guidance such as, "The customer has shown interest. Let's suggest related products."

[0182] Furthermore, the server generates a task list considering each staff member's role and priority, and sends it to their portable device. It also utilizes a cloud-based task management system to monitor each staff member's work progress in real time and send reminders as needed.

[0183] For example, suppose a customer visits a store, and the server collects and analyzes their facial expression data. The emotion engine determines that the customer is "curious." Based on this information, a generative AI model creates promotional information for related products and notifies the staff. The staff can then use this information to proactively explain products to the customer, ultimately improving the customer's purchasing experience.

[0184] An example of a prompt message would be, "Generate the optimal customer service method based on customer sentiment data from the store." This invention makes it possible to simultaneously improve the efficiency of store operations and enhance customer satisfaction.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The server collects environmental and customer information from monitoring devices. Inputs include image data from cameras, audio data from microphones, and environmental data from temperature sensors. The server receives this data and aggregates information from each sensor in the monitoring device in real time. As output, these datasets are sent to the next analysis step.

[0188] Step 2:

[0189] The server processes the collected data using analytical techniques. The input consists of various data collected in step 1. Specifically, image recognition technology is used to analyze image data, and voice analysis technology is used to analyze voice data. As part of the data processing, a generating AI model analyzes the customer's emotions based on their facial expressions and voice, and obtains an output that evaluates the customer's emotional state.

[0190] Step 3:

[0191] The server uses customer sentiment data based on the analysis results to generate notifications on staff members' portable devices. The input is the sentiment evaluation data obtained in step 2. Specifically, it creates customer response guidelines appropriate to the analyzed sentiment and generates a notification that includes suggestions for related products. The output is the specific notification information sent to the staff member's portable device.

[0192] Step 4:

[0193] The terminal displays notifications received from the server to the staff. The input is the notification information generated in step 3. The terminal displays the notification on the screen so that the staff can review it. This allows the staff to obtain the information necessary to provide the best possible service to the customer. The output is an information presentation that the staff can immediately understand.

[0194] Step 5:

[0195] The server uses cloud computing to generate task lists and distribute them to each staff member's portable device. The input is information about each staff member's role and task priorities. Specifically, it uses a task generation algorithm to calculate an efficient task arrangement and create a task list. The output is the task list data sent to the staff member's portable device.

[0196] Step 6:

[0197] The server monitors the progress of the task list and sends reminders if there are any incomplete tasks. The input is real-time updated task progress information. The server identifies incomplete tasks and automatically generates reminder notifications based on that information. The output is the reminder notification sent to the staff member's portable device.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] In today's retail industry, there is a demand for providing optimal service tailored to the individual needs and emotions of each customer. However, due to limited human resources, it is difficult to provide personalized service to all customers. Furthermore, improving employee efficiency and optimizing overall store operations are also crucial challenges. By addressing these challenges, it is necessary to improve customer satisfaction and enhance the efficiency of store operations.

[0201] 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.

[0202] In this invention, the server includes means for aggregating environmental information from numerous sensors within the store and evaluating the store's hygiene and product display status using pattern recognition technology; means for generating a list of tasks based on each employee's role and priority and transmitting the list of tasks to the employee's mobile device; and means for notifying the mobile device of the optimal way to interact with the customer based on emotion data obtained from an emotion analysis device that recognizes the user's emotions. This enables personalized service provision to each customer, as well as improved operational efficiency and optimized store operations.

[0203] A "sensor" is an electronic device that detects environmental information and outputs it as a signal.

[0204] "Environmental information" refers to objective measurement data such as hygiene conditions, temperature, and humidity within the store.

[0205] "Pattern recognition technology" is a technique that identifies regularities and features present in data and uses them to classify or interpret new input data.

[0206] "Hygienic conditions" refers to the cleanliness and orderliness of a store, and includes the degree of disinfection and tidiness.

[0207] "Product display" is a term that describes the arrangement and display method of products, including their visual appeal and ease of access.

[0208] "Employee" refers to an individual employed by a store to perform duties.

[0209] "Portable devices" refer to portable electronic devices capable of sending, receiving, and processing information, and include smartphones and tablets.

[0210] A "task list" is a list that summarizes the tasks necessary for store operations and details the tasks assigned to each employee.

[0211] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0212] "Contact methods" is a concept that refers to the means and approaches for effectively communicating with customers.

[0213] In this invention, a server plays a central role, connecting with multiple sensors to aggregate environmental information within the store. The sensors detect temperature, humidity, noise levels, etc., and are used to evaluate the hygienic conditions and product display status within the store. Pattern recognition technology analyzes this data and extracts useful information about the current state of the store.

[0214] The server has the function of sending task lists to employees' mobile devices. The task list is dynamically generated based on each employee's role and the current store situation, allowing employees to perform their tasks efficiently. The server continuously monitors the progress of tasks, and notifications are sent to the mobile devices for incomplete tasks.

[0215] Furthermore, an emotion analysis device is introduced to analyze the customer's facial expressions, voice, and gestures to recognize their emotional state. Using this data, the server transmits the optimal way to interact with the customer to their mobile device, enabling personalized interactions.

[0216] For example, if a customer shows interest in a technology product, the server can send a notification to a mobile device saying, "The customer is interested in a new product. Please provide a demonstration and explain the details." Such instructions enable employees to communicate more effectively with customers.

[0217] An example of a prompt message might be, "Based on customer sentiment recognition data in a retail store, please design the specifications for an application that recommends the next action." This allows for the use of generative AI models to explore further service improvements.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The server collects environmental information as input from sensors placed throughout the store. This information includes temperature, humidity, and noise levels within the store. The server statistically analyzes this data and processes it using pattern recognition technology to evaluate hygiene conditions and the state of product displays.

[0221] Step 2:

[0222] The server dynamically generates a task list using processed store status data, employee roles, and priorities as input. It then creates a task list as output and sends it to employees' mobile devices. This creates a system that allows employees to perform their tasks efficiently.

[0223] Step 3:

[0224] The terminal receives customer facial expressions, voice, and gestures as input from an emotion analysis device. The terminal processes this data with an emotion analysis engine and outputs the customer's emotional state. The server uses this emotional data to build a foundation for determining the appropriate way to interact with the customer.

[0225] Step 4:

[0226] The server uses emotional data to determine the best way to interact with customers and notifies employees of the results via their mobile devices. The output generates instructions, including service strategies and product recommendations for the customer. This notification enables employees to provide personalized service that meets customer expectations.

[0227] Step 5:

[0228] The server continuously monitors the progress of tasks and collects information on incomplete tasks as input. The server analyzes this information to identify tasks that are behind schedule and sends notifications to the relevant employees' mobile devices as output. This helps ensure that high-priority tasks are completed in a timely manner.

[0229] Step 6:

[0230] The user creates prompt statements for the AI ​​model as input, providing instructions to the system. Based on these prompt statements, the system devises new ideas and methods for service improvement and generates suggestions as output. This allows for continuous service improvement.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] This invention provides a system that utilizes AI technology to support daily operations in order to improve the efficiency of store operations. The system is implemented by linking monitoring devices placed within the store with portable terminals.

[0248] The server first acquires environmental data in real time from monitoring devices within the store. This data includes video footage from cameras and status information from sensors. The server analyzes the acquired data using image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, a notification is immediately sent to the relevant terminal. In this way, users (staff) can take immediate action.

[0249] The server also generates task lists based on staff roles and schedules and sends them to each staff member's mobile device. This allows users to effectively manage their tasks and improve work efficiency. In addition, the server monitors the progress of the task lists and sends reminders to the devices if any tasks remain incomplete.

[0250] Furthermore, the server analyzes sales data to identify key focus points in store sales activities. Based on this, it generates strategic guidelines and sends reports to the user's mobile device so that they can appropriately adjust their sales strategy.

[0251] On the terminal side, a digital reception device efficiently collects customer information and informs customers of waiting times and reservation status. Based on the collected information, the server sends notifications to the appropriate users prompting them to respond to the customer. This enables smooth and effective customer service.

[0252] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server checks the overall status of the store from the monitoring device and detects areas that need cleaning. This information is automatically notified to the terminals of the cleaning staff. Next, the server analyzes sales points to adjust the sales strategy according to the day's sales target and distributes this information to the terminals of the sales staff. After the store opens for the day, the terminals register customers and provide real-time information on waiting times. In this way, the present invention utilizes the system to provide advanced support for store operations, allowing staff to concentrate on more strategic tasks.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server collects environmental data in real time from monitoring devices installed within the store. This includes video data from cameras and situational information from sensors.

[0256] Step 2:

[0257] The server applies image recognition technology to analyze the collected environmental data and evaluates the cleanliness of the store and the arrangement of merchandise displays.

[0258] Step 3:

[0259] Based on the analysis results, if the server detects any abnormalities or areas requiring improvement, it will send a notification to the relevant user's mobile device.

[0260] Step 4:

[0261] The user checks the notification received on their device and confirms the required actions and content for the specified location. The user takes immediate action if necessary.

[0262] Step 5:

[0263] The server generates a task list based on each staff member's role and priority. This task list is then sent to each user's mobile device via the cloud.

[0264] Step 6:

[0265] Users receive a task list on their device and can create effective work plans by reviewing the task content and priority.

[0266] Step 7:

[0267] The server monitors the progress of each task in real time and sends a notification to the user's device if any tasks in the task list remain incomplete.

[0268] Step 8:

[0269] The terminal provides customers with a digital check-in function, allowing them to input waiting times and reservation information. The server then uses this information to issue instructions to the necessary staff.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] Modern commercial facilities demand efficient operations and improved customer service. However, manually checking store cleanliness and product placement is time-consuming and prone to human error. Furthermore, it's difficult to provide instructions to individual staff and track progress, hindering the rapid implementation of effective sales strategies. Prompt and accurate responses to visitors also remain a challenge. A solution is needed to address these issues while simultaneously improving overall operational efficiency and customer service quality.

[0273] 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.

[0274] In this invention, the server includes means for acquiring environmental information from monitoring equipment and evaluating the cleanliness of the facility and product placement using video analysis technology; means for generating a list of tasks based on the role and priority of each worker and transmitting the corresponding list of tasks to the workers' mobile devices; and means for sending real-time notifications when an anomaly is detected based on information analysis. This makes it possible to significantly improve the operational efficiency and service quality of the store.

[0275] "Surveillance equipment" refers to devices, including cameras and sensors, installed within stores and facilities to acquire environmental information.

[0276] "Environmental information" refers to all data that indicates the conditions of a store or facility, such as temperature, humidity, and video data.

[0277] "Video analysis technology" is a technology for judging specific conditions and conducting evaluations using acquired video data, and utilizes image processing algorithms.

[0278] "Facility" refers to a space for providing specific operations or services, including commercial facilities and stores.

[0279] "Cleaning status" is information indicating the cleanliness and tidiness of the floors, shelves, and other fixtures within a facility.

[0280] "Product arrangement" refers to information regarding the arrangement and placement status of products displayed within a facility.

[0281] "Task list" means a list of tasks and operations that each worker should execute.

[0282] "Portable device" refers to a portable electronic device held by each worker for receiving and displaying information.

[0283] "Information analysis" refers to a technology for analyzing the meanings and trends of acquired environmental information and sales information.

[0284] "Anomaly detection" is a process for automatically discovering situations that deviate from the normal state.

[0285] "Real-time notification" means a process for transmitting relevant information to a terminal without delay immediately after an anomaly is detected.

[0286] "Sales information" is data related to transactions conducted within a facility, including sales data and the like. <00009​​​​​​The server acquires environmental information from multiple monitoring devices installed within the facility. These monitoring devices include network cameras and various sensors, and the data is sent to the server in real time. The acquired video data is analyzed using OpenCV and other image processing libraries to evaluate the cleanliness of the facility and the placement of products. If an anomaly is detected through this data processing and evaluation, a notification is immediately sent to the relevant terminal using Firebase Cloud Messaging (FCM), etc.

[0289] The terminal is an electronic device such as a smartphone or tablet carried by the user. A dedicated application is installed to receive notifications and instructions from the server, providing information in real time. For example, in the event of an anomaly detection, an alert is immediately sent to the worker, enabling them to respond quickly to the problem. The terminal also functions as a digital reception device, efficiently collecting visitor information and providing waiting times and reservation information.

[0290] Users act as facility staff, following instructions from the server via portable devices. They perform daily tasks based on a task list generated by the server, updating the task list's progress in real time as needed. The task list is generated via the Google Tasks API, enabling smooth updates and sharing through cloud computing.

[0291] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server processes data from monitoring equipment and detects any disarray in the arrangement of merchandise on the shelves. In response, the server immediately sends a notification to the terminal of the relevant staff member, who then quickly tidies up the shelves. This entire process is based on prompts from a generative AI model. For example, a possible prompt might be, "Please describe in detail the mechanism for checking the store's tidiness in real time and reporting any anomalies." In this way, the system effectively supports the operation of commercial facilities and provides an environment where staff can focus on strategic activities.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The server acquires environmental information from monitoring equipment within the facility. Its inputs include real-time data from network cameras and environmental sensors. The server collects this data and stores it as video and numerical data. Specifically, it periodically makes HTTP requests to retrieve camera streams and sensor data.

[0295] Step 2:

[0296] The server processes the acquired data using video analysis technology to evaluate the placement and cleanliness of the products. The input is the previously acquired video data, and the server uses image processing libraries such as OpenCV to perform background subtraction and edge detection on the images to detect anomalies in placement and cleanliness. If an anomaly is found as a result of the analysis, it is output as anomaly detection information.

[0297] Step 3:

[0298] The server generates a task list based on each worker's role and schedule and sends it to their portable device. The input includes worker information and schedule data. The server uses the Google Tasks API to generate the task list in JSON format and sends it to the device. Specifically, it sets role-specific work instructions for each worker and distributes them via the cloud.

[0299] Step 4:

[0300] The terminal displays a task list received from the server to the user, who then performs tasks based on this list. The input is the task list received from the server, and the output is progress data of the tasks performed by the user. Specifically, the terminal's application updates the task list in real time and feeds the progress back to the server.

[0301] Step 5:

[0302] The server monitors the completion status of tasks based on progress data and sends reminders for unfinished tasks. The input is the progress data of each worker, and the output is a reminder message. The server analyzes the progress and performs an operation to notify uncompleted tasks through the Slack API or the like.

[0303] Step 6:

[0304] The server analyzes the sales information within the facility and creates guidelines regarding the sales strategy. Sales data from the POS system is used as the input, and the server processes this using the Pandas library. Trend analysis and report generation are performed, and the results are output to the terminal as strategic guidelines.

[0305] Step 7:

[0306] The terminal provides the generated sales strategy report to the user. The input is the strategic guideline data from the server, and based on this, the user adjusts their sales activities. As a specific operation, the terminal displays the report in PDF format so that the user can view it.

[0307] Step 8:

[0308] The terminal collects visitor information through digital reception and displays the waiting time and reservation status. The input is the data entered by the visitor into the terminal, and the output is the displayed guidance information. As a specific operation, the terminal receives data through QR code scanning or manual input and displays this on the screen for the visitor.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In traditional store operations, staff often struggled to work efficiently when cleaning, checking merchandise displays, managing tasks, or assisting customers. This problem stemmed particularly from insufficient real-time information provision and management, reducing both store operational efficiency and staff productivity. Furthermore, there was a lack of systems to enable staff to respond quickly and effectively when customers visited the store.

[0312] 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.

[0313] In this invention, the server includes means for collecting environmental data from multiple monitoring devices within the store and evaluating the cleanliness of the store and the state of product display using image recognition technology; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable terminal; and means for displaying information to the staff member in real time through a portable visualization device. This allows staff members to obtain accurate information in real time, enabling more efficient store operations and faster customer service.

[0314] A "monitoring device" is a device that collects environmental data within a store, and may include cameras and sensors.

[0315] "Image recognition technology" is a technology that analyzes acquired image data to identify and evaluate specific features.

[0316] "Cleanliness" is a standard used to evaluate the cleanliness of a specific area within a store.

[0317] "Product display condition" refers to the criteria used to evaluate how products are arranged and replenished within a store.

[0318] "Staff" refers to the personnel involved in store operations and their role is to complete tasks.

[0319] A "task list" refers to a list of tasks that a staff member needs to complete.

[0320] A "portable terminal" is a device carried by staff members and used to display task lists and notifications.

[0321] "Sales data" refers to information about product sales at stores and is useful for strategic decision-making.

[0322] A "digital reception device" is a device that receives requests from customers and provides them with information such as waiting times and reservation details.

[0323] A "portable visualization device" refers to a visual device that is portable by staff and used to display information in real time.

[0324] The system of this invention utilizes AI technology to streamline in-store operations and consists of the following components.

[0325] The server receives real-time environmental data from multiple monitoring devices placed within the store. This includes video data from surveillance cameras and state data obtained from various sensors. Based on this environmental data, the server uses image recognition software to evaluate the cleanliness of the store and the condition of the merchandise display. It is assumed that an AI platform such as TensorFlow will be used here.

[0326] The portable devices provided to staff members are responsible for receiving task lists generated by the server. The server dynamically generates task lists based on each staff member's role and priority, and sends them to the portable devices. The progress of the task lists is constantly monitored, and if there are any incomplete tasks, the server sends a reminder to the staff member's device.

[0327] Sales data is analyzed by a server to provide important insights into store operations. The results of this analysis are sent to staff members' portable devices and used to adjust sales strategies.

[0328] The digital reception system has the function of receiving customer requests and transmitting them to a server. This allows customers to be provided with current waiting times and reservation information, enabling staff to respond quickly.

[0329] One of the unique features of this system is that it provides information directly to staff in real time using portable visualization devices. For example, smart glasses are used as visualization devices, displaying store conditions and task lists directly in the staff's field of vision.

[0330] As a concrete example of its use, imagine a staff member preparing a store to open one day. The server detects areas requiring cleaning from monitoring devices and displays them in real time on the cleaning staff member's visualization device. Furthermore, if a popular item runs low on stock, a notification is sent to the relevant staff member prompting them to replenish it quickly. This process improves the efficiency of store operations and enhances customer service.

[0331] An example of a prompt message for a generated AI model is: "We want to develop an application that analyzes current store environment data and notifies staff of inventory shortages and areas that need cleaning. Please suggest a method to link a server with smart glasses and display the situation to store staff in real time."

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The server receives real-time environmental data from multiple monitoring devices placed within the store. This data includes camera footage and sensor information, and image recognition technology is used to extract detectable features based on this data. The input to this operation is raw data from the monitoring devices, and the output is evaluation data that indicates the specific conditions within the store.

[0335] Step 2:

[0336] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. Specifically, it uses an AI model to analyze image data and detect abnormalities or areas that require improvement. The input to this process is the evaluation data obtained in the previous step, and the output is a list of detected abnormalities and areas that need cleaning.

[0337] Step 3:

[0338] The server generates task lists based on each staff member's role and priority. To do this, it retrieves staff information from the database and, taking into account previously obtained evaluation data, creates an optimal task schedule. The inputs are staff information and evaluation data, and the output is a task list for each staff member.

[0339] Step 4:

[0340] The server sends the generated task list to each portable terminal. The terminal receives this information and uses it as a guide to improve the user's work efficiency. The input for this step is the task list, and the output is the guide task information displayed on the terminal.

[0341] Step 5:

[0342] The server analyzes sales data and generates strategic guidelines. Specifically, it uses an AI model to analyze past sales history and identify key products and timings for increasing sales. The input to this process is sales history data, and the output is strategic guidelines.

[0343] Step 6:

[0344] The server transmits strategic guidelines to each staff member's portable device, prompting immediate tactical adjustments. The device receives this information, supporting the streamlining of sales activities. The input is strategic guidelines, and the output is tactical information displayed on the device.

[0345] Step 7:

[0346] The server receives customer requests via a digital reception device and generates waiting times and reservation information. It receives requests from the digital reception device and compares them with the schedule data registered in the system. The input is customer request data, and the output is waiting time and reservation information.

[0347] Step 8:

[0348] Using a portable visualization device, the server transmits information to staff in real time. The visualization device displays the received information directly in the staff's field of vision, facilitating efficient responses. The input for this step is various notification information from the server, and the output is real-time information displayed on the visualization device.

[0349] 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.

[0350] This invention provides a system using AI technology aimed at improving efficiency in store operations and enhancing the customer experience. The invention incorporates an emotion engine that recognizes user emotions, thereby enabling more personalized services for individual customers.

[0351] The server collects environmental and user data from multiple monitoring devices within the store and uses image recognition technology to evaluate the store's cleanliness and the condition of merchandise displays. In addition, an emotion engine analyzes data such as the user's facial expressions, voice, and body movements to evaluate the customer's emotional state.

[0352] The server sends notifications to store staff's portable devices based on the collected sentiment data. These notifications include hints on the best way to respond to customers and product suggestions. Based on this, staff can dynamically adjust their interactions with customers. For example, if the data suggests that a customer viewing a product is showing interest, the device will display a notification suggesting related products.

[0353] Furthermore, the task list generation system generates tasks based on each staff member's role and priority, and distributes them to their portable devices. The server monitors task progress and sends reminders for incomplete tasks. This process utilizes cloud computing, enabling efficient task management in real time.

[0354] The terminal greets customers with a digital check-in system and, based on the information received, displays waiting times and reservation information to the customer. The server uses this data to notify staff and dynamically adjust the in-store environment, thereby improving the overall quality of service.

[0355] As a concrete example, suppose a customer enters a store, and the customer's emotion engine on the terminal captures their facial expression and detects that they are showing curiosity. The server processes this data and sends promotional information for related products to the staff's terminal. The staff can then use this information to naturally begin explaining recommended products to the customer. This can improve the customer's overall purchasing experience throughout the store.

[0356] These systems streamline daily operations while simultaneously enhancing customer satisfaction through emotion-based interactions.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] The server collects environmental data and user sentiment data from multiple monitoring devices placed within the store. This includes camera footage, audio data, and motion sensor data.

[0360] Step 2:

[0361] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, it immediately sends a notification to the terminal.

[0362] Step 3:

[0363] The emotion engine analyzes collected user data to determine the user's emotional state. This allows for real-time understanding of customer emotions such as interest, satisfaction, and dissatisfaction.

[0364] Step 4:

[0365] Based on the analysis results from the emotion engine, the server determines the optimal response method and product recommendations for the customer, and transmits that information to the user's mobile device.

[0366] Step 5:

[0367] The user checks the notifications received from their device, considers how to respond, and then responds to the customer. For example, they might recommend related products to a customer who has shown interest.

[0368] Step 6:

[0369] The server generates task lists based on each staff member's role and priority, and sends these task lists to the staff member's mobile device in real time. This utilizes cloud computing technology.

[0370] Step 7:

[0371] Users check their task list on their mobile devices and work on tasks. They input their progress on their devices and send feedback to the server.

[0372] Step 8:

[0373] The server monitors the progress of the task list and sends reminder notifications to staff members' devices about incomplete tasks.

[0374] Step 9:

[0375] The terminal uses a digital reception system to greet customers, collect necessary information, and display waiting times and reservation information. Based on this information, the server sends instructions to the staff.

[0376] Step 10:

[0377] This allows users (staff) to efficiently manage store operations while appropriately adjusting their actions to enhance customer satisfaction.

[0378] (Example 2)

[0379] 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".

[0380] Modern store operations require efficient operational management and personalized service tailored to each customer. However, traditional systems cannot assess customer emotions in real time and provide appropriate guidance to staff based on that assessment. Furthermore, inadequate monitoring of work progress and notifications of incomplete tasks can lead to decreased operational efficiency and reduced customer satisfaction.

[0381] 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.

[0382] In this invention, the server includes means for collecting environmental and customer information from monitoring devices and evaluating the store's condition and customer sentiment using analytical techniques; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable device; and means for monitoring the progress of the task list and transmitting notifications regarding incomplete tasks to the staff member's device. This enables efficient operations within the store, provides optimized service to each individual customer, and improves the customer experience.

[0383] A "monitoring device" is a device installed in a store to collect environmental information and customer information, and includes sensors such as cameras and microphones.

[0384] "Environmental information" refers to information about the physical conditions inside the store, such as temperature, humidity, lighting, and noise levels.

[0385] "Customer information" refers to data that shows a customer's behavior and emotional state, such as their facial expressions, voice, and body movements.

[0386] "Analysis technology" refers to techniques for extracting specific patterns or meanings based on collected information, and includes image recognition technology and speech analysis technology.

[0387] A "task list" is a list of tasks assigned to staff members, organized by priority and role.

[0388] "Portable equipment" refers to electronic devices carried by staff members and used to receive notifications and task lists.

[0389] "Progress" refers to the status indicating the extent to which each task listed in the task list has been completed.

[0390] "Incomplete tasks" refers to tasks listed on the task list that have not yet been completed.

[0391] This invention is a system aimed at improving efficiency in store operations and enhancing the customer experience. In implementing the system, the server collects environmental and customer information from monitoring devices within the store. The monitoring devices include, for example, sensors such as cameras and microphones, and acquire data on temperature, humidity, lighting conditions, and customer facial expressions and voices within the store.

[0392] The server processes the collected data using analytical techniques. Specifically, it analyzes customer facial expressions using image recognition technology and evaluates their emotional state using a generative AI model. For example, it captures a customer's facial expression while they are looking at products in a store, and the AI ​​model evaluates their movements to determine if they are "showing interest."

[0393] Based on the evaluation results, the server sends a notification to the staff's portable devices. These devices are personal information terminals such as smartphones and tablets, which receive notifications from the server and display them to the staff. The notifications include information on customer service guidelines and product suggestions. For example, they might provide specific guidance such as, "The customer has shown interest. Let's suggest related products."

[0394] Furthermore, the server generates a task list considering each staff member's role and priority, and sends it to their portable device. It also utilizes a cloud-based task management system to monitor each staff member's work progress in real time and send reminders as needed.

[0395] For example, suppose a customer visits a store, and the server collects and analyzes their facial expression data. The emotion engine determines that the customer is "curious." Based on this information, a generative AI model creates promotional information for related products and notifies the staff. The staff can then use this information to proactively explain products to the customer, ultimately improving the customer's purchasing experience.

[0396] An example of a prompt message would be, "Generate the optimal customer service method based on customer sentiment data from the store." This invention makes it possible to simultaneously improve the efficiency of store operations and enhance customer satisfaction.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The server collects environmental and customer information from monitoring devices. Inputs include image data from cameras, audio data from microphones, and environmental data from temperature sensors. The server receives this data and aggregates information from each sensor in the monitoring device in real time. As output, these datasets are sent to the next analysis step.

[0400] Step 2:

[0401] The server processes the collected data using analytical techniques. The input consists of various data collected in step 1. Specifically, image recognition technology is used to analyze image data, and voice analysis technology is used to analyze voice data. As part of the data processing, a generating AI model analyzes the customer's emotions based on their facial expressions and voice, and obtains an output that evaluates the customer's emotional state.

[0402] Step 3:

[0403] The server uses customer sentiment data based on the analysis results to generate notifications on staff members' portable devices. The input is the sentiment evaluation data obtained in step 2. Specifically, it creates customer response guidelines appropriate to the analyzed sentiment and generates a notification that includes suggestions for related products. The output is the specific notification information sent to the staff member's portable device.

[0404] Step 4:

[0405] The terminal displays notifications received from the server to the staff. The input is the notification information generated in step 3. The terminal displays the notification on the screen so that the staff can review it. This allows the staff to obtain the information necessary to provide the best possible service to the customer. The output is an information presentation that the staff can immediately understand.

[0406] Step 5:

[0407] The server uses cloud computing to generate task lists and distribute them to each staff member's portable device. The input is information about each staff member's role and task priorities. Specifically, it uses a task generation algorithm to calculate an efficient task arrangement and create a task list. The output is the task list data sent to the staff member's portable device.

[0408] Step 6:

[0409] The server monitors the progress of the task list and sends reminders if there are any incomplete tasks. The input is real-time updated task progress information. The server identifies incomplete tasks and automatically generates reminder notifications based on that information. The output is the reminder notification sent to the staff member's portable device.

[0410] (Application Example 2)

[0411] 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."

[0412] In today's retail industry, there is a demand for providing optimal service tailored to the individual needs and emotions of each customer. However, due to limited human resources, it is difficult to provide personalized service to all customers. Furthermore, improving employee efficiency and optimizing overall store operations are also crucial challenges. By addressing these challenges, it is necessary to improve customer satisfaction and enhance the efficiency of store operations.

[0413] 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.

[0414] In this invention, the server includes means for aggregating environmental information from numerous sensors within the store and evaluating the store's hygiene and product display status using pattern recognition technology; means for generating a list of tasks based on each employee's role and priority and transmitting the list of tasks to the employee's mobile device; and means for notifying the mobile device of the optimal way to interact with the customer based on emotion data obtained from an emotion analysis device that recognizes the user's emotions. This enables personalized service provision to each customer, as well as improved operational efficiency and optimized store operations.

[0415] A "sensor" is an electronic device that detects environmental information and outputs it as a signal.

[0416] "Environmental information" refers to objective measurement data such as hygiene conditions, temperature, and humidity within the store.

[0417] "Pattern recognition technology" is a technique that identifies regularities and features present in data and uses them to classify or interpret new input data.

[0418] "Hygienic conditions" refers to the cleanliness and orderliness of a store, and includes the degree of disinfection and tidiness.

[0419] "Product display" is a term that describes the arrangement and display method of products, including their visual appeal and ease of access.

[0420] "Employee" refers to an individual employed by a store to perform duties.

[0421] "Portable devices" refer to portable electronic devices capable of sending, receiving, and processing information, and include smartphones and tablets.

[0422] A "task list" is a list that summarizes the tasks necessary for store operations and details the tasks assigned to each employee.

[0423] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0424] "Contact methods" is a concept that refers to the means and approaches for effectively communicating with customers.

[0425] In this invention, a server plays a central role, connecting with multiple sensors to aggregate environmental information within the store. The sensors detect temperature, humidity, noise levels, etc., and are used to evaluate the hygienic conditions and product display status within the store. Pattern recognition technology analyzes this data and extracts useful information about the current state of the store.

[0426] The server has the function of sending task lists to employees' mobile devices. The task list is dynamically generated based on each employee's role and the current store situation, allowing employees to perform their tasks efficiently. The server continuously monitors the progress of tasks, and notifications are sent to the mobile devices for incomplete tasks.

[0427] Furthermore, an emotion analysis device is introduced to analyze the customer's facial expressions, voice, and gestures to recognize their emotional state. Using this data, the server transmits the optimal way to interact with the customer to their mobile device, enabling personalized interactions.

[0428] For example, if a customer shows interest in a technology product, the server can send a notification to a mobile device saying, "The customer is interested in a new product. Please provide a demonstration and explain the details." Such instructions enable employees to communicate more effectively with customers.

[0429] An example of a prompt message might be, "Based on customer sentiment recognition data in a retail store, please design the specifications for an application that recommends the next action." This allows for the use of generative AI models to explore further service improvements.

[0430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0431] Step 1:

[0432] The server collects environmental information as input from sensors placed throughout the store. This information includes temperature, humidity, and noise levels within the store. The server statistically analyzes this data and processes it using pattern recognition technology to evaluate hygiene conditions and the state of product displays.

[0433] Step 2:

[0434] The server dynamically generates a task list using processed store status data, employee roles, and priorities as input. It then creates a task list as output and sends it to employees' mobile devices. This creates a system that allows employees to perform their tasks efficiently.

[0435] Step 3:

[0436] The terminal receives customer facial expressions, voice, and gestures as input from an emotion analysis device. The terminal processes this data with an emotion analysis engine and outputs the customer's emotional state. The server uses this emotional data to build a foundation for determining the appropriate way to interact with the customer.

[0437] Step 4:

[0438] The server uses emotional data to determine the best way to interact with customers and notifies employees of the results via their mobile devices. The output generates instructions, including service strategies and product recommendations for the customer. This notification enables employees to provide personalized service that meets customer expectations.

[0439] Step 5:

[0440] The server continuously monitors the progress of tasks and collects information on incomplete tasks as input. The server analyzes this information to identify tasks that are behind schedule and sends notifications to the relevant employees' mobile devices as output. This helps ensure that high-priority tasks are completed in a timely manner.

[0441] Step 6:

[0442] The user creates prompt statements for the AI ​​model as input, providing instructions to the system. Based on these prompt statements, the system devises new ideas and methods for service improvement and generates suggestions as output. This allows for continuous service improvement.

[0443] 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.

[0444] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] 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.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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".

[0459] This invention provides a system that utilizes AI technology to support daily operations in order to improve the efficiency of store operations. The system is implemented by linking monitoring devices placed within the store with portable terminals.

[0460] The server first acquires environmental data in real time from monitoring devices within the store. This data includes video footage from cameras and status information from sensors. The server analyzes the acquired data using image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, a notification is immediately sent to the relevant terminal. In this way, users (staff) can take immediate action.

[0461] The server also generates task lists based on staff roles and schedules and sends them to each staff member's mobile device. This allows users to effectively manage their tasks and improve work efficiency. In addition, the server monitors the progress of the task lists and sends reminders to the devices if any tasks remain incomplete.

[0462] Furthermore, the server analyzes sales data to identify key focus points in store sales activities. Based on this, it generates strategic guidelines and sends reports to the user's mobile device so that they can appropriately adjust their sales strategy.

[0463] On the terminal side, a digital reception device efficiently collects customer information and informs customers of waiting times and reservation status. Based on the collected information, the server sends notifications to the appropriate users prompting them to respond to the customer. This enables smooth and effective customer service.

[0464] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server checks the overall status of the store from the monitoring device and detects areas that need cleaning. This information is automatically notified to the terminals of the cleaning staff. Next, the server analyzes sales points to adjust the sales strategy according to the day's sales target and distributes this information to the terminals of the sales staff. After the store opens for the day, the terminals register customers and provide real-time information on waiting times. In this way, the present invention utilizes the system to provide advanced support for store operations, allowing staff to concentrate on more strategic tasks.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The server collects environmental data in real time from monitoring devices installed within the store. This includes video data from cameras and situational information from sensors.

[0468] Step 2:

[0469] The server applies image recognition technology to analyze the collected environmental data and evaluates the cleanliness of the store and the arrangement of merchandise displays.

[0470] Step 3:

[0471] Based on the analysis results, if the server detects any abnormalities or areas requiring improvement, it will send a notification to the relevant user's mobile device.

[0472] Step 4:

[0473] The user checks the notification received on their device and confirms the required actions and content for the specified location. The user takes immediate action if necessary.

[0474] Step 5:

[0475] The server generates a task list based on each staff member's role and priority. This task list is then sent to each user's mobile device via the cloud.

[0476] Step 6:

[0477] Users receive a task list on their device and can create effective work plans by reviewing the task content and priority.

[0478] Step 7:

[0479] The server monitors the progress of each task in real time and sends a notification to the user's device if any tasks in the task list remain incomplete.

[0480] Step 8:

[0481] The terminal provides customers with a digital check-in function, allowing them to input waiting times and reservation information. The server then uses this information to issue instructions to the necessary staff.

[0482] (Example 1)

[0483] 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."

[0484] Modern commercial facilities demand efficient operations and improved customer service. However, manually checking store cleanliness and product placement is time-consuming and prone to human error. Furthermore, it's difficult to provide instructions to individual staff and track progress, hindering the rapid implementation of effective sales strategies. Prompt and accurate responses to visitors also remain a challenge. A solution is needed to address these issues while simultaneously improving overall operational efficiency and customer service quality.

[0485] 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.

[0486] In this invention, the server includes means for acquiring environmental information from monitoring equipment and evaluating the cleanliness of the facility and product placement using video analysis technology; means for generating a list of tasks based on the role and priority of each worker and transmitting the corresponding list of tasks to the workers' mobile devices; and means for sending real-time notifications when an anomaly is detected based on information analysis. This makes it possible to significantly improve the operational efficiency and service quality of the store.

[0487] "Surveillance equipment" refers to devices, including cameras and sensors, installed within stores and facilities to acquire environmental information.

[0488] "Environmental information" refers to all data that indicates the conditions of a store or facility, such as temperature, humidity, and video data.

[0489] "Video analysis technology" is a technique that uses acquired video data to judge and evaluate specific conditions, and it utilizes image processing algorithms.

[0490] "Facility" refers to a space used for providing specific services or operations, including commercial facilities and shops.

[0491] "Cleanliness status" refers to information indicating the degree of cleanliness and tidiness of the floors, shelves, and other fixtures within the facility.

[0492] "Product placement" refers to information regarding the arrangement and layout of products displayed within a facility.

[0493] A "task list" refers to a list of tasks and duties that each worker is responsible for performing.

[0494] "Portable devices" refer to portable electronic devices carried by each worker for receiving and displaying information.

[0495] "Information analysis" refers to the technique of analyzing the meaning and trends associated with acquired environmental and sales information.

[0496] Anomaly detection is a process that automatically identifies situations that deviate from the normal state.

[0497] "Real-time notification" refers to the process of sending relevant information to the device without delay immediately after an anomaly is detected.

[0498] "Sales information" refers to data related to transactions conducted within the facility, including sales data.

[0499] The system of this invention is designed to streamline the operation of commercial facilities and stores and improve customer service. The system consists of the following important elements:

[0500] The server acquires environmental information from multiple monitoring devices installed within the facility. These monitoring devices include network cameras and various sensors, and the data is sent to the server in real time. The acquired video data is analyzed using OpenCV and other image processing libraries to evaluate the cleanliness of the facility and the placement of products. If an anomaly is detected through this data processing and evaluation, a notification is immediately sent to the relevant terminal using Firebase Cloud Messaging (FCM), etc.

[0501] The terminal is an electronic device such as a smartphone or tablet carried by the user. A dedicated application is installed to receive notifications and instructions from the server, providing information in real time. For example, in the event of an anomaly detection, an alert is immediately sent to the worker, enabling them to respond quickly to the problem. The terminal also functions as a digital reception device, efficiently collecting visitor information and providing waiting times and reservation information.

[0502] Users act as facility staff, following instructions from the server via portable devices. They perform daily tasks based on a task list generated by the server, updating the task list's progress in real time as needed. The task list is generated via the Google Tasks API, enabling smooth updates and sharing through cloud computing.

[0503] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server processes data from monitoring equipment and detects any disarray in the arrangement of merchandise on the shelves. In response, the server immediately sends a notification to the terminal of the relevant staff member, who then quickly tidies up the shelves. This entire process is based on prompts from a generative AI model. For example, a possible prompt might be, "Please describe in detail the mechanism for checking the store's tidiness in real time and reporting any anomalies." In this way, the system effectively supports the operation of commercial facilities and provides an environment where staff can focus on strategic activities.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The server acquires environmental information from monitoring equipment within the facility. Its inputs include real-time data from network cameras and environmental sensors. The server collects this data and stores it as video and numerical data. Specifically, it periodically makes HTTP requests to retrieve camera streams and sensor data.

[0507] Step 2:

[0508] The server processes the acquired data using video analysis technology to evaluate the placement and cleanliness of the products. The input is the previously acquired video data, and the server uses image processing libraries such as OpenCV to perform background subtraction and edge detection on the images to detect anomalies in placement and cleanliness. If an anomaly is found as a result of the analysis, it is output as anomaly detection information.

[0509] Step 3:

[0510] The server generates a task list based on each worker's role and schedule and sends it to their portable device. The input includes worker information and schedule data. The server uses the Google Tasks API to generate the task list in JSON format and sends it to the device. Specifically, it sets role-specific work instructions for each worker and distributes them via the cloud.

[0511] Step 4:

[0512] The terminal displays a task list received from the server to the user, who then performs tasks based on this list. The input is the task list received from the server, and the output is progress data of the tasks performed by the user. Specifically, the terminal's application updates the task list in real time and feeds the progress back to the server.

[0513] Step 5:

[0514] The server monitors task completion status based on progress data and sends reminders for incomplete tasks. Input is progress data from each worker, and output is reminder messages. The server analyzes the progress and notifies users of uncompleted tasks via the Slack API or similar.

[0515] Step 6:

[0516] The server analyzes sales information within the facility and creates guidelines for sales strategies. Sales data from the POS system is used as input, and the server processes this data using the Pandas library. It performs trend analysis and generates reports, outputting the results to terminals as strategic guidelines.

[0517] Step 7:

[0518] The terminal provides the user with a generated sales strategy report. Input is strategic guidance data from the server, which the user uses to adjust their sales activities. Specifically, the terminal displays the report in PDF format, making it available for the user to view.

[0519] Step 8:

[0520] The terminal collects visitor information through a digital reception system and displays waiting times and reservation status. Input is data entered by the visitor into the terminal, and output is the displayed information. Specifically, the terminal receives data by scanning a QR code or manual input and displays it on the screen for the visitor.

[0521] (Application Example 1)

[0522] 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."

[0523] In traditional store operations, staff often struggled to work efficiently when cleaning, checking merchandise displays, managing tasks, or assisting customers. This problem stemmed particularly from insufficient real-time information provision and management, reducing both store operational efficiency and staff productivity. Furthermore, there was a lack of systems to enable staff to respond quickly and effectively when customers visited the store.

[0524] 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.

[0525] In this invention, the server includes means for collecting environmental data from multiple monitoring devices within the store and evaluating the cleanliness of the store and the state of product display using image recognition technology; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable terminal; and means for displaying information to the staff member in real time through a portable visualization device. This allows staff members to obtain accurate information in real time, enabling more efficient store operations and faster customer service.

[0526] A "monitoring device" is a device that collects environmental data within a store, and may include cameras and sensors.

[0527] "Image recognition technology" is a technology that analyzes acquired image data to identify and evaluate specific features.

[0528] "Cleanliness" is a standard used to evaluate the cleanliness of a specific area within a store.

[0529] "Product display condition" refers to the criteria used to evaluate how products are arranged and replenished within a store.

[0530] "Staff" refers to the personnel involved in store operations and their role is to complete tasks.

[0531] A "task list" refers to a list of tasks that a staff member needs to complete.

[0532] A "portable terminal" is a device carried by staff members and used to display task lists and notifications.

[0533] "Sales data" refers to information about product sales at stores and is useful for strategic decision-making.

[0534] A "digital reception device" is a device that receives requests from customers and provides them with information such as waiting times and reservation details.

[0535] A "portable visualization device" refers to a visual device that is portable by staff and used to display information in real time.

[0536] The system of this invention utilizes AI technology to streamline in-store operations and consists of the following components.

[0537] The server receives real-time environmental data from multiple monitoring devices placed within the store. This includes video data from surveillance cameras and state data obtained from various sensors. Based on this environmental data, the server uses image recognition software to evaluate the cleanliness of the store and the condition of the merchandise display. It is assumed that an AI platform such as TensorFlow will be used here.

[0538] The portable devices provided to staff members are responsible for receiving task lists generated by the server. The server dynamically generates task lists based on each staff member's role and priority, and sends them to the portable devices. The progress of the task lists is constantly monitored, and if there are any incomplete tasks, the server sends a reminder to the staff member's device.

[0539] Sales data is analyzed by a server to provide important insights into store operations. The results of this analysis are sent to staff members' portable devices and used to adjust sales strategies.

[0540] The digital reception system has the function of receiving customer requests and transmitting them to a server. This allows customers to be provided with current waiting times and reservation information, enabling staff to respond quickly.

[0541] One of the unique features of this system is that it provides information directly to staff in real time using portable visualization devices. For example, smart glasses are used as visualization devices, displaying store conditions and task lists directly in the staff's field of vision.

[0542] As a concrete example of its use, imagine a staff member preparing a store to open one day. The server detects areas requiring cleaning from monitoring devices and displays them in real time on the cleaning staff member's visualization device. Furthermore, if a popular item runs low on stock, a notification is sent to the relevant staff member prompting them to replenish it quickly. This process improves the efficiency of store operations and enhances customer service.

[0543] An example of a prompt message for a generated AI model is: "We want to develop an application that analyzes current store environment data and notifies staff of inventory shortages and areas that need cleaning. Please suggest a method to link a server with smart glasses and display the situation to store staff in real time."

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] The server receives real-time environmental data from multiple monitoring devices placed within the store. This data includes camera footage and sensor information, and image recognition technology is used to extract detectable features based on this data. The input to this operation is raw data from the monitoring devices, and the output is evaluation data that indicates the specific conditions within the store.

[0547] Step 2:

[0548] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. Specifically, it uses an AI model to analyze image data and detect abnormalities or areas that require improvement. The input to this process is the evaluation data obtained in the previous step, and the output is a list of detected abnormalities and areas that need cleaning.

[0549] Step 3:

[0550] The server generates task lists based on each staff member's role and priority. To do this, it retrieves staff information from the database and, taking into account previously obtained evaluation data, creates an optimal task schedule. The inputs are staff information and evaluation data, and the output is a task list for each staff member.

[0551] Step 4:

[0552] The server sends the generated task list to each portable terminal. The terminal receives this information and uses it as a guide to improve the user's work efficiency. The input for this step is the task list, and the output is the guide task information displayed on the terminal.

[0553] Step 5:

[0554] The server analyzes sales data and generates strategic guidelines. Specifically, it uses an AI model to analyze past sales history and identify key products and timings for increasing sales. The input to this process is sales history data, and the output is strategic guidelines.

[0555] Step 6:

[0556] The server transmits strategic guidelines to each staff member's portable device, prompting immediate tactical adjustments. The device receives this information, supporting the streamlining of sales activities. The input is strategic guidelines, and the output is tactical information displayed on the device.

[0557] Step 7:

[0558] The server receives customer requests via a digital reception device and generates waiting times and reservation information. It receives requests from the digital reception device and compares them with the schedule data registered in the system. The input is customer request data, and the output is waiting time and reservation information.

[0559] Step 8:

[0560] Using a portable visualization device, the server transmits information to staff in real time. The visualization device displays the received information directly in the staff's field of vision, facilitating efficient responses. The input for this step is various notification information from the server, and the output is real-time information displayed on the visualization device.

[0561] 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.

[0562] This invention provides a system using AI technology aimed at improving efficiency in store operations and enhancing the customer experience. The invention incorporates an emotion engine that recognizes user emotions, thereby enabling more personalized services for individual customers.

[0563] The server collects environmental and user data from multiple monitoring devices within the store and uses image recognition technology to evaluate the store's cleanliness and the condition of merchandise displays. In addition, an emotion engine analyzes data such as the user's facial expressions, voice, and body movements to evaluate the customer's emotional state.

[0564] The server sends notifications to store staff's portable devices based on the collected sentiment data. These notifications include hints on the best way to respond to customers and product suggestions. Based on this, staff can dynamically adjust their interactions with customers. For example, if the data suggests that a customer viewing a product is showing interest, the device will display a notification suggesting related products.

[0565] Furthermore, the task list generation system generates tasks based on each staff member's role and priority, and distributes them to their portable devices. The server monitors task progress and sends reminders for incomplete tasks. This process utilizes cloud computing, enabling efficient task management in real time.

[0566] The terminal greets customers with a digital check-in system and, based on the information received, displays waiting times and reservation information to the customer. The server uses this data to notify staff and dynamically adjust the in-store environment, thereby improving the overall quality of service.

[0567] As a concrete example, suppose a customer enters a store, and the customer's emotion engine on the terminal captures their facial expression and detects that they are showing curiosity. The server processes this data and sends promotional information for related products to the staff's terminal. The staff can then use this information to naturally begin explaining recommended products to the customer. This can improve the customer's overall purchasing experience throughout the store.

[0568] These systems streamline daily operations while simultaneously enhancing customer satisfaction through emotion-based interactions.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The server collects environmental data and user sentiment data from multiple monitoring devices placed within the store. This includes camera footage, audio data, and motion sensor data.

[0572] Step 2:

[0573] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, it immediately sends a notification to the terminal.

[0574] Step 3:

[0575] The emotion engine analyzes collected user data to determine the user's emotional state. This allows for real-time understanding of customer emotions such as interest, satisfaction, and dissatisfaction.

[0576] Step 4:

[0577] Based on the analysis results from the emotion engine, the server determines the optimal response method and product recommendations for the customer, and transmits that information to the user's mobile device.

[0578] Step 5:

[0579] The user checks the notifications received from their device, considers how to respond, and then responds to the customer. For example, they might recommend related products to a customer who has shown interest.

[0580] Step 6:

[0581] The server generates task lists based on each staff member's role and priority, and sends these task lists to the staff member's mobile device in real time. This utilizes cloud computing technology.

[0582] Step 7:

[0583] Users check their task list on their mobile devices and work on tasks. They input their progress on their devices and send feedback to the server.

[0584] Step 8:

[0585] The server monitors the progress of the task list and sends reminder notifications to staff members' devices about incomplete tasks.

[0586] Step 9:

[0587] The terminal uses a digital reception system to greet customers, collect necessary information, and display waiting times and reservation information. Based on this information, the server sends instructions to the staff.

[0588] Step 10:

[0589] This allows users (staff) to efficiently manage store operations while appropriately adjusting their actions to enhance customer satisfaction.

[0590] (Example 2)

[0591] 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."

[0592] Modern store operations require efficient operational management and personalized service tailored to each customer. However, traditional systems cannot assess customer emotions in real time and provide appropriate guidance to staff based on that assessment. Furthermore, inadequate monitoring of work progress and notifications of incomplete tasks can lead to decreased operational efficiency and reduced customer satisfaction.

[0593] 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.

[0594] In this invention, the server includes means for collecting environmental and customer information from monitoring devices and evaluating the store's condition and customer sentiment using analytical techniques; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable device; and means for monitoring the progress of the task list and transmitting notifications regarding incomplete tasks to the staff member's device. This enables efficient operations within the store, provides optimized service to each individual customer, and improves the customer experience.

[0595] A "monitoring device" is a device installed in a store to collect environmental information and customer information, and includes sensors such as cameras and microphones.

[0596] "Environmental information" refers to information about the physical conditions inside the store, such as temperature, humidity, lighting, and noise levels.

[0597] "Customer information" refers to data that shows a customer's behavior and emotional state, such as their facial expressions, voice, and body movements.

[0598] "Analysis technology" refers to techniques for extracting specific patterns or meanings based on collected information, and includes image recognition technology and speech analysis technology.

[0599] A "task list" is a list of tasks assigned to staff members, organized by priority and role.

[0600] "Portable equipment" refers to electronic devices carried by staff members and used to receive notifications and task lists.

[0601] "Progress" refers to the status indicating the extent to which each task listed in the task list has been completed.

[0602] "Incomplete tasks" refers to tasks listed on the task list that have not yet been completed.

[0603] This invention is a system aimed at improving efficiency in store operations and enhancing the customer experience. In implementing the system, the server collects environmental and customer information from monitoring devices within the store. The monitoring devices include, for example, sensors such as cameras and microphones, and acquire data on temperature, humidity, lighting conditions, and customer facial expressions and voices within the store.

[0604] The server processes the collected data using analytical techniques. Specifically, it analyzes customer facial expressions using image recognition technology and evaluates their emotional state using a generative AI model. For example, it captures a customer's facial expression while they are looking at products in a store, and the AI ​​model evaluates their movements to determine if they are "showing interest."

[0605] Based on the evaluation results, the server sends a notification to the staff's portable devices. These devices are personal information terminals such as smartphones and tablets, which receive notifications from the server and display them to the staff. The notifications include information on customer service guidelines and product suggestions. For example, they might provide specific guidance such as, "The customer has shown interest. Let's suggest related products."

[0606] Furthermore, the server generates a task list considering each staff member's role and priority, and sends it to their portable device. It also utilizes a cloud-based task management system to monitor each staff member's work progress in real time and send reminders as needed.

[0607] For example, suppose a customer visits a store, and the server collects and analyzes their facial expression data. The emotion engine determines that the customer is "curious." Based on this information, a generative AI model creates promotional information for related products and notifies the staff. The staff can then use this information to proactively explain products to the customer, ultimately improving the customer's purchasing experience.

[0608] An example of a prompt message would be, "Generate the optimal customer service method based on customer sentiment data from the store." This invention makes it possible to simultaneously improve the efficiency of store operations and enhance customer satisfaction.

[0609] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0610] Step 1:

[0611] The server collects environmental and customer information from monitoring devices. Inputs include image data from cameras, audio data from microphones, and environmental data from temperature sensors. The server receives this data and aggregates information from each sensor in the monitoring device in real time. As output, these datasets are sent to the next analysis step.

[0612] Step 2:

[0613] The server processes the collected data using analytical techniques. The input consists of various data collected in step 1. Specifically, image recognition technology is used to analyze image data, and voice analysis technology is used to analyze voice data. As part of the data processing, a generating AI model analyzes the customer's emotions based on their facial expressions and voice, and obtains an output that evaluates the customer's emotional state.

[0614] Step 3:

[0615] The server uses customer sentiment data based on the analysis results to generate notifications on staff members' portable devices. The input is the sentiment evaluation data obtained in step 2. Specifically, it creates customer response guidelines appropriate to the analyzed sentiment and generates a notification that includes suggestions for related products. The output is the specific notification information sent to the staff member's portable device.

[0616] Step 4:

[0617] The terminal displays notifications received from the server to the staff. The input is the notification information generated in step 3. The terminal displays the notification on the screen so that the staff can review it. This allows the staff to obtain the information necessary to provide the best possible service to the customer. The output is an information presentation that the staff can immediately understand.

[0618] Step 5:

[0619] The server uses cloud computing to generate task lists and distribute them to each staff member's portable device. The input is information about each staff member's role and task priorities. Specifically, it uses a task generation algorithm to calculate an efficient task arrangement and create a task list. The output is the task list data sent to the staff member's portable device.

[0620] Step 6:

[0621] The server monitors the progress of the task list and sends reminders if there are any incomplete tasks. The input is real-time updated task progress information. The server identifies incomplete tasks and automatically generates reminder notifications based on that information. The output is the reminder notification sent to the staff member's portable device.

[0622] (Application Example 2)

[0623] 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."

[0624] In today's retail industry, there is a demand for providing optimal service tailored to the individual needs and emotions of each customer. However, due to limited human resources, it is difficult to provide personalized service to all customers. Furthermore, improving employee efficiency and optimizing overall store operations are also crucial challenges. By addressing these challenges, it is necessary to improve customer satisfaction and enhance the efficiency of store operations.

[0625] 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.

[0626] In this invention, the server includes means for aggregating environmental information from numerous sensors within the store and evaluating the store's hygiene and product display status using pattern recognition technology; means for generating a list of tasks based on each employee's role and priority and transmitting the list of tasks to the employee's mobile device; and means for notifying the mobile device of the optimal way to interact with the customer based on emotion data obtained from an emotion analysis device that recognizes the user's emotions. This enables personalized service provision to each customer, as well as improved operational efficiency and optimized store operations.

[0627] A "sensor" is an electronic device that detects environmental information and outputs it as a signal.

[0628] "Environmental information" refers to objective measurement data such as hygiene conditions, temperature, and humidity within the store.

[0629] "Pattern recognition technology" is a technique that identifies regularities and features present in data and uses them to classify or interpret new input data.

[0630] "Hygienic conditions" refers to the cleanliness and orderliness of a store, and includes the degree of disinfection and tidiness.

[0631] "Product display" is a term that describes the arrangement and display method of products, including their visual appeal and ease of access.

[0632] "Employee" refers to an individual employed by a store to perform duties.

[0633] "Portable devices" refer to portable electronic devices capable of sending, receiving, and processing information, and include smartphones and tablets.

[0634] A "task list" is a list that summarizes the tasks necessary for store operations and details the tasks assigned to each employee.

[0635] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0636] "Contact methods" is a concept that refers to the means and approaches for effectively communicating with customers.

[0637] In this invention, a server plays a central role, connecting with multiple sensors to aggregate environmental information within the store. The sensors detect temperature, humidity, noise levels, etc., and are used to evaluate the hygienic conditions and product display status within the store. Pattern recognition technology analyzes this data and extracts useful information about the current state of the store.

[0638] The server has the function of sending task lists to employees' mobile devices. The task list is dynamically generated based on each employee's role and the current store situation, allowing employees to perform their tasks efficiently. The server continuously monitors the progress of tasks, and notifications are sent to the mobile devices for incomplete tasks.

[0639] Furthermore, an emotion analysis device is introduced to analyze the customer's facial expressions, voice, and gestures to recognize their emotional state. Using this data, the server transmits the optimal way to interact with the customer to their mobile device, enabling personalized interactions.

[0640] For example, if a customer shows interest in a technology product, the server can send a notification to a mobile device saying, "The customer is interested in a new product. Please provide a demonstration and explain the details." Such instructions enable employees to communicate more effectively with customers.

[0641] An example of a prompt message might be, "Based on customer sentiment recognition data in a retail store, please design the specifications for an application that recommends the next action." This allows for the use of generative AI models to explore further service improvements.

[0642] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0643] Step 1:

[0644] The server collects environmental information as input from sensors placed throughout the store. This information includes temperature, humidity, and noise levels within the store. The server statistically analyzes this data and processes it using pattern recognition technology to evaluate hygiene conditions and the state of product displays.

[0645] Step 2:

[0646] The server dynamically generates a task list using processed store status data, employee roles, and priorities as input. It then creates a task list as output and sends it to employees' mobile devices. This creates a system that allows employees to perform their tasks efficiently.

[0647] Step 3:

[0648] The terminal receives customer facial expressions, voice, and gestures as input from an emotion analysis device. The terminal processes this data with an emotion analysis engine and outputs the customer's emotional state. The server uses this emotional data to build a foundation for determining the appropriate way to interact with the customer.

[0649] Step 4:

[0650] The server uses emotional data to determine the best way to interact with customers and notifies employees of the results via their mobile devices. The output generates instructions, including service strategies and product recommendations for the customer. This notification enables employees to provide personalized service that meets customer expectations.

[0651] Step 5:

[0652] The server continuously monitors the progress of tasks and collects information on incomplete tasks as input. The server analyzes this information to identify tasks that are behind schedule and sends notifications to the relevant employees' mobile devices as output. This helps ensure that high-priority tasks are completed in a timely manner.

[0653] Step 6:

[0654] The user creates prompt statements for the AI ​​model as input, providing instructions to the system. Based on these prompt statements, the system devises new ideas and methods for service improvement and generates suggestions as output. This allows for continuous service improvement.

[0655] 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.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] 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.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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).

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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".

[0672] This invention provides a system that utilizes AI technology to support daily operations in order to improve the efficiency of store operations. The system is implemented by linking monitoring devices placed within the store with portable terminals.

[0673] The server first acquires environmental data in real time from monitoring devices within the store. This data includes video footage from cameras and status information from sensors. The server analyzes the acquired data using image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, a notification is immediately sent to the relevant terminal. In this way, users (staff) can take immediate action.

[0674] The server also generates task lists based on staff roles and schedules and sends them to each staff member's mobile device. This allows users to effectively manage their tasks and improve work efficiency. In addition, the server monitors the progress of the task lists and sends reminders to the devices if any tasks remain incomplete.

[0675] Furthermore, the server analyzes sales data to identify key focus points in store sales activities. Based on this, it generates strategic guidelines and sends reports to the user's mobile device so that they can appropriately adjust their sales strategy.

[0676] On the terminal side, a digital reception device efficiently collects customer information and informs customers of waiting times and reservation status. Based on the collected information, the server sends notifications to the appropriate users prompting them to respond to the customer. This enables smooth and effective customer service.

[0677] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server checks the overall status of the store from the monitoring device and detects areas that need cleaning. This information is automatically notified to the terminals of the cleaning staff. Next, the server analyzes sales points to adjust the sales strategy according to the day's sales target and distributes this information to the terminals of the sales staff. After the store opens for the day, the terminals register customers and provide real-time information on waiting times. In this way, the present invention utilizes the system to provide advanced support for store operations, allowing staff to concentrate on more strategic tasks.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The server collects environmental data in real time from monitoring devices installed within the store. This includes video data from cameras and situational information from sensors.

[0681] Step 2:

[0682] The server applies image recognition technology to analyze the collected environmental data and evaluates the cleanliness of the store and the arrangement of merchandise displays.

[0683] Step 3:

[0684] Based on the analysis results, if the server detects any abnormalities or areas requiring improvement, it will send a notification to the relevant user's mobile device.

[0685] Step 4:

[0686] The user checks the notification received on their device and confirms the required actions and content for the specified location. The user takes immediate action if necessary.

[0687] Step 5:

[0688] The server generates a task list based on each staff member's role and priority. This task list is then sent to each user's mobile device via the cloud.

[0689] Step 6:

[0690] Users receive a task list on their device and can create effective work plans by reviewing the task content and priority.

[0691] Step 7:

[0692] The server monitors the progress of each task in real time and sends a notification to the user's device if any tasks in the task list remain incomplete.

[0693] Step 8:

[0694] The terminal provides customers with a digital check-in function, allowing them to input waiting times and reservation information. The server then uses this information to issue instructions to the necessary staff.

[0695] (Example 1)

[0696] 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".

[0697] Modern commercial facilities demand efficient operations and improved customer service. However, manually checking store cleanliness and product placement is time-consuming and prone to human error. Furthermore, it's difficult to provide instructions to individual staff and track progress, hindering the rapid implementation of effective sales strategies. Prompt and accurate responses to visitors also remain a challenge. A solution is needed to address these issues while simultaneously improving overall operational efficiency and customer service quality.

[0698] 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.

[0699] In this invention, the server includes means for acquiring environmental information from monitoring equipment and evaluating the cleanliness of the facility and product placement using video analysis technology; means for generating a list of tasks based on the role and priority of each worker and transmitting the corresponding list of tasks to the workers' mobile devices; and means for sending real-time notifications when an anomaly is detected based on information analysis. This makes it possible to significantly improve the operational efficiency and service quality of the store.

[0700] "Surveillance equipment" refers to devices, including cameras and sensors, installed within stores and facilities to acquire environmental information.

[0701] "Environmental information" refers to all data that indicates the conditions of a store or facility, such as temperature, humidity, and video data.

[0702] "Video analysis technology" is a technique that uses acquired video data to judge and evaluate specific conditions, and it utilizes image processing algorithms.

[0703] "Facility" refers to a space used for providing specific services or operations, including commercial facilities and shops.

[0704] "Cleanliness status" refers to information indicating the degree of cleanliness and tidiness of the floors, shelves, and other fixtures within the facility.

[0705] "Product placement" refers to information regarding the arrangement and layout of products displayed within a facility.

[0706] A "task list" refers to a list of tasks and duties that each worker is responsible for performing.

[0707] "Portable devices" refer to portable electronic devices carried by each worker for receiving and displaying information.

[0708] "Information analysis" refers to the technique of analyzing the meaning and trends associated with acquired environmental and sales information.

[0709] Anomaly detection is a process that automatically identifies situations that deviate from the normal state.

[0710] "Real-time notification" refers to the process of sending relevant information to the device without delay immediately after an anomaly is detected.

[0711] "Sales information" refers to data related to transactions conducted within the facility, including sales data.

[0712] The system of this invention is designed to streamline the operation of commercial facilities and stores and improve customer service. The system consists of the following important elements:

[0713] The server acquires environmental information from multiple monitoring devices installed within the facility. These monitoring devices include network cameras and various sensors, and the data is sent to the server in real time. The acquired video data is analyzed using OpenCV and other image processing libraries to evaluate the cleanliness of the facility and the placement of products. If an anomaly is detected through this data processing and evaluation, a notification is immediately sent to the relevant terminal using Firebase Cloud Messaging (FCM), etc.

[0714] The terminal is an electronic device such as a smartphone or tablet carried by the user. A dedicated application is installed to receive notifications and instructions from the server, providing information in real time. For example, in the event of an anomaly detection, an alert is immediately sent to the worker, enabling them to respond quickly to the problem. The terminal also functions as a digital reception device, efficiently collecting visitor information and providing waiting times and reservation information.

[0715] Users act as facility staff, following instructions from the server via portable devices. They perform daily tasks based on a task list generated by the server, updating the task list's progress in real time as needed. The task list is generated via the Google Tasks API, enabling smooth updates and sharing through cloud computing.

[0716] As a concrete example, consider a scenario where the system is in operation at a store on a given day. First thing in the morning, the server processes data from monitoring equipment and detects any disarray in the arrangement of merchandise on the shelves. In response, the server immediately sends a notification to the terminal of the relevant staff member, who then quickly tidies up the shelves. This entire process is based on prompts from a generative AI model. For example, a possible prompt might be, "Please describe in detail the mechanism for checking the store's tidiness in real time and reporting any anomalies." In this way, the system effectively supports the operation of commercial facilities and provides an environment where staff can focus on strategic activities.

[0717] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0718] Step 1:

[0719] The server acquires environmental information from monitoring equipment within the facility. Its inputs include real-time data from network cameras and environmental sensors. The server collects this data and stores it as video and numerical data. Specifically, it periodically makes HTTP requests to retrieve camera streams and sensor data.

[0720] Step 2:

[0721] The server processes the acquired data using video analysis technology to evaluate the placement and cleanliness of the products. The input is the previously acquired video data, and the server uses image processing libraries such as OpenCV to perform background subtraction and edge detection on the images to detect anomalies in placement and cleanliness. If an anomaly is found as a result of the analysis, it is output as anomaly detection information.

[0722] Step 3:

[0723] The server generates a task list based on each worker's role and schedule and sends it to their portable device. The input includes worker information and schedule data. The server uses the Google Tasks API to generate the task list in JSON format and sends it to the device. Specifically, it sets role-specific work instructions for each worker and distributes them via the cloud.

[0724] Step 4:

[0725] The terminal displays a task list received from the server to the user, who then performs tasks based on this list. The input is the task list received from the server, and the output is progress data of the tasks performed by the user. Specifically, the terminal's application updates the task list in real time and feeds the progress back to the server.

[0726] Step 5:

[0727] The server monitors task completion status based on progress data and sends reminders for incomplete tasks. Input is progress data from each worker, and output is reminder messages. The server analyzes the progress and notifies users of uncompleted tasks via the Slack API or similar.

[0728] Step 6:

[0729] The server analyzes sales information within the facility and creates guidelines for sales strategies. Sales data from the POS system is used as input, and the server processes this data using the Pandas library. It performs trend analysis and generates reports, outputting the results to terminals as strategic guidelines.

[0730] Step 7:

[0731] The terminal provides the user with a generated sales strategy report. Input is strategic guidance data from the server, which the user uses to adjust their sales activities. Specifically, the terminal displays the report in PDF format, making it available for the user to view.

[0732] Step 8:

[0733] The terminal collects visitor information through a digital reception system and displays waiting times and reservation status. Input is data entered by the visitor into the terminal, and output is the displayed information. Specifically, the terminal receives data by scanning a QR code or manual input and displays it on the screen for the visitor.

[0734] (Application Example 1)

[0735] 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".

[0736] In traditional store operations, staff often struggled to work efficiently when cleaning, checking merchandise displays, managing tasks, or assisting customers. This problem stemmed particularly from insufficient real-time information provision and management, reducing both store operational efficiency and staff productivity. Furthermore, there was a lack of systems to enable staff to respond quickly and effectively when customers visited the store.

[0737] 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.

[0738] In this invention, the server includes means for collecting environmental data from multiple monitoring devices within the store and evaluating the cleanliness of the store and the state of product display using image recognition technology; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable terminal; and means for displaying information to the staff member in real time through a portable visualization device. This allows staff members to obtain accurate information in real time, enabling more efficient store operations and faster customer service.

[0739] A "monitoring device" is a device that collects environmental data within a store, and may include cameras and sensors.

[0740] "Image recognition technology" is a technology that analyzes acquired image data to identify and evaluate specific features.

[0741] "Cleanliness" is a standard used to evaluate the cleanliness of a specific area within a store.

[0742] "Product display condition" refers to the criteria used to evaluate how products are arranged and replenished within a store.

[0743] "Staff" refers to the personnel involved in store operations and their role is to complete tasks.

[0744] A "task list" refers to a list of tasks that a staff member needs to complete.

[0745] A "portable terminal" is a device carried by staff members and used to display task lists and notifications.

[0746] "Sales data" refers to information about product sales at stores and is useful for strategic decision-making.

[0747] A "digital reception device" is a device that receives requests from customers and provides them with information such as waiting times and reservation details.

[0748] A "portable visualization device" refers to a visual device that is portable by staff and used to display information in real time.

[0749] The system of this invention utilizes AI technology to streamline in-store operations and consists of the following components.

[0750] The server receives real-time environmental data from multiple monitoring devices placed within the store. This includes video data from surveillance cameras and state data obtained from various sensors. Based on this environmental data, the server uses image recognition software to evaluate the cleanliness of the store and the condition of the merchandise display. It is assumed that an AI platform such as TensorFlow will be used here.

[0751] The portable devices provided to staff members are responsible for receiving task lists generated by the server. The server dynamically generates task lists based on each staff member's role and priority, and sends them to the portable devices. The progress of the task lists is constantly monitored, and if there are any incomplete tasks, the server sends a reminder to the staff member's device.

[0752] Sales data is analyzed by a server to provide important insights into store operations. The results of this analysis are sent to staff members' portable devices and used to adjust sales strategies.

[0753] The digital reception system has the function of receiving customer requests and transmitting them to a server. This allows customers to be provided with current waiting times and reservation information, enabling staff to respond quickly.

[0754] One of the unique features of this system is that it provides information directly to staff in real time using portable visualization devices. For example, smart glasses are used as visualization devices, displaying store conditions and task lists directly in the staff's field of vision.

[0755] As a concrete example of its use, imagine a staff member preparing a store to open one day. The server detects areas requiring cleaning from monitoring devices and displays them in real time on the cleaning staff member's visualization device. Furthermore, if a popular item runs low on stock, a notification is sent to the relevant staff member prompting them to replenish it quickly. This process improves the efficiency of store operations and enhances customer service.

[0756] An example of a prompt message for a generated AI model is: "We want to develop an application that analyzes current store environment data and notifies staff of inventory shortages and areas that need cleaning. Please suggest a method to link a server with smart glasses and display the situation to store staff in real time."

[0757] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0758] Step 1:

[0759] The server receives real-time environmental data from multiple monitoring devices placed within the store. This data includes camera footage and sensor information, and image recognition technology is used to extract detectable features based on this data. The input to this operation is raw data from the monitoring devices, and the output is evaluation data that indicates the specific conditions within the store.

[0760] Step 2:

[0761] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. Specifically, it uses an AI model to analyze image data and detect abnormalities or areas that require improvement. The input to this process is the evaluation data obtained in the previous step, and the output is a list of detected abnormalities and areas that need cleaning.

[0762] Step 3:

[0763] The server generates task lists based on each staff member's role and priority. To do this, it retrieves staff information from the database and, taking into account previously obtained evaluation data, creates an optimal task schedule. The inputs are staff information and evaluation data, and the output is a task list for each staff member.

[0764] Step 4:

[0765] The server sends the generated task list to each portable terminal. The terminal receives this information and uses it as a guide to improve the user's work efficiency. The input for this step is the task list, and the output is the guide task information displayed on the terminal.

[0766] Step 5:

[0767] The server analyzes sales data and generates strategic guidelines. Specifically, it uses an AI model to analyze past sales history and identify key products and timings for increasing sales. The input to this process is sales history data, and the output is strategic guidelines.

[0768] Step 6:

[0769] The server transmits strategic guidelines to each staff member's portable device, prompting immediate tactical adjustments. The device receives this information, supporting the streamlining of sales activities. The input is strategic guidelines, and the output is tactical information displayed on the device.

[0770] Step 7:

[0771] The server receives customer requests via a digital reception device and generates waiting times and reservation information. It receives requests from the digital reception device and compares them with the schedule data registered in the system. The input is customer request data, and the output is waiting time and reservation information.

[0772] Step 8:

[0773] Using a portable visualization device, the server transmits information to staff in real time. The visualization device displays the received information directly in the staff's field of vision, facilitating efficient responses. The input for this step is various notification information from the server, and the output is real-time information displayed on the visualization device.

[0774] 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.

[0775] This invention provides a system using AI technology aimed at improving efficiency in store operations and enhancing the customer experience. The invention incorporates an emotion engine that recognizes user emotions, thereby enabling more personalized services for individual customers.

[0776] The server collects environmental and user data from multiple monitoring devices within the store and uses image recognition technology to evaluate the store's cleanliness and the condition of merchandise displays. In addition, an emotion engine analyzes data such as the user's facial expressions, voice, and body movements to evaluate the customer's emotional state.

[0777] The server sends notifications to store staff's portable devices based on the collected sentiment data. These notifications include hints on the best way to respond to customers and product suggestions. Based on this, staff can dynamically adjust their interactions with customers. For example, if the data suggests that a customer viewing a product is showing interest, the device will display a notification suggesting related products.

[0778] Furthermore, the task list generation system generates tasks based on each staff member's role and priority, and distributes them to their portable devices. The server monitors task progress and sends reminders for incomplete tasks. This process utilizes cloud computing, enabling efficient task management in real time.

[0779] The terminal greets customers with a digital check-in system and, based on the information received, displays waiting times and reservation information to the customer. The server uses this data to notify staff and dynamically adjust the in-store environment, thereby improving the overall quality of service.

[0780] As a concrete example, suppose a customer enters a store, and the customer's emotion engine on the terminal captures their facial expression and detects that they are showing curiosity. The server processes this data and sends promotional information for related products to the staff's terminal. The staff can then use this information to naturally begin explaining recommended products to the customer. This can improve the customer's overall purchasing experience throughout the store.

[0781] These systems streamline daily operations while simultaneously enhancing customer satisfaction through emotion-based interactions.

[0782] The following describes the processing flow.

[0783] Step 1:

[0784] The server collects environmental data and user sentiment data from multiple monitoring devices placed within the store. This includes camera footage, audio data, and motion sensor data.

[0785] Step 2:

[0786] The server uses image recognition technology to evaluate the cleanliness of the store and the condition of the merchandise display. If an anomaly is detected, it immediately sends a notification to the terminal.

[0787] Step 3:

[0788] The emotion engine analyzes collected user data to determine the user's emotional state. This allows for real-time understanding of customer emotions such as interest, satisfaction, and dissatisfaction.

[0789] Step 4:

[0790] Based on the analysis results from the emotion engine, the server determines the optimal response method and product recommendations for the customer, and transmits that information to the user's mobile device.

[0791] Step 5:

[0792] The user checks the notifications received from their device, considers how to respond, and then responds to the customer. For example, they might recommend related products to a customer who has shown interest.

[0793] Step 6:

[0794] The server generates task lists based on each staff member's role and priority, and sends these task lists to the staff member's mobile device in real time. This utilizes cloud computing technology.

[0795] Step 7:

[0796] Users check their task list on their mobile devices and work on tasks. They input their progress on their devices and send feedback to the server.

[0797] Step 8:

[0798] The server monitors the progress of the task list and sends reminder notifications to staff members' devices about incomplete tasks.

[0799] Step 9:

[0800] The terminal uses a digital reception system to greet customers, collect necessary information, and display waiting times and reservation information. Based on this information, the server sends instructions to the staff.

[0801] Step 10:

[0802] This allows users (staff) to efficiently manage store operations while appropriately adjusting their actions to enhance customer satisfaction.

[0803] (Example 2)

[0804] 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".

[0805] Modern store operations require efficient operational management and personalized service tailored to each customer. However, traditional systems cannot assess customer emotions in real time and provide appropriate guidance to staff based on that assessment. Furthermore, inadequate monitoring of work progress and notifications of incomplete tasks can lead to decreased operational efficiency and reduced customer satisfaction.

[0806] 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.

[0807] In this invention, the server includes means for collecting environmental and customer information from monitoring devices and evaluating the store's condition and customer sentiment using analytical techniques; means for generating a task list based on each staff member's role and priority and transmitting the task list to the staff member's portable device; and means for monitoring the progress of the task list and transmitting notifications regarding incomplete tasks to the staff member's device. This enables efficient operations within the store, provides optimized service to each individual customer, and improves the customer experience.

[0808] A "monitoring device" is a device installed in a store to collect environmental information and customer information, and includes sensors such as cameras and microphones.

[0809] "Environmental information" refers to information about the physical conditions inside the store, such as temperature, humidity, lighting, and noise levels.

[0810] "Customer information" refers to data that shows a customer's behavior and emotional state, such as their facial expressions, voice, and body movements.

[0811] "Analysis technology" refers to techniques for extracting specific patterns or meanings based on collected information, and includes image recognition technology and speech analysis technology.

[0812] A "task list" is a list of tasks assigned to staff members, organized by priority and role.

[0813] "Portable equipment" refers to electronic devices carried by staff members and used to receive notifications and task lists.

[0814] "Progress" refers to the status indicating the extent to which each task listed in the task list has been completed.

[0815] "Incomplete tasks" refers to tasks listed on the task list that have not yet been completed.

[0816] This invention is a system aimed at improving efficiency in store operations and enhancing the customer experience. In implementing the system, the server collects environmental and customer information from monitoring devices within the store. The monitoring devices include, for example, sensors such as cameras and microphones, and acquire data on temperature, humidity, lighting conditions, and customer facial expressions and voices within the store.

[0817] The server processes the collected data using analytical techniques. Specifically, it analyzes customer facial expressions using image recognition technology and evaluates their emotional state using a generative AI model. For example, it captures a customer's facial expression while they are looking at products in a store, and the AI ​​model evaluates their movements to determine if they are "showing interest."

[0818] Based on the evaluation results, the server sends a notification to the staff's portable devices. These devices are personal information terminals such as smartphones and tablets, which receive notifications from the server and display them to the staff. The notifications include information on customer service guidelines and product suggestions. For example, they might provide specific guidance such as, "The customer has shown interest. Let's suggest related products."

[0819] Furthermore, the server generates a task list considering each staff member's role and priority, and sends it to their portable device. It also utilizes a cloud-based task management system to monitor each staff member's work progress in real time and send reminders as needed.

[0820] For example, suppose a customer visits a store, and the server collects and analyzes their facial expression data. The emotion engine determines that the customer is "curious." Based on this information, a generative AI model creates promotional information for related products and notifies the staff. The staff can then use this information to proactively explain products to the customer, ultimately improving the customer's purchasing experience.

[0821] An example of a prompt message would be, "Generate the optimal customer service method based on customer sentiment data from the store." This invention makes it possible to simultaneously improve the efficiency of store operations and enhance customer satisfaction.

[0822] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0823] Step 1:

[0824] The server collects environmental and customer information from monitoring devices. Inputs include image data from cameras, audio data from microphones, and environmental data from temperature sensors. The server receives this data and aggregates information from each sensor in the monitoring device in real time. As output, these datasets are sent to the next analysis step.

[0825] Step 2:

[0826] The server processes the collected data using analytical techniques. The input consists of various data collected in step 1. Specifically, image recognition technology is used to analyze image data, and voice analysis technology is used to analyze voice data. As part of the data processing, a generating AI model analyzes the customer's emotions based on their facial expressions and voice, and obtains an output that evaluates the customer's emotional state.

[0827] Step 3:

[0828] The server uses customer sentiment data based on the analysis results to generate notifications on staff members' portable devices. The input is the sentiment evaluation data obtained in step 2. Specifically, it creates customer response guidelines appropriate to the analyzed sentiment and generates a notification that includes suggestions for related products. The output is the specific notification information sent to the staff member's portable device.

[0829] Step 4:

[0830] The terminal displays notifications received from the server to the staff. The input is the notification information generated in step 3. The terminal displays the notification on the screen so that the staff can review it. This allows the staff to obtain the information necessary to provide the best possible service to the customer. The output is an information presentation that the staff can immediately understand.

[0831] Step 5:

[0832] The server uses cloud computing to generate task lists and distribute them to each staff member's portable device. The input is information about each staff member's role and task priorities. Specifically, it uses a task generation algorithm to calculate an efficient task arrangement and create a task list. The output is the task list data sent to the staff member's portable device.

[0833] Step 6:

[0834] The server monitors the progress of the task list and sends reminders if there are any incomplete tasks. The input is real-time updated task progress information. The server identifies incomplete tasks and automatically generates reminder notifications based on that information. The output is the reminder notification sent to the staff member's portable device.

[0835] (Application Example 2)

[0836] 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".

[0837] In today's retail industry, there is a demand for providing optimal service tailored to the individual needs and emotions of each customer. However, due to limited human resources, it is difficult to provide personalized service to all customers. Furthermore, improving employee efficiency and optimizing overall store operations are also crucial challenges. By addressing these challenges, it is necessary to improve customer satisfaction and enhance the efficiency of store operations.

[0838] 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.

[0839] In this invention, the server includes means for aggregating environmental information from numerous sensors within the store and evaluating the store's hygiene and product display status using pattern recognition technology; means for generating a list of tasks based on each employee's role and priority and transmitting the list of tasks to the employee's mobile device; and means for notifying the mobile device of the optimal way to interact with the customer based on emotion data obtained from an emotion analysis device that recognizes the user's emotions. This enables personalized service provision to each customer, as well as improved operational efficiency and optimized store operations.

[0840] A "sensor" is an electronic device that detects environmental information and outputs it as a signal.

[0841] "Environmental information" refers to objective measurement data such as hygiene conditions, temperature, and humidity within the store.

[0842] "Pattern recognition technology" is a technique that identifies regularities and features present in data and uses them to classify or interpret new input data.

[0843] "Hygienic conditions" refers to the cleanliness and orderliness of a store, and includes the degree of disinfection and tidiness.

[0844] "Product display" is a term that describes the arrangement and display method of products, including their visual appeal and ease of access.

[0845] "Employee" refers to an individual employed by a store to perform duties.

[0846] "Portable devices" refer to portable electronic devices capable of sending, receiving, and processing information, and include smartphones and tablets.

[0847] A "task list" is a list that summarizes the tasks necessary for store operations and details the tasks assigned to each employee.

[0848] An "emotion analysis device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0849] "Contact methods" is a concept that refers to the means and approaches for effectively communicating with customers.

[0850] In this invention, a server plays a central role, connecting with multiple sensors to aggregate environmental information within the store. The sensors detect temperature, humidity, noise levels, etc., and are used to evaluate the hygienic conditions and product display status within the store. Pattern recognition technology analyzes this data and extracts useful information about the current state of the store.

[0851] The server has the function of sending task lists to employees' mobile devices. The task list is dynamically generated based on each employee's role and the current store situation, allowing employees to perform their tasks efficiently. The server continuously monitors the progress of tasks, and notifications are sent to the mobile devices for incomplete tasks.

[0852] Furthermore, an emotion analysis device is introduced to analyze the customer's facial expressions, voice, and gestures to recognize their emotional state. Using this data, the server transmits the optimal way to interact with the customer to their mobile device, enabling personalized interactions.

[0853] For example, if a customer shows interest in a technology product, the server can send a notification to a mobile device saying, "The customer is interested in a new product. Please provide a demonstration and explain the details." Such instructions enable employees to communicate more effectively with customers.

[0854] An example of a prompt message might be, "Based on customer sentiment recognition data in a retail store, please design the specifications for an application that recommends the next action." This allows for the use of generative AI models to explore further service improvements.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The server collects environmental information as input from sensors placed throughout the store. This information includes temperature, humidity, and noise levels within the store. The server statistically analyzes this data and processes it using pattern recognition technology to evaluate hygiene conditions and the state of product displays.

[0858] Step 2:

[0859] The server dynamically generates a task list using processed store status data, employee roles, and priorities as input. It then creates a task list as output and sends it to employees' mobile devices. This creates a system that allows employees to perform their tasks efficiently.

[0860] Step 3:

[0861] The terminal receives customer facial expressions, voice, and gestures as input from an emotion analysis device. The terminal processes this data with an emotion analysis engine and outputs the customer's emotional state. The server uses this emotional data to build a foundation for determining the appropriate way to interact with the customer.

[0862] Step 4:

[0863] The server uses emotional data to determine the best way to interact with customers and notifies employees of the results via their mobile devices. The output generates instructions, including service strategies and product recommendations for the customer. This notification enables employees to provide personalized service that meets customer expectations.

[0864] Step 5:

[0865] The server continuously monitors the progress of tasks and collects information on incomplete tasks as input. The server analyzes this information to identify tasks that are behind schedule and sends notifications to the relevant employees' mobile devices as output. This helps ensure that high-priority tasks are completed in a timely manner.

[0866] Step 6:

[0867] The user creates prompt statements for the AI ​​model as input, providing instructions to the system. Based on these prompt statements, the system devises new ideas and methods for service improvement and generates suggestions as output. This allows for continuous service improvement.

[0868] 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.

[0869] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0875] 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.

[0876] 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."

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] A means for collecting environmental data from multiple monitoring devices within a store and evaluating the cleanliness of the store and the condition of product displays using image recognition technology,

[0892] A means for generating a task list based on each staff member's role and priority, and transmitting the task list to the staff member's portable device,

[0893] A means for monitoring the progress of the task list and sending notifications to the staff member's terminal regarding incomplete tasks,

[0894] A means for analyzing sales data to generate guidelines regarding sales strategies and providing those guidelines to the terminal,

[0895] A digital reception device that receives customer requests and provides the customer with waiting time and reservation information, and a means for notifying the staff who should be assisting the customer of the information,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the image recognition technology analyzes image data from a monitoring device to automatically detect anomalies within a designated area of ​​the store.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein the task list generation means generates and distributes tasks using cloud computing.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A means for acquiring environmental information from monitoring equipment and evaluating the cleanliness of a facility and product placement using video analysis technology,

[0904] A means for generating a list of tasks based on each worker's role and priority, and transmitting the corresponding list of tasks to the worker's mobile device,

[0905] Means for monitoring the progress of the task list and sending notifications to the mobile device for incomplete tasks,

[0906] A means for analyzing sales information to generate guidelines regarding sales policies and providing those guidelines to the mobile device,

[0907] A digital reception system that receives visitor requests and provides visitors with waiting times and appointment details, and means for notifying the appropriate worker of the information,

[0908] A means of sending real-time notifications when an anomaly is detected based on information analysis,

[0909] A means for analyzing the progress of task lists and optimizing instructions through individual portable devices,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, wherein the video analysis technology processes video information from monitoring equipment to automatically detect abnormalities within a designated area of ​​the facility.

[0913] (Claim 3)

[0914] The system according to claim 1, wherein the task list generation means generates and distributes tasks using a data processing platform.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A means for collecting environmental data from multiple monitoring devices within a store and evaluating the cleanliness of the store and the condition of product displays using image recognition technology,

[0918] A means for generating a task list based on each staff member's role and priority, and transmitting the task list to the staff member's portable device,

[0919] A means for monitoring the progress of the task list and sending notifications to the staff member's terminal regarding incomplete tasks,

[0920] A means for analyzing sales data to generate guidelines regarding sales strategies and providing those guidelines to the terminal,

[0921] A digital reception device that receives customer requests and provides the customer with waiting time and reservation information, and a means for notifying the staff who should be assisting the customer of the information,

[0922] A means of displaying information to staff in real time through a portable visualization device,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the image recognition technology analyzes image data from a monitoring device to automatically detect anomalies within a designated area of ​​the store.

[0926] (Claim 3)

[0927] The system according to claim 1, wherein the task list generation means generates and distributes tasks using cloud computing.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] A means for collecting environmental and customer information from monitoring devices and evaluating the store's condition and customer sentiment using analytical techniques,

[0931] A means for generating a task list based on each staff member's role and priority, and transmitting the task list to the staff member's portable device,

[0932] Means for monitoring the progress of the task list and sending notifications regarding incomplete tasks to the staff's devices,

[0933] A means for generating customer response guidelines based on analysis results and providing said guidelines to the device,

[0934] A device that receives customer requests and provides the customer with waiting time and reservation information, and a means for notifying the staff who should respond,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, wherein the analysis technology analyzes information from a monitoring device to automatically detect abnormal situations and customer emotions within the store.

[0938] (Claim 3)

[0939] The system according to claim 1, wherein the business list generation means generates and distributes business using distributed processing technology.

[0940] "Application example 2 when combining with an emotional engine"

[0941] (Claim 1)

[0942] A means for aggregating environmental information from numerous sensors within a store and evaluating the store's hygiene and product display conditions using pattern recognition technology,

[0943] A means for generating a list of tasks based on each employee's role and priority, and transmitting the list of tasks to the employee's mobile device,

[0944] Means for monitoring the progress of the task list and sending notifications regarding incomplete tasks to the employee's device,

[0945] Means for analyzing trading data to generate policies regarding trading strategies and providing said policies to the device,

[0946] An electronic reception system is provided to receive visitor requests and display waiting times and reservation information to the visitors, and a means of communicating information to the employee who is to handle the request.

[0947] A means for notifying the mobile device of the optimal method of contacting the customer based on emotional data obtained from an emotion analysis device that recognizes the user's emotions,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, wherein the pattern recognition technology analyzes image information from a sensor to automatically detect anomalies within a specified area of ​​the store.

[0951] (Claim 3)

[0952] The system according to claim 1, wherein the task list generation means generates and distributes tasks using cloud computing. [Explanation of symbols]

[0953] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting environmental data from multiple monitoring devices within a store and evaluating the cleanliness of the store and the condition of product displays using image recognition technology, A means for generating a task list based on each staff member's role and priority, and transmitting the task list to the staff member's portable device, A means for monitoring the progress of the task list and sending notifications to the staff member's terminal regarding incomplete tasks, A means for analyzing sales data to generate guidelines regarding sales strategies and providing those guidelines to the terminal, A digital reception device that receives customer requests and provides the customer with waiting time and reservation information, and a means for notifying the staff who should be assisting the customer of the information, A means of displaying information to staff in real time through a portable visualization device, A system that includes this.

2. The system according to claim 1, wherein the image recognition technology analyzes image data from a monitoring device to automatically detect anomalies within a designated area of ​​the store.

3. The system according to claim 1, wherein the task list generation means generates and distributes tasks using cloud computing.

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