system

Abstract

We provide the system. [Solution] A means of collecting anonymized movement data and analyzing visitor behavior patterns, A means of integrating SNS data and purchase history data to infer customer preferences and lifestyles, A method for analyzing population dynamics by time of day and evaluating the impact of competing stores, A means for generating market area analysis and potential customer profiles, and creating reports to optimize store opening strategies, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In conventional store strategies and marketing measures, there is a lack of detailed analysis based on customer behavior and preferences. As a result, there is a problem that it is difficult to formulate an effective store-opening strategy and targeted marketing. In addition, there is a problem that the reliability of analysis decreases due to noise and insufficient accuracy of the collected data. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects and analyzes anonymized movement data to gain a detailed understanding of customer behavior patterns, and further integrates social media data and purchase history to precisely grasp customer preferences. It also analyzes population dynamics by time of day and the impact of competing stores, enabling the development of optimal store opening strategies in response to environmental changes within the trading area. Furthermore, data cleaning and noise reduction improve the accuracy and reliability of the analysis. This system supports strategic decision-making based on customer profiles. 【0006】 "Anonymized movement data" refers to data, including location information, that is collected in a way that does not identify individuals, and is used for the purpose of analyzing the movement and trends of visitors. 【0007】 "Visitor behavior patterns" refer to a series of data that show the characteristics and habits of visitors' movements, such as the routes they take and how long they stay at each location. 【0008】 "SNS data" refers to user activity logs, posting history, and information about interpersonal relationships generated on social networking services. 【0009】 "Purchase history data" refers to detailed information about products a user has purchased or services they have used in the past. 【0010】 "Customer preferences" refer to information about customers' likes and preferences for specific products or services, and contribute to the development of marketing strategies. 【0011】 A "market area" refers to a specific geographical area that is influenced by and attracts customers to a particular store or facility. 【0012】 A "potential customer" refers to a prospective customer who is not currently a customer but is highly likely to use a product or service in the future. 【0013】 "Population dynamics" refers to information that shows the detailed demographic composition of a particular region, including the age, gender, occupation, and household structure of its residents. 【0014】 "Influence of competing stores" refers to the impact that competing stores or facilities have on attracting customers and increasing sales for one's own store. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of 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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0029】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 This invention relates to an AI system for gaining a deep understanding of customer trends in commercial facilities using location information and related data. The system consists of three main components: a server, a terminal, and a user. 【0037】 The server collects anonymized movement data and uses it to analyze visitor behavior patterns. This analysis includes measuring visit frequency, routes, and duration of stay within a specific area. The server also receives social media data and purchase history data with user consent and integrates this data to infer customer preferences and lifestyles. Furthermore, the server cleans the collected data to remove noise and improve the reliability of the analysis. 【0038】 The terminal provides a dashboard that visualizes in detail the characteristics of the trading area and population dynamics by time of day, based on analysis results received from the server. This dashboard is designed to be easy for users to understand and includes data plotted on a map and geographical information of competing stores. 【0039】 Through this dashboard, users can consider store opening strategies and marketing measures. For example, when a commercial facility opens a new store, users can identify the most effective location and target customer base based on visitor inflow data and competitor analysis provided by this system. Users can also obtain lists of potential customers and receive support in developing targeted advertising strategies using this information. 【0040】 In this way, this system analyzes customer behavior in physical stores from all angles, greatly supporting corporate decision-making. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The server collects anonymized location information from mobile devices. This uses GPS data and Wi-Fi connection information obtained from the user's mobile device. 【0044】 Step 2: 【0045】 The server cleans the collected location data and removes noise. It sorts the data chronologically and filters out outliers to prepare it for optimal analysis. 【0046】 Step 3: 【0047】 The server analyzes the cleaned data to identify visitor movement patterns, frequency of visits, and length of stay. In particular, it visualizes customer routes and visit trends to understand their movement behavior. 【0048】 Step 4: 【0049】 Users agree to provide social media data and purchase history data to the server. The server receives this data and performs more comprehensive and in-depth analysis by integrating it with location data. 【0050】 Step 5: 【0051】 The server uses location information and other data to conduct a detailed analysis of demographic trends and the impact of competing stores by time of day. This allows it to measure customer flow within the trading area and the degree to which competition affects customer acquisition. 【0052】 Step 6: 【0053】 The terminal displays the analysis results provided by the server on a dashboard. Through this dashboard, users can visually confirm the market area analysis results and the characteristics of potential customers. 【0054】 Step 7: 【0055】 Users utilize the information on the dashboard to develop store opening strategies and targeted marketing initiatives. This decision-making support, based on actual customer behavior data, contributes to business success. 【0056】 (Example 1) 【0057】 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." 【0058】 To effectively formulate store opening strategies for commercial facilities, it is necessary to comprehensively understand customer behavior patterns, preferences, and the surrounding competitive environment. However, this data is usually provided from separate sources, making it difficult to integrate and analyze in real time. Furthermore, there is a lack of means to provide data in a form that ensures reliability, is visualized, and can be used for management decision-making. 【0059】 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. 【0060】 In this invention, the server includes means for collecting anonymized location data and analyzing crowd behavior patterns, means for integrating social network data and transaction history data to infer consumer preferences and behavioral characteristics, and means for analyzing human movements by time of day and evaluating the impact of the competitive environment. This enables commercial facility managers to analyze comprehensive data in real time and make optimal decisions based on visualized information. 【0061】 "Anonymized location data" refers to information about the location of devices or individuals within a specific area, which has been processed in a way that does not allow for the identification of individuals. 【0062】 "Crowd behavior patterns" is an analysis that examines the movement and behavioral tendencies of a large number of individuals or groups, revealing behavioral characteristics based on time and place. 【0063】 "Social network data" refers to information about relationships and activities between users through online platforms and services, including data such as friendships and interaction history. 【0064】 "Transaction history data" refers to information about purchases and payments made by consumers in the past, and includes data on the purchase history of goods and services. 【0065】 "Consumer preferences" refer to consumers' likes and tendencies in choosing products and services that interest them. 【0066】 "Behavioral characteristics" refer to the specific behavioral patterns and attitudes that consumers or individuals exhibit in particular situations. 【0067】 "Human behavior by time of day" refers to information obtained by analyzing the patterns of people's movement and activities during specific time periods. 【0068】 "The impact of the competitive environment" refers to the effect that the presence of competing businesses or stores has on other stores or businesses in a particular market or area. 【0069】 "Regional market analysis" involves analyzing consumer trends and economic activities within a specific region to clarify market characteristics and trends. 【0070】 "Characteristics of potential consumers" refers to the profiles and behavioral tendencies of individuals who are not yet recognized as customers but who may use a product or service in the future. 【0071】 This invention relates to an AI system that comprehensively analyzes and visualizes customer trends and market environment data necessary for the management of commercial facilities. Specific embodiments are described below. 【0072】 The server first collects anonymized location data. This is done using GPS tracking devices and mobile device location services, for example. This data can be stored on cloud services such as Google Cloud Platform or Amazon Web Services. 【0073】 Next, the server cleans the collected data. Specifically, it uses the Python pandas library to remove outliers and noise, improving data reliability. This ensures the accuracy of the analysis results. 【0074】 The server then integrates additional data, such as social network data and transaction history data. This process utilizes machine learning algorithms to build models for predicting consumer preferences and behavioral characteristics. Analysis is performed using R or Python, and this data is then organically combined. 【0075】 The terminal receives analysis results from the server and displays them in a visually easy-to-understand format. This uses data visualization tools such as Tableau and Power BI. The dashboard on the terminal plots information on human activity and the competitive environment by time period on a map, allowing users to intuitively understand the data. 【0076】 Users utilize this visualized data to make strategic decisions. For example, they can understand peaks based on customer inflow data during specific time periods and implement promotions accordingly. Another example of a prompt for the generated AI model based on the provided data is a request such as, "Please suggest the most effective store location." In this way, the system greatly supports management decisions in commercial facilities. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The server collects anonymized location data. Input data includes location information obtained from GPS tracking devices and mobile terminals. Based on this, the server receives and stores the data, accumulating it on a cloud service. The output of this step is an initial dataset showing visitor movement patterns within the geographical area of ​​a commercial facility. 【0080】 Step 2: 【0081】 The server cleans the collected location data. The input includes the initial dataset accumulated in step 1. The data is processed using the Python pandas library to remove outliers and noise. The output is a reliable, clean dataset, which will be used in the next analysis step. 【0082】 Step 3: 【0083】 The server analyzes crowd behavior patterns based on clean location data. The input data is the clean dataset, which is the output of step 2. Machine learning algorithms are applied to perform specific actions to extract behavioral characteristics. The output is the analysis results showing visitor behavior patterns. 【0084】 Step 4: 【0085】 The server integrates social network data and transaction history data. Inputs include analysis results of behavioral patterns and additional data sources. Based on this, a procedure is performed to infer consumer preferences and behavioral characteristics and generate profiles. The output is a detailed consumer profile. 【0086】 Step 5: 【0087】 The terminal uses data received from the server to create a visualized dashboard. Inputs include consumer profiles and behavioral pattern analysis results. Tableau and Power BI are used to perform specific visualization actions and display them to the user. The output is an intuitive visual interface that supports strategic decision-making. 【0088】 Step 6: 【0089】 Users make strategic decisions using visualized dashboards. The input is dashboard information provided by the device. Users analyze the data and take specific actions to consider promotions and store opening strategies based on specific time periods. The output is actionable business strategies. 【0090】 (Application Example 1) 【0091】 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." 【0092】 Traditional customer behavior analysis systems in commercial facilities have not been sufficiently effective in real-time customer behavior monitoring and in proposing efficient sales promotion strategies. As a result, optimizing store opening strategies and promotion plans is time-consuming and costly, leading to a lack of competitiveness against rivals. 【0093】 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. 【0094】 In this invention, the server includes means for collecting anonymized movement data and analyzing visitor behavior patterns, means for integrating social network data and consumption history data to infer customer preferences and lifestyles, and means for monitoring customer trends in real time in conjunction with location information and proposing efficient sales promotion strategies. This enables the optimization of efficient and effective store opening strategies and promotion plans through market area analysis and the generation of potential customer profiles. 【0095】 "Anonymized movement data" refers to location data that has been processed in a way that does not identify individuals, and is used to collect visitors' location history and movement patterns. 【0096】 "Methods for analyzing behavioral patterns" refer to methods that analyze visitor frequency, routes, and length of stay based on collected movement data to reveal specific trends and behavioral models. 【0097】 "Social network data" refers to data about users' interests and activities obtained through online platforms and networks. 【0098】 "Consumption history data" refers to data about purchases made by customers in the past, and represents their consumption trends and purchasing power. 【0099】 "Means for inferring preferences and lifestyles" refers to methods of analyzing social network data and consumption history data to infer the interests, preferences, and daily life patterns of individual customers. 【0100】 "A means of monitoring customer behavior in real time in conjunction with location information" refers to a method of tracking customer movements and locations at the present moment by combining current location data with real-time information processing. 【0101】 "A means of proposing an efficient sales promotion strategy" refers to a method of advising on optimal marketing activities and product placement aimed at increasing sales, based on collected and analyzed data. 【0102】 "Market area analysis" is a process that involves a detailed analysis of demographics and visitor data within a target area to evaluate the economic activity and market potential of that area. 【0103】 "Profile generation of potential customers" is a method of creating information that serves as the basis for marketing and targeting by inferring the characteristics and attributes of individuals who may become future customers. 【0104】 The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server collects anonymized movement data, social network data, and consumption history data, and runs a program to analyze customer behavior patterns, preferences, and lifestyles based on this data. The server is built using Python and utilizes libraries such as pandas, numpy, and scikit-learn for data collection and analysis. Geopy is used to process location data with high accuracy and analyze real-time trends. 【0105】 The terminal plays a role in visualizing the analysis results received from the server, assisting users in making strategic decisions. Using matplotlib and seaborn for visualization, detailed plots of demographics and visitor patterns are displayed on the dashboard, making them intuitively understandable to users. 【0106】 Users can easily obtain information to optimize commercial facility layout changes and promotional activities through a dashboard provided on their devices. For example, if a store is found to receive many customers in the afternoon, users can effectively place protein bars and related products and conduct promotional activities. 【0107】 The generative AI model proposes a variety of strategies based on the prompts used for data analysis. For example, the following prompts are used: 【0108】 "We created a prompt for a customer behavior analysis app for commercial facilities. It's a support tool for devising optimal promotional strategies based on location data and behavioral patterns. Based on the following conditions…" 【0109】 Based on this prompt, the AI ​​model can propose even more sophisticated marketing strategies. 【0110】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0111】 Step 1: 【0112】 The server collects anonymized movement data from various data sources. It receives location data transmitted from mobile devices as input. This data is processed through a collection module and standardized using geopy. The output is a formatted movement dataset, which establishes the basis for the dynamic movement patterns of individual visitors. 【0113】 Step 2: 【0114】 The server integrates social network data and consumption history data to infer customer preferences and lifestyles. Input includes SNS data obtained via API and purchase history from a database. Data cleaning is performed using the pandas library to eliminate inconsistencies. The output is a cleaned, integrated dataset. This process forms a customer behavior model. 【0115】 Step 3: 【0116】 The server monitors customer behavior in real time and proposes efficient sales promotion strategies. Real-time location data and an integrated dataset are referenced as input. Clustering analysis is performed using scikit-learn to identify preferred routes and locations within the store. Recommended promotional areas are generated as output. These suggestions are further optimized by a generative AI model. 【0117】 Step 4: 【0118】 The terminal displays the analysis results received from the server as a visualized dashboard. The input is the analysis results sent from the server. Using matplotlib and seaborn, appropriate charts and heatmaps are generated. The output is a visualized dashboard that is intuitive and easy for the user to understand. 【0119】 Step 5: 【0120】 Users utilize a dashboard on their device to optimize commercial facility layout changes and sales promotion strategies. The input consists of analytical data and prompts displayed on the dashboard. Based on this data, users develop action plans for appropriate product placement and promotions. The output is an efficient and results-oriented strategic implementation plan. 【0121】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0122】 This invention is an advanced analytical system that integrates location data, social media data, and purchase history data, and further combines them with an emotion engine to support strategic decision-making for commercial facilities. The system mainly consists of three main components: a server, terminals, and users. 【0123】 The server collects anonymized movement data and extracts visitor behavior patterns. After data cleaning and noise reduction, it measures movement routes, visit frequency, and dwell time. It also gains a deep understanding of customer preferences based on social media data and purchase history. By introducing an emotion engine, it performs sentiment analysis of natural language obtained from social media data to recognize the customer's emotional state. This analysis identifies which emotions are at the root of purchase intent and interest in services. 【0124】 The terminal visualizes the analysis results sent from the server as a dashboard and displays it to the user. The dashboard displays data that reflects the user's emotional state, along with market area analysis, competitive impact, and information on potential customers. Based on this information, users can optimize their store opening strategies and develop targeted marketing strategies. 【0125】 Users can select appropriate contact methods and timings based on customer emotional data provided by the emotion engine. For example, when promoting a new product, users can choose a time when customers are experiencing positive emotions and launch promotional activities accordingly. Furthermore, emotional data can be used to analyze customers' potential purchasing motivations for specific products or services, enabling more personalized marketing strategies. 【0126】 Thus, this system provides strong support for the operation and marketing activities of commercial facilities through a comprehensive approach that combines data analysis and sentiment recognition. 【0127】 The following describes the processing flow. 【0128】 Step 1: 【0129】 The server collects anonymized location data from mobile devices. This includes GPS data and Wi-Fi connection history, and is used to record visitors' movements. 【0130】 Step 2: 【0131】 The server cleans and removes noise from the collected location data. It organizes the data to improve the accuracy of the analysis by removing outliers and formatting the data. 【0132】 Step 3: 【0133】 The server analyzes visitor behavior patterns based on their location information. This involves calculating visit timing, frequency, and duration, and using this data to predict visitor behavior. 【0134】 Step 4: 【0135】 Users consent to the provision of social media data and purchase history data, which the server uses to analyze customer preferences. The data is processed anonymously and used in a privacy-protected manner. 【0136】 Step 5: 【0137】 The server analyzes the received SNS data using an emotion engine and extracts the user's emotions from the text. This process classifies the emotions into categories such as positive, negative, and neutral, allowing the server to understand each customer's emotional state. 【0138】 Step 6: 【0139】 The server integrates location information, purchase history, and sentiment data to generate customer profiles. These profiles help identify potential customers within the service area based on individual customers' purchasing motivations and interest in the service. 【0140】 Step 7: 【0141】 The terminal displays analysis results from the server on a dashboard. The dashboard visualizes market area analysis, competitive impact, and sentiment profiles, and is presented in a format that is easy for the user to understand. 【0142】 Step 8: 【0143】 Users utilize the information on the dashboard to plan store opening strategies and implement marketing measures. In particular, they can use sentiment data to select the most effective timing for campaigns. 【0144】 (Example 2) 【0145】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0146】 Store opening strategies and marketing activities in commercial facilities require detailed analysis based on visitor behavior and customer preferences. However, traditional methods have made it difficult to gain a comprehensive understanding, including customer emotional states, which can sometimes lead to insufficient decision-making. To address this challenge, there is a need for a system that can comprehensively analyze visitor behavior patterns, customer preferences, and emotional states. 【0147】 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. 【0148】 In this invention, the server includes means for collecting anonymized spatial information and analyzing visitor behavior patterns, means for combining social media data and consumption history data to infer customer preferences and lifestyles, and means for evaluating customer emotional states and analyzing purchase intent and interest in services using natural language processing. This enables detailed data analysis based on visitor behavior and emotional states, making it possible to improve the accuracy of store opening strategies and marketing activities for commercial facilities. 【0149】 "Anonymized spatial information" refers to geographic location data that is collected in a way that removes personally identifiable information and protects the privacy of visitors. 【0150】 "Visitor behavior patterns" refer to specific behavioral tendencies and habits obtained by analyzing visitors' movement routes, frequency of actions, and length of stay. 【0151】 "Social media data" refers to digital information such as customer posts and comments obtained from various social media platforms. 【0152】 "Consumption history data" refers to information about customers' purchasing trends and product choices, collected based on past purchase records. 【0153】 "Natural language processing" refers to the techniques and methods used by computers to understand and analyze human language. 【0154】 "Customer emotional state" refers to the emotional state a customer is experiencing at a particular time or in a particular situation, as analyzed through natural language processing. 【0155】 "Purchase intent" refers to the psychological motivation or degree of interest a customer has in purchasing a product or service. 【0156】 "Interest in the service" refers to the degree of interest and attention a customer has in the service being provided. 【0157】 A "potential market" is a market area that is not currently apparent but is expected to be developed in the future. 【0158】 "Prospective customer characteristics" refer to the attributes and behavioral patterns of individuals who are perceived to have an interest in or need for a particular product or service, but who have not yet become customers. 【0159】 The system implementing this invention includes a server, a terminal, and a user as its main components. The specific roles and operations of each are described below. 【0160】 First, the server plays a central role in data collection and analysis. In this system, the server collects anonymized spatial information, social media data, and consumption history data. Location information is collected via mobile devices and communication networks and stored in a database. Data cleaning is performed using the Python Pandas library. Visitor behavior patterns are also analyzed using K-means clustering and other machine learning algorithms. For data collected from social media, emotional states are analyzed using a natural language processing engine to evaluate customer purchase intent and interest in services. Libraries such as Hugging Face's Transformers are utilized in this process. 【0161】 Next, the terminal is responsible for data visualization. The terminal receives the analysis results sent from the server and visualizes them in a dashboard format using a front-end development framework (such as React.js or Vue.js). The dashboard displays heatmaps of behavioral patterns and graphs showing emotional tendencies, enabling users to make strategic decisions based on this information. 【0162】 Users optimize the store opening strategy and marketing activities of commercial facilities based on data displayed on their devices. For example, if a user confirms that the number of visitors increases during a specific time period and that customers are in a positive emotional state, they can plan promotional and advertising strategies tailored to that timing. A concrete example of input to the generating AI model would be a prompt message such as, "In a shopping mall, identify areas where visitors spend a long time and suggest effective promotional activities when those visitors are in a positive emotional state." 【0163】 In summary, the present invention enhances data-driven decision-making in commercial facilities and supports the development of more accurate marketing and store opening strategies. 【0164】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0165】 Step 1: 【0166】 The server collects anonymized spatial information. Location data sent from various mobile devices and communication networks serves as input. The server stores this data in a database and performs anonymization by removing personally identifiable information. The output is clean, anonymized location data. Specifically, it collects GPS data from mobile devices and ensures anonymity by removing personal information such as user IDs. 【0167】 Step 2: 【0168】 The server analyzes the behavioral patterns. The anonymized location data obtained in Step 1 is used as input. The server uses machine learning algorithms, such as K-means clustering, to analyze the visitors' behavioral patterns. As output, it generates data on the visitors' movement paths, activity frequency, and time spent at each location. Specifically, it identifies the routes and spots that are visited most frequently within a given time frame. 【0169】 Step 3: 【0170】 The server collects social media data and performs sentiment analysis. Input data consists of posts and comments obtained from social networking platforms. Natural language processing is used to evaluate the emotional state of this data. The output is numerical data indicating the customer's emotional tendencies. Specifically, it uses a text analysis engine to calculate positive, negative, and neutral sentiment scores. 【0171】 Step 4: 【0172】 The server analyzes consumption history data to infer customer preferences. The input is consumption history data, i.e., data on purchased items. This data is analyzed to infer customer preferences. The output generates a profile of the customer's purchasing trends and preferences. Specifically, it identifies frequently purchased product categories and uses this to determine the characteristics of potential customers. 【0173】 Step 5: 【0174】 The terminal visualizes the analysis results from the server on a dashboard. The data obtained in steps 2, 3, and 4 serves as input. The terminal visualizes this data in a dashboard format using a front-end development framework and provides it to the user. As output, the user receives a visualization of behavioral patterns, emotional states, and purchasing trends that are intuitively understandable. Specifically, it displays heatmaps and emotion score charts, and provides an interface that allows the user to freely manipulate the data. 【0175】 Step 6: 【0176】 Users make strategic decisions based on the information in the dashboard. Visualized analytical data displayed on the device serves as input. Users use this to determine how to optimize their store opening strategy and marketing activities. The output includes the development of more targeted promotions and specific marketing measures. Specifically, users can select when to promote a particular product and determine the appropriate timing for advertising campaigns. 【0177】 (Application Example 2) 【0178】 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". 【0179】 In recent years, competition has intensified in commercial facilities and brick-and-mortar stores, requiring personalized service based on customer needs. However, traditional methods make it difficult to provide accurate information based on customers' emotional states and behavior in real time, posing a challenge to improving customer satisfaction and achieving effective marketing activities. 【0180】 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. 【0181】 In this invention, the server includes means for collecting anonymized motion data and analyzing visitor behavior patterns, means for integrating information exchange network data and purchase history data to infer consumer preferences and lifestyles, and means for providing information to smart devices in real time and suggesting appropriate contact methods and timings to store staff based on the customer's emotional state. This enables personalized service and real-time optimization of customer service at the store level. 【0182】 "Anonymized movement data" refers to a collection of movement information that has been processed in a way that prevents the identification of individuals. 【0183】 "Visitor behavior patterns" refer to data that shows the tendencies and characteristics of the behavior of people who visit stores or facilities. 【0184】 "Information exchange network data" refers to information about user activity on social networking sites and similar platforms. 【0185】 "Purchase history data" refers to records of products that consumers have purchased in the past. 【0186】 "Consumer preferences and lifestyles" refer to the goods and services that individual customers prefer, as well as their lifestyle habits. 【0187】 "Smart devices" refer to all devices capable of sending and receiving data via the internet, and include mobile terminals and wearable devices. 【0188】 "Providing information in real time" means that data is acquired and processed immediately, and results are provided with virtually no time lag. 【0189】 "Customer emotional state" refers to information about the emotions and moods a consumer is experiencing at a particular point in time. 【0190】 "Individualized service at the store level" refers to providing services and information tailored to specific customers on an individual basis. 【0191】 "Real-time customer service optimization" means instantly understanding customer needs and emotions, and immediately adjusting services and approaches to address them. 【0192】 To realize this invention, a system is required in which a server, terminals, and users work together. The server collects anonymized motion data and uses it to analyze visitor behavior patterns. This data is cleansed and noise is removed. Next, information exchange network data and purchase history data are integrated to infer consumer preferences and lifestyles. Using an emotion engine, the customer's emotional state is determined from the information exchange network data, and this is reflected in the analysis results. 【0193】 The terminal receives analysis results sent from the server and displays them visually as an interface. This interface can be viewed by store staff on-site via their smart devices, allowing them to suggest the optimal contact method and timing based on the customer's emotional state in real time. 【0194】 Users will utilize this information to provide personalized service at the store. For example, if a customer is in a positive emotional state, store staff can use that information to proactively recommend specific products or services. 【0195】 As a concrete example, one bookstore checks what genres of books a customer has purchased in the past and their emotional state at the time, and then quickly presents a list of books that should be recommended to the customer next, based on pre-set conditions. An example of a prompt message is, "Analyze the customer's emotional state towards a specific product based on social media data, and indicate how to propose a personalized promotion." 【0196】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0197】 Step 1: 【0198】 The server collects anonymized motion data. This data forms the basis for extracting visitor behavior patterns. During this process, data cleaning and noise reduction are performed. The input is raw data, and the output is clean motion data with noise removed. 【0199】 Step 2: 【0200】 The server integrates information exchange network data and purchase history data. This integration allows for the analysis of consumer preferences and lifestyles. The input consists of data sets in different formats, and the output is a consumer profile based on these. Here, a generative AI model is used to identify relationships between the data with high accuracy. 【0201】 Step 3: 【0202】 The server uses an emotion engine to analyze customer emotional states from information exchange network data. The input is raw information exchange network data, and the output is each customer's emotional score. This emotional state is then analyzed using natural language processing techniques. 【0203】 Step 4: 【0204】 The terminal acquires analysis results from the server in real time and displays them visually through its interface. The input is the analysis information received from the server, and the output is a visually organized interface. The display on the terminal helps users make quick decisions. 【0205】 Step 5: 【0206】 Users plan appropriate customer interactions based on customer emotional states and shopping habits provided by the device. Input is visualized data, and output is individually optimized responses. Based on this, users construct specific sales strategies and promotions. 【0207】 Step 6: 【0208】 The server aggregates overall feedback and monitors system performance. Inputs are user feedback data, and outputs are metrics that help continuously improve the system. This feedback loop allows the system to constantly improve its accuracy. 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 [Second Embodiment] 【0213】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0214】 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. 【0215】 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). 【0216】 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. 【0217】 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. 【0218】 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). 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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". 【0225】 This invention relates to an AI system for gaining a deep understanding of customer trends in commercial facilities using location information and related data. The system consists of three main components: a server, a terminal, and a user. 【0226】 The server collects anonymized movement data and uses it to analyze visitor behavior patterns. This analysis includes measuring visit frequency, routes, and duration of stay within a specific area. The server also receives social media data and purchase history data with user consent and integrates this data to infer customer preferences and lifestyles. Furthermore, the server cleans the collected data to remove noise and improve the reliability of the analysis. 【0227】 The terminal provides a dashboard that visualizes in detail the characteristics of the trading area and population dynamics by time of day, based on analysis results received from the server. This dashboard is designed to be easy for users to understand and includes data plotted on a map and geographical information of competing stores. 【0228】 Through this dashboard, users can consider store opening strategies and marketing measures. For example, when a commercial facility opens a new store, users can identify the most effective location and target customer base based on visitor inflow data and competitor analysis provided by this system. Users can also obtain lists of potential customers and receive support in developing targeted advertising strategies using this information. 【0229】 In this way, this system analyzes customer behavior in physical stores from all angles, greatly supporting corporate decision-making. 【0230】 The following describes the processing flow. 【0231】 Step 1: 【0232】 The server collects anonymized location information from mobile devices. This uses GPS data and Wi-Fi connection information obtained from the user's mobile device. 【0233】 Step 2: 【0234】 The server cleans the collected location data and removes noise. It sorts the data chronologically and filters out outliers to prepare it for optimal analysis. 【0235】 Step 3: 【0236】 The server analyzes the cleaned data to identify visitor movement patterns, frequency of visits, and length of stay. In particular, it visualizes customer routes and visit trends to understand their movement behavior. 【0237】 Step 4: 【0238】 Users agree to provide social media data and purchase history data to the server. The server receives this data and performs more comprehensive and in-depth analysis by integrating it with location data. 【0239】 Step 5: 【0240】 The server uses location information and other data to conduct a detailed analysis of demographic trends and the impact of competing stores by time of day. This allows it to measure customer flow within the trading area and the degree to which competition affects customer acquisition. 【0241】 Step 6: 【0242】 The terminal displays the analysis results provided by the server on a dashboard. Through this dashboard, users can visually confirm the market area analysis results and the characteristics of potential customers. 【0243】 Step 7: 【0244】 Users utilize the information on the dashboard to develop store opening strategies and targeted marketing initiatives. This decision-making support, based on actual customer behavior data, contributes to business success. 【0245】 (Example 1) 【0246】 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." 【0247】 To effectively formulate store opening strategies for commercial facilities, it is necessary to comprehensively understand customer behavior patterns, preferences, and the surrounding competitive environment. However, this data is usually provided from separate sources, making it difficult to integrate and analyze in real time. Furthermore, there is a lack of means to provide data in a form that ensures reliability, is visualized, and can be used for management decision-making. 【0248】 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. 【0249】 In this invention, the server includes means for collecting anonymized location data and analyzing crowd behavior patterns, means for integrating social network data and transaction history data to infer consumer preferences and behavioral characteristics, and means for analyzing human movements by time of day and evaluating the impact of the competitive environment. This enables commercial facility managers to analyze comprehensive data in real time and make optimal decisions based on visualized information. 【0250】 "Anonymized location data" refers to information about the location of devices or individuals within a specific area, which has been processed in a way that does not allow for the identification of individuals. 【0251】 "Crowd behavior patterns" is an analysis that examines the movement and behavioral tendencies of a large number of individuals or groups, revealing behavioral characteristics based on time and place. 【0252】 "Social network data" refers to information about relationships and activities between users through online platforms and services, including data such as friendships and interaction history. 【0253】 "Transaction history data" refers to information about purchases and payments made by consumers in the past, and includes data on the purchase history of goods and services. 【0254】 "Consumer preferences" refer to consumers' likes and tendencies in choosing products and services that interest them. 【0255】 "Behavioral characteristics" refer to the specific behavioral patterns and attitudes that consumers or individuals exhibit in particular situations. 【0256】 "Human behavior by time of day" refers to information obtained by analyzing the patterns of people's movement and activities during specific time periods. 【0257】 "The impact of the competitive environment" refers to the effect that the presence of competing businesses or stores has on other stores or businesses in a particular market or area. 【0258】 "Regional market analysis" involves analyzing consumer trends and economic activities within a specific region to clarify market characteristics and trends. 【0259】 "Characteristics of potential consumers" refers to the profiles and behavioral tendencies of individuals who are not yet recognized as customers but who may use a product or service in the future. 【0260】 This invention relates to an AI system that comprehensively analyzes and visualizes customer trends and market environment data necessary for the management of commercial facilities. Specific embodiments are described below. 【0261】 The server first collects anonymized location data. This is done using GPS tracking devices or location services on mobile devices. This data can be stored on cloud services such as Google Cloud Platform or Amazon Web Services. 【0262】 Next, the server cleans the collected data. Specifically, it uses the Python pandas library to remove outliers and noise, improving data reliability. This ensures the accuracy of the analysis results. 【0263】 The server then integrates additional data, such as social network data and transaction history data. This process utilizes machine learning algorithms to build models for predicting consumer preferences and behavioral characteristics. Analysis is performed using R or Python, and this data is then organically combined. 【0264】 The terminal receives analysis results from the server and displays them in a visually easy-to-understand format. This uses data visualization tools such as Tableau and Power BI. The dashboard on the terminal plots information on human activity and the competitive environment by time period on a map, allowing users to intuitively understand the data. 【0265】 Users utilize this visualized data to make strategic decisions. For example, they can understand peaks based on customer inflow data during specific time periods and implement promotions accordingly. Another example of a prompt for the generated AI model based on the provided data is a request such as, "Please suggest the most effective store location." In this way, the system greatly supports management decisions in commercial facilities. 【0266】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0267】 Step 1: 【0268】 The server collects anonymized location data. Input data includes location information obtained from GPS tracking devices and mobile terminals. Based on this, the server receives and stores the data, accumulating it on a cloud service. The output of this step is an initial dataset showing visitor movement patterns within the geographical area of ​​a commercial facility. 【0269】 Step 2: 【0270】 The server cleans the collected location data. The input includes the initial dataset accumulated in step 1. The data is processed using the Python pandas library to remove outliers and noise. The output is a reliable, clean dataset, which will be used in the next analysis step. 【0271】 Step 3: 【0272】 The server analyzes crowd behavior patterns based on clean location data. The input data is the clean dataset, which is the output of step 2. Machine learning algorithms are applied to perform specific actions to extract behavioral characteristics. The output is the analysis results showing visitor behavior patterns. 【0273】 Step 4: 【0274】 The server integrates social network data and transaction history data. Inputs include analysis results of behavioral patterns and additional data sources. Based on this, a procedure is performed to infer consumer preferences and behavioral characteristics and generate profiles. The output is a detailed consumer profile. 【0275】 Step 5: 【0276】 The terminal uses data received from the server to create a visualized dashboard. Inputs include consumer profiles and behavioral pattern analysis results. Tableau and Power BI are used to perform specific visualization actions and display them to the user. The output is an intuitive visual interface that supports strategic decision-making. 【0277】 Step 6: 【0278】 Users make strategic decisions using visualized dashboards. The input is dashboard information provided by the device. Users analyze the data and take specific actions to consider promotions and store opening strategies based on specific time periods. The output is actionable business strategies. 【0279】 (Application Example 1) 【0280】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0281】 Traditional customer behavior analysis systems in commercial facilities have not been sufficiently effective in real-time customer behavior monitoring and in proposing efficient sales promotion strategies. As a result, optimizing store opening strategies and promotion plans is time-consuming and costly, leading to a lack of competitiveness against rivals. 【0282】 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. 【0283】 In this invention, the server includes means for collecting anonymized movement data and analyzing the behavior patterns of visitors, means for integrating social network data and consumption history data to infer customer preferences and lifestyles, and means for monitoring customer trends in real time in conjunction with location information and proposing efficient sales promotion strategies. As a result, through商圈 analysis and the generation of profiles of potential customers, it is possible to optimize efficient and effective store opening strategies and promotion plans. 【0284】 "Anonymized movement data" refers to location information data processed in a form where personal information cannot be identified, and is used to collect the location history and movement patterns of visitors. 【0285】 "Means for analyzing behavior patterns" refers to a method of analyzing the frequency, visit route, stay time, etc. of visitors based on the collected movement data to clarify specific trends and behavior models. 【0286】 "Social network data" refers to data on users' interests and activities obtained through online platforms and networks. 【0287】 "Consumption history data" refers to data related to past purchases made by customers, representing consumption trends and purchasing power, etc. 【0288】 "Means for inferring preferences and lifestyles" refers to a method of analyzing social network data and consumption history data to infer the interests and daily life patterns of individual customers. 【0289】 "Means for monitoring customer trends in real time in conjunction with location information" refers to a method of combining current location data and real-time information processing to track the movements and locations of customers at the current time. 【0290】 "Means for proposing efficient sales promotion strategies" refers to a method of advising on optimal marketing activities and product placement aimed at improving sales based on the collected and analyzed data. 【0291】 "Market area analysis" is a process that involves a detailed analysis of demographics and visitor data within a target area to evaluate the economic activity and market potential of that area. 【0292】 "Profile generation of potential customers" is a method of creating information that serves as the basis for marketing and targeting by inferring the characteristics and attributes of individuals who may become future customers. 【0293】 The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server collects anonymized movement data, social network data, and consumption history data, and runs a program to analyze customer behavior patterns, preferences, and lifestyles based on this data. The server is built using Python and utilizes libraries such as pandas, numpy, and scikit-learn for data collection and analysis. Geopy is used to process location data with high accuracy and analyze real-time trends. 【0294】 The terminal plays a role in visualizing the analysis results received from the server, assisting users in making strategic decisions. Using matplotlib and seaborn for visualization, detailed plots of demographics and visitor patterns are displayed on the dashboard, making them intuitively understandable to users. 【0295】 Users can easily obtain information to optimize commercial facility layout changes and promotional activities through a dashboard provided on their devices. For example, if a store is found to receive many customers in the afternoon, users can effectively place protein bars and related products and conduct promotional activities. 【0296】 The generative AI model proposes a variety of strategies based on the prompts used for data analysis. For example, the following prompts are used: 【0297】 "We created a prompt for a customer behavior analysis app for commercial facilities. It's a support tool for devising optimal promotional strategies based on location data and behavioral patterns. Based on the following conditions…" 【0298】 Based on this prompt, the AI ​​model can propose even more sophisticated marketing strategies. 【0299】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0300】 Step 1: 【0301】 The server collects anonymized movement data from various data sources. It receives location data transmitted from mobile devices as input. This data is processed through a collection module and standardized using geopy. The output is a formatted movement dataset, which establishes the basis for the dynamic movement patterns of individual visitors. 【0302】 Step 2: 【0303】 The server integrates social network data and consumption history data to infer customer preferences and lifestyles. Input includes SNS data obtained via API and purchase history from a database. Data cleaning is performed using the pandas library to eliminate inconsistencies. The output is a cleaned, integrated dataset. This process forms a customer behavior model. 【0304】 Step 3: 【0305】 The server monitors customer trends in real time and proposes efficient sales promotion strategies. As inputs, real-time location information and an integrated dataset are referenced. Scikit-learn is utilized to perform clustering analysis to identify preferred routes and locations within the store. As output, recommended promotion areas are generated. This proposal is further optimized by a generative AI model. 【0306】 Step 4: 【0307】 The terminal displays the analysis results received from the server as a visualization dashboard. As input, there are the analysis results sent from the server. Using matplotlib and seaborn, appropriate charts and heatmaps are generated. As output, a visualized dashboard that is intuitive and easy for users to understand is completed. 【0308】 Step 5: 【0309】 The user utilizes the dashboard on the terminal to optimize the layout change and sales promotion strategy of the commercial facility. As inputs, the analysis data and prompt text displayed on the dashboard are used. Based on this data, the user formulates an action plan for implementing appropriate product placement and promotions. As output, an efficient and result-oriented strategic implementation plan is derived. 【0310】 Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions. 【0311】 This invention is an advanced analysis system that integrates location information data, SNS data, and purchase history data, and further combines an emotion engine to support strategic decision-making in commercial facilities. The system is mainly composed of three main components: a server, a terminal, and a user. 【0312】 The server collects anonymized movement data and extracts visitor behavior patterns. After data cleaning and noise reduction, it measures movement routes, visit frequency, and dwell time. It also gains a deep understanding of customer preferences based on social media data and purchase history. By introducing an emotion engine, it performs sentiment analysis of natural language obtained from social media data to recognize the customer's emotional state. This analysis identifies which emotions are at the root of purchase intent and interest in services. 【0313】 The terminal visualizes the analysis results sent from the server as a dashboard and displays it to the user. The dashboard displays data that reflects the user's emotional state, along with market area analysis, competitive impact, and information on potential customers. Based on this information, users can optimize their store opening strategies and develop targeted marketing strategies. 【0314】 Users can select appropriate contact methods and timings based on customer emotional data provided by the emotion engine. For example, when promoting a new product, users can choose a time when customers are experiencing positive emotions and launch promotional activities accordingly. Furthermore, emotional data can be used to analyze customers' potential purchasing motivations for specific products or services, enabling more personalized marketing strategies. 【0315】 Thus, this system provides strong support for the operation and marketing activities of commercial facilities through a comprehensive approach that combines data analysis and sentiment recognition. 【0316】 The following describes the processing flow. 【0317】 Step 1: 【0318】 The server collects anonymized location data from mobile devices. This includes GPS data and Wi-Fi connection history, and is used to record visitors' movements. 【0319】 Step 2: 【0320】 The server cleans and removes noise from the collected location data. It organizes the data to improve the accuracy of the analysis by removing outliers and formatting the data. 【0321】 Step 3: 【0322】 The server analyzes visitor behavior patterns based on their location information. This involves calculating visit timing, frequency, and duration, and using this data to predict visitor behavior. 【0323】 Step 4: 【0324】 Users consent to the provision of social media data and purchase history data, which the server uses to analyze customer preferences. The data is processed anonymously and used in a privacy-protected manner. 【0325】 Step 5: 【0326】 The server analyzes the received SNS data using an emotion engine and extracts the user's emotions from the text. This process classifies the emotions into categories such as positive, negative, and neutral, allowing the server to understand each customer's emotional state. 【0327】 Step 6: 【0328】 The server integrates location information, purchase history, and sentiment data to generate customer profiles. These profiles help identify potential customers within the service area based on individual customers' purchasing motivations and interest in the service. 【0329】 Step 7: 【0330】 The terminal displays analysis results from the server on a dashboard. The dashboard visualizes market area analysis, competitive impact, and sentiment profiles, and is presented in a format that is easy for the user to understand. 【0331】 Step 8: 【0332】 Users utilize the information on the dashboard to plan store opening strategies and implement marketing measures. In particular, they can use sentiment data to select the most effective timing for campaigns. 【0333】 (Example 2) 【0334】 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". 【0335】 Store opening strategies and marketing activities in commercial facilities require detailed analysis based on visitor behavior and customer preferences. However, traditional methods have made it difficult to gain a comprehensive understanding, including customer emotional states, which can sometimes lead to insufficient decision-making. To address this challenge, there is a need for a system that can comprehensively analyze visitor behavior patterns, customer preferences, and emotional states. 【0336】 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. 【0337】 In this invention, the server includes means for collecting anonymized spatial information and analyzing visitor behavior patterns, means for combining social media data and consumption history data to infer customer preferences and lifestyles, and means for evaluating customer emotional states and analyzing purchase intent and interest in services using natural language processing. This enables detailed data analysis based on visitor behavior and emotional states, making it possible to improve the accuracy of store opening strategies and marketing activities for commercial facilities. 【0338】 "Anonymized spatial information" refers to geographic location data that is collected in a way that removes personally identifiable information and protects the privacy of visitors. 【0339】 "Visitor behavior patterns" refer to specific behavioral tendencies and habits obtained by analyzing visitors' movement routes, frequency of actions, and length of stay. 【0340】 "Social media data" refers to digital information such as customer posts and comments obtained from various social media platforms. 【0341】 "Consumption history data" refers to information about customers' purchasing trends and product choices, collected based on past purchase records. 【0342】 "Natural language processing" refers to the techniques and methods used by computers to understand and analyze human language. 【0343】 "Customer emotional state" refers to the emotional state a customer is experiencing at a particular time or in a particular situation, as analyzed through natural language processing. 【0344】 "Purchase intent" refers to the psychological motivation or degree of interest a customer has in purchasing a product or service. 【0345】 "Interest in the service" refers to the degree of interest and attention a customer has in the service being provided. 【0346】 A "potential market" is a market area that is not currently apparent but is expected to be developed in the future. 【0347】 "Prospective customer characteristics" refer to the attributes and behavioral patterns of individuals who are perceived to have an interest in or need for a particular product or service, but who have not yet become customers. 【0348】 The system implementing this invention includes a server, a terminal, and a user as its main components. The specific roles and operations of each are described below. 【0349】 First, the server plays a central role in data collection and analysis. In this system, the server collects anonymized spatial information, social media data, and consumption history data. Location information is collected via mobile devices and communication networks and stored in a database. Data cleaning is performed using the Python Pandas library. Visitor behavior patterns are also analyzed using K-means clustering and other machine learning algorithms. For data collected from social media, emotional states are analyzed using a natural language processing engine to evaluate customer purchase intent and interest in services. Libraries such as Hugging Face's Transformers are utilized in this process. 【0350】 Next, the terminal is responsible for data visualization. The terminal receives the analysis results sent from the server and visualizes them in a dashboard format using a front-end development framework (such as React.js or Vue.js). The dashboard displays heatmaps of behavioral patterns and graphs showing emotional tendencies, enabling users to make strategic decisions based on this information. 【0351】 Users optimize the store opening strategy and marketing activities of commercial facilities based on data displayed on their devices. For example, if a user confirms that the number of visitors increases during a specific time period and that customers are in a positive emotional state, they can plan promotional and advertising strategies tailored to that timing. A concrete example of input to the generating AI model would be a prompt message such as, "In a shopping mall, identify areas where visitors spend a long time and suggest effective promotional activities when those visitors are in a positive emotional state." 【0352】 In summary, the present invention enhances data-driven decision-making in commercial facilities and supports the development of more accurate marketing and store opening strategies. 【0353】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0354】 Step 1: 【0355】 The server collects anonymized spatial information. Location data sent from various mobile devices and communication networks serves as input. The server stores this data in a database and performs anonymization by removing personally identifiable information. The output is clean, anonymized location data. Specifically, it collects GPS data from mobile devices and ensures anonymity by removing personal information such as user IDs. 【0356】 Step 2: 【0357】 The server analyzes the behavioral patterns. The anonymized location data obtained in Step 1 is used as input. The server uses machine learning algorithms, such as K-means clustering, to analyze the visitors' behavioral patterns. As output, it generates data on the visitors' movement paths, activity frequency, and time spent at each location. Specifically, it identifies the routes and spots that are visited most frequently within a given time frame. 【0358】 Step 3: 【0359】 The server collects social media data and performs sentiment analysis. Input data consists of posts and comments obtained from social networking platforms. Natural language processing is used to evaluate the emotional state of this data. The output is numerical data indicating the customer's emotional tendencies. Specifically, it uses a text analysis engine to calculate positive, negative, and neutral sentiment scores. 【0360】 Step 4: 【0361】 The server analyzes consumption history data to infer customer preferences. The input is consumption history data, i.e., data on purchased items. This data is analyzed to infer customer preferences. The output generates a profile of the customer's purchasing trends and preferences. Specifically, it identifies frequently purchased product categories and uses this to determine the characteristics of potential customers. 【0362】 Step 5: 【0363】 The terminal visualizes the analysis results from the server on a dashboard. The data obtained in steps 2, 3, and 4 serves as input. The terminal visualizes this data in a dashboard format using a front-end development framework and provides it to the user. As output, the user receives a visualization of behavioral patterns, emotional states, and purchasing trends that are intuitively understandable. Specifically, it displays heatmaps and emotion score charts, and provides an interface that allows the user to freely manipulate the data. 【0364】 Step 6: 【0365】 Users make strategic decisions based on the information in the dashboard. Visualized analytical data displayed on the device serves as input. Users use this to determine how to optimize their store opening strategy and marketing activities. The output includes the development of more targeted promotions and specific marketing measures. Specifically, users can select when to promote a particular product and determine the appropriate timing for advertising campaigns. 【0366】 (Application Example 2) 【0367】 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." 【0368】 In recent years, competition has intensified in commercial facilities and brick-and-mortar stores, requiring personalized service based on customer needs. However, traditional methods make it difficult to provide accurate information based on customers' emotional states and behavior in real time, posing a challenge to improving customer satisfaction and achieving effective marketing activities. 【0369】 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. 【0370】 In this invention, the server includes means for collecting anonymized motion data and analyzing visitor behavior patterns, means for integrating information exchange network data and purchase history data to infer consumer preferences and lifestyles, and means for providing information to smart devices in real time and suggesting appropriate contact methods and timings to store staff based on the customer's emotional state. This enables personalized service and real-time optimization of customer service at the store level. 【0371】 "Anonymized movement data" refers to a collection of movement information that has been processed in a way that prevents the identification of individuals. 【0372】 "Visitor behavior patterns" refer to data that shows the tendencies and characteristics of the behavior of people who visit stores or facilities. 【0373】 "Information exchange network data" refers to information about user activity on social networking sites and similar platforms. 【0374】 "Purchase history data" refers to records of products that consumers have purchased in the past. 【0375】 "Consumer preferences and lifestyles" refer to the goods and services that individual customers prefer, as well as their lifestyle habits. 【0376】 "Smart devices" refer to all devices capable of sending and receiving data via the internet, and include mobile terminals and wearable devices. 【0377】 "Providing information in real time" means that data is acquired and processed immediately, and results are provided with virtually no time lag. 【0378】 "Customer emotional state" refers to information about the emotions and moods a consumer is experiencing at a particular point in time. 【0379】 "Individualized service at the store level" refers to providing services and information tailored to specific customers on an individual basis. 【0380】 "Real-time customer service optimization" means instantly understanding customer needs and emotions, and immediately adjusting services and approaches to address them. 【0381】 To realize this invention, a system is required in which a server, terminals, and users work together. The server collects anonymized motion data and uses it to analyze visitor behavior patterns. This data is cleansed and noise is removed. Next, information exchange network data and purchase history data are integrated to infer consumer preferences and lifestyles. Using an emotion engine, the customer's emotional state is determined from the information exchange network data, and this is reflected in the analysis results. 【0382】 The terminal receives analysis results sent from the server and displays them visually as an interface. This interface can be viewed by store staff on-site via their smart devices, allowing them to suggest the optimal contact method and timing based on the customer's emotional state in real time. 【0383】 Users will utilize this information to provide personalized service at the store. For example, if a customer is in a positive emotional state, store staff can use that information to proactively recommend specific products or services. 【0384】 As a concrete example, one bookstore checks what genres of books a customer has purchased in the past and their emotional state at the time, and then quickly presents a list of books that should be recommended to the customer next, based on pre-set conditions. An example of a prompt message is, "Analyze the customer's emotional state towards a specific product based on social media data, and indicate how to propose a personalized promotion." 【0385】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0386】 Step 1: 【0387】 The server collects anonymized motion data. This data forms the basis for extracting visitor behavior patterns. During this process, data cleaning and noise reduction are performed. The input is raw data, and the output is clean motion data with noise removed. 【0388】 Step 2: 【0389】 The server integrates information exchange network data and purchase history data. This integration allows for the analysis of consumer preferences and lifestyles. The input consists of data sets in different formats, and the output is a consumer profile based on these. Here, a generative AI model is used to identify relationships between the data with high accuracy. 【0390】 Step 3: 【0391】 The server uses an emotion engine to analyze customer emotional states from information exchange network data. The input is raw information exchange network data, and the output is each customer's emotional score. This emotional state is then analyzed using natural language processing techniques. 【0392】 Step 4: 【0393】 The terminal acquires analysis results from the server in real time and displays them visually through its interface. The input is the analysis information received from the server, and the output is a visually organized interface. The display on the terminal helps users make quick decisions. 【0394】 Step 5: 【0395】 Users plan appropriate customer interactions based on customer emotional states and shopping habits provided by the device. Input is visualized data, and output is individually optimized responses. Based on this, users construct specific sales strategies and promotions. 【0396】 Step 6: 【0397】 The server aggregates overall feedback and monitors system performance. Inputs are user feedback data, and outputs are metrics that help continuously improve the system. This feedback loop allows the system to constantly improve its accuracy. 【0398】 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. 【0399】 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. 【0400】 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. 【0401】 [Third Embodiment] 【0402】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0403】 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. 【0404】 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). 【0405】 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. 【0406】 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. 【0407】 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). 【0408】 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. 【0409】 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. 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 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". 【0414】 This invention relates to an AI system for gaining a deep understanding of customer trends in commercial facilities using location information and related data. The system consists of three main components: a server, a terminal, and a user. 【0415】 The server collects anonymized movement data and uses it to analyze visitor behavior patterns. This analysis includes measuring visit frequency, routes, and duration of stay within a specific area. The server also receives social media data and purchase history data with user consent and integrates this data to infer customer preferences and lifestyles. Furthermore, the server cleans the collected data to remove noise and improve the reliability of the analysis. 【0416】 The terminal provides a dashboard that visualizes in detail the characteristics of the trading area and population dynamics by time of day, based on analysis results received from the server. This dashboard is designed to be easy for users to understand and includes data plotted on a map and geographical information of competing stores. 【0417】 Through this dashboard, users can consider store opening strategies and marketing measures. For example, when a commercial facility opens a new store, users can identify the most effective location and target customer base based on visitor inflow data and competitor analysis provided by this system. Users can also obtain lists of potential customers and receive support in developing targeted advertising strategies using this information. 【0418】 In this way, this system analyzes customer behavior in physical stores from all angles, greatly supporting corporate decision-making. 【0419】 The following describes the processing flow. 【0420】 Step 1: 【0421】 The server collects anonymized location information from mobile devices. This uses GPS data and Wi-Fi connection information obtained from the user's mobile device. 【0422】 Step 2: 【0423】 The server cleans the collected location data and removes noise. It sorts the data chronologically and filters out outliers to prepare it for optimal analysis. 【0424】 Step 3: 【0425】 The server analyzes the cleaned data to identify visitor movement patterns, frequency of visits, and length of stay. In particular, it visualizes customer routes and visit trends to understand their movement behavior. 【0426】 Step 4: 【0427】 Users agree to provide social media data and purchase history data to the server. The server receives this data and performs more comprehensive and in-depth analysis by integrating it with location data. 【0428】 Step 5: 【0429】 The server uses location information and other data to conduct a detailed analysis of demographic trends and the impact of competing stores by time of day. This allows it to measure customer flow within the trading area and the degree to which competition affects customer acquisition. 【0430】 Step 6: 【0431】 The terminal displays the analysis results provided by the server on a dashboard. Through this dashboard, users can visually confirm the market area analysis results and the characteristics of potential customers. 【0432】 Step 7: 【0433】 Users utilize the information on the dashboard to develop store opening strategies and targeted marketing initiatives. This decision-making support, based on actual customer behavior data, contributes to business success. 【0434】 (Example 1) 【0435】 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." 【0436】 To effectively formulate store opening strategies for commercial facilities, it is necessary to comprehensively understand customer behavior patterns, preferences, and the surrounding competitive environment. However, this data is usually provided from separate sources, making it difficult to integrate and analyze in real time. Furthermore, there is a lack of means to provide data in a form that ensures reliability, is visualized, and can be used for management decision-making. 【0437】 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. 【0438】 In this invention, the server includes means for collecting anonymized location data and analyzing crowd behavior patterns, means for integrating social network data and transaction history data to infer consumer preferences and behavioral characteristics, and means for analyzing human movements by time of day and evaluating the impact of the competitive environment. This enables commercial facility managers to analyze comprehensive data in real time and make optimal decisions based on visualized information. 【0439】 "Anonymized location data" refers to information about the location of devices or individuals within a specific area, which has been processed in a way that does not allow for the identification of individuals. 【0440】 "Crowd behavior patterns" is an analysis that examines the movement and behavioral tendencies of a large number of individuals or groups, revealing behavioral characteristics based on time and place. 【0441】 "Social network data" refers to information about relationships and activities between users through online platforms and services, including data such as friendships and interaction history. 【0442】 "Transaction history data" refers to information about purchases and payments made by consumers in the past, and includes data on the purchase history of goods and services. 【0443】 "Consumer preferences" refer to consumers' likes and tendencies in choosing products and services that interest them. 【0444】 "Behavioral characteristics" refer to the specific behavioral patterns and attitudes that consumers or individuals exhibit in particular situations. 【0445】 "Human behavior by time of day" refers to information obtained by analyzing the patterns of people's movement and activities during specific time periods. 【0446】 "The impact of the competitive environment" refers to the effect that the presence of competing businesses or stores has on other stores or businesses in a particular market or area. 【0447】 "Regional market analysis" involves analyzing consumer trends and economic activities within a specific region to clarify market characteristics and trends. 【0448】 "Characteristics of potential consumers" refers to the profiles and behavioral tendencies of individuals who are not yet recognized as customers but who may use a product or service in the future. 【0449】 This invention relates to an AI system that comprehensively analyzes and visualizes customer trends and market environment data necessary for the management of commercial facilities. Specific embodiments are described below. 【0450】 The server first collects anonymized location data. This is done using GPS tracking devices or location services on mobile devices. This data can be stored on cloud services such as Google Cloud Platform or Amazon Web Services. 【0451】 Next, the server cleans the collected data. Specifically, it uses the Python pandas library to remove outliers and noise, improving data reliability. This ensures the accuracy of the analysis results. 【0452】 The server then integrates additional data, such as social network data and transaction history data. This process utilizes machine learning algorithms to build models for predicting consumer preferences and behavioral characteristics. Analysis is performed using R or Python, and this data is then organically combined. 【0453】 The terminal receives analysis results from the server and displays them in a visually easy-to-understand format. This uses data visualization tools such as Tableau and Power BI. The dashboard on the terminal plots information on human activity and the competitive environment by time period on a map, allowing users to intuitively understand the data. 【0454】 Users utilize this visualized data to make strategic decisions. For example, they can understand peaks based on customer inflow data during specific time periods and implement promotions accordingly. Another example of a prompt for the generated AI model based on the provided data is a request such as, "Please suggest the most effective store location." In this way, the system greatly supports management decisions in commercial facilities. 【0455】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0456】 Step 1: 【0457】 The server collects anonymized location data. Input data includes location information obtained from GPS tracking devices and mobile terminals. Based on this, the server receives and stores the data, accumulating it on a cloud service. The output of this step is an initial dataset showing visitor movement patterns within the geographical area of ​​a commercial facility. 【0458】 Step 2: 【0459】 The server cleans the collected location data. The input includes the initial dataset accumulated in step 1. The data is processed using the Python pandas library to remove outliers and noise. The output is a reliable, clean dataset, which will be used in the next analysis step. 【0460】 Step 3: 【0461】 The server analyzes crowd behavior patterns based on clean location data. The input data is the clean dataset, which is the output of step 2. Machine learning algorithms are applied to perform specific actions to extract behavioral characteristics. The output is the analysis results showing visitor behavior patterns. 【0462】 Step 4: 【0463】 The server integrates social network data and transaction history data. Inputs include analysis results of behavioral patterns and additional data sources. Based on this, a procedure is performed to infer consumer preferences and behavioral characteristics and generate profiles. The output is a detailed consumer profile. 【0464】 Step 5: 【0465】 The terminal uses data received from the server to create a visualized dashboard. Inputs include consumer profiles and behavioral pattern analysis results. Tableau and Power BI are used to perform specific visualization actions and display them to the user. The output is an intuitive visual interface that supports strategic decision-making. 【0466】 Step 6: 【0467】 Users make strategic decisions using visualized dashboards. The input is dashboard information provided by the device. Users analyze the data and take specific actions to consider promotions and store opening strategies based on specific time periods. The output is actionable business strategies. 【0468】 (Application Example 1) 【0469】 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." 【0470】 Traditional customer behavior analysis systems in commercial facilities have not been sufficiently effective in real-time customer behavior monitoring and in proposing efficient sales promotion strategies. As a result, optimizing store opening strategies and promotion plans is time-consuming and costly, leading to a lack of competitiveness against rivals. 【0471】 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. 【0472】 In this invention, the server includes means for collecting anonymized movement data and analyzing visitor behavior patterns, means for integrating social network data and consumption history data to infer customer preferences and lifestyles, and means for monitoring customer trends in real time in conjunction with location information and proposing efficient sales promotion strategies. This enables the optimization of efficient and effective store opening strategies and promotion plans through market area analysis and the generation of potential customer profiles. 【0473】 "Anonymized movement data" refers to location data that has been processed in a way that does not identify individuals, and is used to collect visitors' location history and movement patterns. 【0474】 "Methods for analyzing behavioral patterns" refer to methods that analyze visitor frequency, routes, and length of stay based on collected movement data to reveal specific trends and behavioral models. 【0475】 "Social network data" refers to data about users' interests and activities obtained through online platforms and networks. 【0476】 "Consumption history data" refers to data about purchases made by customers in the past, and represents their consumption trends and purchasing power. 【0477】 "Means for inferring preferences and lifestyles" refers to methods of analyzing social network data and consumption history data to infer the interests, preferences, and daily life patterns of individual customers. 【0478】 "A means of monitoring customer behavior in real time in conjunction with location information" refers to a method of tracking customer movements and locations at the present moment by combining current location data with real-time information processing. 【0479】 "A means of proposing an efficient sales promotion strategy" refers to a method of advising on optimal marketing activities and product placement aimed at increasing sales, based on collected and analyzed data. 【0480】 "Market area analysis" is a process that involves a detailed analysis of demographics and visitor data within a target area to evaluate the economic activity and market potential of that area. 【0481】 "Profile generation of potential customers" is a method of creating information that serves as the basis for marketing and targeting by inferring the characteristics and attributes of individuals who may become future customers. 【0482】 The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server collects anonymized movement data, social network data, and consumption history data, and runs a program to analyze customer behavior patterns, preferences, and lifestyles based on this data. The server is built using Python and utilizes libraries such as pandas, numpy, and scikit-learn for data collection and analysis. Geopy is used to process location data with high accuracy and analyze real-time trends. 【0483】 The terminal plays a role in visualizing the analysis results received from the server, assisting users in making strategic decisions. Using matplotlib and seaborn for visualization, detailed plots of demographics and visitor patterns are displayed on the dashboard, making them intuitively understandable to users. 【0484】 Users can easily obtain information to optimize commercial facility layout changes and promotional activities through a dashboard provided on their devices. For example, if a store is found to receive many customers in the afternoon, users can effectively place protein bars and related products and conduct promotional activities. 【0485】 The generative AI model proposes a variety of strategies based on the prompts used for data analysis. For example, the following prompts are used: 【0486】 "We created a prompt for a customer behavior analysis app for commercial facilities. It's a support tool for devising optimal promotional strategies based on location data and behavioral patterns. Based on the following conditions…" 【0487】 Based on this prompt, the AI ​​model can propose even more sophisticated marketing strategies. 【0488】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0489】 Step 1: 【0490】 The server collects anonymized movement data from various data sources. It receives location data transmitted from mobile devices as input. This data is processed through a collection module and standardized using geopy. The output is a formatted movement dataset, which establishes the basis for the dynamic movement patterns of individual visitors. 【0491】 Step 2: 【0492】 The server integrates social network data and consumption history data to infer customer preferences and lifestyles. Input includes SNS data obtained via API and purchase history from a database. Data cleaning is performed using the pandas library to eliminate inconsistencies. The output is a cleaned, integrated dataset. This process forms a customer behavior model. 【0493】 Step 3: 【0494】 The server monitors customer behavior in real time and proposes efficient sales promotion strategies. Real-time location data and an integrated dataset are referenced as input. Clustering analysis is performed using scikit-learn to identify preferred routes and locations within the store. Recommended promotional areas are generated as output. These suggestions are further optimized by a generative AI model. 【0495】 Step 4: 【0496】 The terminal displays the analysis results received from the server as a visualized dashboard. The input is the analysis results sent from the server. Using matplotlib and seaborn, appropriate charts and heatmaps are generated. The output is a visualized dashboard that is intuitive and easy for the user to understand. 【0497】 Step 5: 【0498】 Users utilize a dashboard on their device to optimize commercial facility layout changes and sales promotion strategies. The input consists of analytical data and prompts displayed on the dashboard. Based on this data, users develop action plans for appropriate product placement and promotions. The output is an efficient and results-oriented strategic implementation plan. 【0499】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0500】 This invention is an advanced analytical system that integrates location data, social media data, and purchase history data, and further combines them with an emotion engine to support strategic decision-making for commercial facilities. The system mainly consists of three main components: a server, terminals, and users. 【0501】 The server collects anonymized movement data and extracts visitor behavior patterns. After data cleaning and noise reduction, it measures movement routes, visit frequency, and dwell time. It also gains a deep understanding of customer preferences based on social media data and purchase history. By introducing an emotion engine, it performs sentiment analysis of natural language obtained from social media data to recognize the customer's emotional state. This analysis identifies which emotions are at the root of purchase intent and interest in services. 【0502】 The terminal visualizes the analysis results sent from the server as a dashboard and displays it to the user. The dashboard displays data that reflects the user's emotional state, along with market area analysis, competitive impact, and information on potential customers. Based on this information, users can optimize their store opening strategies and develop targeted marketing strategies. 【0503】 Users can select appropriate contact methods and timings based on customer emotional data provided by the emotion engine. For example, when promoting a new product, users can choose a time when customers are experiencing positive emotions and launch promotional activities accordingly. Furthermore, emotional data can be used to analyze customers' potential purchasing motivations for specific products or services, enabling more personalized marketing strategies. 【0504】 Thus, this system provides strong support for the operation and marketing activities of commercial facilities through a comprehensive approach that combines data analysis and sentiment recognition. 【0505】 The following describes the processing flow. 【0506】 Step 1: 【0507】 The server collects anonymized location data from mobile devices. This includes GPS data and Wi-Fi connection history, and is used to record visitors' movements. 【0508】 Step 2: 【0509】 The server cleans and removes noise from the collected location data. It organizes the data to improve the accuracy of the analysis by removing outliers and formatting the data. 【0510】 Step 3: 【0511】 The server analyzes visitor behavior patterns based on their location information. This involves calculating visit timing, frequency, and duration, and using this data to predict visitor behavior. 【0512】 Step 4: 【0513】 Users consent to the provision of social media data and purchase history data, which the server uses to analyze customer preferences. The data is processed anonymously and used in a privacy-protected manner. 【0514】 Step 5: 【0515】 The server analyzes the received SNS data using an emotion engine and extracts the user's emotions from the text. This process classifies the emotions into categories such as positive, negative, and neutral, allowing the server to understand each customer's emotional state. 【0516】 Step 6: 【0517】 The server integrates location information, purchase history, and sentiment data to generate customer profiles. These profiles help identify potential customers within the service area based on individual customers' purchasing motivations and interest in the service. 【0518】 Step 7: 【0519】 The terminal displays analysis results from the server on a dashboard. The dashboard visualizes market area analysis, competitive impact, and sentiment profiles, and is presented in a format that is easy for the user to understand. 【0520】 Step 8: 【0521】 Users utilize the information on the dashboard to plan store opening strategies and implement marketing measures. In particular, they can use sentiment data to select the most effective timing for campaigns. 【0522】 (Example 2) 【0523】 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." 【0524】 Store opening strategies and marketing activities in commercial facilities require detailed analysis based on visitor behavior and customer preferences. However, traditional methods have made it difficult to gain a comprehensive understanding, including customer emotional states, which can sometimes lead to insufficient decision-making. To address this challenge, there is a need for a system that can comprehensively analyze visitor behavior patterns, customer preferences, and emotional states. 【0525】 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. 【0526】 In this invention, the server includes means for collecting anonymized spatial information and analyzing visitor behavior patterns, means for combining social media data and consumption history data to infer customer preferences and lifestyles, and means for evaluating customer emotional states and analyzing purchase intent and interest in services using natural language processing. This enables detailed data analysis based on visitor behavior and emotional states, making it possible to improve the accuracy of store opening strategies and marketing activities for commercial facilities. 【0527】 "Anonymized spatial information" refers to geographic location data that is collected in a way that removes personally identifiable information and protects the privacy of visitors. 【0528】 "Visitor behavior patterns" refer to specific behavioral tendencies and habits obtained by analyzing visitors' movement routes, frequency of actions, and length of stay. 【0529】 "Social media data" refers to digital information such as customer posts and comments obtained from various social media platforms. 【0530】 "Consumption history data" refers to information about customers' purchasing trends and product choices, collected based on past purchase records. 【0531】 "Natural language processing" refers to the techniques and methods used by computers to understand and analyze human language. 【0532】 "Customer emotional state" refers to the emotional state a customer is experiencing at a particular time or in a particular situation, as analyzed through natural language processing. 【0533】 "Purchase intent" refers to the psychological motivation or degree of interest a customer has in purchasing a product or service. 【0534】 "Interest in the service" refers to the degree of interest and attention a customer has in the service being provided. 【0535】 A "potential market" is a market area that is not currently apparent but is expected to be developed in the future. 【0536】 "Prospective customer characteristics" refer to the attributes and behavioral patterns of individuals who are perceived to have an interest in or need for a particular product or service, but who have not yet become customers. 【0537】 The system implementing this invention includes a server, a terminal, and a user as its main components. The specific roles and operations of each are described below. 【0538】 First, the server plays a central role in data collection and analysis. In this system, the server collects anonymized spatial information, social media data, and consumption history data. Location information is collected via mobile devices and communication networks and stored in a database. Data cleaning is performed using the Python Pandas library. Visitor behavior patterns are also analyzed using K-means clustering and other machine learning algorithms. For data collected from social media, emotional states are analyzed using a natural language processing engine to evaluate customer purchase intent and interest in services. Libraries such as Hugging Face's Transformers are utilized in this process. 【0539】 Next, the terminal is responsible for data visualization. The terminal receives the analysis results sent from the server and visualizes them in a dashboard format using a front-end development framework (such as React.js or Vue.js). The dashboard displays heatmaps of behavioral patterns and graphs showing emotional tendencies, enabling users to make strategic decisions based on this information. 【0540】 Users optimize the store opening strategy and marketing activities of commercial facilities based on data displayed on their devices. For example, if a user confirms that the number of visitors increases during a specific time period and that customers are in a positive emotional state, they can plan promotional and advertising strategies tailored to that timing. A concrete example of input to the generating AI model would be a prompt message such as, "In a shopping mall, identify areas where visitors spend a long time and suggest effective promotional activities when those visitors are in a positive emotional state." 【0541】 In summary, the present invention enhances data-driven decision-making in commercial facilities and supports the development of more accurate marketing and store opening strategies. 【0542】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0543】 Step 1: 【0544】 The server collects anonymized spatial information. Location data sent from various mobile devices and communication networks serves as input. The server stores this data in a database and performs anonymization by removing personally identifiable information. The output is clean, anonymized location data. Specifically, it collects GPS data from mobile devices and ensures anonymity by removing personal information such as user IDs. 【0545】 Step 2: 【0546】 The server analyzes the behavioral patterns. The anonymized location data obtained in Step 1 is used as input. The server uses machine learning algorithms, such as K-means clustering, to analyze the visitors' behavioral patterns. As output, it generates data on the visitors' movement paths, activity frequency, and time spent at each location. Specifically, it identifies the routes and spots that are visited most frequently within a given time frame. 【0547】 Step 3: 【0548】 The server collects social media data and performs sentiment analysis. Input data consists of posts and comments obtained from social networking platforms. Natural language processing is used to evaluate the emotional state of this data. The output is numerical data indicating the customer's emotional tendencies. Specifically, it uses a text analysis engine to calculate positive, negative, and neutral sentiment scores. 【0549】 Step 4: 【0550】 The server analyzes consumption history data to infer customer preferences. The input is consumption history data, i.e., data on purchased items. This data is analyzed to infer customer preferences. The output generates a profile of the customer's purchasing trends and preferences. Specifically, it identifies frequently purchased product categories and uses this to determine the characteristics of potential customers. 【0551】 Step 5: 【0552】 The terminal visualizes the analysis results from the server on a dashboard. The data obtained in steps 2, 3, and 4 serves as input. The terminal visualizes this data in a dashboard format using a front-end development framework and provides it to the user. As output, the user receives a visualization of behavioral patterns, emotional states, and purchasing trends that are intuitively understandable. Specifically, it displays heatmaps and emotion score charts, and provides an interface that allows the user to freely manipulate the data. 【0553】 Step 6: 【0554】 Users make strategic decisions based on the information in the dashboard. Visualized analytical data displayed on the device serves as input. Users use this to determine how to optimize their store opening strategy and marketing activities. The output includes the development of more targeted promotions and specific marketing measures. Specifically, users can select when to promote a particular product and determine the appropriate timing for advertising campaigns. 【0555】 (Application Example 2) 【0556】 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." 【0557】 In recent years, competition has intensified in commercial facilities and brick-and-mortar stores, requiring personalized service based on customer needs. However, traditional methods make it difficult to provide accurate information based on customers' emotional states and behavior in real time, posing a challenge to improving customer satisfaction and achieving effective marketing activities. 【0558】 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. 【0559】 In this invention, the server includes means for collecting anonymized motion data and analyzing visitor behavior patterns, means for integrating information exchange network data and purchase history data to infer consumer preferences and lifestyles, and means for providing information to smart devices in real time and suggesting appropriate contact methods and timings to store staff based on the customer's emotional state. This enables personalized service and real-time optimization of customer service at the store level. 【0560】 "Anonymized movement data" refers to a collection of movement information that has been processed in a way that prevents the identification of individuals. 【0561】 "Visitor behavior patterns" refer to data that shows the tendencies and characteristics of the behavior of people who visit stores or facilities. 【0562】 "Information exchange network data" refers to information about user activity on social networking sites and similar platforms. 【0563】 "Purchase history data" refers to records of products that consumers have purchased in the past. 【0564】 "Consumer preferences and lifestyles" refer to the goods and services that individual customers prefer, as well as their lifestyle habits. 【0565】 "Smart devices" refer to all devices capable of sending and receiving data via the internet, and include mobile terminals and wearable devices. 【0566】 "Providing information in real time" means that data is acquired and processed immediately, and results are provided with virtually no time lag. 【0567】 "Customer emotional state" refers to information about the emotions and moods a consumer is experiencing at a particular point in time. 【0568】 "Individualized service at the store level" refers to providing services and information tailored to specific customers on an individual basis. 【0569】 "Real-time customer service optimization" means instantly understanding customer needs and emotions, and immediately adjusting services and approaches to address them. 【0570】 To realize this invention, a system is required in which a server, terminals, and users work together. The server collects anonymized motion data and uses it to analyze visitor behavior patterns. This data is cleansed and noise is removed. Next, information exchange network data and purchase history data are integrated to infer consumer preferences and lifestyles. Using an emotion engine, the customer's emotional state is determined from the information exchange network data, and this is reflected in the analysis results. 【0571】 The terminal receives analysis results sent from the server and displays them visually as an interface. This interface can be viewed by store staff on-site via their smart devices, allowing them to suggest the optimal contact method and timing based on the customer's emotional state in real time. 【0572】 Users will utilize this information to provide personalized service at the store. For example, if a customer is in a positive emotional state, store staff can use that information to proactively recommend specific products or services. 【0573】 As a concrete example, one bookstore checks what genres of books a customer has purchased in the past and their emotional state at the time, and then quickly presents a list of books that should be recommended to the customer next, based on pre-set conditions. An example of a prompt message is, "Analyze the customer's emotional state towards a specific product based on social media data, and indicate how to propose a personalized promotion." 【0574】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0575】 Step 1: 【0576】 The server collects anonymized motion data. This data forms the basis for extracting visitor behavior patterns. During this process, data cleaning and noise reduction are performed. The input is raw data, and the output is clean motion data with noise removed. 【0577】 Step 2: 【0578】 The server integrates information exchange network data and purchase history data. This integration allows for the analysis of consumer preferences and lifestyles. The input consists of data sets in different formats, and the output is a consumer profile based on these. Here, a generative AI model is used to identify relationships between the data with high accuracy. 【0579】 Step 3: 【0580】 The server uses an emotion engine to analyze customer emotional states from information exchange network data. The input is raw information exchange network data, and the output is each customer's emotional score. This emotional state is then analyzed using natural language processing techniques. 【0581】 Step 4: 【0582】 The terminal acquires analysis results from the server in real time and displays them visually through its interface. The input is the analysis information received from the server, and the output is a visually organized interface. The display on the terminal helps users make quick decisions. 【0583】 Step 5: 【0584】 Users plan appropriate customer interactions based on customer emotional states and shopping habits provided by the device. Input is visualized data, and output is individually optimized responses. Based on this, users construct specific sales strategies and promotions. 【0585】 Step 6: 【0586】 The server aggregates overall feedback and monitors system performance. Inputs are user feedback data, and outputs are metrics that help continuously improve the system. This feedback loop allows the system to constantly improve its accuracy. 【0587】 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. 【0588】 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. 【0589】 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. 【0590】 [Fourth Embodiment] 【0591】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0592】 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. 【0593】 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). 【0594】 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. 【0595】 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. 【0596】 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). 【0597】 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. 【0598】 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. 【0599】 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. 【0600】 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. 【0601】 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. 【0602】 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. 【0603】 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". 【0604】 This invention relates to an AI system for gaining a deep understanding of customer trends in commercial facilities using location information and related data. The system consists of three main components: a server, a terminal, and a user. 【0605】 The server collects anonymized movement data and uses it to analyze visitor behavior patterns. This analysis includes measuring visit frequency, routes, and duration of stay within a specific area. The server also receives social media data and purchase history data with user consent and integrates this data to infer customer preferences and lifestyles. Furthermore, the server cleans the collected data to remove noise and improve the reliability of the analysis. 【0606】 The terminal provides a dashboard that visualizes in detail the characteristics of the trading area and population dynamics by time of day, based on analysis results received from the server. This dashboard is designed to be easy for users to understand and includes data plotted on a map and geographical information of competing stores. 【0607】 Through this dashboard, users can consider store opening strategies and marketing measures. For example, when a commercial facility opens a new store, users can identify the most effective location and target customer base based on visitor inflow data and competitor analysis provided by this system. Users can also obtain lists of potential customers and receive support in developing targeted advertising strategies using this information. 【0608】 In this way, this system analyzes customer behavior in physical stores from all angles, greatly supporting corporate decision-making. 【0609】 The following describes the processing flow. 【0610】 Step 1: 【0611】 The server collects anonymized location information from mobile devices. This uses GPS data and Wi-Fi connection information obtained from the user's mobile device. 【0612】 Step 2: 【0613】 The server cleans the collected location data and removes noise. It sorts the data chronologically and filters out outliers to prepare it for optimal analysis. 【0614】 Step 3: 【0615】 The server analyzes the cleaned data to identify visitor movement patterns, frequency of visits, and length of stay. In particular, it visualizes customer routes and visit trends to understand their movement behavior. 【0616】 Step 4: 【0617】 Users agree to provide social media data and purchase history data to the server. The server receives this data and performs more comprehensive and in-depth analysis by integrating it with location data. 【0618】 Step 5: 【0619】 The server uses location information and other data to conduct a detailed analysis of demographic trends and the impact of competing stores by time of day. This allows it to measure customer flow within the trading area and the degree to which competition affects customer acquisition. 【0620】 Step 6: 【0621】 The terminal displays the analysis results provided by the server on a dashboard. Through this dashboard, users can visually confirm the market area analysis results and the characteristics of potential customers. 【0622】 Step 7: 【0623】 Users utilize the information on the dashboard to develop store opening strategies and targeted marketing initiatives. This decision-making support, based on actual customer behavior data, contributes to business success. 【0624】 (Example 1) 【0625】 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". 【0626】 To effectively formulate store opening strategies for commercial facilities, it is necessary to comprehensively understand customer behavior patterns, preferences, and the surrounding competitive environment. However, this data is usually provided from separate sources, making it difficult to integrate and analyze in real time. Furthermore, there is a lack of means to provide data in a form that ensures reliability, is visualized, and can be used for management decision-making. 【0627】 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. 【0628】 In this invention, the server includes means for collecting anonymized location data and analyzing crowd behavior patterns, means for integrating social network data and transaction history data to infer consumer preferences and behavioral characteristics, and means for analyzing human movements by time of day and evaluating the impact of the competitive environment. This enables commercial facility managers to analyze comprehensive data in real time and make optimal decisions based on visualized information. 【0629】 "Anonymized location data" refers to information about the location of devices or individuals within a specific area, which has been processed in a way that does not allow for the identification of individuals. 【0630】 "Crowd behavior patterns" is an analysis that examines the movement and behavioral tendencies of a large number of individuals or groups, revealing behavioral characteristics based on time and place. 【0631】 "Social network data" refers to information about relationships and activities between users through online platforms and services, including data such as friendships and interaction history. 【0632】 "Transaction history data" refers to information about purchases and payments made by consumers in the past, and includes data on the purchase history of goods and services. 【0633】 "Consumer preferences" refer to consumers' likes and tendencies in choosing products and services that interest them. 【0634】 "Behavioral characteristics" refer to the specific behavioral patterns and attitudes that consumers or individuals exhibit in particular situations. 【0635】 "Human behavior by time of day" refers to information obtained by analyzing the patterns of people's movement and activities during specific time periods. 【0636】 "The impact of the competitive environment" refers to the effect that the presence of competing businesses or stores has on other stores or businesses in a particular market or area. 【0637】 "Regional market analysis" involves analyzing consumer trends and economic activities within a specific region to clarify market characteristics and trends. 【0638】 "Characteristics of potential consumers" refers to the profiles and behavioral tendencies of individuals who are not yet recognized as customers but who may use a product or service in the future. 【0639】 This invention relates to an AI system that comprehensively analyzes and visualizes customer trends and market environment data necessary for the management of commercial facilities. Specific embodiments are described below. 【0640】 The server first collects anonymized location data. This is done using GPS tracking devices or location services on mobile devices. This data can be stored on cloud services such as Google Cloud Platform or Amazon Web Services. 【0641】 Next, the server cleans the collected data. Specifically, it uses the Python pandas library to remove outliers and noise, improving data reliability. This ensures the accuracy of the analysis results. 【0642】 The server then integrates additional data, such as social network data and transaction history data. This process utilizes machine learning algorithms to build models for predicting consumer preferences and behavioral characteristics. Analysis is performed using R or Python, and this data is then organically combined. 【0643】 The terminal receives analysis results from the server and displays them in a visually easy-to-understand format. This uses data visualization tools such as Tableau and Power BI. The dashboard on the terminal plots information on human activity and the competitive environment by time period on a map, allowing users to intuitively understand the data. 【0644】 Users utilize this visualized data to make strategic decisions. For example, they can understand peaks based on customer inflow data during specific time periods and implement promotions accordingly. Another example of a prompt for the generated AI model based on the provided data is a request such as, "Please suggest the most effective store location." In this way, the system greatly supports management decisions in commercial facilities. 【0645】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0646】 Step 1: 【0647】 The server collects anonymized location data. Input data includes location information obtained from GPS tracking devices and mobile terminals. Based on this, the server receives and stores the data, accumulating it on a cloud service. The output of this step is an initial dataset showing visitor movement patterns within the geographical area of ​​a commercial facility. 【0648】 Step 2: 【0649】 The server cleans the collected location data. The input includes the initial dataset accumulated in step 1. The data is processed using the Python pandas library to remove outliers and noise. The output is a reliable, clean dataset, which will be used in the next analysis step. 【0650】 Step 3: 【0651】 The server analyzes crowd behavior patterns based on clean location data. The input data is the clean dataset, which is the output of step 2. Machine learning algorithms are applied to perform specific actions to extract behavioral characteristics. The output is the analysis results showing visitor behavior patterns. 【0652】 Step 4: 【0653】 The server integrates social network data and transaction history data. Inputs include analysis results of behavioral patterns and additional data sources. Based on this, a procedure is performed to infer consumer preferences and behavioral characteristics and generate profiles. The output is a detailed consumer profile. 【0654】 Step 5: 【0655】 The terminal uses data received from the server to create a visualized dashboard. Inputs include consumer profiles and behavioral pattern analysis results. Tableau and Power BI are used to perform specific visualization actions and display them to the user. The output is an intuitive visual interface that supports strategic decision-making. 【0656】 Step 6: 【0657】 Users make strategic decisions using visualized dashboards. The input is dashboard information provided by the device. Users analyze the data and take specific actions to consider promotions and store opening strategies based on specific time periods. The output is actionable business strategies. 【0658】 (Application Example 1) 【0659】 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". 【0660】 Traditional customer behavior analysis systems in commercial facilities have not been sufficiently effective in real-time customer behavior monitoring and in proposing efficient sales promotion strategies. As a result, optimizing store opening strategies and promotion plans is time-consuming and costly, leading to a lack of competitiveness against rivals. 【0661】 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. 【0662】 In this invention, the server includes means for collecting anonymized movement data and analyzing visitor behavior patterns, means for integrating social network data and consumption history data to infer customer preferences and lifestyles, and means for monitoring customer trends in real time in conjunction with location information and proposing efficient sales promotion strategies. This enables the optimization of efficient and effective store opening strategies and promotion plans through market area analysis and the generation of potential customer profiles. 【0663】 "Anonymized movement data" refers to location data that has been processed in a way that does not identify individuals, and is used to collect visitors' location history and movement patterns. 【0664】 "Methods for analyzing behavioral patterns" refer to methods that analyze visitor frequency, routes, and length of stay based on collected movement data to reveal specific trends and behavioral models. 【0665】 "Social network data" refers to data about users' interests and activities obtained through online platforms and networks. 【0666】 "Consumption history data" refers to data about purchases made by customers in the past, and represents their consumption trends and purchasing power. 【0667】 "Means for inferring preferences and lifestyles" refers to methods of analyzing social network data and consumption history data to infer the interests, preferences, and daily life patterns of individual customers. 【0668】 "A means of monitoring customer behavior in real time in conjunction with location information" refers to a method of tracking customer movements and locations at the present moment by combining current location data with real-time information processing. 【0669】 "A means of proposing an efficient sales promotion strategy" refers to a method of advising on optimal marketing activities and product placement aimed at increasing sales, based on collected and analyzed data. 【0670】 "Market area analysis" is a process that involves a detailed analysis of demographics and visitor data within a target area to evaluate the economic activity and market potential of that area. 【0671】 "Profile generation of potential customers" is a method of creating information that serves as the basis for marketing and targeting by inferring the characteristics and attributes of individuals who may become future customers. 【0672】 The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server collects anonymized movement data, social network data, and consumption history data, and runs a program to analyze customer behavior patterns, preferences, and lifestyles based on this data. The server is built using Python and utilizes libraries such as pandas, numpy, and scikit-learn for data collection and analysis. Geopy is used to process location data with high accuracy and analyze real-time trends. 【0673】 The terminal plays a role in visualizing the analysis results received from the server, assisting users in making strategic decisions. Using matplotlib and seaborn for visualization, detailed plots of demographics and visitor patterns are displayed on the dashboard, making them intuitively understandable to users. 【0674】 Users can easily obtain information to optimize commercial facility layout changes and promotional activities through a dashboard provided on their devices. For example, if a store is found to receive many customers in the afternoon, users can effectively place protein bars and related products and conduct promotional activities. 【0675】 The generative AI model proposes a variety of strategies based on the prompts used for data analysis. For example, the following prompts are used: 【0676】 "We created a prompt for a customer behavior analysis app for commercial facilities. It's a support tool for devising optimal promotional strategies based on location data and behavioral patterns. Based on the following conditions…" 【0677】 Based on this prompt, the AI ​​model can propose even more sophisticated marketing strategies. 【0678】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0679】 Step 1: 【0680】 The server collects anonymized movement data from various data sources. It receives location data transmitted from mobile devices as input. This data is processed through a collection module and standardized using geopy. The output is a formatted movement dataset, which establishes the basis for the dynamic movement patterns of individual visitors. 【0681】 Step 2: 【0682】 The server integrates social network data and consumption history data to infer customer preferences and lifestyles. Input includes SNS data obtained via API and purchase history from a database. Data cleaning is performed using the pandas library to eliminate inconsistencies. The output is a cleaned, integrated dataset. This process forms a customer behavior model. 【0683】 Step 3: 【0684】 The server monitors customer behavior in real time and proposes efficient sales promotion strategies. Real-time location data and an integrated dataset are referenced as input. Clustering analysis is performed using scikit-learn to identify preferred routes and locations within the store. Recommended promotional areas are generated as output. These suggestions are further optimized by a generative AI model. 【0685】 Step 4: 【0686】 The terminal displays the analysis results received from the server as a visualized dashboard. The input is the analysis results sent from the server. Using matplotlib and seaborn, appropriate charts and heatmaps are generated. The output is a visualized dashboard that is intuitive and easy for the user to understand. 【0687】 Step 5: 【0688】 Users utilize a dashboard on their device to optimize commercial facility layout changes and sales promotion strategies. The input consists of analytical data and prompts displayed on the dashboard. Based on this data, users develop action plans for appropriate product placement and promotions. The output is an efficient and results-oriented strategic implementation plan. 【0689】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0690】 This invention is an advanced analytical system that integrates location data, social media data, and purchase history data, and further combines them with an emotion engine to support strategic decision-making for commercial facilities. The system mainly consists of three main components: a server, terminals, and users. 【0691】 The server collects anonymized movement data and extracts visitor behavior patterns. After data cleaning and noise reduction, it measures movement routes, visit frequency, and dwell time. It also gains a deep understanding of customer preferences based on social media data and purchase history. By introducing an emotion engine, it performs sentiment analysis of natural language obtained from social media data to recognize the customer's emotional state. This analysis identifies which emotions are at the root of purchase intent and interest in services. 【0692】 The terminal visualizes the analysis results sent from the server as a dashboard and displays it to the user. The dashboard displays data that reflects the user's emotional state, along with market area analysis, competitive impact, and information on potential customers. Based on this information, users can optimize their store opening strategies and develop targeted marketing strategies. 【0693】 Users can select appropriate contact methods and timings based on customer emotional data provided by the emotion engine. For example, when promoting a new product, users can choose a time when customers are experiencing positive emotions and launch promotional activities accordingly. Furthermore, emotional data can be used to analyze customers' potential purchasing motivations for specific products or services, enabling more personalized marketing strategies. 【0694】 Thus, this system provides strong support for the operation and marketing activities of commercial facilities through a comprehensive approach that combines data analysis and sentiment recognition. 【0695】 The following describes the processing flow. 【0696】 Step 1: 【0697】 The server collects anonymized location data from mobile devices. This includes GPS data and Wi-Fi connection history, and is used to record visitors' movements. 【0698】 Step 2: 【0699】 The server cleans and removes noise from the collected location data. It organizes the data to improve the accuracy of the analysis by removing outliers and formatting the data. 【0700】 Step 3: 【0701】 The server analyzes visitor behavior patterns based on their location information. This involves calculating visit timing, frequency, and duration, and using this data to predict visitor behavior. 【0702】 Step 4: 【0703】 Users consent to the provision of social media data and purchase history data, which the server uses to analyze customer preferences. The data is processed anonymously and used in a privacy-protected manner. 【0704】 Step 5: 【0705】 The server analyzes the received SNS data using an emotion engine and extracts the user's emotions from the text. This process classifies the emotions into categories such as positive, negative, and neutral, allowing the server to understand each customer's emotional state. 【0706】 Step 6: 【0707】 The server integrates location information, purchase history, and sentiment data to generate customer profiles. These profiles help identify potential customers within the service area based on individual customers' purchasing motivations and interest in the service. 【0708】 Step 7: 【0709】 The terminal displays analysis results from the server on a dashboard. The dashboard visualizes market area analysis, competitive impact, and sentiment profiles, and is presented in a format that is easy for the user to understand. 【0710】 Step 8: 【0711】 Users utilize the information on the dashboard to plan store opening strategies and implement marketing measures. In particular, they can use sentiment data to select the most effective timing for campaigns. 【0712】 (Example 2) 【0713】 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". 【0714】 Store opening strategies and marketing activities in commercial facilities require detailed analysis based on visitor behavior and customer preferences. However, traditional methods have made it difficult to gain a comprehensive understanding, including customer emotional states, which can sometimes lead to insufficient decision-making. To address this challenge, there is a need for a system that can comprehensively analyze visitor behavior patterns, customer preferences, and emotional states. 【0715】 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. 【0716】 In this invention, the server includes means for collecting anonymized spatial information and analyzing visitor behavior patterns, means for combining social media data and consumption history data to infer customer preferences and lifestyles, and means for evaluating customer emotional states and analyzing purchase intent and interest in services using natural language processing. This enables detailed data analysis based on visitor behavior and emotional states, making it possible to improve the accuracy of store opening strategies and marketing activities for commercial facilities. 【0717】 "Anonymized spatial information" refers to geographic location data that is collected in a way that removes personally identifiable information and protects the privacy of visitors. 【0718】 "Visitor behavior patterns" refer to specific behavioral tendencies and habits obtained by analyzing visitors' movement routes, frequency of actions, and length of stay. 【0719】 "Social media data" refers to digital information such as customer posts and comments obtained from various social media platforms. 【0720】 "Consumption history data" refers to information about customers' purchasing trends and product choices, collected based on past purchase records. 【0721】 "Natural language processing" refers to the techniques and methods used by computers to understand and analyze human language. 【0722】 "Customer emotional state" refers to the emotional state a customer is experiencing at a particular time or in a particular situation, as analyzed through natural language processing. 【0723】 "Purchase intent" refers to the psychological motivation or degree of interest a customer has in purchasing a product or service. 【0724】 "Interest in the service" refers to the degree of interest and attention a customer has in the service being provided. 【0725】 A "potential market" is a market area that is not currently apparent but is expected to be developed in the future. 【0726】 "Prospective customer characteristics" refer to the attributes and behavioral patterns of individuals who are perceived to have an interest in or need for a particular product or service, but who have not yet become customers. 【0727】 The system implementing this invention includes a server, a terminal, and a user as its main components. The specific roles and operations of each are described below. 【0728】 First, the server plays a central role in data collection and analysis. In this system, the server collects anonymized spatial information, social media data, and consumption history data. Location information is collected via mobile devices and communication networks and stored in a database. Data cleaning is performed using the Python Pandas library. Visitor behavior patterns are also analyzed using K-means clustering and other machine learning algorithms. For data collected from social media, emotional states are analyzed using a natural language processing engine to evaluate customer purchase intent and interest in services. Libraries such as Hugging Face's Transformers are utilized in this process. 【0729】 Next, the terminal is responsible for data visualization. The terminal receives the analysis results sent from the server and visualizes them in a dashboard format using a front-end development framework (such as React.js or Vue.js). The dashboard displays heatmaps of behavioral patterns and graphs showing emotional tendencies, enabling users to make strategic decisions based on this information. 【0730】 Users optimize the store opening strategy and marketing activities of commercial facilities based on data displayed on their devices. For example, if a user confirms that the number of visitors increases during a specific time period and that customers are in a positive emotional state, they can plan promotional and advertising strategies tailored to that timing. A concrete example of input to the generating AI model would be a prompt message such as, "In a shopping mall, identify areas where visitors spend a long time and suggest effective promotional activities when those visitors are in a positive emotional state." 【0731】 In summary, the present invention enhances data-driven decision-making in commercial facilities and supports the development of more accurate marketing and store opening strategies. 【0732】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0733】 Step 1: 【0734】 The server collects anonymized spatial information. Location data sent from various mobile devices and communication networks serves as input. The server stores this data in a database and performs anonymization by removing personally identifiable information. The output is clean, anonymized location data. Specifically, it collects GPS data from mobile devices and ensures anonymity by removing personal information such as user IDs. 【0735】 Step 2: 【0736】 The server analyzes the behavioral patterns. The anonymized location data obtained in Step 1 is used as input. The server uses machine learning algorithms, such as K-means clustering, to analyze the visitors' behavioral patterns. As output, it generates data on the visitors' movement paths, activity frequency, and time spent at each location. Specifically, it identifies the routes and spots that are visited most frequently within a given time frame. 【0737】 Step 3: 【0738】 The server collects social media data and performs sentiment analysis. Input data consists of posts and comments obtained from social networking platforms. Natural language processing is used to evaluate the emotional state of this data. The output is numerical data indicating the customer's emotional tendencies. Specifically, it uses a text analysis engine to calculate positive, negative, and neutral sentiment scores. 【0739】 Step 4: 【0740】 The server analyzes consumption history data to infer customer preferences. The input is consumption history data, i.e., data on purchased items. This data is analyzed to infer customer preferences. The output generates a profile of the customer's purchasing trends and preferences. Specifically, it identifies frequently purchased product categories and uses this to determine the characteristics of potential customers. 【0741】 Step 5: 【0742】 The terminal visualizes the analysis results from the server on a dashboard. The data obtained in steps 2, 3, and 4 serves as input. The terminal visualizes this data in a dashboard format using a front-end development framework and provides it to the user. As output, the user receives a visualization of behavioral patterns, emotional states, and purchasing trends that are intuitively understandable. Specifically, it displays heatmaps and emotion score charts, and provides an interface that allows the user to freely manipulate the data. 【0743】 Step 6: 【0744】 Users make strategic decisions based on the information in the dashboard. Visualized analytical data displayed on the device serves as input. Users use this to determine how to optimize their store opening strategy and marketing activities. The output includes the development of more targeted promotions and specific marketing measures. Specifically, users can select when to promote a particular product and determine the appropriate timing for advertising campaigns. 【0745】 (Application Example 2) 【0746】 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". 【0747】 In recent years, competition has intensified in commercial facilities and brick-and-mortar stores, requiring personalized service based on customer needs. However, traditional methods make it difficult to provide accurate information based on customers' emotional states and behavior in real time, posing a challenge to improving customer satisfaction and achieving effective marketing activities. 【0748】 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. 【0749】 In this invention, the server includes means for collecting anonymized motion data and analyzing visitor behavior patterns, means for integrating information exchange network data and purchase history data to infer consumer preferences and lifestyles, and means for providing information to smart devices in real time and suggesting appropriate contact methods and timings to store staff based on the customer's emotional state. This enables personalized service and real-time optimization of customer service at the store level. 【0750】 "Anonymized movement data" refers to a collection of movement information that has been processed in a way that prevents the identification of individuals. 【0751】 "Visitor behavior patterns" refer to data that shows the tendencies and characteristics of the behavior of people who visit stores or facilities. 【0752】 "Information exchange network data" refers to information about user activity on social networking sites and similar platforms. 【0753】 "Purchase history data" refers to records of products that consumers have purchased in the past. 【0754】 "Consumer preferences and lifestyles" refer to the goods and services that individual customers prefer, as well as their lifestyle habits. 【0755】 "Smart devices" refer to all devices capable of sending and receiving data via the internet, and include mobile terminals and wearable devices. 【0756】 "Providing information in real time" means that data is acquired and processed immediately, and results are provided with virtually no time lag. 【0757】 "Customer emotional state" refers to information about the emotions and moods a consumer is experiencing at a particular point in time. 【0758】 "Individualized service at the store level" refers to providing services and information tailored to specific customers on an individual basis. 【0759】 "Real-time customer service optimization" means instantly understanding customer needs and emotions, and immediately adjusting services and approaches to address them. 【0760】 To realize this invention, a system is required in which a server, terminals, and users work together. The server collects anonymized motion data and uses it to analyze visitor behavior patterns. This data is cleansed and noise is removed. Next, information exchange network data and purchase history data are integrated to infer consumer preferences and lifestyles. Using an emotion engine, the customer's emotional state is determined from the information exchange network data, and this is reflected in the analysis results. 【0761】 The terminal receives analysis results sent from the server and displays them visually as an interface. This interface can be viewed by store staff on-site via their smart devices, allowing them to suggest the optimal contact method and timing based on the customer's emotional state in real time. 【0762】 Users will utilize this information to provide personalized service at the store. For example, if a customer is in a positive emotional state, store staff can use that information to proactively recommend specific products or services. 【0763】 As a concrete example, one bookstore checks what genres of books a customer has purchased in the past and their emotional state at the time, and then quickly presents a list of books that should be recommended to the customer next, based on pre-set conditions. An example of a prompt message is, "Analyze the customer's emotional state towards a specific product based on social media data, and indicate how to propose a personalized promotion." 【0764】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0765】 Step 1: 【0766】 The server collects anonymized motion data. This data forms the basis for extracting visitor behavior patterns. During this process, data cleaning and noise reduction are performed. The input is raw data, and the output is clean motion data with noise removed. 【0767】 Step 2: 【0768】 The server integrates information exchange network data and purchase history data. This integration allows for the analysis of consumer preferences and lifestyles. The input consists of data sets in different formats, and the output is a consumer profile based on these. Here, a generative AI model is used to identify relationships between the data with high accuracy. 【0769】 Step 3: 【0770】 The server uses an emotion engine to analyze customer emotional states from information exchange network data. The input is raw information exchange network data, and the output is each customer's emotional score. This emotional state is then analyzed using natural language processing techniques. 【0771】 Step 4: 【0772】 The terminal acquires analysis results from the server in real time and displays them visually through its interface. The input is the analysis information received from the server, and the output is a visually organized interface. The display on the terminal helps users make quick decisions. 【0773】 Step 5: 【0774】 Users plan appropriate customer interactions based on customer emotional states and shopping habits provided by the device. Input is visualized data, and output is individually optimized responses. Based on this, users construct specific sales strategies and promotions. 【0775】 Step 6: 【0776】 The server aggregates overall feedback and monitors system performance. Inputs are user feedback data, and outputs are metrics that help continuously improve the system. This feedback loop allows the system to constantly improve its accuracy. 【0777】 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. 【0778】 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. 【0779】 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. 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0784】 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. 【0785】 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." 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0798】 The following is further disclosed regarding the embodiments described above. 【0799】 (Claim 1) 【0800】 A means of collecting anonymized movement data and analyzing visitor behavior patterns, 【0801】 A means of integrating SNS data and purchase history data to infer customer preferences and lifestyles, 【0802】 A method for analyzing population dynamics by time of day and evaluating the impact of competing stores, 【0803】 A means for generating market area analysis and potential customer profiles, and creating reports to optimize store opening strategies, 【0804】 A system that includes this. 【0805】 (Claim 2) 【0806】 The system according to claim 1, which cleans and denoises location data to improve the accuracy of the analysis. 【0807】 (Claim 3) 【0808】 The system according to claim 1, which visually displays reports generated on a terminal as a dashboard that can be used by the user for strategic decision-making. 【0809】 "Example 1" 【0810】 (Claim 1) 【0811】 A means of collecting anonymized location data and analyzing crowd behavior patterns, 【0812】 A means of integrating social network data and transaction history data to infer consumer preferences and behavioral characteristics, 【0813】 A method for analyzing human behavior at different times of the day and evaluating the impact of the competitive environment, 【0814】 A means for analyzing regional markets and generating characteristics of potential consumers, and for creating information to optimize business strategies, 【0815】 A system that includes this. 【0816】 (Claim 2) 【0817】 The system according to claim 1, which organizes location data and removes outliers to improve the accuracy of the analysis. 【0818】 (Claim 3) 【0819】 The system according to claim 1, which displays information generated on a terminal as a visualization tool that can be used by the user for strategic decision-making. 【0820】 "Application Example 1" 【0821】 (Claim 1) 【0822】 A means of collecting anonymized movement data and analyzing visitor behavior patterns, 【0823】 A means of integrating social network data and consumption history data to infer customer preferences and lifestyles, 【0824】 A means of monitoring customer behavior in real time in conjunction with location information and proposing efficient sales promotion strategies, 【0825】 A means for generating market area analysis and potential customer profiles, and creating reports to optimize store opening strategies, 【0826】 A system that includes this. 【0827】 (Claim 2) 【0828】 The system according to claim 1, which cleans and denoises location data to improve the accuracy of the analysis. 【0829】 (Claim 3) 【0830】 The system according to claim 1, which presents reports generated on a terminal as a visualization dashboard that can be used by the user to implement an optimized deployment strategy. 【0831】 "Example 2 of combining an emotion engine" 【0832】 (Claim 1) 【0833】 A means for collecting anonymized spatial information and analyzing visitor behavior patterns, 【0834】 A means of inferring customer preferences and lifestyles by combining social media data and consumption history data, 【0835】 A method for evaluating customer emotional states and analyzing their purchasing intent and interest in services using natural language processing, 【0836】 A means for analyzing potential markets and generating characteristics of prospective customers, and creating reports to optimize installation plans, 【0837】 A system that includes this. 【0838】 (Claim 2) 【0839】 The system according to claim 1, which performs scrutiny and noise reduction of spatial information data to improve the accuracy of the analysis. 【0840】 (Claim 3) 【0841】 The system according to claim 1, which visually displays data including the user's emotional state as a dashboard on the terminal, and which the user can use for strategic decision-making. 【0842】 "Application example 2 when combining with an emotional engine" 【0843】 (Claim 1) 【0844】 A means of collecting anonymized exercise data and analyzing visitor behavior patterns, 【0845】 A means of integrating information exchange network data and purchase history data to infer consumer preferences and lifestyles, 【0846】 A method for analyzing population trends by time of day and evaluating the impact of competing stores, 【0847】 A means for generating market area analysis and potential customer characteristics, and creating reports to optimize store opening strategies, 【0848】 A means of providing information to smart devices in real time and suggesting appropriate contact methods and timings to store staff based on the customer's emotional state, 【0849】 A system that includes this. 【0850】 (Claim 2) 【0851】 The system according to claim 1, which cleans and denoises location data to improve the accuracy of the analysis. 【0852】 (Claim 3) 【0853】 The system according to claim 1, which visually displays reports generated on a terminal as an interface, and which can be used by the user for strategic decision-making. [Explanation of Symbols] 【0854】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of collecting anonymized movement data and analyzing visitor behavior patterns, A means of integrating SNS data and purchase history data to infer customer preferences and lifestyles, A method for analyzing population dynamics by time of day and evaluating the impact of competing stores, A means for generating market area analysis and potential customer profiles, and creating reports to optimize store opening strategies, A system that includes this. [Claim 2] The system according to claim 1, which cleans and removes noise from location data to improve the accuracy of the analysis. [Claim 3] The system according to claim 1, which visually displays reports generated on a terminal as a dashboard that can be used by the user for strategic decision-making.

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