system

Abstract

We provide the system. [Solution] A means of acquiring regional information from a data collection device, A means for preprocessing acquired regional information and converting it into a unified format, A means for analyzing pre-processed regional information to extract features, A means for generating a predictive model based on extracted features, A method for performing region-specific scoring using a predictive model, A system that includes means for suggesting the optimal location based on the scoring results.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the opening, relocation, or closure of a new store, site selection is an important factor that affects the success or failure of a business. Especially in regions where it is difficult to understand local characteristics, it is difficult to make an appropriate decision on store opening, and as a result, there is a risk of selecting an inappropriate location. Furthermore, conventional store opening strategies rely heavily on intuition and experience, and data-based decision-making is not sufficient. Improvement of such a current situation is required. 【Means for Solving the Problems】 【0005】 This invention provides a system for evaluating the potential for opening a store in each region by preprocessing diverse regional information acquired from a data collection device into a unified format and extracting features. A predictive model is used to score each region, and further filtering is performed according to user-defined conditions to propose the optimal store location. Furthermore, the system visually presents the proposed results, enabling users to intuitively understand them and supporting data-driven, rational decision-making. This system can improve the accuracy of store location decisions and increase the success rate of businesses. 【0006】 A "data collection device" is a device used to collect relevant data from various data sources necessary for obtaining regional information. 【0007】 "Regional information" refers to information that includes statistical data, infrastructure data, and economic information related to a specific geographical area. 【0008】 "Preprocessing" refers to processes such as data cleaning, formatting standardization, and missing value imputation, which are necessary to convert acquired data into a format suitable for analysis. 【0009】 "Features" refer to important parameters or numerical values ​​extracted in data analysis to build a model. 【0010】 A "predictive model" is a mathematical model used to estimate future outcomes based on past data. 【0011】 "Scoring" is the process of assigning points to data and information based on specific criteria to evaluate their value and suitability. 【0012】 "Filtering" is the process of selecting data and information according to user-defined conditions and extracting those that meet specific criteria. 【0013】 "Visual presentation" refers to displaying information in a way that is easy for users to understand, using diagrams, illustrations, charts, and other visual aids. [Brief explanation of the drawing] 【0014】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention provides a system for site selection that is implemented through a series of processes based on data. The main component of the system is a program that includes multiple processing steps for processing information and supporting decision-making. 【0036】 This system first aggregates regional information from diverse data sources. The server collects information from government statistics, traffic sensor data, and commercial real estate databases. Next, this data is preprocessed and converted into a unified, analyzable format. By removing data noise and performing appropriate missing value imputation, the accuracy of the analysis is improved. 【0037】 Next, the server extracts key features from the pre-processed data to quantitatively understand regional characteristics. This includes data on population density, age groups, traffic volume, and lifestyle. Once the features are complete, the predictive model is trained. Using past successful store opening data as a reference, a machine learning algorithm is used to build a predictive model and score the potential of each candidate region. 【0038】 Next, the device suggests the most suitable candidate locations from scored areas based on the user's set conditions. At this stage, the suggestions are filtered considering the user's budget, desired property size, preferred area, and other conditions. Finally, the device visually presents the selected candidate locations, making them easy to understand intuitively on a map. The user then makes a decision based on this information. 【0039】 To give a concrete example, when a telecommunications company is opening a new store, the system first collects detailed traffic sensor data and density information of competing stores in a local city. The server analyzes population dynamics and traffic convenience scores for a specific area within the city to evaluate its potential for opening a store. Next, the terminal takes into account the user's preferences, such as a specific area and budget constraints, and proposes the three locations with the highest scores to the user. This result is mapped on a map, and detailed information for each location is displayed in a pop-up. Based on this information, the user can ultimately decide on the store location that best suits their company's strategy. 【0040】 In this way, the present invention supports efficient and rational location selection through a data-driven approach. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The server collects local information from external data sources. This includes government statistics, real-time data from traffic sensors, and property information from commercial real estate databases. This data is automatically retrieved via APIs. 【0044】 Step 2: 【0045】 The server preprocesses the collected data. Preprocessing includes cleaning the data, standardizing the format, and imputing missing values. For example, it synchronizes timestamps across different datasets and detects and removes outliers. 【0046】 Step 3: 【0047】 The server extracts features from pre-processed data. This includes calculations such as population density, age distribution, transportation accessibility score, and density of competing businesses. Regional characteristics are quantified to prepare the input dataset for the model. 【0048】 Step 4: 【0049】 The server trains a predictive model using machine learning algorithms. This process involves referencing past store opening success data and building a model to score each region using random forests or other appropriate models. 【0050】 Step 5: 【0051】 The server uses a trained model to score the potential for opening a store in each region and generates a ranking for each region. This identifies regions where business opportunities are predicted to be significant. 【0052】 Step 6: 【0053】 The terminal considers the user's input criteria, such as budget and location, to filter the most suitable store locations. The user can then adjust the criteria to further refine the suggested results. 【0054】 Step 7: 【0055】 The device visually displays proposed store locations on a map. By clicking on the details of each location, users can view information such as population data, traffic volume, and competitive landscape in a pop-up window. 【0056】 Step 8: 【0057】 Users make their final store location decision based on the information provided. After the decision, the actual data is fed back into the system and used to improve the accuracy of future analyses. 【0058】 (Example 1) 【0059】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0060】 In selecting locations for regional development, there is a need to efficiently and accurately process information obtained from diverse data sources and quickly propose the optimal location that meets the user's requirements. However, conventional methods suffer from the problems of complex data integration and analysis, which are time-consuming and costly. Furthermore, there is the challenge of presenting information in a visually easy-to-understand format. 【0061】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0062】 In this invention, the server includes means for acquiring area information from a data acquisition unit, means for preprocessing the acquired area information and converting it into a unified format, and means for analyzing the preprocessed area information and extracting feature data. This enables efficient processing of diverse data and allows for the suggestion of the optimal location based on the user's conditions. 【0063】 The "data acquisition unit" is the part that has the function of collecting area information from multiple data sources. 【0064】 "Regional information" is a general term for data related to a specific region, such as demographics, transportation data, and real estate information. 【0065】 "Preprocessing" refers to the process of converting collected data into a unified, analyzable format, removing noise, and imputing missing values. 【0066】 "Feature data" refers to data that shows important indicators and attributes extracted from pre-processed data. 【0067】 A "predictive structure" is a model built to predict future regional characteristics based on past data. 【0068】 "Evaluation" is the process of quantitatively calculating the potential of each area using a predictive structure. 【0069】 A "location" refers to a specific area selected based on evaluation, representing the point that best suits the user's requirements. 【0070】 A "display unit" is a device or software that provides visual information to the user. 【0071】 This invention is a system that supports location selection, proposing the optimal location through data collection, processing, and analysis. Specifically, the server, terminal, and user elements each play their respective roles. 【0072】 The server uses a data acquisition unit to collect area information from various data sources. Specifically, it uses a computer server equipped with a high-performance processor and large-capacity storage, and the software utilizes programs that perform data collection via APIs and SQL database queries. This makes it possible to obtain information in real time from government statistics data, traffic sensor data, and commercial real estate databases. 【0073】 The server also converts the data into a unified format during the preprocessing step, preparing it for analysis. This process uses algorithms to remove noise and impute missing values. The software used here includes data cleansing tools, noise filtering programs, and so-called data mining tools. 【0074】 The server then extracts feature data and constructs a prediction structure as a machine learning algorithm. Examples of algorithms used include random forests and support vector machines. This process generates predictions about the commercial potential and demographics of each region. 【0075】 Next, the terminal takes into account the user's settings and plays a role in suggesting the optimal location based on evaluation results obtained from the server. The user's settings include budget, desired property size, and preferred regional characteristics. The terminal integrates these conditions and evaluation results to provide visual suggestions. Specifically, it uses mapping software to display the best candidate locations on a map and presents detailed information about each location in a pop-up window. 【0076】 As a concrete example, when a company opens a new store, the server collects and analyzes traffic sensor data and competitor store density information for a local city. The terminal then presents three optimal locations based on the budget constraints and required property area criteria set by the user. This proposal is mapped onto a map and provided to the user along with detailed store information. 【0077】 An example of a prompt sentence to be input to the generating AI model is, "Please propose urban locations that are expected to yield the greatest profit within a budget of 100 million yen." In this way, the present invention provides a data-driven approach to achieving efficient location selection. 【0078】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0079】 Step 1: 【0080】 The server collects area information using a data acquisition unit. This involves obtaining information from government statistics data, traffic sensor data, and commercial real estate databases via APIs and database queries. The input requires access information to each data source, and the output is raw, unprocessed data. 【0081】 Step 2: 【0082】 The server preprocesses the collected raw data. Specifically, it removes noise from the data, appropriately imputes missing values, and converts it into a unified format that can be analyzed. The input is raw data, and after applying filtering algorithms and imputation techniques, processed data is obtained as output. 【0083】 Step 3: 【0084】 The server analyzes pre-processed data and extracts feature data. This process uses statistical methods and data mining algorithms to generate key indicators such as population density and traffic volume. The input is the pre-processed data, and the output is the feature data used in subsequent predictive models. 【0085】 Step 4: 【0086】 The server generates a predictive structure using the extracted feature data. In this step, machine learning algorithms, such as random forests and support vector machines, are used to create predictive models for store opening success rates, etc. The input is the feature data, and the output is the trained predictive model. 【0087】 Step 5: 【0088】 The server uses a predictive structure to evaluate each area. In this evaluation, the predictive model scores the commercial potential of each area. The input is a trained predictive model and current regional information, and the output is the scored evaluation result. 【0089】 Step 6: 【0090】 The terminal suggests the optimal location based on the user's input conditions and scored evaluation results. User input includes budget and property conditions, which are taken into consideration when filtering, and the output is a list of suggested locations. 【0091】 Step 7: 【0092】 The terminal visually presents the suggested locations. In this step, mapping software is used to pin locations on a map and display detailed information in a pop-up window. The input is the suggested locations, and the output is visualized map information. 【0093】 (Application Example 1) 【0094】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0095】 In modern urban development and facility design, quickly selecting the optimal location is a crucial challenge. In particular, optimizing facility placement, taking into account local demographics and transportation convenience, is essential for improving urban functionality and resident convenience. However, traditional methods are time-consuming and labor-intensive, making rapid decision-making difficult. 【0096】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0097】 In this invention, the server includes means for acquiring regional information from a data collection device, means for analyzing pre-processed regional information to extract features, and means for visually displaying candidate locations on a map using an information display device. This enables efficient location selection and rapid facility placement proposals in urban development. 【0098】 A "data collection device" is a device used to acquire local information, and its role is to aggregate necessary information from various data sources. 【0099】 "Preprocessing" refers to the process of converting collected regional information into a unified format and performing noise removal and missing value imputation to improve the accuracy of the analysis. 【0100】 "Feature extraction" is the process of extracting important elements from pre-processed regional information to create a dataset necessary for predicting location selection. 【0101】 A "predictive model" is an algorithmic model built based on extracted features, and is used to score the potential of each candidate location. 【0102】 "Scoring" is the process of evaluating the potential of each region using predictive models and providing quantifiable indicators. 【0103】 An "information display device" is a device used to visually confirm scoring results and suggested candidate locations on a map, providing users with an intuitive understanding. 【0104】 "Filtering" is the process of narrowing down suggested candidates based on the conditions entered by the user and providing the most suitable option. 【0105】 "Facility placement" refers to proposing the optimal locations for facilities that should be placed in specific areas within a city, thereby contributing to the improvement of urban functions. 【0106】 To implement this invention, a data collection device, an analysis server, and a user terminal are required. The data collection device is responsible for acquiring regional information from various data sources, such as government statistics, traffic conditions, and commercial conditions. The information collected by the server is preprocessed and converted into a unified format. This processing uses Python and Pandas to perform noise reduction and missing value imputation. 【0107】 Next, the Scikit-learn library is used to extract features from the pre-processed regional data. This reveals important features such as population density and traffic flow. Once the features are complete, a predictive model is generated using Scikit-learn's Random Forest. The server then uses this model to score the potential of each region. 【0108】 On the user's device, candidate locations are presented visually. An application developed using React Native then displays information about these candidate locations on a map via a map API (e.g., Google® Maps API). Based on this information, the user can view filtered candidate locations according to their set criteria and make the optimal selection. 【0109】 As a concrete example, consider the case of selecting a location for a health-promoting sports center. The server scores population density and accessibility to sports facilities to identify areas of interest in health and fitness. As a result, the most suitable location is displayed on a map. 【0110】 An example of a prompt message would be: "Please propose the optimal location for constructing a sports center for health-conscious users within a smart city. In particular, please prioritize locations with good population density and convenient transportation." 【0111】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0112】 Step 1: 【0113】 The server uses data collection devices to collect government statistics, transportation data, and commercial real estate data. Input consists of raw data from various data sources, which the server integrates and stores in a database. Output is roughly processed, integrated data. 【0114】 Step 2: 【0115】 The server uses Python and Pandas to preprocess the collected data. The input is integrated data, which is denoised, imputed for missing values, and converted to a unified format. Standardizing the data format improves the accuracy of the analysis. The output is a clean, preprocessed dataset. 【0116】 Step 3: 【0117】 The server extracts important features based on preprocessed data. The input is a clean dataset, and features such as population density and traffic volume are selected using the Scikit-learn library. This prepares the data necessary for the predictive model. The output is the identified set of features. 【0118】 Step 4: 【0119】 The server generates a predictive model using Scikit-learn's Random Forest. The input is an extracted feature set, and the model is trained based on data from past successful cases. This constructs a predictive model capable of evaluating the potential of each region. The output is the predictive model. 【0120】 Step 5: 【0121】 The server uses the generated predictive model to score each region. The input is the predictive model and new regional data. The model quantifies the potential of each region and generates an evaluation score. The output is a list of scores for each region. 【0122】 Step 6: 【0123】 The terminal filters the scoring results based on user-specified conditions. The input consists of a regional score list and user conditions; the terminal selects the candidate location with the highest score that also meets the user's desired conditions. The output is a filtered list of candidate locations. 【0124】 Step 7: 【0125】 The device, via a React Native application, visually presents filtered candidate locations using a map API. The input is a filtered list of candidate locations, displayed on the map in an intuitively understandable format for the user. The output is the group of candidate locations displayed on the map. 【0126】 Step 8: 【0127】 The user makes decisions regarding the specific placement of the facility based on candidate sites presented on the map. This allows for the selection of the optimal location for the new facility based on visually confirmed information. The output is the optimal location selected by the user. 【0128】 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. 【0129】 This invention provides a new function for a system that supports the selection of potential store locations, which recognizes the user's emotions and further utilizes that information to customize the proposed content. The system has the following components and performs the following specific processing. 【0130】 First, the system collects regional information from external data sources and preprocesses it into a standard format to prepare a unified analytical platform. The server acquires demographic and traffic data, competitive information, etc., generates predictive models using machine learning algorithms, and scores the potential for opening a store in each region. Then, it proposes the optimal candidate locations for store openings based on user-defined conditions. 【0131】 A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses a camera and microphone to analyze the user's facial expressions and voice tone, recognizing their emotional state in real time. This emotional data is transmitted to a server and used to generate suggestions tailored to the user's needs. 【0132】 For example, if a user shows strong interest in a proposed location, the server will present additional information or similar potential store locations. Conversely, if a user expresses dissatisfaction with the proposal, it will present alternative locations or new proposals. Furthermore, the system dynamically adjusts elements of the user interface based on sentiment data to provide a more user-friendly environment. 【0133】 For example, when a user receives a suggestion for a potential store location, if they smile, the system highlights the relevant suggestion and offers options to explore it further. On the other hand, if the user frowns or shows other signs of dissatisfaction, the system re-evaluates the suggestion and either presents new options or changes how the information is displayed. 【0134】 In this way, the emotion engine recognizes the user's emotional state and dynamically changes the suggested content accordingly, enabling more personalized selection of store locations. This improves user satisfaction and enhances the accuracy of business strategies. 【0135】 The following describes the processing flow. 【0136】 Step 1: 【0137】 The server collects local information from external data sources. This includes demographic data, traffic data, commercial real estate information, and competitor information. The data is automatically retrieved periodically via an API. 【0138】 Step 2: 【0139】 The server preprocesses the collected data. It cleans each dataset, standardizes the format, and appropriately imputes missing values. This ensures data integrity and improves the accuracy of the analysis. 【0140】 Step 3: 【0141】 The server extracts features from pre-processed data. This includes quantifying factors such as population density, competition density, and accessibility. A dataset is then prepared to quantitatively understand the characteristics of each region. 【0142】 Step 4: 【0143】 The server trains a predictive model using machine learning algorithms. It learns from past success stories and builds a model that scores the potential for opening a store in each region. Through this scoring, it identifies the regions with the greatest business opportunities. 【0144】 Step 5: 【0145】 The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. To recognize the user's emotional state, an emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) and sends the data to a server. 【0146】 Step 6: 【0147】 The server dynamically adjusts the suggested store locations for the user based on emotion recognition data. If positive emotions are recognized, relevant information is highlighted and further details are presented. If negative emotions are recognized, alternatives are suggested, and the order and content of the information displayed are changed. 【0148】 Step 7: 【0149】 The device visually displays the adjusted suggestion results. Potential store locations are shown on a map, and detailed information about each location is highlighted according to the user's level of interest. Users can select a location on the map to view more detailed information. 【0150】 Step 8: 【0151】 Users make decisions based on the suggested information. They provide feedback on the selected store locations via their terminals, and the server incorporates this feedback into subsequent analyses. This improves the overall accuracy of the system. 【0152】 (Example 2) 【0153】 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." 【0154】 In today's business environment, selecting potential store locations is a crucial element of a company's strategy. However, selecting locations based solely on regional information presents a challenge: it fails to adequately reflect the individual needs and emotions of users, thus hindering customer satisfaction. Furthermore, existing systems are limited to presenting static information, making it difficult to effectively incorporate user emotions and real-time feedback. 【0155】 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. 【0156】 In this invention, the server includes means for acquiring regional information from a data collection device, means for preprocessing the acquired regional information and converting it into a unified format, means for analyzing the preprocessed regional information and extracting features, means for analyzing image and audio information to recognize the user's emotional state, and means for dynamically customizing the suggested content based on the recognized emotional data. This makes it possible to utilize the user's emotional data to propose more personalized and optimal store location candidates for the user. 【0157】 A "data collection device" is a device that collects information via various sensors and internet connections in order to acquire local information. 【0158】 "Local information" refers to a collection of various data related to a specific area, such as demographic statistics, transportation data, and competitive information. 【0159】 "Preprocessing" refers to the data cleaning and format conversion processes performed to transform raw data into a format that is easy to analyze. 【0160】 A "unified format" is a standardized data structure used to unify data from different formats into a consistent format. 【0161】 "Features" are analyzable attributes or values ​​extracted from data, and are parameters used to generate predictive models. 【0162】 A "predictive model" is a model created using machine learning algorithms to learn specific patterns from data and estimate future trends. 【0163】 "Scoring" is the process of numerically evaluating the potential for opening stores in each region using the generated predictive model. 【0164】 "Emotional state" refers to the type and intensity of emotions a user is currently experiencing, as judged from factors such as their facial expressions and tone of voice. 【0165】 "Dynamic customization" refers to the process of changing information and services in real time based on user emotions and feedback. 【0166】 "Proposed content" refers to suggestions to users, including various information regarding potential store locations. 【0167】 This invention relates to a system that innovates the process of selecting potential store locations. This system is characterized by its ability to recognize user emotions and dynamically customize the suggested content. The hardware and software configurations are described in detail below. 【0168】 The server first acquires local information through data collection devices. This data includes demographic data, traffic data, and competitive information. This data is collected via edge device technology and cloud services. The server then uses data analysis software such as Python and R to preprocess the acquired data and convert it into a unified format. 【0169】 Next, the server analyzes the pre-processed data and extracts features. Libraries such as Pandas and NumPy are used for this purpose. Furthermore, based on the extracted features, a predictive model is generated using machine learning frameworks such as TENSORFLOW® and PyTorch, and region-specific scoring is performed. 【0170】 The device uses its camera and microphone to capture the user's facial expressions and voice tone in real time. This utilizes image and audio processing technologies such as OpenCV and Audacity. The device then sends this captured data to a server, where a generative AI model is used to analyze the user's emotions. 【0171】 If a user shows a specific emotional response to a suggested store location provided via their device, the server dynamically customizes the suggestions based on that emotional data. If the user is interested, the server suggests additional related information or similar locations. If the user expresses dissatisfaction, new locations are presented or existing suggestions are improved. For example, a prompt such as "Analyze the user's level of interest regarding store locations in a specific area based on emotional data and suggest optimized locations" can be input into the generating AI model to provide information optimized for the user. 【0172】 This system enables more personalized suggestions for potential store locations based on user sentiment data, improving the user experience and increasing the accuracy of business strategies. 【0173】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0174】 Step 1: 【0175】 The server acquires local information from the internet through data collection devices. This information includes demographic data, traffic data, and competitive information. The acquired data is input to the server as raw data. 【0176】 Step 2: 【0177】 The server uses Python to preprocess the input regional information. Specifically, it performs data cleaning and imputation of missing values, and converts the data into a unified format. As a result, clean data suitable for analysis is output. 【0178】 Step 3: 【0179】 The server analyzes the cleansed data and extracts features. Statistical analysis is performed using NumPy and Pandas to identify important data points. This process yields the features necessary for generating a predictive model. 【0180】 Step 4: 【0181】 The server generates a predictive model using TensorFlow based on the extracted features. It then applies machine learning algorithms to build a model for evaluating the potential of store openings in each region. This results in the output of a score for each region. 【0182】 Step 5: 【0183】 The server uses the generated predictive model to perform region-specific scoring. Regional information is used as input data to quantify the potential for opening a store in each region. This yields scoring results, enabling the selection of the optimal candidate locations based on specific criteria. 【0184】 Step 6: 【0185】 The device uses a camera and microphone to collect the user's facial expressions and voice tone. This utilizes OpenCV and Audacity to acquire real-time emotion data from the user. 【0186】 Step 7: 【0187】 The device sends collected emotional data to the server. The server analyzes the emotional data using a generative AI model to determine the user's level of interest and dissatisfaction. Based on this input, it dynamically customizes the suggestions and outputs optimized suggestion results. 【0188】 Step 8: 【0189】 The user receives the suggestions via their device. If the user expresses a positive sentiment towards the suggestions, the server adds relevant information and similar locations to enhance the suggestions. In the case of a negative reaction, the server re-evaluates the suggestions and presents alternative options. 【0190】 This process enables the provision of personalized store location suggestions tailored to each user. 【0191】 (Application Example 2) 【0192】 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". 【0193】 Conventional store location selection systems make suggestions based on the analysis of regional data, but these suggestions cannot adapt to the individual emotions and reactions of users. Therefore, the suggested locations do not always meet user expectations, and there is a need to improve the user experience. Accordingly, a system is needed that enables more personalized store location selection that takes user emotions into consideration. 【0194】 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. 【0195】 In this invention, the server includes means for acquiring regional data from a data acquisition device, means for recognizing the user's emotional state using visual and auditory information, and means for dynamically adjusting the suggested content based on the recognized emotional state. This makes it possible to provide personalized suggestions that respond to the user's emotions. 【0196】 A "data acquisition device" is a device used to collect various types of data related to a region, and provides that data as basic information for analysis. 【0197】 "Regional data" refers to data that includes information such as the population, transportation, and competitive landscape of a region, and is used to understand the characteristics and features of that region. 【0198】 A "unified data format" is a data format that converts data obtained from different sources into a consistent format, making subsequent analysis easier. 【0199】 "Features" are important patterns and indicators extracted from regional data, and are used as raw materials for predictive algorithms. 【0200】 A "predictive algorithm" is a computational method used to predict future trends and suitability based on the characteristics of regional data. 【0201】 "Evaluation" is the act of numerically representing the potential and value of each region using predictive algorithms, and it serves as a foundation for improving the accuracy of proposals. 【0202】 "Location" refers to the proposed store locations suggested by the system, which are considered to have a high probability of business success based on the characteristics of that region. 【0203】 "Visual information" refers to visual data such as facial expressions collected by the user's camera, and is used to analyze the user's emotional state. 【0204】 "Voice" refers to audio information such as the tone of voice and the content of speech acquired by the user's microphone, and is used to understand the user's emotional state. 【0205】 "Emotional state" refers to a psychological state estimated based on the user's facial expressions and tone of voice, and is a factor used to optimize the suggested content. 【0206】 "Dynamic adjustment of proposal content" refers to the act of changing the content and display method of proposals in real time in accordance with the user's emotions, with the aim of improving the user experience. 【0207】 In an embodiment of the present invention, first, a server collects various data about a region via a data acquisition device. This collected data includes demographics, traffic information, and competitive analysis. The server preprocesses the data and converts it into a unified data format. Next, it extracts features from the preprocessed data and generates a prediction algorithm. This enables evaluation on a region-by-region basis and prepares the system to suggest optimal locations for store openings. 【0208】 The device uses a camera and microphone to acquire the user's visual and auditory information in real time, and analyzes the user's emotional state using emotion recognition software. This analysis is sent to a server and used to dynamically adjust the suggested content. Specifically, the server customizes the suggested content and display method based on the user's emotional state to improve user satisfaction. 【0209】 For example, if a user responds positively to a suggestion of potential store locations, the server will display more detailed information and provide information to help them consider similar areas. On the other hand, if the user shows indecisiveness, the server will suggest alternatives and re-evaluate the suggestions. In this way, the system provides personalized suggestions and supports the user's decision-making. 【0210】 By utilizing generative AI models, it is also possible to automatically generate prompt messages that correspond to the user's emotional state. For example, the following prompt messages are possible: 【0211】 "If the user's facial expression is 'smiling,' display detailed information about the suggested location." 【0212】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0213】 Step 1: 【0214】 The server collects regional data via data acquisition devices. This data includes demographics, traffic information, and competition information. Based on this input information, the server preprocesses the data and converts it into a unified data format. This conversion process unifies different data sources and standardizes the data into a format that can be used for subsequent analysis. 【0215】 Step 2: 【0216】 The server analyzes pre-processed data and extracts regional characteristics. Specifically, the server applies machine learning algorithms to extract valuable features from the data. In this process, characteristics such as population trends and transportation convenience for each region are quantified and supplied as input to the prediction algorithm. 【0217】 Step 3: 【0218】 The server generates a prediction algorithm based on the extracted features and performs an evaluation for each region. The server uses these features to score the regional potential and generates basic data to identify the optimal candidate locations. Based on the scoring results, regions with high potential for opening a store are selected. 【0219】 Step 4: 【0220】 The device uses its camera and microphone to capture the user's visual and auditory information and analyzes their emotional state in real time. Using the user's facial expressions and tone of voice as input, the device employs emotion recognition software to determine the user's emotional state. This output serves as an indicator of how the user is reacting to the presented information. 【0221】 Step 5: 【0222】 The server dynamically adjusts the suggestions based on the user's emotional state. Using the emotional data obtained in step 4 as input, the server automatically generates prompt sentences using a generative AI model and customizes the suggestions. Specific actions include displaying additional details if the user shows interest, and presenting alternatives if the user expresses dissatisfaction. 【0223】 Step 6: 【0224】 The user reviews dynamic suggestions provided by the server and makes a decision. Based on the information displayed on the device's screen, the user selects which candidate location best matches their needs. The user's selection results are fed back as data to improve the accuracy of future suggestions. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 [Second Embodiment] 【0229】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0230】 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. 【0231】 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). 【0232】 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. 【0233】 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. 【0234】 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). 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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. 【0239】 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. 【0240】 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". 【0241】 This invention provides a system for site selection that is implemented through a series of processes based on data. The main component of the system is a program that includes multiple processing steps for processing information and supporting decision-making. 【0242】 This system first aggregates regional information from diverse data sources. The server collects information from government statistics, traffic sensor data, and commercial real estate databases. Next, this data is preprocessed and converted into a unified, analyzable format. By removing data noise and performing appropriate missing value imputation, the accuracy of the analysis is improved. 【0243】 Next, the server extracts key features from the pre-processed data to quantitatively understand regional characteristics. This includes data on population density, age groups, traffic volume, and lifestyle. Once the features are complete, the predictive model is trained. Using past successful store opening data as a reference, a machine learning algorithm is used to build a predictive model and score the potential of each candidate region. 【0244】 Next, the device suggests the most suitable candidate locations from scored areas based on the user's set conditions. At this stage, the suggestions are filtered considering the user's budget, desired property size, preferred area, and other conditions. Finally, the device visually presents the selected candidate locations, making them easy to understand intuitively on a map. The user then makes a decision based on this information. 【0245】 To give a concrete example, when a telecommunications company is opening a new store, the system first collects detailed traffic sensor data and density information of competing stores in a local city. The server analyzes population dynamics and traffic convenience scores for a specific area within the city to evaluate its potential for opening a store. Next, the terminal takes into account the user's preferences, such as a specific area and budget constraints, and proposes the three locations with the highest scores to the user. This result is mapped on a map, and detailed information for each location is displayed in a pop-up. Based on this information, the user can ultimately decide on the store location that best suits their company's strategy. 【0246】 In this way, the present invention supports efficient and rational location selection through a data-driven approach. 【0247】 The following describes the processing flow. 【0248】 Step 1: 【0249】 The server collects local information from external data sources. This includes government statistics, real-time data from traffic sensors, and property information from commercial real estate databases. This data is automatically retrieved via APIs. 【0250】 Step 2: 【0251】 The server preprocesses the collected data. Preprocessing includes cleaning the data, standardizing the format, and imputing missing values. For example, it synchronizes timestamps across different datasets and detects and removes outliers. 【0252】 Step 3: 【0253】 The server extracts features from pre-processed data. This includes calculations such as population density, age distribution, transportation accessibility score, and density of competing businesses. Regional characteristics are quantified to prepare the input dataset for the model. 【0254】 Step 4: 【0255】 The server trains a predictive model using machine learning algorithms. This process involves referencing past store opening success data and building a model to score each region using random forests or other appropriate models. 【0256】 Step 5: 【0257】 The server uses a trained model to score the potential for opening a store in each region and generates a ranking for each region. This identifies regions where business opportunities are predicted to be significant. 【0258】 Step 6: 【0259】 The terminal considers the user's input criteria, such as budget and location, to filter the most suitable store locations. The user can then adjust the criteria to further refine the suggested results. 【0260】 Step 7: 【0261】 The device visually displays proposed store locations on a map. By clicking on the details of each location, users can view information such as population data, traffic volume, and competitive landscape in a pop-up window. 【0262】 Step 8: 【0263】 Users make their final store location decision based on the information provided. After the decision, the actual data is fed back into the system and used to improve the accuracy of future analyses. 【0264】 (Example 1) 【0265】 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". 【0266】 In selecting locations for regional development, there is a need to efficiently and accurately process information obtained from diverse data sources and quickly propose the optimal location that meets the user's requirements. However, conventional methods suffer from the problems of complex data integration and analysis, which are time-consuming and costly. Furthermore, there is the challenge of presenting information in a visually easy-to-understand format. 【0267】 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. 【0268】 In this invention, the server includes means for acquiring area information from a data acquisition unit, means for preprocessing the acquired area information and converting it into a unified format, and means for analyzing the preprocessed area information and extracting feature data. This enables efficient processing of diverse data and allows for the suggestion of the optimal location based on the user's conditions. 【0269】 The "data acquisition unit" is the part that has the function of collecting area information from multiple data sources. 【0270】 "Regional information" is a general term for data related to a specific region, such as demographics, transportation data, and real estate information. 【0271】 "Preprocessing" refers to the process of converting collected data into a unified, analyzable format, removing noise, and imputing missing values. 【0272】 "Feature data" refers to data that shows important indicators and attributes extracted from pre-processed data. 【0273】 A "predictive structure" is a model built to predict future regional characteristics based on past data. 【0274】 "Evaluation" is the process of quantitatively calculating the potential of each area using a predictive structure. 【0275】 A "location" refers to a specific area selected based on evaluation, representing the point that best suits the user's requirements. 【0276】 A "display unit" is a device or software that provides visual information to the user. 【0277】 This invention is a system that supports location selection, proposing the optimal location through data collection, processing, and analysis. Specifically, the server, terminal, and user elements each play their respective roles. 【0278】 The server uses a data acquisition unit to collect area information from various data sources. Specifically, it uses a computer server equipped with a high-performance processor and large-capacity storage, and the software utilizes programs that perform data collection via APIs and SQL database queries. This makes it possible to obtain information in real time from government statistics data, traffic sensor data, and commercial real estate databases. 【0279】 The server also converts the data into a unified format during the preprocessing step, preparing it for analysis. This process uses algorithms to remove noise and impute missing values. The software used here includes data cleansing tools, noise filtering programs, and so-called data mining tools. 【0280】 The server then extracts feature data and constructs a prediction structure as a machine learning algorithm. Examples of algorithms used include random forests and support vector machines. This process generates predictions about the commercial potential and demographics of each region. 【0281】 Next, the terminal takes into account the user's settings and plays a role in suggesting the optimal location based on evaluation results obtained from the server. The user's settings include budget, desired property size, and preferred regional characteristics. The terminal integrates these conditions and evaluation results to provide visual suggestions. Specifically, it uses mapping software to display the best candidate locations on a map and presents detailed information about each location in a pop-up window. 【0282】 As a concrete example, when a company opens a new store, the server collects and analyzes traffic sensor data and competitor store density information for a local city. The terminal then presents three optimal locations based on the budget constraints and required property area criteria set by the user. This proposal is mapped onto a map and provided to the user along with detailed store information. 【0283】 An example of a prompt sentence to be input into the generative AI model is "Please propose candidate locations in urban areas where the maximum profit can be expected within a budget of 100 million yen." Thus, the present invention provides a data-driven approach for realizing efficient site selection. 【0284】 The flow of the specific process in Example 1 will be described with reference to FIG. 11. 【0285】 Step 1: 【0286】 The server collects area information using the data acquisition unit. At this time, information is obtained from government statistical data, traffic sensor data, and commercial real estate databases via APIs and database queries. Access information to each data source is required as input, and raw data is obtained as output. 【0287】 Step 2: 【0288】 The server preprocesses the collected raw data. Specifically, a process is performed to remove data noise, appropriately fill in missing values, and convert it into a unified format that can be analyzed. The input is raw data, and as a result of applying filtering algorithms and filling methods, processed data is obtained as output. 【0289】 Step 3: 【0290】 The server analyzes the preprocessed data and extracts feature data. In this process, statistical methods and data mining algorithms are used to generate important indicators such as population density and traffic volume. The input is the data after preprocessing, and the output is the feature data to be used in the subsequent prediction model. 【0291】 Step 4: 【0292】 The server generates a predictive structure using the extracted feature data. In this step, machine learning algorithms, such as random forests and support vector machines, are used to create predictive models for store opening success rates, etc. The input is the feature data, and the output is the trained predictive model. 【0293】 Step 5: 【0294】 The server uses a predictive structure to evaluate each area. In this evaluation, the predictive model scores the commercial potential of each area. The input is a trained predictive model and current regional information, and the output is the scored evaluation result. 【0295】 Step 6: 【0296】 The terminal suggests the optimal location based on the user's input conditions and scored evaluation results. User input includes budget and property conditions, which are taken into consideration when filtering, and the output is a list of suggested locations. 【0297】 Step 7: 【0298】 The terminal visually presents the suggested locations. In this step, mapping software is used to pin locations on a map and display detailed information in a pop-up window. The input is the suggested locations, and the output is visualized map information. 【0299】 (Application Example 1) 【0300】 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." 【0301】 In modern urban development and facility design, quickly making an optimal site selection is an important issue. In particular, an optimal facility layout considering the population dynamics and transportation convenience of the region is essential for improving the functionality of the city and the convenience of its residents. However, conventional methods require a lot of time and effort, making it difficult to make rapid decisions. 【0302】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0303】 In this invention, the server includes means for acquiring regional information from a data collection device, means for analyzing the preprocessed regional information to extract feature quantities, and means for visually displaying candidate locations on a map using an information display device. This enables efficient site selection and rapid facility layout proposals in urban development. 【0304】 The "data collection device" is a device used to acquire regional information and has the role of aggregating necessary information from various data sources. 【0305】 "Preprocessing" is a process of converting the collected regional information into a unified format and performing noise removal and missing value complementation to improve the accuracy of analysis. 【0306】 "Feature quantity extraction" is a process of extracting important elements from the preprocessed regional information and creating a data set necessary for site selection prediction. 【0307】 The "prediction model" is an algorithm model constructed based on the extracted feature quantities and is used to score the potential of each candidate location. 【0308】 "Scoring" is a process of evaluating the potential of each region using a prediction model and providing a quantified index. 【0309】 An "information display device" is a device used to visually confirm scoring results and suggested candidate locations on a map, providing users with an intuitive understanding. 【0310】 "Filtering" is the process of narrowing down suggested candidates based on the conditions entered by the user and providing the most suitable option. 【0311】 "Facility placement" refers to proposing the optimal locations for facilities that should be placed in specific areas within a city, thereby contributing to the improvement of urban functions. 【0312】 To implement this invention, a data collection device, an analysis server, and a user terminal are required. The data collection device is responsible for acquiring regional information from various data sources, such as government statistics, traffic conditions, and commercial conditions. The information collected by the server is preprocessed and converted into a unified format. This processing uses Python and Pandas to perform noise reduction and missing value imputation. 【0313】 Next, the Scikit-learn library is used to extract features from the pre-processed regional data. This reveals important features such as population density and traffic flow. Once the features are complete, a predictive model is generated using Scikit-learn's Random Forest. The server then uses this model to score the potential of each region. 【0314】 On the user's device, potential locations are presented visually. An application developed using React Native then displays information about these locations on a map via a map API (e.g., Google Maps API). Based on this information, the user can view filtered locations according to their set criteria and make the optimal selection. 【0315】 As a concrete example, consider the case of selecting a location for a health-promoting sports center. The server scores population density and accessibility to sports facilities to identify areas of interest in health and fitness. As a result, the most suitable location is displayed on a map. 【0316】 An example of a prompt message would be: "Please propose the optimal location for constructing a sports center for health-conscious users within a smart city. In particular, please prioritize locations with good population density and convenient transportation." 【0317】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0318】 Step 1: 【0319】 The server uses data collection devices to collect government statistics, transportation data, and commercial real estate data. Input consists of raw data from various data sources, which the server integrates and stores in a database. Output is roughly processed, integrated data. 【0320】 Step 2: 【0321】 The server uses Python and Pandas to preprocess the collected data. The input is integrated data, which is denoised, imputed for missing values, and converted to a unified format. Standardizing the data format improves the accuracy of the analysis. The output is a clean, preprocessed dataset. 【0322】 Step 3: 【0323】 The server extracts important features based on preprocessed data. The input is a clean dataset, and features such as population density and traffic volume are selected using the Scikit-learn library. This prepares the data necessary for the predictive model. The output is the identified set of features. 【0324】 Step 4: 【0325】 The server generates a predictive model using Scikit-learn's Random Forest. The input is an extracted feature set, and the model is trained based on data from past successful cases. This constructs a predictive model capable of evaluating the potential of each region. The output is the predictive model. 【0326】 Step 5: 【0327】 The server uses the generated predictive model to score each region. The input is the predictive model and new regional data. The model quantifies the potential of each region and generates an evaluation score. The output is a list of scores for each region. 【0328】 Step 6: 【0329】 The terminal filters the scoring results based on user-specified conditions. The input consists of a regional score list and user conditions; the terminal selects the candidate location with the highest score that also meets the user's desired conditions. The output is a filtered list of candidate locations. 【0330】 Step 7: 【0331】 The device, via a React Native application, visually presents filtered candidate locations using a map API. The input is a filtered list of candidate locations, displayed on the map in an intuitively understandable format for the user. The output is the group of candidate locations displayed on the map. 【0332】 Step 8: 【0333】 The user makes decisions regarding the specific placement of the facility based on candidate sites presented on the map. This allows for the selection of the optimal location for the new facility based on visually confirmed information. The output is the optimal location selected by the user. 【0334】 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. 【0335】 This invention provides a new function for a system that supports the selection of potential store locations, which recognizes the user's emotions and further utilizes that information to customize the proposed content. The system has the following components and performs the following specific processing. 【0336】 First, the system collects regional information from external data sources and preprocesses it into a standard format to prepare a unified analytical platform. The server acquires demographic and traffic data, competitive information, etc., generates predictive models using machine learning algorithms, and scores the potential for opening a store in each region. Then, it proposes the optimal candidate locations for store openings based on user-defined conditions. 【0337】 A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses a camera and microphone to analyze the user's facial expressions and voice tone, recognizing their emotional state in real time. This emotional data is transmitted to a server and used to generate suggestions tailored to the user's needs. 【0338】 For example, if a user shows strong interest in a proposed location, the server will present additional information or similar potential store locations. Conversely, if a user expresses dissatisfaction with the proposal, it will present alternative locations or new proposals. Furthermore, the system dynamically adjusts elements of the user interface based on sentiment data to provide a more user-friendly environment. 【0339】 For example, when a user receives a suggestion for a potential store location, if they smile, the system highlights the relevant suggestion and offers options to explore it further. On the other hand, if the user frowns or shows other signs of dissatisfaction, the system re-evaluates the suggestion and either presents new options or changes how the information is displayed. 【0340】 In this way, the emotion engine recognizes the user's emotional state and dynamically changes the suggested content accordingly, enabling more personalized selection of store locations. This improves user satisfaction and enhances the accuracy of business strategies. 【0341】 The following describes the processing flow. 【0342】 Step 1: 【0343】 The server collects local information from external data sources. This includes demographic data, traffic data, commercial real estate information, and competitor information. The data is automatically retrieved periodically via an API. 【0344】 Step 2: 【0345】 The server preprocesses the collected data. It cleans each dataset, standardizes the format, and appropriately imputes missing values. This ensures data integrity and improves the accuracy of the analysis. 【0346】 Step 3: 【0347】 The server extracts features from pre-processed data. This includes quantifying factors such as population density, competition density, and accessibility. A dataset is then prepared to quantitatively understand the characteristics of each region. 【0348】 Step 4: 【0349】 The server trains a predictive model using machine learning algorithms. It learns from past success stories and builds a model that scores the potential for opening a store in each region. Through this scoring, it identifies the regions with the greatest business opportunities. 【0350】 Step 5: 【0351】 The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. To recognize the user's emotional state, an emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) and sends the data to a server. 【0352】 Step 6: 【0353】 The server dynamically adjusts the suggested store locations for the user based on emotion recognition data. If positive emotions are recognized, relevant information is highlighted and further details are presented. If negative emotions are recognized, alternatives are suggested, and the order and content of the information displayed are changed. 【0354】 Step 7: 【0355】 The device visually displays the adjusted suggestion results. Potential store locations are shown on a map, and detailed information about each location is highlighted according to the user's level of interest. Users can select a location on the map to view more detailed information. 【0356】 Step 8: 【0357】 Users make decisions based on the suggested information. They provide feedback on the selected store locations via their terminals, and the server incorporates this feedback into subsequent analyses. This improves the overall accuracy of the system. 【0358】 (Example 2) 【0359】 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". 【0360】 In today's business environment, selecting potential store locations is a crucial element of a company's strategy. However, selecting locations based solely on regional information presents a challenge: it fails to adequately reflect the individual needs and emotions of users, thus hindering customer satisfaction. Furthermore, existing systems are limited to presenting static information, making it difficult to effectively incorporate user emotions and real-time feedback. 【0361】 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. 【0362】 In this invention, the server includes means for acquiring regional information from a data collection device, means for preprocessing the acquired regional information and converting it into a unified format, means for analyzing the preprocessed regional information and extracting features, means for analyzing image and audio information to recognize the user's emotional state, and means for dynamically customizing the suggested content based on the recognized emotional data. This makes it possible to utilize the user's emotional data to propose more personalized and optimal store location candidates for the user. 【0363】 A "data collection device" is a device that collects information via various sensors and internet connections in order to acquire local information. 【0364】 "Local information" refers to a collection of various data related to a specific area, such as demographic statistics, transportation data, and competitive information. 【0365】 "Preprocessing" refers to the data cleaning and format conversion processes performed to transform raw data into a format that is easy to analyze. 【0366】 A "unified format" is a standardized data structure used to unify data from different formats into a consistent format. 【0367】 "Features" are analyzable attributes or values ​​extracted from data, and are parameters used to generate predictive models. 【0368】 A "predictive model" is a model created using machine learning algorithms to learn specific patterns from data and estimate future trends. 【0369】 "Scoring" is the process of numerically evaluating the potential for opening stores in each region using the generated predictive model. 【0370】 "Emotional state" refers to the type and intensity of emotions a user is currently experiencing, as judged from factors such as their facial expressions and tone of voice. 【0371】 "Dynamic customization" refers to the process of changing information and services in real time based on user emotions and feedback. 【0372】 "Proposed content" refers to suggestions to users, including various information regarding potential store locations. 【0373】 This invention relates to a system that innovates the process of selecting potential store locations. This system is characterized by its ability to recognize user emotions and dynamically customize the suggested content. The hardware and software configurations are described in detail below. 【0374】 The server first acquires local information through data collection devices. This data includes demographic data, traffic data, and competitive information. This data is collected via edge device technology and cloud services. The server then uses data analysis software such as Python and R to preprocess the acquired data and convert it into a unified format. 【0375】 Next, the server analyzes the preprocessed data and extracts features. Libraries such as Pandas and NumPy are used for this purpose. Furthermore, based on the extracted features, a predictive model is generated using machine learning frameworks such as TensorFlow and PyTorch, and region-specific scoring is performed. 【0376】 The device uses its camera and microphone to capture the user's facial expressions and voice tone in real time. This utilizes image and audio processing technologies such as OpenCV and Audacity. The device then sends this captured data to a server, where a generative AI model is used to analyze the user's emotions. 【0377】 If a user shows a specific emotional response to a suggested store location provided via their device, the server dynamically customizes the suggestions based on that emotional data. If the user is interested, the server suggests additional related information or similar locations. If the user expresses dissatisfaction, new locations are presented or existing suggestions are improved. For example, a prompt such as "Analyze the user's level of interest regarding store locations in a specific area based on emotional data and suggest optimized locations" can be input into the generating AI model to provide information optimized for the user. 【0378】 This system enables more personalized suggestions for potential store locations based on user sentiment data, improving the user experience and increasing the accuracy of business strategies. 【0379】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0380】 Step 1: 【0381】 The server acquires local information from the internet through data collection devices. This information includes demographic data, traffic data, and competitive information. The acquired data is input to the server as raw data. 【0382】 Step 2: 【0383】 The server uses Python to preprocess the input regional information. Specifically, it performs data cleaning and imputation of missing values, and converts the data into a unified format. As a result, clean data suitable for analysis is output. 【0384】 Step 3: 【0385】 The server analyzes the cleansed data and extracts features. Statistical analysis is performed using NumPy and Pandas to identify important data points. This process yields the features necessary for generating a predictive model. 【0386】 Step 4: 【0387】 The server generates a predictive model using TensorFlow based on the extracted features. It then applies machine learning algorithms to build a model for evaluating the potential of store openings in each region. This results in the output of a score for each region. 【0388】 Step 5: 【0389】 The server uses the generated predictive model to perform region-specific scoring. Regional information is used as input data to quantify the potential for opening a store in each region. This yields scoring results, enabling the selection of the optimal candidate locations based on specific criteria. 【0390】 Step 6: 【0391】 The device uses a camera and microphone to collect the user's facial expressions and voice tone. This utilizes OpenCV and Audacity to acquire real-time emotion data from the user. 【0392】 Step 7: 【0393】 The device sends collected emotional data to the server. The server analyzes the emotional data using a generative AI model to determine the user's level of interest and dissatisfaction. Based on this input, it dynamically customizes the suggestions and outputs optimized suggestion results. 【0394】 Step 8: 【0395】 The user receives the suggestions via their device. If the user expresses a positive sentiment towards the suggestions, the server adds relevant information and similar locations to enhance the suggestions. In the case of a negative reaction, the server re-evaluates the suggestions and presents alternative options. 【0396】 This process enables the provision of personalized store location suggestions tailored to each user. 【0397】 (Application Example 2) 【0398】 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." 【0399】 Conventional store location selection systems make suggestions based on the analysis of regional data, but these suggestions cannot adapt to the individual emotions and reactions of users. Therefore, the suggested locations do not always meet user expectations, and there is a need to improve the user experience. Accordingly, a system is needed that enables more personalized store location selection that takes user emotions into consideration. 【0400】 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. 【0401】 In this invention, the server includes means for acquiring regional data from a data acquisition device, means for recognizing the user's emotional state using visual and auditory information, and means for dynamically adjusting the suggested content based on the recognized emotional state. This makes it possible to provide personalized suggestions that respond to the user's emotions. 【0402】 A "data acquisition device" is a device used to collect various types of data related to a region, and provides that data as basic information for analysis. 【0403】 "Regional data" refers to data that includes information such as the population, transportation, and competitive landscape of a region, and is used to understand the characteristics and features of that region. 【0404】 A "unified data format" is a data format that converts data obtained from different sources into a consistent format, making subsequent analysis easier. 【0405】 "Features" are important patterns and indicators extracted from regional data, and are used as raw materials for predictive algorithms. 【0406】 A "predictive algorithm" is a computational method used to predict future trends and suitability based on the characteristics of regional data. 【0407】 "Evaluation" is the act of numerically representing the potential and value of each region using predictive algorithms, and it serves as a foundation for improving the accuracy of proposals. 【0408】 "Location" refers to the proposed store locations suggested by the system, which are considered to have a high probability of business success based on the characteristics of that region. 【0409】 "Visual information" refers to visual data such as facial expressions collected by the user's camera, and is used to analyze the user's emotional state. 【0410】 "Voice" refers to audio information such as the tone of voice and the content of speech acquired by the user's microphone, and is used to understand the user's emotional state. 【0411】 "Emotional state" refers to a psychological state estimated based on the user's facial expressions and tone of voice, and is a factor used to optimize the suggested content. 【0412】 "Dynamic adjustment of proposal content" refers to the act of changing the content and display method of proposals in real time in accordance with the user's emotions, with the aim of improving the user experience. 【0413】 In an embodiment of the present invention, first, a server collects various data about a region via a data acquisition device. This collected data includes demographics, traffic information, and competitive analysis. The server preprocesses the data and converts it into a unified data format. Next, it extracts features from the preprocessed data and generates a prediction algorithm. This enables evaluation on a region-by-region basis and prepares the system to suggest optimal locations for store openings. 【0414】 The device uses a camera and microphone to acquire the user's visual and auditory information in real time, and analyzes the user's emotional state using emotion recognition software. This analysis is sent to a server and used to dynamically adjust the suggested content. Specifically, the server customizes the suggested content and display method based on the user's emotional state to improve user satisfaction. 【0415】 For example, if a user responds positively to a suggestion of potential store locations, the server will display more detailed information and provide information to help them consider similar areas. On the other hand, if the user shows indecisiveness, the server will suggest alternatives and re-evaluate the suggestions. In this way, the system provides personalized suggestions and supports the user's decision-making. 【0416】 By utilizing generative AI models, it is also possible to automatically generate prompt messages that correspond to the user's emotional state. For example, the following prompt messages are possible: 【0417】 "If the user's facial expression is 'smiling,' display detailed information about the suggested location." 【0418】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0419】 Step 1: 【0420】 The server collects regional data via data acquisition devices. This data includes demographics, traffic information, and competition information. Based on this input information, the server preprocesses the data and converts it into a unified data format. This conversion process unifies different data sources and standardizes the data into a format that can be used for subsequent analysis. 【0421】 Step 2: 【0422】 The server analyzes pre-processed data and extracts regional characteristics. Specifically, the server applies machine learning algorithms to extract valuable features from the data. In this process, characteristics such as population trends and transportation convenience for each region are quantified and supplied as input to the prediction algorithm. 【0423】 Step 3: 【0424】 The server generates a prediction algorithm based on the extracted features and performs an evaluation for each region. The server uses these features to score the regional potential and generates basic data to identify the optimal candidate locations. Based on the scoring results, regions with high potential for opening a store are selected. 【0425】 Step 4: 【0426】 The device uses its camera and microphone to capture the user's visual and auditory information and analyzes their emotional state in real time. Using the user's facial expressions and tone of voice as input, the device employs emotion recognition software to determine the user's emotional state. This output serves as an indicator of how the user is reacting to the presented information. 【0427】 Step 5: 【0428】 The server dynamically adjusts the suggestions based on the user's emotional state. Using the emotional data obtained in step 4 as input, the server automatically generates prompt sentences using a generative AI model and customizes the suggestions. Specific actions include displaying additional details if the user shows interest, and presenting alternatives if the user expresses dissatisfaction. 【0429】 Step 6: 【0430】 The user reviews dynamic suggestions provided by the server and makes a decision. Based on the information displayed on the device's screen, the user selects which candidate location best matches their needs. The user's selection results are fed back as data to improve the accuracy of future suggestions. 【0431】 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. 【0432】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0433】 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. 【0434】 [Third Embodiment] 【0435】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0436】 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. 【0437】 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). 【0438】 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. 【0439】 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. 【0440】 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). 【0441】 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. 【0442】 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. 【0443】 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. 【0444】 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. 【0445】 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. 【0446】 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". 【0447】 This invention provides a system for site selection that is implemented through a series of processes based on data. The main component of the system is a program that includes multiple processing steps for processing information and supporting decision-making. 【0448】 This system first aggregates regional information from diverse data sources. The server collects information from government statistics, traffic sensor data, and commercial real estate databases. Next, this data is preprocessed and converted into a unified, analyzable format. By removing data noise and performing appropriate missing value imputation, the accuracy of the analysis is improved. 【0449】 Next, the server extracts key features from the pre-processed data to quantitatively understand regional characteristics. This includes data on population density, age groups, traffic volume, and lifestyle. Once the features are complete, the predictive model is trained. Using past successful store opening data as a reference, a machine learning algorithm is used to build a predictive model and score the potential of each candidate region. 【0450】 Next, the device suggests the most suitable candidate locations from scored areas based on the user's set conditions. At this stage, the suggestions are filtered considering the user's budget, desired property size, preferred area, and other conditions. Finally, the device visually presents the selected candidate locations, making them easy to understand intuitively on a map. The user then makes a decision based on this information. 【0451】 To give a concrete example, when a telecommunications company is opening a new store, the system first collects detailed traffic sensor data and density information of competing stores in a local city. The server analyzes population dynamics and traffic convenience scores for a specific area within the city to evaluate its potential for opening a store. Next, the terminal takes into account the user's preferences, such as a specific area and budget constraints, and proposes the three locations with the highest scores to the user. This result is mapped on a map, and detailed information for each location is displayed in a pop-up. Based on this information, the user can ultimately decide on the store location that best suits their company's strategy. 【0452】 In this way, the present invention supports efficient and rational location selection through a data-driven approach. 【0453】 The following describes the processing flow. 【0454】 Step 1: 【0455】 The server collects local information from external data sources. This includes government statistics, real-time data from traffic sensors, and property information from commercial real estate databases. This data is automatically retrieved via APIs. 【0456】 Step 2: 【0457】 The server preprocesses the collected data. Preprocessing includes cleaning the data, standardizing the format, and imputing missing values. For example, it synchronizes timestamps across different datasets and detects and removes outliers. 【0458】 Step 3: 【0459】 The server extracts features from pre-processed data. This includes calculations such as population density, age distribution, transportation accessibility score, and density of competing businesses. Regional characteristics are quantified to prepare the input dataset for the model. 【0460】 Step 4: 【0461】 The server trains a predictive model using machine learning algorithms. This process involves referencing past store opening success data and building a model to score each region using random forests or other appropriate models. 【0462】 Step 5: 【0463】 The server uses a trained model to score the potential for opening a store in each region and generates a ranking for each region. This identifies regions where business opportunities are predicted to be significant. 【0464】 Step 6: 【0465】 The terminal considers the user's input criteria, such as budget and location, to filter the most suitable store locations. The user can then adjust the criteria to further refine the suggested results. 【0466】 Step 7: 【0467】 The device visually displays proposed store locations on a map. By clicking on the details of each location, users can view information such as population data, traffic volume, and competitive landscape in a pop-up window. 【0468】 Step 8: 【0469】 Users make their final store location decision based on the information provided. After the decision, the actual data is fed back into the system and used to improve the accuracy of future analyses. 【0470】 (Example 1) 【0471】 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." 【0472】 In selecting locations for regional development, there is a need to efficiently and accurately process information obtained from diverse data sources and quickly propose the optimal location that meets the user's requirements. However, conventional methods suffer from the problems of complex data integration and analysis, which are time-consuming and costly. Furthermore, there is the challenge of presenting information in a visually easy-to-understand format. 【0473】 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. 【0474】 In this invention, the server includes means for acquiring area information from a data acquisition unit, means for preprocessing the acquired area information and converting it into a unified format, and means for analyzing the preprocessed area information and extracting feature data. This enables efficient processing of diverse data and allows for the suggestion of the optimal location based on the user's conditions. 【0475】 The "data acquisition unit" is the part that has the function of collecting area information from multiple data sources. 【0476】 "Regional information" is a general term for data related to a specific region, such as demographics, transportation data, and real estate information. 【0477】 "Preprocessing" refers to the process of converting collected data into a unified, analyzable format, removing noise, and imputing missing values. 【0478】 "Feature data" refers to data that shows important indicators and attributes extracted from pre-processed data. 【0479】 A "predictive structure" is a model built to predict future regional characteristics based on past data. 【0480】 "Evaluation" is the process of quantitatively calculating the potential of each area using a predictive structure. 【0481】 A "location" refers to a specific area selected based on evaluation, representing the point that best suits the user's requirements. 【0482】 A "display unit" is a device or software that provides visual information to the user. 【0483】 This invention is a system that supports location selection, proposing the optimal location through data collection, processing, and analysis. Specifically, the server, terminal, and user elements each play their respective roles. 【0484】 The server uses a data acquisition unit to collect area information from various data sources. Specifically, it uses a computer server equipped with a high-performance processor and large-capacity storage, and the software utilizes programs that perform data collection via APIs and SQL database queries. This makes it possible to obtain information in real time from government statistics data, traffic sensor data, and commercial real estate databases. 【0485】 The server also converts the data into a unified format during the preprocessing step, preparing it for analysis. This process uses algorithms to remove noise and impute missing values. The software used here includes data cleansing tools, noise filtering programs, and so-called data mining tools. 【0486】 The server then extracts feature data and constructs a prediction structure as a machine learning algorithm. Examples of algorithms used include random forests and support vector machines. This process generates predictions about the commercial potential and demographics of each region. 【0487】 Next, the terminal takes into account the user's settings and plays a role in suggesting the optimal location based on evaluation results obtained from the server. The user's settings include budget, desired property size, and preferred regional characteristics. The terminal integrates these conditions and evaluation results to provide visual suggestions. Specifically, it uses mapping software to display the best candidate locations on a map and presents detailed information about each location in a pop-up window. 【0488】 As a concrete example, when a company opens a new store, the server collects and analyzes traffic sensor data and competitor store density information for a local city. The terminal then presents three optimal locations based on the budget constraints and required property area criteria set by the user. This proposal is mapped onto a map and provided to the user along with detailed store information. 【0489】 An example of a prompt sentence to be input to the generating AI model is, "Please propose urban locations that are expected to yield the greatest profit within a budget of 100 million yen." In this way, the present invention provides a data-driven approach to achieving efficient location selection. 【0490】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0491】 Step 1: 【0492】 The server collects area information using a data acquisition unit. This involves obtaining information from government statistics data, traffic sensor data, and commercial real estate databases via APIs and database queries. The input requires access information to each data source, and the output is raw, unprocessed data. 【0493】 Step 2: 【0494】 The server preprocesses the collected raw data. Specifically, it removes noise from the data, appropriately imputes missing values, and converts it into a unified format that can be analyzed. The input is raw data, and after applying filtering algorithms and imputation techniques, processed data is obtained as output. 【0495】 Step 3: 【0496】 The server analyzes pre-processed data and extracts feature data. This process uses statistical methods and data mining algorithms to generate key indicators such as population density and traffic volume. The input is the pre-processed data, and the output is the feature data used in subsequent predictive models. 【0497】 Step 4: 【0498】 The server generates a predictive structure using the extracted feature data. In this step, machine learning algorithms, such as random forests and support vector machines, are used to create predictive models for store opening success rates, etc. The input is the feature data, and the output is the trained predictive model. 【0499】 Step 5: 【0500】 The server uses a predictive structure to evaluate each area. In this evaluation, the predictive model scores the commercial potential of each area. The input is a trained predictive model and current regional information, and the output is the scored evaluation result. 【0501】 Step 6: 【0502】 The terminal suggests the optimal location based on the user's input conditions and scored evaluation results. User input includes budget and property conditions, which are taken into consideration when filtering, and the output is a list of suggested locations. 【0503】 Step 7: 【0504】 The terminal visually presents the suggested locations. In this step, mapping software is used to pin locations on a map and display detailed information in a pop-up window. The input is the suggested locations, and the output is visualized map information. 【0505】 (Application Example 1) 【0506】 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." 【0507】 In modern urban development and facility design, quickly selecting the optimal location is a crucial challenge. In particular, optimizing facility placement, taking into account local demographics and transportation convenience, is essential for improving urban functionality and resident convenience. However, traditional methods are time-consuming and labor-intensive, making rapid decision-making difficult. 【0508】 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. 【0509】 In this invention, the server includes means for acquiring regional information from a data collection device, means for analyzing pre-processed regional information to extract features, and means for visually displaying candidate locations on a map using an information display device. This enables efficient location selection and rapid facility placement proposals in urban development. 【0510】 A "data collection device" is a device used to acquire local information, and its role is to aggregate necessary information from various data sources. 【0511】 "Preprocessing" refers to the process of converting collected regional information into a unified format and performing noise removal and missing value imputation to improve the accuracy of the analysis. 【0512】 "Feature extraction" is the process of extracting important elements from pre-processed regional information to create a dataset necessary for predicting location selection. 【0513】 A "predictive model" is an algorithmic model built based on extracted features, and is used to score the potential of each candidate location. 【0514】 "Scoring" is the process of evaluating the potential of each region using predictive models and providing quantifiable indicators. 【0515】 An "information display device" is a device used to visually confirm scoring results and suggested candidate locations on a map, providing users with an intuitive understanding. 【0516】 "Filtering" is the process of narrowing down suggested candidates based on the conditions entered by the user and providing the most suitable option. 【0517】 "Facility placement" refers to proposing the optimal locations for facilities that should be placed in specific areas within a city, thereby contributing to the improvement of urban functions. 【0518】 To implement this invention, a data collection device, an analysis server, and a user terminal are required. The data collection device is responsible for acquiring regional information from various data sources, such as government statistics, traffic conditions, and commercial conditions. The information collected by the server is preprocessed and converted into a unified format. This processing uses Python and Pandas to perform noise reduction and missing value imputation. 【0519】 Next, the Scikit-learn library is used to extract features from the pre-processed regional data. This reveals important features such as population density and traffic flow. Once the features are complete, a predictive model is generated using Scikit-learn's Random Forest. The server then uses this model to score the potential of each region. 【0520】 On the user's device, potential locations are presented visually. An application developed using React Native then displays information about these locations on a map via a map API (e.g., Google Maps API). Based on this information, the user can view filtered locations according to their set criteria and make the optimal selection. 【0521】 As a concrete example, consider the case of selecting a location for a health-promoting sports center. The server scores population density and accessibility to sports facilities to identify areas of interest in health and fitness. As a result, the most suitable location is displayed on a map. 【0522】 An example of a prompt message would be: "Please propose the optimal location for constructing a sports center for health-conscious users within a smart city. In particular, please prioritize locations with good population density and convenient transportation." 【0523】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0524】 Step 1: 【0525】 The server uses data collection devices to collect government statistics, transportation data, and commercial real estate data. Input consists of raw data from various data sources, which the server integrates and stores in a database. Output is roughly processed, integrated data. 【0526】 Step 2: 【0527】 The server uses Python and Pandas to preprocess the collected data. The input is integrated data, which is denoised, imputed for missing values, and converted to a unified format. Standardizing the data format improves the accuracy of the analysis. The output is a clean, preprocessed dataset. 【0528】 Step 3: 【0529】 The server extracts important features based on preprocessed data. The input is a clean dataset, and features such as population density and traffic volume are selected using the Scikit-learn library. This prepares the data necessary for the predictive model. The output is the identified set of features. 【0530】 Step 4: 【0531】 The server generates a predictive model using Scikit-learn's Random Forest. The input is an extracted feature set, and the model is trained based on data from past successful cases. This constructs a predictive model capable of evaluating the potential of each region. The output is the predictive model. 【0532】 Step 5: 【0533】 The server uses the generated predictive model to score each region. The input is the predictive model and new regional data. The model quantifies the potential of each region and generates an evaluation score. The output is a list of scores for each region. 【0534】 Step 6: 【0535】 The terminal filters the scoring results based on user-specified conditions. The input consists of a regional score list and user conditions; the terminal selects the candidate location with the highest score that also meets the user's desired conditions. The output is a filtered list of candidate locations. 【0536】 Step 7: 【0537】 The device, via a React Native application, visually presents filtered candidate locations using a map API. The input is a filtered list of candidate locations, displayed on the map in an intuitively understandable format for the user. The output is the group of candidate locations displayed on the map. 【0538】 Step 8: 【0539】 The user makes decisions regarding the specific placement of the facility based on candidate sites presented on the map. This allows for the selection of the optimal location for the new facility based on visually confirmed information. The output is the optimal location selected by the user. 【0540】 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. 【0541】 This invention provides a new function for a system that supports the selection of potential store locations, which recognizes the user's emotions and further utilizes that information to customize the proposed content. The system has the following components and performs the following specific processing. 【0542】 First, the system collects regional information from external data sources and preprocesses it into a standard format to prepare a unified analytical platform. The server acquires demographic and traffic data, competitive information, etc., generates predictive models using machine learning algorithms, and scores the potential for opening a store in each region. Then, it proposes the optimal candidate locations for store openings based on user-defined conditions. 【0543】 A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses a camera and microphone to analyze the user's facial expressions and voice tone, recognizing their emotional state in real time. This emotional data is transmitted to a server and used to generate suggestions tailored to the user's needs. 【0544】 For example, if a user shows strong interest in a proposed location, the server will present additional information or similar potential store locations. Conversely, if a user expresses dissatisfaction with the proposal, it will present alternative locations or new proposals. Furthermore, the system dynamically adjusts elements of the user interface based on sentiment data to provide a more user-friendly environment. 【0545】 For example, when a user receives a suggestion for a potential store location, if they smile, the system highlights the relevant suggestion and offers options to explore it further. On the other hand, if the user frowns or shows other signs of dissatisfaction, the system re-evaluates the suggestion and either presents new options or changes how the information is displayed. 【0546】 In this way, the emotion engine recognizes the user's emotional state and dynamically changes the suggested content accordingly, enabling more personalized selection of store locations. This improves user satisfaction and enhances the accuracy of business strategies. 【0547】 The following describes the processing flow. 【0548】 Step 1: 【0549】 The server collects local information from external data sources. This includes demographic data, traffic data, commercial real estate information, and competitor information. The data is automatically retrieved periodically via an API. 【0550】 Step 2: 【0551】 The server preprocesses the collected data. It cleans each dataset, standardizes the format, and appropriately imputes missing values. This ensures data integrity and improves the accuracy of the analysis. 【0552】 Step 3: 【0553】 The server extracts features from pre-processed data. This includes quantifying factors such as population density, competition density, and accessibility. A dataset is then prepared to quantitatively understand the characteristics of each region. 【0554】 Step 4: 【0555】 The server trains a predictive model using machine learning algorithms. It learns from past success stories and builds a model that scores the potential for opening a store in each region. Through this scoring, it identifies the regions with the greatest business opportunities. 【0556】 Step 5: 【0557】 The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. To recognize the user's emotional state, an emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) and sends the data to a server. 【0558】 Step 6: 【0559】 The server dynamically adjusts the suggested store locations for the user based on emotion recognition data. If positive emotions are recognized, relevant information is highlighted and further details are presented. If negative emotions are recognized, alternatives are suggested, and the order and content of the information displayed are changed. 【0560】 Step 7: 【0561】 The device visually displays the adjusted suggestion results. Potential store locations are shown on a map, and detailed information about each location is highlighted according to the user's level of interest. Users can select a location on the map to view more detailed information. 【0562】 Step 8: 【0563】 Users make decisions based on the suggested information. They provide feedback on the selected store locations via their terminals, and the server incorporates this feedback into subsequent analyses. This improves the overall accuracy of the system. 【0564】 (Example 2) 【0565】 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." 【0566】 In today's business environment, selecting potential store locations is a crucial element of a company's strategy. However, selecting locations based solely on regional information presents a challenge: it fails to adequately reflect the individual needs and emotions of users, thus hindering customer satisfaction. Furthermore, existing systems are limited to presenting static information, making it difficult to effectively incorporate user emotions and real-time feedback. 【0567】 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. 【0568】 In this invention, the server includes means for acquiring regional information from a data collection device, means for preprocessing the acquired regional information and converting it into a unified format, means for analyzing the preprocessed regional information and extracting features, means for analyzing image and audio information to recognize the user's emotional state, and means for dynamically customizing the suggested content based on the recognized emotional data. This makes it possible to utilize the user's emotional data to propose more personalized and optimal store location candidates for the user. 【0569】 A "data collection device" is a device that collects information via various sensors and internet connections in order to acquire local information. 【0570】 "Local information" refers to a collection of various data related to a specific area, such as demographic statistics, transportation data, and competitive information. 【0571】 "Preprocessing" refers to the data cleaning and format conversion processes performed to transform raw data into a format that is easy to analyze. 【0572】 A "unified format" is a standardized data structure used to unify data from different formats into a consistent format. 【0573】 "Features" are analyzable attributes or values ​​extracted from data, and are parameters used to generate predictive models. 【0574】 A "predictive model" is a model created using machine learning algorithms to learn specific patterns from data and estimate future trends. 【0575】 "Scoring" is the process of numerically evaluating the potential for opening stores in each region using the generated predictive model. 【0576】 "Emotional state" refers to the type and intensity of emotions a user is currently experiencing, as judged from factors such as their facial expressions and tone of voice. 【0577】 "Dynamic customization" refers to the process of changing information and services in real time based on user emotions and feedback. 【0578】 "Proposed content" refers to suggestions to users, including various information regarding potential store locations. 【0579】 This invention relates to a system that innovates the process of selecting potential store locations. This system is characterized by its ability to recognize user emotions and dynamically customize the suggested content. The hardware and software configurations are described in detail below. 【0580】 The server first acquires local information through data collection devices. This data includes demographic data, traffic data, and competitive information. This data is collected via edge device technology and cloud services. The server then uses data analysis software such as Python and R to preprocess the acquired data and convert it into a unified format. 【0581】 Next, the server analyzes the preprocessed data and extracts features. Libraries such as Pandas and NumPy are used for this purpose. Furthermore, based on the extracted features, a predictive model is generated using machine learning frameworks such as TensorFlow and PyTorch, and region-specific scoring is performed. 【0582】 The device uses its camera and microphone to capture the user's facial expressions and voice tone in real time. This utilizes image and audio processing technologies such as OpenCV and Audacity. The device then sends this captured data to a server, where a generative AI model is used to analyze the user's emotions. 【0583】 If a user shows a specific emotional response to a suggested store location provided via their device, the server dynamically customizes the suggestions based on that emotional data. If the user is interested, the server suggests additional related information or similar locations. If the user expresses dissatisfaction, new locations are presented or existing suggestions are improved. For example, a prompt such as "Analyze the user's level of interest regarding store locations in a specific area based on emotional data and suggest optimized locations" can be input into the generating AI model to provide information optimized for the user. 【0584】 This system enables more personalized suggestions for potential store locations based on user sentiment data, improving the user experience and increasing the accuracy of business strategies. 【0585】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0586】 Step 1: 【0587】 The server acquires local information from the internet through data collection devices. This information includes demographic data, traffic data, and competitive information. The acquired data is input to the server as raw data. 【0588】 Step 2: 【0589】 The server uses Python to preprocess the input regional information. Specifically, it performs data cleaning and imputation of missing values, and converts the data into a unified format. As a result, clean data suitable for analysis is output. 【0590】 Step 3: 【0591】 The server analyzes the cleansed data and extracts features. Statistical analysis is performed using NumPy and Pandas to identify important data points. This process yields the features necessary for generating a predictive model. 【0592】 Step 4: 【0593】 The server generates a predictive model using TensorFlow based on the extracted features. It then applies machine learning algorithms to build a model for evaluating the potential of store openings in each region. This results in the output of a score for each region. 【0594】 Step 5: 【0595】 The server uses the generated predictive model to perform region-specific scoring. Regional information is used as input data to quantify the potential for opening a store in each region. This yields scoring results, enabling the selection of the optimal candidate locations based on specific criteria. 【0596】 Step 6: 【0597】 The device uses a camera and microphone to collect the user's facial expressions and voice tone. This utilizes OpenCV and Audacity to acquire real-time emotion data from the user. 【0598】 Step 7: 【0599】 The device sends collected emotional data to the server. The server analyzes the emotional data using a generative AI model to determine the user's level of interest and dissatisfaction. Based on this input, it dynamically customizes the suggestions and outputs optimized suggestion results. 【0600】 Step 8: 【0601】 The user receives the suggestions via their device. If the user expresses a positive sentiment towards the suggestions, the server adds relevant information and similar locations to enhance the suggestions. In the case of a negative reaction, the server re-evaluates the suggestions and presents alternative options. 【0602】 This process enables the provision of personalized store location suggestions tailored to each user. 【0603】 (Application Example 2) 【0604】 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." 【0605】 Conventional store location selection systems make suggestions based on the analysis of regional data, but these suggestions cannot adapt to the individual emotions and reactions of users. Therefore, the suggested locations do not always meet user expectations, and there is a need to improve the user experience. Accordingly, a system is needed that enables more personalized store location selection that takes user emotions into consideration. 【0606】 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. 【0607】 In this invention, the server includes means for acquiring regional data from a data acquisition device, means for recognizing the user's emotional state using visual and auditory information, and means for dynamically adjusting the suggested content based on the recognized emotional state. This makes it possible to provide personalized suggestions that respond to the user's emotions. 【0608】 A "data acquisition device" is a device used to collect various types of data related to a region, and provides that data as basic information for analysis. 【0609】 "Regional data" refers to data that includes information such as the population, transportation, and competitive landscape of a region, and is used to understand the characteristics and features of that region. 【0610】 A "unified data format" is a data format that converts data obtained from different sources into a consistent format, making subsequent analysis easier. 【0611】 "Features" are important patterns and indicators extracted from regional data, and are used as raw materials for predictive algorithms. 【0612】 A "predictive algorithm" is a computational method used to predict future trends and suitability based on the characteristics of regional data. 【0613】 "Evaluation" is the act of numerically representing the potential and value of each region using predictive algorithms, and it serves as a foundation for improving the accuracy of proposals. 【0614】 "Location" refers to the proposed store locations suggested by the system, which are considered to have a high probability of business success based on the characteristics of that region. 【0615】 "Visual information" refers to visual data such as facial expressions collected by the user's camera, and is used to analyze the user's emotional state. 【0616】 "Voice" refers to audio information such as the tone of voice and the content of speech acquired by the user's microphone, and is used to understand the user's emotional state. 【0617】 "Emotional state" refers to a psychological state estimated based on the user's facial expressions and tone of voice, and is a factor used to optimize the suggested content. 【0618】 "Dynamic adjustment of proposal content" refers to the act of changing the content and display method of proposals in real time in accordance with the user's emotions, with the aim of improving the user experience. 【0619】 In an embodiment of the present invention, first, a server collects various data about a region via a data acquisition device. This collected data includes demographics, traffic information, and competitive analysis. The server preprocesses the data and converts it into a unified data format. Next, it extracts features from the preprocessed data and generates a prediction algorithm. This enables evaluation on a region-by-region basis and prepares the system to suggest optimal locations for store openings. 【0620】 The device uses a camera and microphone to acquire the user's visual and auditory information in real time, and analyzes the user's emotional state using emotion recognition software. This analysis is sent to a server and used to dynamically adjust the suggested content. Specifically, the server customizes the suggested content and display method based on the user's emotional state to improve user satisfaction. 【0621】 For example, if a user responds positively to a suggestion of potential store locations, the server will display more detailed information and provide information to help them consider similar areas. On the other hand, if the user shows indecisiveness, the server will suggest alternatives and re-evaluate the suggestions. In this way, the system provides personalized suggestions and supports the user's decision-making. 【0622】 By utilizing generative AI models, it is also possible to automatically generate prompt messages that correspond to the user's emotional state. For example, the following prompt messages are possible: 【0623】 "If the user's facial expression is 'smiling,' display detailed information about the suggested location." 【0624】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0625】 Step 1: 【0626】 The server collects regional data via data acquisition devices. This data includes demographics, traffic information, and competition information. Based on this input information, the server preprocesses the data and converts it into a unified data format. This conversion process unifies different data sources and standardizes the data into a format that can be used for subsequent analysis. 【0627】 Step 2: 【0628】 The server analyzes pre-processed data and extracts regional characteristics. Specifically, the server applies machine learning algorithms to extract valuable features from the data. In this process, characteristics such as population trends and transportation convenience for each region are quantified and supplied as input to the prediction algorithm. 【0629】 Step 3: 【0630】 The server generates a prediction algorithm based on the extracted features and performs an evaluation for each region. The server uses these features to score the regional potential and generates basic data to identify the optimal candidate locations. Based on the scoring results, regions with high potential for opening a store are selected. 【0631】 Step 4: 【0632】 The device uses its camera and microphone to capture the user's visual and auditory information and analyzes their emotional state in real time. Using the user's facial expressions and tone of voice as input, the device employs emotion recognition software to determine the user's emotional state. This output serves as an indicator of how the user is reacting to the presented information. 【0633】 Step 5: 【0634】 The server dynamically adjusts the suggestions based on the user's emotional state. Using the emotional data obtained in step 4 as input, the server automatically generates prompt sentences using a generative AI model and customizes the suggestions. Specific actions include displaying additional details if the user shows interest, and presenting alternatives if the user expresses dissatisfaction. 【0635】 Step 6: 【0636】 The user reviews dynamic suggestions provided by the server and makes a decision. Based on the information displayed on the device's screen, the user selects which candidate location best matches their needs. The user's selection results are fed back as data to improve the accuracy of future suggestions. 【0637】 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. 【0638】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0639】 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. 【0640】 [Fourth Embodiment] 【0641】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0642】 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. 【0643】 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). 【0644】 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. 【0645】 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. 【0646】 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). 【0647】 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. 【0648】 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. 【0649】 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. 【0650】 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. 【0651】 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. 【0652】 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. 【0653】 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". 【0654】 This invention provides a system for site selection that is implemented through a series of processes based on data. The main component of the system is a program that includes multiple processing steps for processing information and supporting decision-making. 【0655】 This system first aggregates regional information from diverse data sources. The server collects information from government statistics, traffic sensor data, and commercial real estate databases. Next, this data is preprocessed and converted into a unified, analyzable format. By removing data noise and performing appropriate missing value imputation, the accuracy of the analysis is improved. 【0656】 Next, the server extracts key features from the pre-processed data to quantitatively understand regional characteristics. This includes data on population density, age groups, traffic volume, and lifestyle. Once the features are complete, the predictive model is trained. Using past successful store opening data as a reference, a machine learning algorithm is used to build a predictive model and score the potential of each candidate region. 【0657】 Next, the device suggests the most suitable candidate locations from scored areas based on the user's set conditions. At this stage, the suggestions are filtered considering the user's budget, desired property size, preferred area, and other conditions. Finally, the device visually presents the selected candidate locations, making them easy to understand intuitively on a map. The user then makes a decision based on this information. 【0658】 To give a concrete example, when a telecommunications company is opening a new store, the system first collects detailed traffic sensor data and density information of competing stores in a local city. The server analyzes population dynamics and traffic convenience scores for a specific area within the city to evaluate its potential for opening a store. Next, the terminal takes into account the user's preferences, such as a specific area and budget constraints, and proposes the three locations with the highest scores to the user. This result is mapped on a map, and detailed information for each location is displayed in a pop-up. Based on this information, the user can ultimately decide on the store location that best suits their company's strategy. 【0659】 In this way, the present invention supports efficient and rational location selection through a data-driven approach. 【0660】 The following describes the processing flow. 【0661】 Step 1: 【0662】 The server collects local information from external data sources. This includes government statistics, real-time data from traffic sensors, and property information from commercial real estate databases. This data is automatically retrieved via APIs. 【0663】 Step 2: 【0664】 The server preprocesses the collected data. Preprocessing includes cleaning the data, standardizing the format, and imputing missing values. For example, it synchronizes timestamps across different datasets and detects and removes outliers. 【0665】 Step 3: 【0666】 The server extracts features from pre-processed data. This includes calculations such as population density, age distribution, transportation accessibility score, and density of competing businesses. Regional characteristics are quantified to prepare the input dataset for the model. 【0667】 Step 4: 【0668】 The server trains a predictive model using machine learning algorithms. This process involves referencing past store opening success data and building a model to score each region using random forests or other appropriate models. 【0669】 Step 5: 【0670】 The server uses a trained model to score the potential for opening a store in each region and generates a ranking for each region. This identifies regions where business opportunities are predicted to be significant. 【0671】 Step 6: 【0672】 The terminal considers the user's input criteria, such as budget and location, to filter the most suitable store locations. The user can then adjust the criteria to further refine the suggested results. 【0673】 Step 7: 【0674】 The device visually displays proposed store locations on a map. By clicking on the details of each location, users can view information such as population data, traffic volume, and competitive landscape in a pop-up window. 【0675】 Step 8: 【0676】 Users make their final store location decision based on the information provided. After the decision, the actual data is fed back into the system and used to improve the accuracy of future analyses. 【0677】 (Example 1) 【0678】 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". 【0679】 In selecting locations for regional development, there is a need to efficiently and accurately process information obtained from diverse data sources and quickly propose the optimal location that meets the user's requirements. However, conventional methods suffer from the problems of complex data integration and analysis, which are time-consuming and costly. Furthermore, there is the challenge of presenting information in a visually easy-to-understand format. 【0680】 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. 【0681】 In this invention, the server includes means for acquiring area information from a data acquisition unit, means for preprocessing the acquired area information and converting it into a unified format, and means for analyzing the preprocessed area information and extracting feature data. This enables efficient processing of diverse data and allows for the suggestion of the optimal location based on the user's conditions. 【0682】 The "data acquisition unit" is the part that has the function of collecting area information from multiple data sources. 【0683】 "Regional information" is a general term for data related to a specific region, such as demographics, transportation data, and real estate information. 【0684】 "Preprocessing" refers to the process of converting collected data into a unified, analyzable format, removing noise, and imputing missing values. 【0685】 "Feature data" refers to data that shows important indicators and attributes extracted from pre-processed data. 【0686】 A "predictive structure" is a model built to predict future regional characteristics based on past data. 【0687】 "Evaluation" is the process of quantitatively calculating the potential of each area using a predictive structure. 【0688】 A "location" refers to a specific area selected based on evaluation, representing the point that best suits the user's requirements. 【0689】 A "display unit" is a device or software that provides visual information to the user. 【0690】 This invention is a system that supports location selection, proposing the optimal location through data collection, processing, and analysis. Specifically, the server, terminal, and user elements each play their respective roles. 【0691】 The server uses a data acquisition unit to collect area information from various data sources. Specifically, it uses a computer server equipped with a high-performance processor and large-capacity storage, and the software utilizes programs that perform data collection via APIs and SQL database queries. This makes it possible to obtain information in real time from government statistics data, traffic sensor data, and commercial real estate databases. 【0692】 The server also converts the data into a unified format during the preprocessing step, preparing it for analysis. This process uses algorithms to remove noise and impute missing values. The software used here includes data cleansing tools, noise filtering programs, and so-called data mining tools. 【0693】 The server then extracts feature data and constructs a prediction structure as a machine learning algorithm. Examples of algorithms used include random forests and support vector machines. This process generates predictions about the commercial potential and demographics of each region. 【0694】 Next, the terminal takes into account the user's settings and plays a role in suggesting the optimal location based on evaluation results obtained from the server. The user's settings include budget, desired property size, and preferred regional characteristics. The terminal integrates these conditions and evaluation results to provide visual suggestions. Specifically, it uses mapping software to display the best candidate locations on a map and presents detailed information about each location in a pop-up window. 【0695】 As a concrete example, when a company opens a new store, the server collects and analyzes traffic sensor data and competitor store density information for a local city. The terminal then presents three optimal locations based on the budget constraints and required property area criteria set by the user. This proposal is mapped onto a map and provided to the user along with detailed store information. 【0696】 An example of a prompt sentence to be input to the generating AI model is, "Please propose urban locations that are expected to yield the greatest profit within a budget of 100 million yen." In this way, the present invention provides a data-driven approach to achieving efficient location selection. 【0697】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0698】 Step 1: 【0699】 The server collects area information using a data acquisition unit. This involves obtaining information from government statistics data, traffic sensor data, and commercial real estate databases via APIs and database queries. The input requires access information to each data source, and the output is raw, unprocessed data. 【0700】 Step 2: 【0701】 The server preprocesses the collected raw data. Specifically, it removes noise from the data, appropriately imputes missing values, and converts it into a unified format that can be analyzed. The input is raw data, and after applying filtering algorithms and imputation techniques, processed data is obtained as output. 【0702】 Step 3: 【0703】 The server analyzes pre-processed data and extracts feature data. This process uses statistical methods and data mining algorithms to generate key indicators such as population density and traffic volume. The input is the pre-processed data, and the output is the feature data used in subsequent predictive models. 【0704】 Step 4: 【0705】 The server generates a predictive structure using the extracted feature data. In this step, machine learning algorithms, such as random forests and support vector machines, are used to create predictive models for store opening success rates, etc. The input is the feature data, and the output is the trained predictive model. 【0706】 Step 5: 【0707】 The server uses a predictive structure to evaluate each area. In this evaluation, the predictive model scores the commercial potential of each area. The input is a trained predictive model and current regional information, and the output is the scored evaluation result. 【0708】 Step 6: 【0709】 The terminal suggests the optimal location based on the user's input conditions and scored evaluation results. User input includes budget and property conditions, which are taken into consideration when filtering, and the output is a list of suggested locations. 【0710】 Step 7: 【0711】 The terminal visually presents the suggested locations. In this step, mapping software is used to pin locations on a map and display detailed information in a pop-up window. The input is the suggested locations, and the output is visualized map information. 【0712】 (Application Example 1) 【0713】 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". 【0714】 In modern urban development and facility design, quickly selecting the optimal location is a crucial challenge. In particular, optimizing facility placement, taking into account local demographics and transportation convenience, is essential for improving urban functionality and resident convenience. However, traditional methods are time-consuming and labor-intensive, making rapid decision-making difficult. 【0715】 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. 【0716】 In this invention, the server includes means for acquiring regional information from a data collection device, means for analyzing pre-processed regional information to extract features, and means for visually displaying candidate locations on a map using an information display device. This enables efficient location selection and rapid facility placement proposals in urban development. 【0717】 A "data collection device" is a device used to acquire local information, and its role is to aggregate necessary information from various data sources. 【0718】 "Preprocessing" refers to the process of converting collected regional information into a unified format and performing noise removal and missing value imputation to improve the accuracy of the analysis. 【0719】 "Feature extraction" is the process of extracting important elements from pre-processed regional information to create a dataset necessary for predicting location selection. 【0720】 A "predictive model" is an algorithmic model built based on extracted features, and is used to score the potential of each candidate location. 【0721】 "Scoring" is the process of evaluating the potential of each region using predictive models and providing quantifiable indicators. 【0722】 An "information display device" is a device used to visually confirm scoring results and suggested candidate locations on a map, providing users with an intuitive understanding. 【0723】 "Filtering" is the process of narrowing down suggested candidates based on the conditions entered by the user and providing the most suitable option. 【0724】 "Facility placement" refers to proposing the optimal locations for facilities that should be placed in specific areas within a city, thereby contributing to the improvement of urban functions. 【0725】 To implement this invention, a data collection device, an analysis server, and a user terminal are required. The data collection device is responsible for acquiring regional information from various data sources, such as government statistics, traffic conditions, and commercial conditions. The information collected by the server is preprocessed and converted into a unified format. This processing uses Python and Pandas to perform noise reduction and missing value imputation. 【0726】 Next, the Scikit-learn library is used to extract features from the pre-processed regional data. This reveals important features such as population density and traffic flow. Once the features are complete, a predictive model is generated using Scikit-learn's Random Forest. The server then uses this model to score the potential of each region. 【0727】 On the user's device, potential locations are presented visually. An application developed using React Native then displays information about these locations on a map via a map API (e.g., Google Maps API). Based on this information, the user can view filtered locations according to their set criteria and make the optimal selection. 【0728】 As a concrete example, consider the case of selecting a location for a health-promoting sports center. The server scores population density and accessibility to sports facilities to identify areas of interest in health and fitness. As a result, the most suitable location is displayed on a map. 【0729】 An example of a prompt message would be: "Please propose the optimal location for constructing a sports center for health-conscious users within a smart city. In particular, please prioritize locations with good population density and convenient transportation." 【0730】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0731】 Step 1: 【0732】 The server uses data collection devices to collect government statistics, transportation data, and commercial real estate data. Input consists of raw data from various data sources, which the server integrates and stores in a database. Output is roughly processed, integrated data. 【0733】 Step 2: 【0734】 The server uses Python and Pandas to preprocess the collected data. The input is integrated data, which is denoised, imputed for missing values, and converted to a unified format. Standardizing the data format improves the accuracy of the analysis. The output is a clean, preprocessed dataset. 【0735】 Step 3: 【0736】 The server extracts important features based on preprocessed data. The input is a clean dataset, and features such as population density and traffic volume are selected using the Scikit-learn library. This prepares the data necessary for the predictive model. The output is the identified set of features. 【0737】 Step 4: 【0738】 The server generates a predictive model using Scikit-learn's Random Forest. The input is an extracted feature set, and the model is trained based on data from past successful cases. This constructs a predictive model capable of evaluating the potential of each region. The output is the predictive model. 【0739】 Step 5: 【0740】 The server uses the generated predictive model to score each region. The input is the predictive model and new regional data. The model quantifies the potential of each region and generates an evaluation score. The output is a list of scores for each region. 【0741】 Step 6: 【0742】 The terminal filters the scoring results based on user-specified conditions. The input consists of a regional score list and user conditions; the terminal selects the candidate location with the highest score that also meets the user's desired conditions. The output is a filtered list of candidate locations. 【0743】 Step 7: 【0744】 The device, via a React Native application, visually presents filtered candidate locations using a map API. The input is a filtered list of candidate locations, displayed on the map in an intuitively understandable format for the user. The output is the group of candidate locations displayed on the map. 【0745】 Step 8: 【0746】 The user makes decisions regarding the specific placement of the facility based on candidate sites presented on the map. This allows for the selection of the optimal location for the new facility based on visually confirmed information. The output is the optimal location selected by the user. 【0747】 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. 【0748】 This invention provides a new function for a system that supports the selection of potential store locations, which recognizes the user's emotions and further utilizes that information to customize the proposed content. The system has the following components and performs the following specific processing. 【0749】 First, the system collects regional information from external data sources and preprocesses it into a standard format to prepare a unified analytical platform. The server acquires demographic and traffic data, competitive information, etc., generates predictive models using machine learning algorithms, and scores the potential for opening a store in each region. Then, it proposes the optimal candidate locations for store openings based on user-defined conditions. 【0750】 A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses a camera and microphone to analyze the user's facial expressions and voice tone, recognizing their emotional state in real time. This emotional data is transmitted to a server and used to generate suggestions tailored to the user's needs. 【0751】 For example, if a user shows strong interest in a proposed location, the server will present additional information or similar potential store locations. Conversely, if a user expresses dissatisfaction with the proposal, it will present alternative locations or new proposals. Furthermore, the system dynamically adjusts elements of the user interface based on sentiment data to provide a more user-friendly environment. 【0752】 For example, when a user receives a suggestion for a potential store location, if they smile, the system highlights the relevant suggestion and offers options to explore it further. On the other hand, if the user frowns or shows other signs of dissatisfaction, the system re-evaluates the suggestion and either presents new options or changes how the information is displayed. 【0753】 In this way, the emotion engine recognizes the user's emotional state and dynamically changes the suggested content accordingly, enabling more personalized selection of store locations. This improves user satisfaction and enhances the accuracy of business strategies. 【0754】 The following describes the processing flow. 【0755】 Step 1: 【0756】 The server collects local information from external data sources. This includes demographic data, traffic data, commercial real estate information, and competitor information. The data is automatically retrieved periodically via an API. 【0757】 Step 2: 【0758】 The server preprocesses the collected data. It cleans each dataset, standardizes the format, and appropriately imputes missing values. This ensures data integrity and improves the accuracy of the analysis. 【0759】 Step 3: 【0760】 The server extracts features from pre-processed data. This includes quantifying factors such as population density, competition density, and accessibility. A dataset is then prepared to quantitatively understand the characteristics of each region. 【0761】 Step 4: 【0762】 The server trains a predictive model using machine learning algorithms. It learns from past success stories and builds a model that scores the potential for opening a store in each region. Through this scoring, it identifies the regions with the greatest business opportunities. 【0763】 Step 5: 【0764】 The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. To recognize the user's emotional state, an emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) and sends the data to a server. 【0765】 Step 6: 【0766】 The server dynamically adjusts the suggested store locations for the user based on emotion recognition data. If positive emotions are recognized, relevant information is highlighted and further details are presented. If negative emotions are recognized, alternatives are suggested, and the order and content of the information displayed are changed. 【0767】 Step 7: 【0768】 The device visually displays the adjusted suggestion results. Potential store locations are shown on a map, and detailed information about each location is highlighted according to the user's level of interest. Users can select a location on the map to view more detailed information. 【0769】 Step 8: 【0770】 Users make decisions based on the suggested information. They provide feedback on the selected store locations via their terminals, and the server incorporates this feedback into subsequent analyses. This improves the overall accuracy of the system. 【0771】 (Example 2) 【0772】 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". 【0773】 In today's business environment, selecting potential store locations is a crucial element of a company's strategy. However, selecting locations based solely on regional information presents a challenge: it fails to adequately reflect the individual needs and emotions of users, thus hindering customer satisfaction. Furthermore, existing systems are limited to presenting static information, making it difficult to effectively incorporate user emotions and real-time feedback. 【0774】 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. 【0775】 In this invention, the server includes means for acquiring regional information from a data collection device, means for preprocessing the acquired regional information and converting it into a unified format, means for analyzing the preprocessed regional information and extracting features, means for analyzing image and audio information to recognize the user's emotional state, and means for dynamically customizing the suggested content based on the recognized emotional data. This makes it possible to utilize the user's emotional data to propose more personalized and optimal store location candidates for the user. 【0776】 A "data collection device" is a device that collects information via various sensors and internet connections in order to acquire local information. 【0777】 "Local information" refers to a collection of various data related to a specific area, such as demographic statistics, transportation data, and competitive information. 【0778】 "Preprocessing" refers to the data cleaning and format conversion processes performed to transform raw data into a format that is easy to analyze. 【0779】 A "unified format" is a standardized data structure used to unify data from different formats into a consistent format. 【0780】 "Features" are analyzable attributes or values ​​extracted from data, and are parameters used to generate predictive models. 【0781】 A "predictive model" is a model created using machine learning algorithms to learn specific patterns from data and estimate future trends. 【0782】 "Scoring" is the process of numerically evaluating the potential for opening stores in each region using the generated predictive model. 【0783】 "Emotional state" refers to the type and intensity of emotions a user is currently experiencing, as judged from factors such as their facial expressions and tone of voice. 【0784】 "Dynamic customization" refers to the process of changing information and services in real time based on user emotions and feedback. 【0785】 "Proposed content" refers to suggestions to users, including various information regarding potential store locations. 【0786】 This invention relates to a system that innovates the process of selecting potential store locations. This system is characterized by its ability to recognize user emotions and dynamically customize the suggested content. The hardware and software configurations are described in detail below. 【0787】 The server first acquires local information through data collection devices. This data includes demographic data, traffic data, and competitive information. This data is collected via edge device technology and cloud services. The server then uses data analysis software such as Python and R to preprocess the acquired data and convert it into a unified format. 【0788】 Next, the server analyzes the preprocessed data and extracts features. Libraries such as Pandas and NumPy are used for this purpose. Furthermore, based on the extracted features, a predictive model is generated using machine learning frameworks such as TensorFlow and PyTorch, and region-specific scoring is performed. 【0789】 The device uses its camera and microphone to capture the user's facial expressions and voice tone in real time. This utilizes image and audio processing technologies such as OpenCV and Audacity. The device then sends this captured data to a server, where a generative AI model is used to analyze the user's emotions. 【0790】 If a user shows a specific emotional response to a suggested store location provided via their device, the server dynamically customizes the suggestions based on that emotional data. If the user is interested, the server suggests additional related information or similar locations. If the user expresses dissatisfaction, new locations are presented or existing suggestions are improved. For example, a prompt such as "Analyze the user's level of interest regarding store locations in a specific area based on emotional data and suggest optimized locations" can be input into the generating AI model to provide information optimized for the user. 【0791】 This system enables more personalized suggestions for potential store locations based on user sentiment data, improving the user experience and increasing the accuracy of business strategies. 【0792】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0793】 Step 1: 【0794】 The server acquires local information from the internet through data collection devices. This information includes demographic data, traffic data, and competitive information. The acquired data is input to the server as raw data. 【0795】 Step 2: 【0796】 The server uses Python to preprocess the input regional information. Specifically, it performs data cleaning and imputation of missing values, and converts the data into a unified format. As a result, clean data suitable for analysis is output. 【0797】 Step 3: 【0798】 The server analyzes the cleansed data and extracts features. Statistical analysis is performed using NumPy and Pandas to identify important data points. This process yields the features necessary for generating a predictive model. 【0799】 Step 4: 【0800】 The server generates a predictive model using TensorFlow based on the extracted features. It then applies machine learning algorithms to build a model for evaluating the potential of store openings in each region. This results in the output of a score for each region. 【0801】 Step 5: 【0802】 The server uses the generated predictive model to perform region-specific scoring. Regional information is used as input data to quantify the potential for opening a store in each region. This yields scoring results, enabling the selection of the optimal candidate locations based on specific criteria. 【0803】 Step 6: 【0804】 The device uses a camera and microphone to collect the user's facial expressions and voice tone. This utilizes OpenCV and Audacity to acquire real-time emotion data from the user. 【0805】 Step 7: 【0806】 The device sends collected emotional data to the server. The server analyzes the emotional data using a generative AI model to determine the user's level of interest and dissatisfaction. Based on this input, it dynamically customizes the suggestions and outputs optimized suggestion results. 【0807】 Step 8: 【0808】 The user receives the suggestions via their device. If the user expresses a positive sentiment towards the suggestions, the server adds relevant information and similar locations to enhance the suggestions. In the case of a negative reaction, the server re-evaluates the suggestions and presents alternative options. 【0809】 This process enables the provision of personalized store location suggestions tailored to each user. 【0810】 (Application Example 2) 【0811】 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". 【0812】 Conventional store location selection systems make suggestions based on the analysis of regional data, but these suggestions cannot adapt to the individual emotions and reactions of users. Therefore, the suggested locations do not always meet user expectations, and there is a need to improve the user experience. Accordingly, a system is needed that enables more personalized store location selection that takes user emotions into consideration. 【0813】 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. 【0814】 In this invention, the server includes means for acquiring regional data from a data acquisition device, means for recognizing the user's emotional state using visual and auditory information, and means for dynamically adjusting the suggested content based on the recognized emotional state. This makes it possible to provide personalized suggestions that respond to the user's emotions. 【0815】 A "data acquisition device" is a device used to collect various types of data related to a region, and provides that data as basic information for analysis. 【0816】 "Regional data" refers to data that includes information such as the population, transportation, and competitive landscape of a region, and is used to understand the characteristics and features of that region. 【0817】 A "unified data format" is a data format that converts data obtained from different sources into a consistent format, making subsequent analysis easier. 【0818】 "Features" are important patterns and indicators extracted from regional data, and are used as raw materials for predictive algorithms. 【0819】 A "predictive algorithm" is a computational method used to predict future trends and suitability based on the characteristics of regional data. 【0820】 "Evaluation" is the act of numerically representing the potential and value of each region using predictive algorithms, and it serves as a foundation for improving the accuracy of proposals. 【0821】 "Location" refers to the proposed store locations suggested by the system, which are considered to have a high probability of business success based on the characteristics of that region. 【0822】 "Visual information" refers to visual data such as facial expressions collected by the user's camera, and is used to analyze the user's emotional state. 【0823】 "Voice" refers to audio information such as the tone of voice and the content of speech acquired by the user's microphone, and is used to understand the user's emotional state. 【0824】 "Emotional state" refers to a psychological state estimated based on the user's facial expressions and tone of voice, and is a factor used to optimize the suggested content. 【0825】 "Dynamic adjustment of proposal content" refers to the act of changing the content and display method of proposals in real time in accordance with the user's emotions, with the aim of improving the user experience. 【0826】 In an embodiment of the present invention, first, a server collects various data about a region via a data acquisition device. This collected data includes demographics, traffic information, and competitive analysis. The server preprocesses the data and converts it into a unified data format. Next, it extracts features from the preprocessed data and generates a prediction algorithm. This enables evaluation on a region-by-region basis and prepares the system to suggest optimal locations for store openings. 【0827】 The device uses a camera and microphone to acquire the user's visual and auditory information in real time, and analyzes the user's emotional state using emotion recognition software. This analysis is sent to a server and used to dynamically adjust the suggested content. Specifically, the server customizes the suggested content and display method based on the user's emotional state to improve user satisfaction. 【0828】 For example, if a user responds positively to a suggestion of potential store locations, the server will display more detailed information and provide information to help them consider similar areas. On the other hand, if the user shows indecisiveness, the server will suggest alternatives and re-evaluate the suggestions. In this way, the system provides personalized suggestions and supports the user's decision-making. 【0829】 By utilizing generative AI models, it is also possible to automatically generate prompt messages that correspond to the user's emotional state. For example, the following prompt messages are possible: 【0830】 "If the user's facial expression is 'smiling,' display detailed information about the suggested location." 【0831】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0832】 Step 1: 【0833】 The server collects regional data via data acquisition devices. This data includes demographics, traffic information, and competition information. Based on this input information, the server preprocesses the data and converts it into a unified data format. This conversion process unifies different data sources and standardizes the data into a format that can be used for subsequent analysis. 【0834】 Step 2: 【0835】 The server analyzes pre-processed data and extracts regional characteristics. Specifically, the server applies machine learning algorithms to extract valuable features from the data. In this process, characteristics such as population trends and transportation convenience for each region are quantified and supplied as input to the prediction algorithm. 【0836】 Step 3: 【0837】 The server generates a prediction algorithm based on the extracted features and performs an evaluation for each region. The server uses these features to score the regional potential and generates basic data to identify the optimal candidate locations. Based on the scoring results, regions with high potential for opening a store are selected. 【0838】 Step 4: 【0839】 The device uses its camera and microphone to capture the user's visual and auditory information and analyzes their emotional state in real time. Using the user's facial expressions and tone of voice as input, the device employs emotion recognition software to determine the user's emotional state. This output serves as an indicator of how the user is reacting to the presented information. 【0840】 Step 5: 【0841】 The server dynamically adjusts the suggestions based on the user's emotional state. Using the emotional data obtained in step 4 as input, the server automatically generates prompt sentences using a generative AI model and customizes the suggestions. Specific actions include displaying additional details if the user shows interest, and presenting alternatives if the user expresses dissatisfaction. 【0842】 Step 6: 【0843】 The user reviews dynamic suggestions provided by the server and makes a decision. Based on the information displayed on the device's screen, the user selects which candidate location best matches their needs. The user's selection results are fed back as data to improve the accuracy of future suggestions. 【0844】 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. 【0845】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0846】 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 robot 414. 【0847】 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. 【0848】 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. 【0849】 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. 【0850】 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. 【0851】 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. 【0852】 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." 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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. 【0862】 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. 【0863】 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. 【0864】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0865】 The following is further disclosed regarding the embodiments described above. 【0866】 (Claim 1) 【0867】 A means of acquiring regional information from a data collection device, 【0868】 A means for preprocessing acquired regional information and converting it into a unified format, 【0869】 A means for analyzing pre-processed regional information to extract features, 【0870】 A means for generating a predictive model based on extracted features, 【0871】 A method for performing region-specific scoring using a predictive model, 【0872】 A system that includes means for suggesting the optimal location based on the scoring results. 【0873】 (Claim 2) 【0874】 The system according to claim 1, further comprising means for filtering suggestion results based on user input conditions. 【0875】 (Claim 3) 【0876】 The system according to claim 1, further comprising means for visually presenting the proposed results using a display device. 【0877】 "Example 1" 【0878】 (Claim 1) 【0879】 A means for obtaining area information from the data acquisition unit, 【0880】 A means for preprocessing acquired area information and converting it into a unified format, 【0881】 A means for analyzing pre-processed area information to extract feature data, 【0882】 A means for generating a predictive structure based on extracted feature data, 【0883】 A means of performing evaluation for each area using a predictive structure, 【0884】 A system that includes means for suggesting the optimal location based on evaluation results. 【0885】 (Claim 2) 【0886】 The system according to claim 1, further comprising means for narrowing down the suggested results based on user input conditions. 【0887】 (Claim 3) 【0888】 The system according to claim 1, further comprising means for visually presenting the proposed results using a display unit. 【0889】 "Application Example 1" 【0890】 (Claim 1) 【0891】 A means of acquiring regional information from a data collection device, 【0892】 A means for preprocessing acquired regional information and converting it into a unified format, 【0893】 A means for analyzing pre-processed regional information to extract features, 【0894】 A means for generating a predictive model based on extracted features, 【0895】 A method for performing region-specific scoring using a predictive model, 【0896】 A means of suggesting the optimal location based on the scoring results, 【0897】 A means of visually displaying candidate locations on a map using an information display device, 【0898】 A means of proposing the optimal placement of specific facilities, 【0899】 A system that includes this. 【0900】 (Claim 2) 【0901】 The system according to claim 1, further comprising means for filtering suggestion results based on user input conditions. 【0902】 (Claim 3) 【0903】 The system according to claim 1, further comprising means for displaying the proposed results on a terminal via wireless communication using an information display device. 【0904】 "Example 2 of combining an emotion engine" 【0905】 (Claim 1) 【0906】 A means of acquiring regional information from a data collection device, 【0907】 A means for preprocessing acquired regional information and converting it into a unified format, 【0908】 A means for analyzing pre-processed regional information to extract features, 【0909】 A means for generating a predictive model based on extracted features, 【0910】 A method for performing region-specific scoring using a predictive model, 【0911】 A means for analyzing image and audio information in order to recognize the user's emotional state, 【0912】 A means of dynamically customizing the suggested content based on recognized emotion data, 【0913】 A system that integrates scoring results with dynamic customization and includes means for suggesting the optimal location. 【0914】 (Claim 2) 【0915】 The system according to claim 1, further comprising means for filtering suggestion results based on user input conditions. 【0916】 (Claim 3) 【0917】 The system according to claim 1, further comprising means for visually presenting the proposed results using a display device. 【0918】 "Application example 2 of combining emotional engines" 【0919】 (Claim 1) 【0920】 A means of acquiring regional data from a data acquisition device, 【0921】 A means of preprocessing acquired regional data and converting it into a unified data format, 【0922】 A means for analyzing pre-processed regional data to extract features, 【0923】 A means for generating a prediction algorithm based on extracted features, 【0924】 A means of performing regional evaluations using a prediction algorithm, 【0925】 A means of proposing the optimal location based on the evaluation results, 【0926】 A means of recognizing the emotional state of a user using visual and auditory information, 【0927】 A system that includes means for dynamically adjusting the content of suggestions based on recognized emotional states. 【0928】 (Claim 2) 【0929】 The system according to claim 1, further comprising means for filtering suggestion results based on user input conditions. 【0930】 (Claim 3) 【0931】 The system according to claim 1, further comprising means for visually presenting the proposed results using a presentation device. [Explanation of symbols] 【0932】 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 acquiring regional information from a data collection device, A means for preprocessing acquired regional information and converting it into a unified format, A means for analyzing pre-processed regional information to extract features, A means for generating a predictive model based on extracted features, A method for performing region-specific scoring using a predictive model, A system that includes means for suggesting the optimal location based on the scoring results. [Claim 2] The system according to claim 1, further comprising means for filtering suggestion results based on user input conditions. [Claim 3] The system according to claim 1, further comprising means for visually presenting the proposed results using a display device.

Images