System

Abstract

A system is provided.SOLUTION: The system includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data, a data preprocessing means for organizing the collected data, supplementing missing data, and correcting abnormal values, a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis result, a result presentation means for providing the generated store opening guide map to the user and causing the user to perform feedback, and a feedback collection means for collecting feedback from the user and using the feedback to improve the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Store opening strategies are a very important topic in many industries, but selecting the appropriate location can be very difficult. It is not easy to collect and properly analyze a wide range of data, such as regional demographics, traffic conditions, property information, and the status of competing stores. As a result, it is currently difficult to develop a rational location strategy based on data, and selecting a store location often involves significant risk. As a result, it is difficult to develop an effective store opening strategy necessary for business success. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a data collection means is provided for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. Next, a data preprocessing means is provided for organizing the collected data, supplementing missing data, and correcting outliers. Next, a data analysis means is provided for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results. In addition, a result presentation means is provided for providing the generated store opening guide map to the user and allowing the user to provide feedback. Furthermore, a feedback collection means is provided for collecting feedback from users and using it to improve the system, thereby providing a system that supports effective store opening strategies. This system allows users to create rational location strategies based on data and increase the success rate of their business.

[0006] "Data collection means" refers to means for collecting local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data.

[0007] "Data preprocessing means" refers to means for organizing collected data, filling in missing data, and correcting outliers.

[0008] The "data analysis means" is a means for analyzing pre-processed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results.

[0009] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0010] The "feedback collection means" is a means for collecting feedback from users and using it to improve the system.

[0011] A "store opening guide map" is a map that contains data for displaying the optimal store opening location on a map and providing detailed information. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a 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.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0026] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention is an AI system for optimizing store opening strategies. This system derives the optimal store location by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection. Each major component and its processing are explained below.

[0034] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The collected data is obtained via the Internet, public databases, cloud services, etc. Population data includes the population size of the specified region and the population distribution by age group. Traffic volume data includes the flow of people at major stations and bus stops, and the frequency of road use. Access status data indicates the use of public transportation in the region and the ease of access to major facilities. Lifestyle data is used to evaluate residents' hobbies, preferences, and purchasing power, and property status data includes the area, rent, vacancy status, etc. of the property.

[0035] The server then sorts the collected data, fills in any missing data, and corrects any outliers. For example, if the latest traffic volume data or information on newly built properties is missing, it can be supplemented from the internet or cloud services. Correcting outliers involves adjusting extremely high rents or extremely low traffic volume data to values ​​closer to the average.

[0036] The server then performs data analysis. Using the preprocessed data, it uses machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, a location with high traffic volume and few competing stores is rated as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0037] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check potential store locations while looking at the guide map and view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal provides interactive functions, allowing the user to zoom in on the information they need and click to display detailed information.

[0038] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0039] As a concrete example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they would like to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0040] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0041] The processing flow will be explained below.

[0042] Step 1:

[0043] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0044] Step 2:

[0045] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0046] Step 3:

[0047] The server preprocesses the collected data. First, it organizes the data and adds missing data. If there are outliers, it corrects or removes them. For example, if traffic volume data is out of date or property rents are abnormally high, it updates them with the latest data and normalizes the values.

[0048] Step 4:

[0049] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0050] Step 5:

[0051] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0052] Step 6:

[0053] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via the user interface. The user can view detailed information by clicking on the candidate site on the map.

[0054] Step 7:

[0055] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0056] Step 8:

[0057] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0058] Through the above steps, the system of the present invention can assist users in formulating rational location strategies based on data, thereby reducing the risks involved in opening a store.

[0059] Example 1

[0060] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0061] Conventional store opening strategy formulation methods have made it difficult to effectively collect and analyze the diverse data (population data, age group data, traffic volume data, etc.) required to select appropriate locations. Furthermore, the analytical methods for uniformly organizing this data and evaluating suitability were limited, limiting the accuracy of the results. Furthermore, the inability to properly collect and reflect user feedback made it difficult to improve the system and enhance the accuracy of store opening strategies.

[0062] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0063] In this invention, the server includes: information collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; data preprocessing means for organizing the collected data into a unified format, supplementing missing data from external data sources, and correcting outliers; information analysis means for analyzing the preprocessed data using machine learning algorithms and statistical analysis to evaluate location suitability and generate a store opening guide map based on the analysis results; result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; and feedback collection means for collecting user feedback and using it to improve the system. This enables the effective collection and analysis of diverse data and the improvement of suitability evaluation accuracy. Furthermore, by collecting and incorporating user feedback, the system can be continuously improved and store opening strategies can be optimized.

[0064] "Regional population data" is information about the number and distribution of people living in a specified region.

[0065] "Age group data" is information that classifies residents of a particular area by age.

[0066] "Traffic volume data" refers to information about traffic flow in a specific area over a certain period of time and the frequency of use of transportation means.

[0067] "Accessibility data" refers to information about the use of public transportation in a particular area and the ease of access to major facilities.

[0068] "Lifestyle data" is information related to the hobbies, preferences, purchasing power, and lifestyles of residents living in a particular area.

[0069] "Property status data" refers to information about a specific property, such as area, rent, and availability.

[0070] "Information collection means" refers to the means of collecting data from the Internet, public databases, cloud services, etc.

[0071] "Data preprocessing means" refers to a means for organizing collected data into a unified format, supplementing missing data, and correcting outliers.

[0072] "Information analysis means" refers to a means of analyzing pre-processed data using machine learning algorithms and statistical analysis to evaluate the suitability of the location and generate a store opening guide map.

[0073] The "result presentation means" is a means for providing the user with the store opening guide map generated as the analysis result via a user interface.

[0074] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0075] A "machine learning algorithm" is an algorithm that learns patterns and regularities from data and makes predictions and classifications.

[0076] "Statistical analysis" is a method of analyzing relationships and patterns in data to derive statistical conclusions.

[0077] The "Store Opening Guide Map" is a map that visually shows the optimal store location based on various data.

[0078] A "user interface" is an interface through which a user inputs information and checks the results.

[0079] The "type of business in which the user wishes to open a store" refers to the type of business in which the user wishes to open a new store.

[0080] "Desired conditions for opening a store" refer to the conditions a user sets when wishing to open a new store.

[0081] This invention is an AI system for optimizing store opening strategies. The system includes a server, a terminal, and a user. The specific functions and usage of each element are explained below.

[0082] System configuration

[0083] server

[0084] The server is designed to perform the following main functions:

[0085] 1. Information collection method: The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specific data sources used include census data, Google Maps API, and public transportation operation information.

[0086] 2. Data preprocessing: The server organizes the collected data into a unified format, fills in missing data, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and converts outliers to the average.

[0087] 3. Information analysis method: Using the preprocessed data, machine learning algorithms and statistical analysis are performed to evaluate the suitability of locations. Specifically, regression analysis and clustering are performed using the Scikit-learn library. Based on the suitability evaluation results, a store opening guide map is generated.

[0088] Terminal

[0089] The terminal is a device that receives information provided by the server and presents it to the user through a user interface.

[0090] 1. Result presentation method: The terminal uses HTML and JavaScript to display the generated store guide map. Markers are placed on the map and related detailed information is displayed as tooltips.

[0091] 2. Interactive features: Users can click on a potential store location on the map to view detailed information (such as demographics, traffic volume, rent, and competition), and provide a form for providing feedback.

[0092] User

[0093] The user is the subject who operates the terminal and interacts with the system.

[0094] 1. Entering the desired business type and conditions: The user enters the desired business type and conditions via the terminal. The following is a specific example.

[0095] I would like to open a new cafe. My desired conditions are as follows:

[0096] city ​​center

[0097] Within a 10-minute walk from a major station

[0098] The maximum rent is 500,000 yen per month

[0099] 2. Check the guide map: Check the store opening guide map sent from the server and consider the best possible location for the store.

[0100] 3. Providing feedback: Evaluate the proposed store location and the usability of the system, and send feedback to the server via the terminal.

[0101] Specific examples

[0102] Consider a case where a user wants to open a new cafe. The user uses a terminal to enter the following prompt sentence:

[0103] I would like to open a new cafe. My desired conditions are as follows:

[0104] city ​​center

[0105] Within a 10-minute walk from a major station

[0106] The maximum rent is 500,000 yen per month

[0107] The server receives this prompt, collects the necessary data, performs preprocessing, and then analyzes it. The resulting store opening guide map is sent to the terminal and presented to the user. The user refers to the guide map and identifies the optimal store location. The user then provides feedback on the proposed location and the system's usability, which is used to improve the system.

[0108] This system effectively collects and analyzes a variety of data to improve the accuracy of aptitude assessments. In addition, by collecting and incorporating user feedback, the system can be continuously improved, leading to the optimization of store opening strategies.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1: Gather information

[0111] The server obtains local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, it uses census data, Google Maps API, and public transportation information. Based on the region and conditions specified by the user as input, the server builds an appropriate data source. The output is the various collected data that will be used in the next step.

[0112] Step 2: Data Preprocessing

[0113] The server organizes the collected data into a unified format, imputes missing data from the internet or cloud services, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and imputes missing values ​​with the mean or mode. In the case of extremely high rent data, it uses statistical methods to correct outliers. The data collected in step 1 is required as input, and preprocessed data is generated as output.

[0114] Step 3: Data analysis

[0115] The server evaluates the suitability of locations using machine learning algorithms and statistical analysis based on the preprocessed data. Specifically, it uses the Scikit-learn library to apply regression analysis and clustering algorithms. For example, it builds a model to evaluate the commercial suitability of an area using traffic volume, rent, age group data, etc. Preprocessed data is required as input, and the suitability evaluation results and a store opening guide map are generated as output.

[0116] Step 4: Presenting the results

[0117] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. Specifically, it uses HTML and JavaScript to mark candidate locations on the map and displays related detailed information (such as demographics, traffic volume, rent, and competitive situation) as tooltips and detail panels. The inputs are the store opening guide map and evaluation results sent from the server, and the output is visual information provided to the user.

[0118] Step 5: Gather feedback

[0119] The user provides feedback on the presented store opening guide map and proposals. For example, the user enters their opinion via a feedback form, such as "This area has good commercial suitability, but the rent is too high." The terminal then sends this feedback information to the server. The input is the feedback information from the user, and the output is the feedback sent to the server.

[0120] Step 6: Feedback analysis and system improvement

[0121] The server analyzes the collected feedback and identifies areas for improvement in the system. For example, if the analysis results for a particular region do not match user expectations, the data collection method or analysis algorithm for that region can be revised. Based on the analyzed feedback, the system makes improvements that will be reflected in the next analysis. The input is user feedback information, and the output is a revised version of the system.

[0122] Through the above steps, the system can assist users in formulating store opening strategies rationally and efficiently.

[0123] (Application example 1)

[0124] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] The present invention solves the problem in the technical field of optimizing store opening strategies: conventional methods were unable to effectively utilize large amounts of data, making it difficult to quickly and accurately identify optimal store locations. Furthermore, there was a lack of means to properly analyze this data and provide it to users in an easy-to-understand manner, resulting in a high likelihood of store opening risks and misjudgments. Furthermore, there was a lack of a mechanism for incorporating user feedback into the system, making it difficult to continuously improve the system's accuracy.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0127] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers; a data analysis means for analyzing the preprocessed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results; a result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; a feedback collection means for collecting feedback from users and using it to improve the system; and a visualization means for visually displaying the scoring of potential store locations. This enables rapid analysis of large amounts of data and visually suggesting optimal store locations. Furthermore, the system can be continuously improved based on user feedback, thereby increasing the accuracy of store opening strategies.

[0128] "Data collection means" refers to means for collecting local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0129] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0130] The "data analysis method" is a method for analyzing pre-processed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results. The data is analyzed using statistical analysis and machine learning algorithms.

[0131] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0132] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0133] The "visualization means" is a means for visually displaying the scoring of a potential store location.

[0134] This invention relates to an AI system for optimizing store opening strategies. This system derives optimal store locations by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection.

[0135] 1. Data Collection

[0136] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, this data is obtained through APIs. For example, population data includes the population size of a specified area and its population distribution by age group, while traffic volume data includes the flow of people at major stations and bus stops, and frequency of road use.

[0137] 2. Data Preprocessing

[0138] The server organizes the collected data, fills in missing data, and corrects outliers. For example, if the latest traffic volume data or information on newly built properties is lacking, it can be supplemented from the internet or cloud services. It also adjusts outliers such as extremely high rents or low traffic volume data to values ​​closer to the average. Data preprocessing is performed using numpy.

[0139] 3. Data Analysis

[0140] The server uses preprocessed data and machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, using KMeans clustering, it evaluates locations with high traffic volume and few competing stores as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0141] 4. Presentation of results

[0142] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check the candidate store locations by looking at the guide map and view detailed information (demographic composition, traffic volume, rent, competitive situation, etc.). The scored locations are visualized using matplotlib.

[0143] 5. Feedback Collection

[0144] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0145] Specific examples

[0146] For example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they want to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0147] Prompt Sentence Examples

[0148] To optimize the location of a store in the city center, please find the best location within a 10-minute walk from a major station with a maximum rent of XXX yen. Please collect and analyze the necessary demographics, traffic volume, accessibility, lifestyle data, and property status data. Please visually display the best store location and the reasons for it.

[0149] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0150] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0151] Step 1: Data collection

[0152] The server collects local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. based on the area and conditions specified by the user. Specifically, it obtains these datasets through API endpoints. It receives the specific area and conditions as input and obtains the collected, unprocessed raw data as output.

[0153] Step 2: Data Preprocessing

[0154] The server organizes the raw data collected in step 1, fills in missing data, and corrects outliers. Specifically, if the latest traffic volume data or new property information is lacking, it fills in the missing data from the internet or cloud services. It also uses numpy to clip extreme values ​​to values ​​closer to the average. It receives raw data as input and obtains preprocessed data as output.

[0155] Step 3: Data analysis

[0156] The server analyzes the data preprocessed in step 2 and evaluates the suitability of locations. Specifically, it uses a machine learning algorithm (e.g., KMeans clustering) to calculate a suitability score for each location. It receives the preprocessed data as input and obtains the suitability evaluation score and analysis results as output.

[0157] Step 4: Presenting the results

[0158] The terminal receives the analysis results provided by the server and presents them to the user via a user interface. Specifically, it uses matplotlib to visually display a store opening guide map and show the suitability score for each location. The terminal receives the analysis results from the server as input and obtains a visually displayed store opening guide map as output.

[0159] Step 5: Gather feedback

[0160] Users provide feedback on the displayed store opening guide map and proposals. Specifically, they input their evaluations and opinions and send them from their terminals to the server. The system receives user feedback as input and obtains collected feedback data as output.

[0161] Step 6: Feedback analysis and system improvement

[0162] The server analyzes the feedback collected in step 5 and uses it for future improvements to the system. Specifically, it analyzes the feedback data and uses it to retrain the machine learning model to improve the accuracy of the system. It receives the feedback data as input and obtains an improved analytical model as output.

[0163] Specific examples

[0164] For example, a user who wants to open a new cafe uses a terminal to input the conditions for opening a cafe (city center, within 10 minutes' walk from a major station, maximum rent, etc.). Based on these conditions, the server collects the necessary data, performs preprocessing, and then analyzes it using a machine learning algorithm. The generated cafe opening guide map is presented to the user, who provides feedback, which the server analyzes and improves the system.

[0165] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0166] This invention combines an emotion engine with an AI system for optimizing store opening strategies. This system combines the methods of data collection, data preprocessing, data analysis, result presentation, and feedback collection, and is equipped with an emotion engine that recognizes user emotions and adjusts the analysis results and recommendations to determine the optimal store location. Each of the main components and their processing are explained below.

[0167] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data collection is done via the Internet, public databases, cloud services, etc. For example, population data includes the population size and age distribution of a specified area, traffic volume data includes the flow of people at major stations and bus stops, access status data includes the usage status of public transportation, lifestyle data includes residents' hobbies and purchasing power, and property status data includes the area, rent, and availability of properties.

[0168] The server then preprocesses the collected data: it cleans it up, fills in gaps, and corrects or removes outliers. For example, outdated traffic data is updated with more recent data, and extremely high rents are adjusted to be closer to the average.

[0169] The server then analyzes the preprocessed data and evaluates the suitability of the location using statistical analysis and machine learning algorithms (regression analysis, clustering, deep learning, etc.). For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable." Based on this, a store opening guide map showing the optimal store locations is generated.

[0170] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. The user can click on a potential location on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal also provides interactive functions, allowing the user to zoom in on the information they need and click to view detailed information.

[0171] Users provide feedback on the presented store opening guide map and the usability of the system. This feedback is sent from the terminal to the server. The server analyzes the collected feedback and uses it to improve the system. This feedback loop allows the system to continuously improve its accuracy.

[0172] Furthermore, the system of the present invention is equipped with an emotion engine. This emotion engine has the function of recognizing the user's emotions from the user's input data and feedback, and adjusting the analysis results and the proposed contents of the store opening guide map based on the user's emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's text input. It is also possible to recognize emotions in real time by analyzing the user's facial expressions and voice through the user interface.

[0173] As a concrete example, consider the case of someone wanting to open a new cafe. Using a terminal, the user inputs the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal location for opening a cafe, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0174] In this way, the system of the present invention allows users to create rational location strategies based on data and emotions, significantly reducing the risks of opening a store.

[0175] The processing flow will be explained below.

[0176] Step 1:

[0177] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0178] Step 2:

[0179] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0180] Step 3:

[0181] The server preprocesses the collected data. First, it organizes the data and fills in any missing parts. If there are any outliers, it corrects or removes them. For example, old traffic volume data is updated with the latest data, and extremely high rents are corrected to values ​​closer to the average.

[0182] Step 4:

[0183] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0184] Step 5:

[0185] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0186] Step 6:

[0187] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via a user interface. The user can view detailed information by clicking on a candidate site on the map. The terminal also provides interactive functions, allowing the user to zoom in on the information they need and display more detailed information.

[0188] Step 7:

[0189] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0190] Step 8:

[0191] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0192] Step 9:

[0193] The server sends the user's input data and feedback to the emotion engine, which uses natural language processing technology to analyze emotions from the user's text input. The device also analyzes the user's facial expressions and voice through the user interface and sends the emotional data to the server.

[0194] Step 10:

[0195] The server receives the analysis results from the emotion engine and dynamically adjusts the analysis results and the store opening guide map suggestions based on the user's emotions. For example, if the user is feeling stressed, the server will prioritize suggestions to open a store in a relaxing location.

[0196] Step 11:

[0197] The device then presents the adjusted store opening guide map and suggestions to the user again, allowing the user to select the optimal store opening location that suits their emotional state.

[0198] Through these steps, the system of the present invention can help users develop rational location strategies based on data, and can also provide more personalized suggestions by taking into account the user's emotional state.

[0199] Example 2

[0200] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] Conventional store opening strategy support systems simply provide analysis results based on collected data, and do not provide enough information that takes into account user emotions and feedback. This makes it difficult to create a more effective store opening strategy that reflects user emotions and intuition.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0203] In this invention, the server includes an information collection means, a data preprocessing means, a data analysis means, a presentation means, a feedback collection means, and a sentiment analysis means, which makes it possible to propose a store opening strategy that takes into account the sentiments of users.

[0204] "Information collection means" is a function that collects information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from various data sources.

[0205] "Data preprocessing means" refers to a function for organizing collected data, supplementing missing data, and correcting or removing outliers.

[0206] "Data analysis means" is a function that analyzes pre-processed data, evaluates the suitability of the location using statistical analysis and machine learning algorithms, and generates a store opening guide map based on the analysis results.

[0207] The "presentation means" is a function for providing the generated store opening guide map to the user and allowing the user to provide feedback.

[0208] The "feedback collection means" is a function for collecting feedback from users and using it to improve the system.

[0209] The "emotion analysis means" is a function for analyzing the user's emotions from the user's input data, feedback, facial expressions, voice, etc., and adjusting the analysis results and proposal content.

[0210] "Communication means" is a function for transmitting input data to a server and receiving a store opening guide map from the server.

[0211] The present invention is a system for optimizing store opening strategies, and includes information collection means, data preprocessing means, data analysis means, presentation means, feedback collection means, and sentiment analysis means. This system collects various local data, processes and analyzes it, and suggests optimal store locations to users. Furthermore, the system is continuously improved by analyzing user feedback and sentiment data.

[0212] First, the server uses information collection methods to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The hardware used for collection uses a general web server and cloud service, and data is automatically acquired using Python libraries (such as requests and BeautifulSoup).

[0213] The server then uses data preprocessing tools to organize the collected data, impute missing data, and correct or remove outliers. Specifically, it creates a data frame using pandas or NumPy, imputes missing data values ​​with the mean or median, and replaces outliers with the mean.

[0214] The server then analyzes the preprocessed data using data analysis tools. It applies statistical analysis and machine learning algorithms (such as scikit-learn regression models, KMeans clustering, and TensorFlow neural network models) to evaluate the suitability of the location. For example, it evaluates locations with high traffic volume and few competing stores as highly suitable and generates a store opening guide map showing the optimal store locations.

[0215] The terminal uses the presentation means to receive the store opening guide map provided by the server and presents it to the user through the user interface. A specific example is to display an interactive map on a web browser, allowing the user to click on a candidate site on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The map is drawn using a JavaScript library (such as Leaflet.js or Google Maps API).

[0216] Users provide feedback on the presented store opening guide map and the usability of the system. The terminal collects this feedback and sends it to the server via a feedback collection means. The server analyzes the collected feedback and uses it to improve the system. The feedback content is analyzed using text analysis tools (such as NLTK and spaCy) to extract specific keywords and emotions.

[0217] Furthermore, the server analyzes the user's emotions using emotion analysis. The server uses natural language processing technology to recognize emotions from the user's input data and feedback. Specifically, it analyzes emotions using an API (e.g., Google Cloud Natural Language API or IBM Watson's emotion analysis API) and adjusts the analysis results and suggestions based on the user's emotions. The device can also use a webcam and microphone to analyze the user's facial expressions and voice in real time and recognize emotions in real time.

[0218] As a specific example of use, consider a user who wants to open a new cafe. The user uses a terminal to input the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). The server collects the necessary data based on this, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting cafe guide map and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal cafe location, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0219] Prompt Sentence Examples

[0220] "Please explain the specific steps for data collection, preprocessing, and data analysis for optimizing store openings using Python's pandas, NumPy, scikit-learn, and TensorFlow. Also, please provide an example of how to collect user feedback and use it to improve the system. Furthermore, please specifically demonstrate how to use an emotion engine to recognize user emotions and adjust the suggestions."

[0221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0222] Step 1: Data collection

[0223] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from public databases and cloud services available on the Internet.

[0224] Input: local keywords or queries

[0225] Processing: We use Python's requests library to collect data from the internet, parse HTML with BeautifulSoup to extract the necessary information, and use cloud service APIs to obtain more detailed data.

[0226] Output: A data frame containing the various regional data collected.

[0227] Step 2: Data Preprocessing

[0228] The server organizes the collected data using data pre-processing means, fills in missing data, and corrects or removes outliers.

[0229] Input: Collected data frame

[0230] Processing: Create a data frame using the pandas library, impute missing values ​​(mean, median, etc.), detect and correct outliers, standardize the data format, and normalize and scale the data.

[0231] Output: Preprocessed data frame

[0232] Step 3: Data analysis

[0233] The server analyzes the pre-processed data using data analysis tools, specifically statistical analysis and machine learning algorithms, to evaluate the suitability of the location.

[0234] Input: Preprocessed data frame

[0235] Processing: Regression analysis using scikit-learn, KMeans clustering, and neural networks using TensorFlow are applied. Factors such as traffic volume, number of stores, and competitive situation are evaluated to calculate a suitability score.

[0236] Output: A store opening guide map generated along with suitability scores

[0237] Step 4: Presenting the results

[0238] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface.

[0239] Input: Store opening guide map and suitability score

[0240] Processing: Use JavaScript libraries (Leaflet.js and Google Maps API) to display an interactive map and allow users to click on potential locations on the map to view more information.

[0241] Output: An interactive store opening guide map displayed on the user interface

[0242] Step 5: Gather feedback

[0243] Users provide feedback on the presented store opening guide map and the usability of the system.

[0244] Input: User feedback (text, selection, rating, etc.)

[0245] Processing: The terminal collects input from the feedback form and sends it to the server.

[0246] Output: Feedback data sent to the server

[0247] Step 6: Analyze feedback and improve the system

[0248] The server analyzes the collected feedback and uses it to improve the system.

[0249] Input: Feedback data from users

[0250] Processing: Analyze the feedback content using text analysis tools (NLTK or spaCy) to extract specific keywords and sentiments. Also, analyze the sentiment of the feedback using sentiment analysis API.

[0251] Output: System improvement instructions based on analysis results

[0252] Step 7: Perform sentiment analysis

[0253] The server uses emotion analysis means to recognize emotions from the user's input data and feedback.

[0254] Input: User text input, facial expression data, voice data

[0255] Processing: Natural language processing technology (Google Cloud Natural Language API and IBM Watson Sentiment Analysis API) is used to analyze emotions from text and adjust the content displayed and suggestions on the user interface as appropriate.

[0256] Output: Sentiment analysis results and tailored recommendations

[0257] (Application example 2)

[0258] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0259] Conventional store opening strategy optimization systems propose store locations based on regional data analysis, but do not take user emotions into consideration when making proposals. As a result, the proposed store locations may not match the user's wishes or emotions, resulting in low user satisfaction. Furthermore, there was a need for a system that could recognize user emotions in real time and instantly adjust the analysis results and proposals.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0261] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data, a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers, a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results, and an emotion analysis means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and proposal content. This makes it possible to make proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[0262] "Data collection means" refers to means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data.

[0263] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0264] The "data analysis means" is a means for analyzing pre-processed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results.

[0265] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0266] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0267] The "emotion analysis means" is a means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and suggestions.

[0268] The "input means" is a means for inputting the desired business type and desired conditions for opening a store.

[0269] The "transmission and reception means" is a means for transmitting input data to a server and receiving a store opening guide map from the server.

[0270] "Statistical analysis" is a method of analyzing data using statistical techniques in data analysis means.

[0271] A "machine learning algorithm" is an algorithm that analyzes data using machine learning technology in a data analysis means.

[0272] This invention combines an emotion analysis method with an AI system for optimizing store opening strategies. This system makes it possible to propose optimal store locations that take user emotions into consideration. The system configuration and processing are explained below.

[0273] System Configuration

[0274] The system is centered around a server and is composed of data collection means, data preprocessing means, data analysis means, result presentation means, feedback collection means, sentiment analysis means, input means, and transmission / reception means.

[0275] Hardware and software used

[0276] Hardware: A head-mounted display (HMD) is used as the device used by the user.

[0277] Software: TensorFlow (machine learning), NLTK (natural language processing), OpenCV (facial expression recognition), Google Maps API (map display)

[0278] Data collection methods

[0279] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data is obtained from the Internet, public databases, cloud services, etc. For example, the Google Maps API can be used to obtain demographics and traffic volume of key locations.

[0280] Data preprocessing measures

[0281] The collected data is then processed by data pre-processing methods, where missing data is filled in and outliers are corrected or removed, for example, old data is updated to the latest data, and extremely high outliers are corrected to the average value.

[0282] Data Analysis Methods

[0283] Statistical analysis and machine learning algorithms are used to analyze the pre-processed data and evaluate the suitability of locations. For example, TensorFlow is used to build predictive models based on traffic and population data to evaluate optimal store locations.

[0284] Results presentation means

[0285] The terminal (HMD) receives the store opening guide map provided by the server. The user can interactively view the map through the HMD and check detailed information about the candidate site (such as demographics, traffic volume, rent, and competitive situation).

[0286] Feedback collection methods

[0287] Users provide feedback on the guide map and the usability of the system. This feedback is sent to the server and used to improve the system. This feedback loop allows the system to continually improve its accuracy.

[0288] Emotion analysis means

[0289] The emotion analysis means analyzes the user's input data, facial expressions, and voice to recognize emotions. Emotions are analyzed from the user's text input using NLTK, and the user's facial expressions are analyzed using OpenCV. Based on the obtained emotional information, the analysis results and the proposed content of the store opening guide map are adjusted.

[0290] Specific processing flow

[0291] For example, if a user inputs "I want to open a new cafe," this input is analyzed using NLTK to recognize emotions. Furthermore, the user's facial image is captured using an HMD and facial expression analysis is performed using OpenCV. The system then comprehensively evaluates the user's emotions, such as whether they are excited or anxious, and adjusts the suggestions accordingly.

[0292] Example prompts for generative AI models

[0293] "Please provide the coordinates of the area where you would like to open a store and your desired conditions (e.g., maximum rent, distance from major stations, etc.). We will recommend potential locations using AI models and natural language analysis, and adjust the suggestions based on user sentiment."

[0294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0295] Step 1:

[0296] The user puts on the HMD and inputs the desired conditions for opening a store, such as "I want to open a new cafe." Using the input means, the user inputs the conditions (desired business type, city center location, within 10 minutes' walk from a major station, maximum rent, etc.). The input data is sent to the server via the sending and receiving means. The desired business type and conditions are obtained as input data and sent to the server as output.

[0297] Step 2:

[0298] The server uses data collection means to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. These data are obtained from the Internet, public databases, cloud services, etc. Various data related to the local area are obtained as input data, and the collected data is obtained as output.

[0299] Step 3:

[0300] The server preprocesses the collected data using a data preprocessing means. It organizes the collected data, fills in missing data, and corrects or removes outliers. For example, it updates the traffic volume data to the latest one, and corrects extremely high rents to values ​​closer to the average. The collected data is obtained as input data, and the preprocessed data is obtained as output data.

[0301] Step 4:

[0302] The server analyzes the preprocessed data using data analysis tools. It evaluates the suitability of the location using statistical analysis and machine learning algorithms. For example, it builds a model using TensorFlow and inputs traffic volume and population data to evaluate the optimal store location. The preprocessed data is obtained as input data, and the analysis results are output.

[0303] Step 5:

[0304] The server generates a store opening guide map based on the generated analysis results and sends it to the terminal via the results presentation means. The user can view the map interactively through the HMD and check detailed information about the candidate site (demographic composition, traffic volume, rent, competitive situation, etc.). The analysis results are obtained as input data, and the store opening guide map is obtained as output.

[0305] Step 6:

[0306] Users provide feedback on the store opening guide map and the usability of the system. The feedback is sent from the terminal to the server and collected by the feedback collection means. The user feedback is obtained as input data, and the feedback data is saved in the server as output.

[0307] Step 7:

[0308] The server uses emotion analysis means to analyze the user's input data, facial expressions, and voice to recognize the user's emotions. For example, OpenCV is used to analyze facial expressions from the user's facial video, and NLTK is used to analyze emotions from text input. Based on the obtained emotional information, the server adjusts the analysis results and the proposed content of the store opening guide map. The user's text input and facial expression data are obtained as input data, and the recognized emotional data is obtained as output.

[0309] Step 8:

[0310] Finally, the proposal content that takes into account the emotional information is provided to the user. The proposal content adjusted based on the user's emotions can improve the user's satisfaction. The proposal content before adjustment is obtained as input data, and the final proposal content that reflects the emotional information is displayed on the terminal as output.

[0311] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0312] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0313] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0314] [Second embodiment]

[0315] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0316] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0317] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0318] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0319] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0320] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0321] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0322] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0323] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0324] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0325] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0326] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0327] This invention is an AI system for optimizing store opening strategies. This system derives the optimal store location by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection. Each major component and its processing are explained below.

[0328] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The collected data is obtained via the Internet, public databases, cloud services, etc. Population data includes the population size of the specified region and the population distribution by age group. Traffic volume data includes the flow of people at major stations and bus stops, and the frequency of road use. Access status data indicates the use of public transportation in the region and the ease of access to major facilities. Lifestyle data is used to evaluate residents' hobbies, preferences, and purchasing power, and property status data includes the area, rent, vacancy status, etc. of the property.

[0329] The server then sorts the collected data, fills in any missing data, and corrects any outliers. For example, if the latest traffic volume data or information on newly built properties is missing, it can be supplemented from the internet or cloud services. Correcting outliers involves adjusting extremely high rents or extremely low traffic volume data to values ​​closer to the average.

[0330] The server then performs data analysis. Using the preprocessed data, it uses machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, a location with high traffic volume and few competing stores is rated as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0331] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check potential store locations while looking at the guide map and view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal provides interactive functions, allowing the user to zoom in on the information they need and click to display detailed information.

[0332] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0333] As a concrete example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they would like to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0334] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0335] The processing flow will be explained below.

[0336] Step 1:

[0337] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0338] Step 2:

[0339] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0340] Step 3:

[0341] The server preprocesses the collected data. First, it organizes the data and adds missing data. If there are outliers, it corrects or removes them. For example, if traffic volume data is out of date or property rents are abnormally high, it updates them with the latest data and normalizes the values.

[0342] Step 4:

[0343] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0344] Step 5:

[0345] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0346] Step 6:

[0347] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via the user interface. The user can view detailed information by clicking on the candidate site on the map.

[0348] Step 7:

[0349] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0350] Step 8:

[0351] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0352] Through the above steps, the system of the present invention can assist users in formulating rational location strategies based on data, thereby reducing the risks involved in opening a store.

[0353] Example 1

[0354] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0355] Conventional store opening strategy formulation methods have made it difficult to effectively collect and analyze the diverse data (population data, age group data, traffic volume data, etc.) required to select appropriate locations. Furthermore, the analytical methods for uniformly organizing this data and evaluating suitability were limited, limiting the accuracy of the results. Furthermore, the inability to properly collect and reflect user feedback made it difficult to improve the system and enhance the accuracy of store opening strategies.

[0356] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0357] In this invention, the server includes: information collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; data preprocessing means for organizing the collected data into a unified format, supplementing missing data from external data sources, and correcting outliers; information analysis means for analyzing the preprocessed data using machine learning algorithms and statistical analysis to evaluate location suitability and generate a store opening guide map based on the analysis results; result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; and feedback collection means for collecting user feedback and using it to improve the system. This enables the effective collection and analysis of diverse data and the improvement of suitability evaluation accuracy. Furthermore, by collecting and incorporating user feedback, the system can be continuously improved and store opening strategies can be optimized.

[0358] "Regional population data" is information about the number and distribution of people living in a specified region.

[0359] "Age group data" is information that classifies residents of a particular area by age.

[0360] "Traffic volume data" refers to information about traffic flow in a specific area over a certain period of time and the frequency of use of transportation means.

[0361] "Accessibility data" refers to information about the use of public transportation in a particular area and the ease of access to major facilities.

[0362] "Lifestyle data" is information related to the hobbies, preferences, purchasing power, and lifestyles of residents living in a particular area.

[0363] "Property status data" refers to information about a specific property, such as area, rent, and availability.

[0364] "Information collection means" refers to the means of collecting data from the Internet, public databases, cloud services, etc.

[0365] "Data preprocessing means" refers to a means for organizing collected data into a unified format, supplementing missing data, and correcting outliers.

[0366] "Information analysis means" refers to a means of analyzing pre-processed data using machine learning algorithms and statistical analysis to evaluate the suitability of the location and generate a store opening guide map.

[0367] The "result presentation means" is a means for providing the user with the store opening guide map generated as the analysis result via a user interface.

[0368] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0369] A "machine learning algorithm" is an algorithm that learns patterns and regularities from data and makes predictions and classifications.

[0370] "Statistical analysis" is a method of analyzing relationships and patterns in data to derive statistical conclusions.

[0371] The "Store Opening Guide Map" is a map that visually shows the optimal store location based on various data.

[0372] A "user interface" is an interface through which a user inputs information and checks the results.

[0373] The "type of business in which the user wishes to open a store" refers to the type of business in which the user wishes to open a new store.

[0374] "Desired conditions for opening a store" refer to the conditions a user sets when wishing to open a new store.

[0375] This invention is an AI system for optimizing store opening strategies. The system includes a server, a terminal, and a user. The specific functions and usage of each element are explained below.

[0376] System configuration

[0377] server

[0378] The server is designed to perform the following main functions:

[0379] 1. Information collection method: The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specific data sources used include census data, Google Maps API, and public transportation operation information.

[0380] 2. Data preprocessing: The server organizes the collected data into a unified format, fills in missing data, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and converts outliers to the average.

[0381] 3. Information analysis method: Using the preprocessed data, machine learning algorithms and statistical analysis are performed to evaluate the suitability of locations. Specifically, regression analysis and clustering are performed using the Scikit-learn library. Based on the suitability evaluation results, a store opening guide map is generated.

[0382] Terminal

[0383] The terminal is a device that receives information provided by the server and presents it to the user through a user interface.

[0384] 1. Result presentation method: The terminal uses HTML and JavaScript to display the generated store guide map. Markers are placed on the map and related detailed information is displayed as tooltips.

[0385] 2. Interactive features: Users can click on a potential store location on the map to view detailed information (such as demographics, traffic volume, rent, and competition), and provide a form for providing feedback.

[0386] User

[0387] The user is the subject who operates the terminal and interacts with the system.

[0388] 1. Entering the desired business type and conditions: The user enters the desired business type and conditions via the terminal. The following is a specific example.

[0389] I would like to open a new cafe. My desired conditions are as follows:

[0390] city ​​center

[0391] Within a 10-minute walk from a major station

[0392] The maximum rent is 500,000 yen per month

[0393] 2. Check the guide map: Check the store opening guide map sent from the server and consider the best possible location for the store.

[0394] 3. Providing feedback: Evaluate the proposed store location and the usability of the system, and send feedback to the server via the terminal.

[0395] Specific examples

[0396] Consider a case where a user wants to open a new cafe. The user uses a terminal to enter the following prompt sentence:

[0397] I would like to open a new cafe. My desired conditions are as follows:

[0398] city ​​center

[0399] Within a 10-minute walk from a major station

[0400] The maximum rent is 500,000 yen per month

[0401] The server receives this prompt, collects the necessary data, performs preprocessing, and then analyzes it. The resulting store opening guide map is sent to the terminal and presented to the user. The user refers to the guide map and identifies the optimal store location. The user then provides feedback on the proposed location and the system's usability, which is used to improve the system.

[0402] This system effectively collects and analyzes a variety of data to improve the accuracy of aptitude assessments. In addition, by collecting and incorporating user feedback, the system can be continuously improved, leading to the optimization of store opening strategies.

[0403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0404] Step 1: Gather information

[0405] The server obtains local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, it uses census data, Google Maps API, and public transportation information. Based on the region and conditions specified by the user as input, the server builds an appropriate data source. The output is the various collected data that will be used in the next step.

[0406] Step 2: Data Preprocessing

[0407] The server organizes the collected data into a unified format, imputes missing data from the internet or cloud services, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and imputes missing values ​​with the mean or mode. In the case of extremely high rent data, it uses statistical methods to correct outliers. The data collected in step 1 is required as input, and preprocessed data is generated as output.

[0408] Step 3: Data analysis

[0409] The server evaluates the suitability of locations using machine learning algorithms and statistical analysis based on the preprocessed data. Specifically, it uses the Scikit-learn library to apply regression analysis and clustering algorithms. For example, it builds a model to evaluate the commercial suitability of an area using traffic volume, rent, age group data, etc. Preprocessed data is required as input, and the suitability evaluation results and a store opening guide map are generated as output.

[0410] Step 4: Presenting the results

[0411] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. Specifically, it uses HTML and JavaScript to mark candidate locations on the map and displays related detailed information (such as demographics, traffic volume, rent, and competitive situation) as tooltips and detail panels. The inputs are the store opening guide map and evaluation results sent from the server, and the output is visual information provided to the user.

[0412] Step 5: Gather feedback

[0413] The user provides feedback on the presented store opening guide map and proposals. For example, the user enters their opinion via a feedback form, such as "This area has good commercial suitability, but the rent is too high." The terminal then sends this feedback information to the server. The input is the feedback information from the user, and the output is the feedback sent to the server.

[0414] Step 6: Feedback analysis and system improvement

[0415] The server analyzes the collected feedback and identifies areas for improvement in the system. For example, if the analysis results for a particular region do not match user expectations, the data collection method or analysis algorithm for that region can be revised. Based on the analyzed feedback, the system makes improvements that will be reflected in the next analysis. The input is user feedback information, and the output is a revised version of the system.

[0416] Through the above steps, the system can assist users in formulating store opening strategies rationally and efficiently.

[0417] (Application example 1)

[0418] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0419] The present invention solves the problem in the technical field of optimizing store opening strategies: conventional methods were unable to effectively utilize large amounts of data, making it difficult to quickly and accurately identify optimal store locations. Furthermore, there was a lack of means to properly analyze this data and provide it to users in an easy-to-understand manner, resulting in a high likelihood of store opening risks and misjudgments. Furthermore, there was a lack of a mechanism for incorporating user feedback into the system, making it difficult to continuously improve the system's accuracy.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0421] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers; a data analysis means for analyzing the preprocessed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results; a result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; a feedback collection means for collecting feedback from users and using it to improve the system; and a visualization means for visually displaying the scoring of potential store locations. This enables rapid analysis of large amounts of data and visually suggesting optimal store locations. Furthermore, the system can be continuously improved based on user feedback, thereby increasing the accuracy of store opening strategies.

[0422] "Data collection means" refers to means for collecting local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0423] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0424] The "data analysis method" is a method for analyzing pre-processed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results. The data is analyzed using statistical analysis and machine learning algorithms.

[0425] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0426] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0427] The "visualization means" is a means for visually displaying the scoring of a potential store location.

[0428] This invention relates to an AI system for optimizing store opening strategies. This system derives optimal store locations by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection.

[0429] 1. Data Collection

[0430] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, this data is obtained through APIs. For example, population data includes the population size of a specified area and its population distribution by age group, while traffic volume data includes the flow of people at major stations and bus stops, and frequency of road use.

[0431] 2. Data Preprocessing

[0432] The server organizes the collected data, fills in missing data, and corrects outliers. For example, if the latest traffic volume data or information on newly built properties is lacking, it can be supplemented from the internet or cloud services. It also adjusts outliers such as extremely high rents or low traffic volume data to values ​​closer to the average. Data preprocessing is performed using numpy.

[0433] 3. Data Analysis

[0434] The server uses preprocessed data and machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, using KMeans clustering, it evaluates locations with high traffic volume and few competing stores as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0435] 4. Presentation of results

[0436] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check the candidate store locations by looking at the guide map and view detailed information (demographic composition, traffic volume, rent, competitive situation, etc.). The scored locations are visualized using matplotlib.

[0437] 5. Feedback Collection

[0438] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0439] Specific examples

[0440] For example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they want to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0441] Prompt Sentence Examples

[0442] To optimize the location of a store in the city center, please find the best location within a 10-minute walk from a major station with a maximum rent of XXX yen. Please collect and analyze the necessary demographics, traffic volume, accessibility, lifestyle data, and property status data. Please visually display the best store location and the reasons for it.

[0443] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0444] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0445] Step 1: Data collection

[0446] The server collects local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. based on the area and conditions specified by the user. Specifically, it obtains these datasets through API endpoints. It receives the specific area and conditions as input and obtains the collected, unprocessed raw data as output.

[0447] Step 2: Data Preprocessing

[0448] The server organizes the raw data collected in step 1, fills in missing data, and corrects outliers. Specifically, if the latest traffic volume data or new property information is lacking, it fills in the missing data from the internet or cloud services. It also uses numpy to clip extreme values ​​to values ​​closer to the average. It receives raw data as input and obtains preprocessed data as output.

[0449] Step 3: Data analysis

[0450] The server analyzes the data preprocessed in step 2 and evaluates the suitability of locations. Specifically, it uses a machine learning algorithm (e.g., KMeans clustering) to calculate a suitability score for each location. It receives the preprocessed data as input and obtains the suitability evaluation score and analysis results as output.

[0451] Step 4: Presenting the results

[0452] The terminal receives the analysis results provided by the server and presents them to the user via a user interface. Specifically, it uses matplotlib to visually display a store opening guide map and show the suitability score for each location. The terminal receives the analysis results from the server as input and obtains a visually displayed store opening guide map as output.

[0453] Step 5: Gather feedback

[0454] Users provide feedback on the displayed store opening guide map and proposals. Specifically, they input their evaluations and opinions and send them from their terminals to the server. The system receives user feedback as input and obtains collected feedback data as output.

[0455] Step 6: Feedback analysis and system improvement

[0456] The server analyzes the feedback collected in step 5 and uses it for future improvements to the system. Specifically, it analyzes the feedback data and uses it to retrain the machine learning model to improve the accuracy of the system. It receives the feedback data as input and obtains an improved analytical model as output.

[0457] Specific examples

[0458] For example, a user who wants to open a new cafe uses a terminal to input the conditions for opening a cafe (city center, within 10 minutes' walk from a major station, maximum rent, etc.). Based on these conditions, the server collects the necessary data, performs preprocessing, and then analyzes it using a machine learning algorithm. The generated cafe opening guide map is presented to the user, who provides feedback, which the server analyzes and improves the system.

[0459] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0460] This invention combines an emotion engine with an AI system for optimizing store opening strategies. This system combines the methods of data collection, data preprocessing, data analysis, result presentation, and feedback collection, and is equipped with an emotion engine that recognizes user emotions and adjusts the analysis results and recommendations to determine the optimal store location. Each of the main components and their processing are explained below.

[0461] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data collection is done via the Internet, public databases, cloud services, etc. For example, population data includes the population size and age distribution of a specified area, traffic volume data includes the flow of people at major stations and bus stops, access status data includes the usage status of public transportation, lifestyle data includes residents' hobbies and purchasing power, and property status data includes the area, rent, and availability of properties.

[0462] The server then preprocesses the collected data: it cleans it up, fills in gaps, and corrects or removes outliers. For example, outdated traffic data is updated with more recent data, and extremely high rents are adjusted to be closer to the average.

[0463] The server then analyzes the preprocessed data and evaluates the suitability of the location using statistical analysis and machine learning algorithms (regression analysis, clustering, deep learning, etc.). For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable." Based on this, a store opening guide map showing the optimal store locations is generated.

[0464] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. The user can click on a potential location on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal also provides interactive functions, allowing the user to zoom in on the information they need and click to view detailed information.

[0465] Users provide feedback on the presented store opening guide map and the usability of the system. This feedback is sent from the terminal to the server. The server analyzes the collected feedback and uses it to improve the system. This feedback loop allows the system to continuously improve its accuracy.

[0466] Furthermore, the system of the present invention is equipped with an emotion engine. This emotion engine has the function of recognizing the user's emotions from the user's input data and feedback, and adjusting the analysis results and the proposed contents of the store opening guide map based on the user's emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's text input. It is also possible to recognize emotions in real time by analyzing the user's facial expressions and voice through the user interface.

[0467] As a concrete example, consider the case of someone wanting to open a new cafe. Using a terminal, the user inputs the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal location for opening a cafe, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0468] In this way, the system of the present invention allows users to create rational location strategies based on data and emotions, significantly reducing the risks of opening a store.

[0469] The processing flow will be explained below.

[0470] Step 1:

[0471] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0472] Step 2:

[0473] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0474] Step 3:

[0475] The server preprocesses the collected data. First, it organizes the data and fills in any missing parts. If there are any outliers, it corrects or removes them. For example, old traffic volume data is updated with the latest data, and extremely high rents are corrected to values ​​closer to the average.

[0476] Step 4:

[0477] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0478] Step 5:

[0479] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0480] Step 6:

[0481] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via a user interface. The user can view detailed information by clicking on a candidate site on the map. The terminal also provides interactive functions, allowing the user to zoom in on the information they need and display more detailed information.

[0482] Step 7:

[0483] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0484] Step 8:

[0485] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0486] Step 9:

[0487] The server sends the user's input data and feedback to the emotion engine, which uses natural language processing technology to analyze emotions from the user's text input. The device also analyzes the user's facial expressions and voice through the user interface and sends the emotional data to the server.

[0488] Step 10:

[0489] The server receives the analysis results from the emotion engine and dynamically adjusts the analysis results and the store opening guide map suggestions based on the user's emotions. For example, if the user is feeling stressed, the server will prioritize suggestions to open a store in a relaxing location.

[0490] Step 11:

[0491] The device then presents the adjusted store opening guide map and suggestions to the user again, allowing the user to select the optimal store opening location that suits their emotional state.

[0492] Through these steps, the system of the present invention can help users develop rational location strategies based on data, and can also provide more personalized suggestions by taking into account the user's emotional state.

[0493] Example 2

[0494] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0495] Conventional store opening strategy support systems simply provide analysis results based on collected data, and do not provide enough information that takes into account user emotions and feedback. This makes it difficult to create a more effective store opening strategy that reflects user emotions and intuition.

[0496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0497] In this invention, the server includes an information collection means, a data preprocessing means, a data analysis means, a presentation means, a feedback collection means, and a sentiment analysis means, which makes it possible to propose a store opening strategy that takes into account the sentiments of users.

[0498] "Information collection means" is a function that collects information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from various data sources.

[0499] "Data preprocessing means" refers to a function for organizing collected data, supplementing missing data, and correcting or removing outliers.

[0500] "Data analysis means" is a function that analyzes pre-processed data, evaluates the suitability of the location using statistical analysis and machine learning algorithms, and generates a store opening guide map based on the analysis results.

[0501] The "presentation means" is a function for providing the generated store opening guide map to the user and allowing the user to provide feedback.

[0502] The "feedback collection means" is a function for collecting feedback from users and using it to improve the system.

[0503] The "emotion analysis means" is a function for analyzing the user's emotions from the user's input data, feedback, facial expressions, voice, etc., and adjusting the analysis results and proposal content.

[0504] "Communication means" is a function for transmitting input data to a server and receiving a store opening guide map from the server.

[0505] The present invention is a system for optimizing store opening strategies, and includes information collection means, data preprocessing means, data analysis means, presentation means, feedback collection means, and sentiment analysis means. This system collects various local data, processes and analyzes it, and suggests optimal store locations to users. Furthermore, the system is continuously improved by analyzing user feedback and sentiment data.

[0506] First, the server uses information collection methods to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The hardware used for collection uses a general web server and cloud service, and data is automatically acquired using Python libraries (such as requests and BeautifulSoup).

[0507] The server then uses data preprocessing tools to organize the collected data, impute missing data, and correct or remove outliers. Specifically, it creates a data frame using pandas or NumPy, imputes missing data values ​​with the mean or median, and replaces outliers with the mean.

[0508] The server then analyzes the preprocessed data using data analysis tools. It applies statistical analysis and machine learning algorithms (such as scikit-learn regression models, KMeans clustering, and TensorFlow neural network models) to evaluate the suitability of the location. For example, it evaluates locations with high traffic volume and few competing stores as highly suitable and generates a store opening guide map showing the optimal store locations.

[0509] The terminal uses the presentation means to receive the store opening guide map provided by the server and presents it to the user through the user interface. A specific example is to display an interactive map on a web browser, allowing the user to click on a candidate site on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The map is drawn using a JavaScript library (such as Leaflet.js or Google Maps API).

[0510] Users provide feedback on the presented store opening guide map and the usability of the system. The terminal collects this feedback and sends it to the server via a feedback collection means. The server analyzes the collected feedback and uses it to improve the system. The feedback content is analyzed using text analysis tools (such as NLTK and spaCy) to extract specific keywords and emotions.

[0511] Furthermore, the server analyzes the user's emotions using emotion analysis. The server uses natural language processing technology to recognize emotions from the user's input data and feedback. Specifically, it analyzes emotions using an API (e.g., Google Cloud Natural Language API or IBM Watson's emotion analysis API) and adjusts the analysis results and suggestions based on the user's emotions. The device can also use a webcam and microphone to analyze the user's facial expressions and voice in real time and recognize emotions in real time.

[0512] As a specific example of use, consider a user who wants to open a new cafe. The user uses a terminal to input the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). The server collects the necessary data based on this, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting cafe guide map and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal cafe location, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0513] Prompt Sentence Examples

[0514] "Please explain the specific steps for data collection, preprocessing, and data analysis for optimizing store openings using Python's pandas, NumPy, scikit-learn, and TensorFlow. Also, please provide an example of how to collect user feedback and use it to improve the system. Furthermore, please specifically demonstrate how to use an emotion engine to recognize user emotions and adjust the suggestions."

[0515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0516] Step 1: Data collection

[0517] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from public databases and cloud services available on the Internet.

[0518] Input: local keywords or queries

[0519] Processing: We use Python's requests library to collect data from the internet, parse HTML with BeautifulSoup to extract the necessary information, and use cloud service APIs to obtain more detailed data.

[0520] Output: A data frame containing the various regional data collected.

[0521] Step 2: Data Preprocessing

[0522] The server organizes the collected data using data pre-processing means, fills in missing data, and corrects or removes outliers.

[0523] Input: Collected data frame

[0524] Processing: Create a data frame using the pandas library, impute missing values ​​(mean, median, etc.), detect and correct outliers, standardize the data format, and normalize and scale the data.

[0525] Output: Preprocessed data frame

[0526] Step 3: Data analysis

[0527] The server analyzes the pre-processed data using data analysis tools, specifically statistical analysis and machine learning algorithms, to evaluate the suitability of the location.

[0528] Input: Preprocessed data frame

[0529] Processing: Regression analysis using scikit-learn, KMeans clustering, and neural networks using TensorFlow are applied. Factors such as traffic volume, number of stores, and competitive situation are evaluated to calculate a suitability score.

[0530] Output: A store opening guide map generated along with suitability scores

[0531] Step 4: Presenting the results

[0532] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface.

[0533] Input: Store opening guide map and suitability score

[0534] Processing: Use JavaScript libraries (Leaflet.js and Google Maps API) to display an interactive map and allow users to click on potential locations on the map to view more information.

[0535] Output: An interactive store opening guide map displayed on the user interface

[0536] Step 5: Gather feedback

[0537] Users provide feedback on the presented store opening guide map and the usability of the system.

[0538] Input: User feedback (text, selection, rating, etc.)

[0539] Processing: The terminal collects input from the feedback form and sends it to the server.

[0540] Output: Feedback data sent to the server

[0541] Step 6: Analyze feedback and improve the system

[0542] The server analyzes the collected feedback and uses it to improve the system.

[0543] Input: Feedback data from users

[0544] Processing: Analyze the feedback content using text analysis tools (NLTK or spaCy) to extract specific keywords and sentiments. Also, analyze the sentiment of the feedback using sentiment analysis API.

[0545] Output: System improvement instructions based on analysis results

[0546] Step 7: Perform sentiment analysis

[0547] The server uses emotion analysis means to recognize emotions from the user's input data and feedback.

[0548] Input: User text input, facial expression data, voice data

[0549] Processing: Natural language processing technology (Google Cloud Natural Language API and IBM Watson Sentiment Analysis API) is used to analyze emotions from text and adjust the content displayed and suggestions on the user interface as appropriate.

[0550] Output: Sentiment analysis results and tailored recommendations

[0551] (Application example 2)

[0552] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] Conventional store opening strategy optimization systems propose store locations based on regional data analysis, but do not take user emotions into consideration when making proposals. As a result, the proposed store locations may not match the user's wishes or emotions, resulting in low user satisfaction. Furthermore, there was a need for a system that could recognize user emotions in real time and instantly adjust the analysis results and proposals.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0555] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data, a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers, a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results, and an emotion analysis means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and proposal content. This makes it possible to make proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[0556] "Data collection means" refers to means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data.

[0557] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0558] The "data analysis means" is a means for analyzing pre-processed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results.

[0559] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0560] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0561] The "emotion analysis means" is a means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and suggestions.

[0562] The "input means" is a means for inputting the desired business type and desired conditions for opening a store.

[0563] The "transmission and reception means" is a means for transmitting input data to a server and receiving a store opening guide map from the server.

[0564] "Statistical analysis" is a method of analyzing data using statistical techniques in data analysis means.

[0565] A "machine learning algorithm" is an algorithm that analyzes data using machine learning technology in a data analysis means.

[0566] This invention combines an emotion analysis method with an AI system for optimizing store opening strategies. This system makes it possible to propose optimal store locations that take user emotions into consideration. The system configuration and processing are explained below.

[0567] System Configuration

[0568] The system is centered around a server and is composed of data collection means, data preprocessing means, data analysis means, result presentation means, feedback collection means, sentiment analysis means, input means, and transmission / reception means.

[0569] Hardware and software used

[0570] Hardware: A head-mounted display (HMD) is used as the device used by the user.

[0571] Software: TensorFlow (machine learning), NLTK (natural language processing), OpenCV (facial expression recognition), Google Maps API (map display)

[0572] Data collection methods

[0573] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data is obtained from the Internet, public databases, cloud services, etc. For example, the Google Maps API can be used to obtain demographics and traffic volume of key locations.

[0574] Data preprocessing measures

[0575] The collected data is then processed by data pre-processing methods, where missing data is filled in and outliers are corrected or removed, for example, old data is updated to the latest data, and extremely high outliers are corrected to the average value.

[0576] Data Analysis Methods

[0577] Statistical analysis and machine learning algorithms are used to analyze the pre-processed data and evaluate the suitability of locations. For example, TensorFlow is used to build predictive models based on traffic and population data to evaluate optimal store locations.

[0578] Results presentation means

[0579] The terminal (HMD) receives the store opening guide map provided by the server. The user can interactively view the map through the HMD and check detailed information about the candidate site (such as demographics, traffic volume, rent, and competitive situation).

[0580] Feedback collection methods

[0581] Users provide feedback on the guide map and the usability of the system. This feedback is sent to the server and used to improve the system. This feedback loop allows the system to continually improve its accuracy.

[0582] Emotion analysis means

[0583] The emotion analysis means analyzes the user's input data, facial expressions, and voice to recognize emotions. Emotions are analyzed from the user's text input using NLTK, and the user's facial expressions are analyzed using OpenCV. Based on the obtained emotional information, the analysis results and the proposed content of the store opening guide map are adjusted.

[0584] Specific processing flow

[0585] For example, if a user inputs "I want to open a new cafe," this input is analyzed using NLTK to recognize emotions. Furthermore, the user's facial image is captured using an HMD and facial expression analysis is performed using OpenCV. The system then comprehensively evaluates the user's emotions, such as whether they are excited or anxious, and adjusts the suggestions accordingly.

[0586] Example prompts for generative AI models

[0587] "Please provide the coordinates of the area where you would like to open a store and your desired conditions (e.g., maximum rent, distance from major stations, etc.). We will recommend potential locations using AI models and natural language analysis, and adjust the suggestions based on user sentiment."

[0588] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0589] Step 1:

[0590] The user puts on the HMD and inputs the desired conditions for opening a store, such as "I want to open a new cafe." Using the input means, the user inputs the conditions (desired business type, city center location, within 10 minutes' walk from a major station, maximum rent, etc.). The input data is sent to the server via the sending and receiving means. The desired business type and conditions are obtained as input data and sent to the server as output.

[0591] Step 2:

[0592] The server uses data collection means to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. These data are obtained from the Internet, public databases, cloud services, etc. Various data related to the local area are obtained as input data, and the collected data is obtained as output.

[0593] Step 3:

[0594] The server preprocesses the collected data using a data preprocessing means. It organizes the collected data, fills in missing data, and corrects or removes outliers. For example, it updates the traffic volume data to the latest one, and corrects extremely high rents to values ​​closer to the average. The collected data is obtained as input data, and the preprocessed data is obtained as output data.

[0595] Step 4:

[0596] The server analyzes the preprocessed data using data analysis tools. It evaluates the suitability of the location using statistical analysis and machine learning algorithms. For example, it builds a model using TensorFlow and inputs traffic volume and population data to evaluate the optimal store location. The preprocessed data is obtained as input data, and the analysis results are output.

[0597] Step 5:

[0598] The server generates a store opening guide map based on the generated analysis results and sends it to the terminal via the results presentation means. The user can view the map interactively through the HMD and check detailed information about the candidate site (demographic composition, traffic volume, rent, competitive situation, etc.). The analysis results are obtained as input data, and the store opening guide map is obtained as output.

[0599] Step 6:

[0600] Users provide feedback on the store opening guide map and the usability of the system. The feedback is sent from the terminal to the server and collected by the feedback collection means. The user feedback is obtained as input data, and the feedback data is saved in the server as output.

[0601] Step 7:

[0602] The server uses emotion analysis means to analyze the user's input data, facial expressions, and voice to recognize the user's emotions. For example, OpenCV is used to analyze facial expressions from the user's facial video, and NLTK is used to analyze emotions from text input. Based on the obtained emotional information, the server adjusts the analysis results and the proposed content of the store opening guide map. The user's text input and facial expression data are obtained as input data, and the recognized emotional data is obtained as output.

[0603] Step 8:

[0604] Finally, the proposal content that takes into account the emotional information is provided to the user. The proposal content adjusted based on the user's emotions can improve the user's satisfaction. The proposal content before adjustment is obtained as input data, and the final proposal content that reflects the emotional information is displayed on the terminal as output.

[0605] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0606] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0607] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0608] [Third embodiment]

[0609] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0610] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0611] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0612] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0613] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0614] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0615] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0616] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0617] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0618] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0619] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0620] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0621] This invention is an AI system for optimizing store opening strategies. This system derives the optimal store location by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection. Each major component and its processing are explained below.

[0622] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The collected data is obtained via the Internet, public databases, cloud services, etc. Population data includes the population size of the specified region and the population distribution by age group. Traffic volume data includes the flow of people at major stations and bus stops, and the frequency of road use. Access status data indicates the use of public transportation in the region and the ease of access to major facilities. Lifestyle data is used to evaluate residents' hobbies, preferences, and purchasing power, and property status data includes the area, rent, vacancy status, etc. of the property.

[0623] The server then sorts the collected data, fills in any missing data, and corrects any outliers. For example, if the latest traffic volume data or information on newly built properties is missing, it can be supplemented from the internet or cloud services. Correcting outliers involves adjusting extremely high rents or extremely low traffic volume data to values ​​closer to the average.

[0624] The server then performs data analysis. Using the preprocessed data, it uses machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, a location with high traffic volume and few competing stores is rated as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0625] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check potential store locations while looking at the guide map and view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal provides interactive functions, allowing the user to zoom in on the information they need and click to display detailed information.

[0626] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0627] As a concrete example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they would like to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0628] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0629] The processing flow will be explained below.

[0630] Step 1:

[0631] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0632] Step 2:

[0633] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0634] Step 3:

[0635] The server preprocesses the collected data. First, it organizes the data and adds missing data. If there are outliers, it corrects or removes them. For example, if traffic volume data is out of date or property rents are abnormally high, it updates them with the latest data and normalizes the values.

[0636] Step 4:

[0637] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0638] Step 5:

[0639] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0640] Step 6:

[0641] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via the user interface. The user can view detailed information by clicking on the candidate site on the map.

[0642] Step 7:

[0643] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0644] Step 8:

[0645] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0646] Through the above steps, the system of the present invention can assist users in formulating rational location strategies based on data, thereby reducing the risks involved in opening a store.

[0647] Example 1

[0648] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0649] Conventional store opening strategy formulation methods have made it difficult to effectively collect and analyze the diverse data (population data, age group data, traffic volume data, etc.) required to select appropriate locations. Furthermore, the analytical methods for uniformly organizing this data and evaluating suitability were limited, limiting the accuracy of the results. Furthermore, the inability to properly collect and reflect user feedback made it difficult to improve the system and enhance the accuracy of store opening strategies.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0651] In this invention, the server includes: information collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; data preprocessing means for organizing the collected data into a unified format, supplementing missing data from external data sources, and correcting outliers; information analysis means for analyzing the preprocessed data using machine learning algorithms and statistical analysis to evaluate location suitability and generate a store opening guide map based on the analysis results; result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; and feedback collection means for collecting user feedback and using it to improve the system. This enables the effective collection and analysis of diverse data and the improvement of suitability evaluation accuracy. Furthermore, by collecting and incorporating user feedback, the system can be continuously improved and store opening strategies can be optimized.

[0652] "Regional population data" is information about the number and distribution of people living in a specified region.

[0653] "Age group data" is information that classifies residents of a particular area by age.

[0654] "Traffic volume data" refers to information about traffic flow in a specific area over a certain period of time and the frequency of use of transportation means.

[0655] "Accessibility data" refers to information about the use of public transportation in a particular area and the ease of access to major facilities.

[0656] "Lifestyle data" is information related to the hobbies, preferences, purchasing power, and lifestyles of residents living in a particular area.

[0657] "Property status data" refers to information about a specific property, such as area, rent, and availability.

[0658] "Information collection means" refers to the means of collecting data from the Internet, public databases, cloud services, etc.

[0659] "Data preprocessing means" refers to a means for organizing collected data into a unified format, supplementing missing data, and correcting outliers.

[0660] "Information analysis means" refers to a means of analyzing pre-processed data using machine learning algorithms and statistical analysis to evaluate the suitability of the location and generate a store opening guide map.

[0661] The "result presentation means" is a means for providing the user with the store opening guide map generated as the analysis result via a user interface.

[0662] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0663] A "machine learning algorithm" is an algorithm that learns patterns and regularities from data and makes predictions and classifications.

[0664] "Statistical analysis" is a method of analyzing relationships and patterns in data to derive statistical conclusions.

[0665] The "Store Opening Guide Map" is a map that visually shows the optimal store location based on various data.

[0666] A "user interface" is an interface through which a user inputs information and checks the results.

[0667] The "type of business in which the user wishes to open a store" refers to the type of business in which the user wishes to open a new store.

[0668] "Desired conditions for opening a store" refer to the conditions a user sets when wishing to open a new store.

[0669] This invention is an AI system for optimizing store opening strategies. The system includes a server, a terminal, and a user. The specific functions and usage of each element are explained below.

[0670] System configuration

[0671] server

[0672] The server is designed to perform the following main functions:

[0673] 1. Information collection method: The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specific data sources used include census data, Google Maps API, and public transportation operation information.

[0674] 2. Data preprocessing: The server organizes the collected data into a unified format, fills in missing data, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and converts outliers to the average.

[0675] 3. Information analysis method: Using the preprocessed data, machine learning algorithms and statistical analysis are performed to evaluate the suitability of locations. Specifically, regression analysis and clustering are performed using the Scikit-learn library. Based on the suitability evaluation results, a store opening guide map is generated.

[0676] Terminal

[0677] The terminal is a device that receives information provided by the server and presents it to the user through a user interface.

[0678] 1. Result presentation method: The terminal uses HTML and JavaScript to display the generated store guide map. Markers are placed on the map and related detailed information is displayed as tooltips.

[0679] 2. Interactive features: Users can click on a potential store location on the map to view detailed information (such as demographics, traffic volume, rent, and competition), and provide a form for providing feedback.

[0680] User

[0681] The user is the subject who operates the terminal and interacts with the system.

[0682] 1. Entering the desired business type and conditions: The user enters the desired business type and conditions via the terminal. The following is a specific example.

[0683] I would like to open a new cafe. My desired conditions are as follows:

[0684] city ​​center

[0685] Within a 10-minute walk from a major station

[0686] The maximum rent is 500,000 yen per month

[0687] 2. Check the guide map: Check the store opening guide map sent from the server and consider the best possible location for the store.

[0688] 3. Providing feedback: Evaluate the proposed store location and the usability of the system, and send feedback to the server via the terminal.

[0689] Specific examples

[0690] Consider a case where a user wants to open a new cafe. The user uses a terminal to enter the following prompt sentence:

[0691] I would like to open a new cafe. My desired conditions are as follows:

[0692] city ​​center

[0693] Within a 10-minute walk from a major station

[0694] The maximum rent is 500,000 yen per month

[0695] The server receives this prompt, collects the necessary data, performs preprocessing, and then analyzes it. The resulting store opening guide map is sent to the terminal and presented to the user. The user refers to the guide map and identifies the optimal store location. The user then provides feedback on the proposed location and the system's usability, which is used to improve the system.

[0696] This system effectively collects and analyzes a variety of data to improve the accuracy of aptitude assessments. In addition, by collecting and incorporating user feedback, the system can be continuously improved, leading to the optimization of store opening strategies.

[0697] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0698] Step 1: Gather information

[0699] The server obtains local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, it uses census data, Google Maps API, and public transportation information. Based on the region and conditions specified by the user as input, the server builds an appropriate data source. The output is the various collected data that will be used in the next step.

[0700] Step 2: Data Preprocessing

[0701] The server organizes the collected data into a unified format, imputes missing data from the internet or cloud services, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and imputes missing values ​​with the mean or mode. In the case of extremely high rent data, it uses statistical methods to correct outliers. The data collected in step 1 is required as input, and preprocessed data is generated as output.

[0702] Step 3: Data analysis

[0703] The server evaluates the suitability of locations using machine learning algorithms and statistical analysis based on the preprocessed data. Specifically, it uses the Scikit-learn library to apply regression analysis and clustering algorithms. For example, it builds a model to evaluate the commercial suitability of an area using traffic volume, rent, age group data, etc. Preprocessed data is required as input, and the suitability evaluation results and a store opening guide map are generated as output.

[0704] Step 4: Presenting the results

[0705] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. Specifically, it uses HTML and JavaScript to mark candidate locations on the map and displays related detailed information (such as demographics, traffic volume, rent, and competitive situation) as tooltips and detail panels. The inputs are the store opening guide map and evaluation results sent from the server, and the output is visual information provided to the user.

[0706] Step 5: Gather feedback

[0707] The user provides feedback on the presented store opening guide map and proposals. For example, the user enters their opinion via a feedback form, such as "This area has good commercial suitability, but the rent is too high." The terminal then sends this feedback information to the server. The input is the feedback information from the user, and the output is the feedback sent to the server.

[0708] Step 6: Feedback analysis and system improvement

[0709] The server analyzes the collected feedback and identifies areas for improvement in the system. For example, if the analysis results for a particular region do not match user expectations, the data collection method or analysis algorithm for that region can be revised. Based on the analyzed feedback, the system makes improvements that will be reflected in the next analysis. The input is user feedback information, and the output is a revised version of the system.

[0710] Through the above steps, the system can assist users in formulating store opening strategies rationally and efficiently.

[0711] (Application example 1)

[0712] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0713] The present invention solves the problem in the technical field of optimizing store opening strategies: conventional methods were unable to effectively utilize large amounts of data, making it difficult to quickly and accurately identify optimal store locations. Furthermore, there was a lack of means to properly analyze this data and provide it to users in an easy-to-understand manner, resulting in a high likelihood of store opening risks and misjudgments. Furthermore, there was a lack of a mechanism for incorporating user feedback into the system, making it difficult to continuously improve the system's accuracy.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0715] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers; a data analysis means for analyzing the preprocessed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results; a result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; a feedback collection means for collecting feedback from users and using it to improve the system; and a visualization means for visually displaying the scoring of potential store locations. This enables rapid analysis of large amounts of data and visually suggesting optimal store locations. Furthermore, the system can be continuously improved based on user feedback, thereby increasing the accuracy of store opening strategies.

[0716] "Data collection means" refers to means for collecting local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0717] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0718] The "data analysis method" is a method for analyzing pre-processed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results. The data is analyzed using statistical analysis and machine learning algorithms.

[0719] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0720] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0721] The "visualization means" is a means for visually displaying the scoring of a potential store location.

[0722] This invention relates to an AI system for optimizing store opening strategies. This system derives optimal store locations by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection.

[0723] 1. Data Collection

[0724] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, this data is obtained through APIs. For example, population data includes the population size of a specified area and its population distribution by age group, while traffic volume data includes the flow of people at major stations and bus stops, and frequency of road use.

[0725] 2. Data Preprocessing

[0726] The server organizes the collected data, fills in missing data, and corrects outliers. For example, if the latest traffic volume data or information on newly built properties is lacking, it can be supplemented from the internet or cloud services. It also adjusts outliers such as extremely high rents or low traffic volume data to values ​​closer to the average. Data preprocessing is performed using numpy.

[0727] 3. Data Analysis

[0728] The server uses preprocessed data and machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, using KMeans clustering, it evaluates locations with high traffic volume and few competing stores as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0729] 4. Presentation of results

[0730] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check the candidate store locations by looking at the guide map and view detailed information (demographic composition, traffic volume, rent, competitive situation, etc.). The scored locations are visualized using matplotlib.

[0731] 5. Feedback Collection

[0732] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0733] Specific examples

[0734] For example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they want to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0735] Prompt Sentence Examples

[0736] To optimize the location of a store in the city center, please find the best location within a 10-minute walk from a major station with a maximum rent of XXX yen. Please collect and analyze the necessary demographics, traffic volume, accessibility, lifestyle data, and property status data. Please visually display the best store location and the reasons for it.

[0737] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0738] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0739] Step 1: Data collection

[0740] The server collects local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. based on the area and conditions specified by the user. Specifically, it obtains these datasets through API endpoints. It receives the specific area and conditions as input and obtains the collected, unprocessed raw data as output.

[0741] Step 2: Data Preprocessing

[0742] The server organizes the raw data collected in step 1, fills in missing data, and corrects outliers. Specifically, if the latest traffic volume data or new property information is lacking, it fills in the missing data from the internet or cloud services. It also uses numpy to clip extreme values ​​to values ​​closer to the average. It receives raw data as input and obtains preprocessed data as output.

[0743] Step 3: Data analysis

[0744] The server analyzes the data preprocessed in step 2 and evaluates the suitability of locations. Specifically, it uses a machine learning algorithm (e.g., KMeans clustering) to calculate a suitability score for each location. It receives the preprocessed data as input and obtains the suitability evaluation score and analysis results as output.

[0745] Step 4: Presenting the results

[0746] The terminal receives the analysis results provided by the server and presents them to the user via a user interface. Specifically, it uses matplotlib to visually display a store opening guide map and show the suitability score for each location. The terminal receives the analysis results from the server as input and obtains a visually displayed store opening guide map as output.

[0747] Step 5: Gather feedback

[0748] Users provide feedback on the displayed store opening guide map and proposals. Specifically, they input their evaluations and opinions and send them from their terminals to the server. The system receives user feedback as input and obtains collected feedback data as output.

[0749] Step 6: Feedback analysis and system improvement

[0750] The server analyzes the feedback collected in step 5 and uses it for future improvements to the system. Specifically, it analyzes the feedback data and uses it to retrain the machine learning model to improve the accuracy of the system. It receives the feedback data as input and obtains an improved analytical model as output.

[0751] Specific examples

[0752] For example, a user who wants to open a new cafe uses a terminal to input the conditions for opening a cafe (city center, within 10 minutes' walk from a major station, maximum rent, etc.). Based on these conditions, the server collects the necessary data, performs preprocessing, and then analyzes it using a machine learning algorithm. The generated cafe opening guide map is presented to the user, who provides feedback, which the server analyzes and improves the system.

[0753] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0754] This invention combines an emotion engine with an AI system for optimizing store opening strategies. This system combines the methods of data collection, data preprocessing, data analysis, result presentation, and feedback collection, and is equipped with an emotion engine that recognizes user emotions and adjusts the analysis results and recommendations to determine the optimal store location. Each of the main components and their processing are explained below.

[0755] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data collection is done via the Internet, public databases, cloud services, etc. For example, population data includes the population size and age distribution of a specified area, traffic volume data includes the flow of people at major stations and bus stops, access status data includes the usage status of public transportation, lifestyle data includes residents' hobbies and purchasing power, and property status data includes the area, rent, and availability of properties.

[0756] The server then preprocesses the collected data: it cleans it up, fills in gaps, and corrects or removes outliers. For example, outdated traffic data is updated with more recent data, and extremely high rents are adjusted to be closer to the average.

[0757] The server then analyzes the preprocessed data and evaluates the suitability of the location using statistical analysis and machine learning algorithms (regression analysis, clustering, deep learning, etc.). For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable." Based on this, a store opening guide map showing the optimal store locations is generated.

[0758] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. The user can click on a potential location on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal also provides interactive functions, allowing the user to zoom in on the information they need and click to view detailed information.

[0759] Users provide feedback on the presented store opening guide map and the usability of the system. This feedback is sent from the terminal to the server. The server analyzes the collected feedback and uses it to improve the system. This feedback loop allows the system to continuously improve its accuracy.

[0760] Furthermore, the system of the present invention is equipped with an emotion engine. This emotion engine has the function of recognizing the user's emotions from the user's input data and feedback, and adjusting the analysis results and the proposed contents of the store opening guide map based on the user's emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's text input. It is also possible to recognize emotions in real time by analyzing the user's facial expressions and voice through the user interface.

[0761] As a concrete example, consider the case of someone wanting to open a new cafe. Using a terminal, the user inputs the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal location for opening a cafe, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0762] In this way, the system of the present invention allows users to create rational location strategies based on data and emotions, significantly reducing the risks of opening a store.

[0763] The processing flow will be explained below.

[0764] Step 1:

[0765] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0766] Step 2:

[0767] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0768] Step 3:

[0769] The server preprocesses the collected data. First, it organizes the data and fills in any missing parts. If there are any outliers, it corrects or removes them. For example, old traffic volume data is updated with the latest data, and extremely high rents are corrected to values ​​closer to the average.

[0770] Step 4:

[0771] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0772] Step 5:

[0773] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0774] Step 6:

[0775] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via a user interface. The user can view detailed information by clicking on a candidate site on the map. The terminal also provides interactive functions, allowing the user to zoom in on the information they need and display more detailed information.

[0776] Step 7:

[0777] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0778] Step 8:

[0779] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0780] Step 9:

[0781] The server sends the user's input data and feedback to the emotion engine, which uses natural language processing technology to analyze emotions from the user's text input. The device also analyzes the user's facial expressions and voice through the user interface and sends the emotional data to the server.

[0782] Step 10:

[0783] The server receives the analysis results from the emotion engine and dynamically adjusts the analysis results and the store opening guide map suggestions based on the user's emotions. For example, if the user is feeling stressed, the server will prioritize suggestions to open a store in a relaxing location.

[0784] Step 11:

[0785] The device then presents the adjusted store opening guide map and suggestions to the user again, allowing the user to select the optimal store opening location that suits their emotional state.

[0786] Through these steps, the system of the present invention can help users develop rational location strategies based on data, and can also provide more personalized suggestions by taking into account the user's emotional state.

[0787] Example 2

[0788] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0789] Conventional store opening strategy support systems simply provide analysis results based on collected data, and do not provide enough information that takes into account user emotions and feedback. This makes it difficult to create a more effective store opening strategy that reflects user emotions and intuition.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0791] In this invention, the server includes an information collection means, a data preprocessing means, a data analysis means, a presentation means, a feedback collection means, and a sentiment analysis means, which makes it possible to propose a store opening strategy that takes into account the sentiments of users.

[0792] "Information collection means" is a function that collects information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from various data sources.

[0793] "Data preprocessing means" refers to a function for organizing collected data, supplementing missing data, and correcting or removing outliers.

[0794] "Data analysis means" is a function that analyzes pre-processed data, evaluates the suitability of the location using statistical analysis and machine learning algorithms, and generates a store opening guide map based on the analysis results.

[0795] The "presentation means" is a function for providing the generated store opening guide map to the user and allowing the user to provide feedback.

[0796] The "feedback collection means" is a function for collecting feedback from users and using it to improve the system.

[0797] The "emotion analysis means" is a function for analyzing the user's emotions from the user's input data, feedback, facial expressions, voice, etc., and adjusting the analysis results and proposal content.

[0798] "Communication means" is a function for transmitting input data to a server and receiving a store opening guide map from the server.

[0799] The present invention is a system for optimizing store opening strategies, and includes information collection means, data preprocessing means, data analysis means, presentation means, feedback collection means, and sentiment analysis means. This system collects various local data, processes and analyzes it, and suggests optimal store locations to users. Furthermore, the system is continuously improved by analyzing user feedback and sentiment data.

[0800] First, the server uses information collection methods to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The hardware used for collection uses a general web server and cloud service, and data is automatically acquired using Python libraries (such as requests and BeautifulSoup).

[0801] The server then uses data preprocessing tools to organize the collected data, impute missing data, and correct or remove outliers. Specifically, it creates a data frame using pandas or NumPy, imputes missing data values ​​with the mean or median, and replaces outliers with the mean.

[0802] The server then analyzes the preprocessed data using data analysis tools. It applies statistical analysis and machine learning algorithms (such as scikit-learn regression models, KMeans clustering, and TensorFlow neural network models) to evaluate the suitability of the location. For example, it evaluates locations with high traffic volume and few competing stores as highly suitable and generates a store opening guide map showing the optimal store locations.

[0803] The terminal uses the presentation means to receive the store opening guide map provided by the server and presents it to the user through the user interface. A specific example is to display an interactive map on a web browser, allowing the user to click on a candidate site on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The map is drawn using a JavaScript library (such as Leaflet.js or Google Maps API).

[0804] Users provide feedback on the presented store opening guide map and the usability of the system. The terminal collects this feedback and sends it to the server via a feedback collection means. The server analyzes the collected feedback and uses it to improve the system. The feedback content is analyzed using text analysis tools (such as NLTK and spaCy) to extract specific keywords and emotions.

[0805] Furthermore, the server analyzes the user's emotions using emotion analysis. The server uses natural language processing technology to recognize emotions from the user's input data and feedback. Specifically, it analyzes emotions using an API (e.g., Google Cloud Natural Language API or IBM Watson's emotion analysis API) and adjusts the analysis results and suggestions based on the user's emotions. The device can also use a webcam and microphone to analyze the user's facial expressions and voice in real time and recognize emotions in real time.

[0806] As a specific example of use, consider a user who wants to open a new cafe. The user uses a terminal to input the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). The server collects the necessary data based on this, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting cafe guide map and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal cafe location, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[0807] Prompt Sentence Examples

[0808] "Please explain the specific steps for data collection, preprocessing, and data analysis for optimizing store openings using Python's pandas, NumPy, scikit-learn, and TensorFlow. Also, please provide an example of how to collect user feedback and use it to improve the system. Furthermore, please specifically demonstrate how to use an emotion engine to recognize user emotions and adjust the suggestions."

[0809] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0810] Step 1: Data collection

[0811] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from public databases and cloud services available on the Internet.

[0812] Input: local keywords or queries

[0813] Processing: We use Python's requests library to collect data from the internet, parse HTML with BeautifulSoup to extract the necessary information, and use cloud service APIs to obtain more detailed data.

[0814] Output: A data frame containing the various regional data collected.

[0815] Step 2: Data Preprocessing

[0816] The server organizes the collected data using data pre-processing means, fills in missing data, and corrects or removes outliers.

[0817] Input: Collected data frame

[0818] Processing: Create a data frame using the pandas library, impute missing values ​​(mean, median, etc.), detect and correct outliers, standardize the data format, and normalize and scale the data.

[0819] Output: Preprocessed data frame

[0820] Step 3: Data analysis

[0821] The server analyzes the pre-processed data using data analysis tools, specifically statistical analysis and machine learning algorithms, to evaluate the suitability of the location.

[0822] Input: Preprocessed data frame

[0823] Processing: Regression analysis using scikit-learn, KMeans clustering, and neural networks using TensorFlow are applied. Factors such as traffic volume, number of stores, and competitive situation are evaluated to calculate a suitability score.

[0824] Output: A store opening guide map generated along with suitability scores

[0825] Step 4: Presenting the results

[0826] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface.

[0827] Input: Store opening guide map and suitability score

[0828] Processing: Use JavaScript libraries (Leaflet.js and Google Maps API) to display an interactive map and allow users to click on potential locations on the map to view more information.

[0829] Output: An interactive store opening guide map displayed on the user interface

[0830] Step 5: Gather feedback

[0831] Users provide feedback on the presented store opening guide map and the usability of the system.

[0832] Input: User feedback (text, selection, rating, etc.)

[0833] Processing: The terminal collects input from the feedback form and sends it to the server.

[0834] Output: Feedback data sent to the server

[0835] Step 6: Analyze feedback and improve the system

[0836] The server analyzes the collected feedback and uses it to improve the system.

[0837] Input: Feedback data from users

[0838] Processing: Analyze the feedback content using text analysis tools (NLTK or spaCy) to extract specific keywords and sentiments. Also, analyze the sentiment of the feedback using sentiment analysis API.

[0839] Output: System improvement instructions based on analysis results

[0840] Step 7: Perform sentiment analysis

[0841] The server uses emotion analysis means to recognize emotions from the user's input data and feedback.

[0842] Input: User text input, facial expression data, voice data

[0843] Processing: Natural language processing technology (Google Cloud Natural Language API and IBM Watson Sentiment Analysis API) is used to analyze emotions from text and adjust the content displayed and suggestions on the user interface as appropriate.

[0844] Output: Sentiment analysis results and tailored recommendations

[0845] (Application example 2)

[0846] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0847] Conventional store opening strategy optimization systems propose store locations based on regional data analysis, but do not take user emotions into consideration when making proposals. As a result, the proposed store locations may not match the user's wishes or emotions, resulting in low user satisfaction. Furthermore, there was a need for a system that could recognize user emotions in real time and instantly adjust the analysis results and proposals.

[0848] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0849] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data, a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers, a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results, and an emotion analysis means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and proposal content. This makes it possible to make proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[0850] "Data collection means" refers to means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data.

[0851] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[0852] The "data analysis means" is a means for analyzing pre-processed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results.

[0853] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[0854] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0855] The "emotion analysis means" is a means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and suggestions.

[0856] The "input means" is a means for inputting the desired business type and desired conditions for opening a store.

[0857] The "transmission and reception means" is a means for transmitting input data to a server and receiving a store opening guide map from the server.

[0858] "Statistical analysis" is a method of analyzing data using statistical techniques in data analysis means.

[0859] A "machine learning algorithm" is an algorithm that analyzes data using machine learning technology in a data analysis means.

[0860] This invention combines an emotion analysis method with an AI system for optimizing store opening strategies. This system makes it possible to propose optimal store locations that take user emotions into consideration. The system configuration and processing are explained below.

[0861] System Configuration

[0862] The system is centered around a server and is composed of data collection means, data preprocessing means, data analysis means, result presentation means, feedback collection means, sentiment analysis means, input means, and transmission / reception means.

[0863] Hardware and software used

[0864] Hardware: A head-mounted display (HMD) is used as the device used by the user.

[0865] Software: TensorFlow (machine learning), NLTK (natural language processing), OpenCV (facial expression recognition), Google Maps API (map display)

[0866] Data collection methods

[0867] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data is obtained from the Internet, public databases, cloud services, etc. For example, the Google Maps API can be used to obtain demographics and traffic volume of key locations.

[0868] Data preprocessing measures

[0869] The collected data is then processed by data pre-processing methods, where missing data is filled in and outliers are corrected or removed, for example, old data is updated to the latest data, and extremely high outliers are corrected to the average value.

[0870] Data Analysis Methods

[0871] Statistical analysis and machine learning algorithms are used to analyze the pre-processed data and evaluate the suitability of locations. For example, TensorFlow is used to build predictive models based on traffic and population data to evaluate optimal store locations.

[0872] Results presentation means

[0873] The terminal (HMD) receives the store opening guide map provided by the server. The user can interactively view the map through the HMD and check detailed information about the candidate site (such as demographics, traffic volume, rent, and competitive situation).

[0874] Feedback collection methods

[0875] Users provide feedback on the guide map and the usability of the system. This feedback is sent to the server and used to improve the system. This feedback loop allows the system to continually improve its accuracy.

[0876] Emotion analysis means

[0877] The emotion analysis means analyzes the user's input data, facial expressions, and voice to recognize emotions. Emotions are analyzed from the user's text input using NLTK, and the user's facial expressions are analyzed using OpenCV. Based on the obtained emotional information, the analysis results and the proposed content of the store opening guide map are adjusted.

[0878] Specific processing flow

[0879] For example, if a user inputs "I want to open a new cafe," this input is analyzed using NLTK to recognize emotions. Furthermore, the user's facial image is captured using an HMD and facial expression analysis is performed using OpenCV. The system then comprehensively evaluates the user's emotions, such as whether they are excited or anxious, and adjusts the suggestions accordingly.

[0880] Example prompts for generative AI models

[0881] "Please provide the coordinates of the area where you would like to open a store and your desired conditions (e.g., maximum rent, distance from major stations, etc.). We will recommend potential locations using AI models and natural language analysis, and adjust the suggestions based on user sentiment."

[0882] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0883] Step 1:

[0884] The user puts on the HMD and inputs the desired conditions for opening a store, such as "I want to open a new cafe." Using the input means, the user inputs the conditions (desired business type, city center location, within 10 minutes' walk from a major station, maximum rent, etc.). The input data is sent to the server via the sending and receiving means. The desired business type and conditions are obtained as input data and sent to the server as output.

[0885] Step 2:

[0886] The server uses data collection means to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. These data are obtained from the Internet, public databases, cloud services, etc. Various data related to the local area are obtained as input data, and the collected data is obtained as output.

[0887] Step 3:

[0888] The server preprocesses the collected data using a data preprocessing means. It organizes the collected data, fills in missing data, and corrects or removes outliers. For example, it updates the traffic volume data to the latest one, and corrects extremely high rents to values ​​closer to the average. The collected data is obtained as input data, and the preprocessed data is obtained as output data.

[0889] Step 4:

[0890] The server analyzes the preprocessed data using data analysis tools. It evaluates the suitability of the location using statistical analysis and machine learning algorithms. For example, it builds a model using TensorFlow and inputs traffic volume and population data to evaluate the optimal store location. The preprocessed data is obtained as input data, and the analysis results are output.

[0891] Step 5:

[0892] The server generates a store opening guide map based on the generated analysis results and sends it to the terminal via the results presentation means. The user can view the map interactively through the HMD and check detailed information about the candidate site (demographic composition, traffic volume, rent, competitive situation, etc.). The analysis results are obtained as input data, and the store opening guide map is obtained as output.

[0893] Step 6:

[0894] Users provide feedback on the store opening guide map and the usability of the system. The feedback is sent from the terminal to the server and collected by the feedback collection means. The user feedback is obtained as input data, and the feedback data is saved in the server as output.

[0895] Step 7:

[0896] The server uses emotion analysis means to analyze the user's input data, facial expressions, and voice to recognize the user's emotions. For example, OpenCV is used to analyze facial expressions from the user's facial video, and NLTK is used to analyze emotions from text input. Based on the obtained emotional information, the server adjusts the analysis results and the proposed content of the store opening guide map. The user's text input and facial expression data are obtained as input data, and the recognized emotional data is obtained as output.

[0897] Step 8:

[0898] Finally, the proposal content that takes into account the emotional information is provided to the user. The proposal content adjusted based on the user's emotions can improve the user's satisfaction. The proposal content before adjustment is obtained as input data, and the final proposal content that reflects the emotional information is displayed on the terminal as output.

[0899] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0900] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0901] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0902] [Fourth embodiment]

[0903] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0904] 7, a 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.

[0905] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0906] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0907] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0908] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0909] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0910] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0911] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0912] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0913] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0914] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0915] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0916] This invention is an AI system for optimizing store opening strategies. This system derives the optimal store location by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection. Each major component and its processing are explained below.

[0917] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The collected data is obtained via the Internet, public databases, cloud services, etc. Population data includes the population size of the specified region and the population distribution by age group. Traffic volume data includes the flow of people at major stations and bus stops, and the frequency of road use. Access status data indicates the use of public transportation in the region and the ease of access to major facilities. Lifestyle data is used to evaluate residents' hobbies, preferences, and purchasing power, and property status data includes the area, rent, vacancy status, etc. of the property.

[0918] The server then sorts the collected data, fills in any missing data, and corrects any outliers. For example, if the latest traffic volume data or information on newly built properties is missing, it can be supplemented from the internet or cloud services. Correcting outliers involves adjusting extremely high rents or extremely low traffic volume data to values ​​closer to the average.

[0919] The server then performs data analysis. Using the preprocessed data, it uses machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, a location with high traffic volume and few competing stores is rated as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[0920] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check potential store locations while looking at the guide map and view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal provides interactive functions, allowing the user to zoom in on the information they need and click to display detailed information.

[0921] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[0922] As a concrete example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they would like to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[0923] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[0924] The processing flow will be explained below.

[0925] Step 1:

[0926] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[0927] Step 2:

[0928] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[0929] Step 3:

[0930] The server preprocesses the collected data. First, it organizes the data and adds missing data. If there are outliers, it corrects or removes them. For example, if traffic volume data is out of date or property rents are abnormally high, it updates them with the latest data and normalizes the values.

[0931] Step 4:

[0932] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[0933] Step 5:

[0934] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[0935] Step 6:

[0936] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via the user interface. The user can view detailed information by clicking on the candidate site on the map.

[0937] Step 7:

[0938] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[0939] Step 8:

[0940] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[0941] Through the above steps, the system of the present invention can assist users in formulating rational location strategies based on data, thereby reducing the risks involved in opening a store.

[0942] Example 1

[0943] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0944] Conventional store opening strategy formulation methods have made it difficult to effectively collect and analyze the diverse data (population data, age group data, traffic volume data, etc.) required to select appropriate locations. Furthermore, the analytical methods for uniformly organizing this data and evaluating suitability were limited, limiting the accuracy of the results. Furthermore, the inability to properly collect and reflect user feedback made it difficult to improve the system and enhance the accuracy of store opening strategies.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0946] In this invention, the server includes: information collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; data preprocessing means for organizing the collected data into a unified format, supplementing missing data from external data sources, and correcting outliers; information analysis means for analyzing the preprocessed data using machine learning algorithms and statistical analysis to evaluate location suitability and generate a store opening guide map based on the analysis results; result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; and feedback collection means for collecting user feedback and using it to improve the system. This enables the effective collection and analysis of diverse data and the improvement of suitability evaluation accuracy. Furthermore, by collecting and incorporating user feedback, the system can be continuously improved and store opening strategies can be optimized.

[0947] "Regional population data" is information about the number and distribution of people living in a specified region.

[0948] "Age group data" is information that classifies residents of a particular area by age.

[0949] "Traffic volume data" refers to information about traffic flow in a specific area over a certain period of time and the frequency of use of transportation means.

[0950] "Accessibility data" refers to information about the use of public transportation in a particular area and the ease of access to major facilities.

[0951] "Lifestyle data" is information related to the hobbies, preferences, purchasing power, and lifestyles of residents living in a particular area.

[0952] "Property status data" refers to information about a specific property, such as area, rent, and availability.

[0953] "Information collection means" refers to the means of collecting data from the Internet, public databases, cloud services, etc.

[0954] "Data preprocessing means" refers to a means for organizing collected data into a unified format, supplementing missing data, and correcting outliers.

[0955] "Information analysis means" refers to a means of analyzing pre-processed data using machine learning algorithms and statistical analysis to evaluate the suitability of the location and generate a store opening guide map.

[0956] The "result presentation means" is a means for providing the user with the store opening guide map generated as the analysis result via a user interface.

[0957] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[0958] A "machine learning algorithm" is an algorithm that learns patterns and regularities from data and makes predictions and classifications.

[0959] "Statistical analysis" is a method of analyzing relationships and patterns in data to derive statistical conclusions.

[0960] The "Store Opening Guide Map" is a map that visually shows the optimal store location based on various data.

[0961] A "user interface" is an interface through which a user inputs information and checks the results.

[0962] The "type of business in which the user wishes to open a store" refers to the type of business in which the user wishes to open a new store.

[0963] "Desired conditions for opening a store" refer to the conditions a user sets when wishing to open a new store.

[0964] This invention is an AI system for optimizing store opening strategies. The system includes a server, a terminal, and a user. The specific functions and usage of each element are explained below.

[0965] System configuration

[0966] server

[0967] The server is designed to perform the following main functions:

[0968] 1. Information collection method: The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specific data sources used include census data, Google Maps API, and public transportation operation information.

[0969] 2. Data preprocessing: The server organizes the collected data into a unified format, fills in missing data, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and converts outliers to the average.

[0970] 3. Information analysis method: Using the preprocessed data, machine learning algorithms and statistical analysis are performed to evaluate the suitability of locations. Specifically, regression analysis and clustering are performed using the Scikit-learn library. Based on the suitability evaluation results, a store opening guide map is generated.

[0971] Terminal

[0972] The terminal is a device that receives information provided by the server and presents it to the user through a user interface.

[0973] 1. Result presentation method: The terminal uses HTML and JavaScript to display the generated store guide map. Markers are placed on the map and related detailed information is displayed as tooltips.

[0974] 2. Interactive features: Users can click on a potential store location on the map to view detailed information (such as demographics, traffic volume, rent, and competition), and provide a form for providing feedback.

[0975] User

[0976] The user is the subject who operates the terminal and interacts with the system.

[0977] 1. Entering the desired business type and conditions: The user enters the desired business type and conditions via the terminal. The following is a specific example.

[0978] I would like to open a new cafe. My desired conditions are as follows:

[0979] city ​​center

[0980] Within a 10-minute walk from a major station

[0981] The maximum rent is 500,000 yen per month

[0982] 2. Check the guide map: Check the store opening guide map sent from the server and consider the best possible location for the store.

[0983] 3. Providing feedback: Evaluate the proposed store location and the usability of the system, and send feedback to the server via the terminal.

[0984] Specific examples

[0985] Consider a case where a user wants to open a new cafe. The user uses a terminal to enter the following prompt sentence:

[0986] I would like to open a new cafe. My desired conditions are as follows:

[0987] city ​​center

[0988] Within a 10-minute walk from a major station

[0989] The maximum rent is 500,000 yen per month

[0990] The server receives this prompt, collects the necessary data, performs preprocessing, and then analyzes it. The resulting store opening guide map is sent to the terminal and presented to the user. The user refers to the guide map and identifies the optimal store location. The user then provides feedback on the proposed location and the system's usability, which is used to improve the system.

[0991] This system effectively collects and analyzes a variety of data to improve the accuracy of aptitude assessments. In addition, by collecting and incorporating user feedback, the system can be continuously improved, leading to the optimization of store opening strategies.

[0992] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0993] Step 1: Gather information

[0994] The server obtains local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, it uses census data, Google Maps API, and public transportation information. Based on the region and conditions specified by the user as input, the server builds an appropriate data source. The output is the various collected data that will be used in the next step.

[0995] Step 2: Data Preprocessing

[0996] The server organizes the collected data into a unified format, imputes missing data from the internet or cloud services, and corrects outliers. For example, it converts the data into a data frame using Python's Pandas library and imputes missing values ​​with the mean or mode. In the case of extremely high rent data, it uses statistical methods to correct outliers. The data collected in step 1 is required as input, and preprocessed data is generated as output.

[0997] Step 3: Data analysis

[0998] The server evaluates the suitability of locations using machine learning algorithms and statistical analysis based on the preprocessed data. Specifically, it uses the Scikit-learn library to apply regression analysis and clustering algorithms. For example, it builds a model to evaluate the commercial suitability of an area using traffic volume, rent, age group data, etc. Preprocessed data is required as input, and the suitability evaluation results and a store opening guide map are generated as output.

[0999] Step 4: Presenting the results

[1000] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. Specifically, it uses HTML and JavaScript to mark candidate locations on the map and displays related detailed information (such as demographics, traffic volume, rent, and competitive situation) as tooltips and detail panels. The inputs are the store opening guide map and evaluation results sent from the server, and the output is visual information provided to the user.

[1001] Step 5: Gather feedback

[1002] The user provides feedback on the presented store opening guide map and proposals. For example, the user enters their opinion via a feedback form, such as "This area has good commercial suitability, but the rent is too high." The terminal then sends this feedback information to the server. The input is the feedback information from the user, and the output is the feedback sent to the server.

[1003] Step 6: Feedback analysis and system improvement

[1004] The server analyzes the collected feedback and identifies areas for improvement in the system. For example, if the analysis results for a particular region do not match user expectations, the data collection method or analysis algorithm for that region can be revised. Based on the analyzed feedback, the system makes improvements that will be reflected in the next analysis. The input is user feedback information, and the output is a revised version of the system.

[1005] Through the above steps, the system can assist users in formulating store opening strategies rationally and efficiently.

[1006] (Application example 1)

[1007] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1008] The present invention solves the problem in the technical field of optimizing store opening strategies: conventional methods were unable to effectively utilize large amounts of data, making it difficult to quickly and accurately identify optimal store locations. Furthermore, there was a lack of means to properly analyze this data and provide it to users in an easy-to-understand manner, resulting in a high likelihood of store opening risks and misjudgments. Furthermore, there was a lack of a mechanism for incorporating user feedback into the system, making it difficult to continuously improve the system's accuracy.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1010] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers; a data analysis means for analyzing the preprocessed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results; a result presentation means for providing the generated store opening guide map to users and allowing them to provide feedback; a feedback collection means for collecting feedback from users and using it to improve the system; and a visualization means for visually displaying the scoring of potential store locations. This enables rapid analysis of large amounts of data and visually suggesting optimal store locations. Furthermore, the system can be continuously improved based on user feedback, thereby increasing the accuracy of store opening strategies.

[1011] "Data collection means" refers to means for collecting local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[1012] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[1013] The "data analysis method" is a method for analyzing pre-processed data, evaluating the suitability of locations, and generating a store opening guide map based on the analysis results. The data is analyzed using statistical analysis and machine learning algorithms.

[1014] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[1015] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[1016] The "visualization means" is a means for visually displaying the scoring of a potential store location.

[1017] This invention relates to an AI system for optimizing store opening strategies. This system derives optimal store locations by combining data collection, data preprocessing, data analysis, result presentation, and feedback collection.

[1018] 1. Data Collection

[1019] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. Specifically, this data is obtained through APIs. For example, population data includes the population size of a specified area and its population distribution by age group, while traffic volume data includes the flow of people at major stations and bus stops, and frequency of road use.

[1020] 2. Data Preprocessing

[1021] The server organizes the collected data, fills in missing data, and corrects outliers. For example, if the latest traffic volume data or information on newly built properties is lacking, it can be supplemented from the internet or cloud services. It also adjusts outliers such as extremely high rents or low traffic volume data to values ​​closer to the average. Data preprocessing is performed using numpy.

[1022] 3. Data Analysis

[1023] The server uses preprocessed data and machine learning algorithms (regression analysis, clustering, deep learning, etc.) to evaluate the suitability of the location. For example, using KMeans clustering, it evaluates locations with high traffic volume and few competing stores as highly suitable. Based on this analysis, a store opening guide map showing the optimal store locations is generated.

[1024] 4. Presentation of results

[1025] The terminal receives the store opening guide map provided by the server and presents it to the user via the user interface. The user can check the candidate store locations by looking at the guide map and view detailed information (demographic composition, traffic volume, rent, competitive situation, etc.). The scored locations are visualized using matplotlib.

[1026] 5. Feedback Collection

[1027] Users provide feedback on the presented store opening guide map and suggestions. This feedback is sent from the device to the server. The server analyzes the collected feedback and uses it to improve the system in the future. This feedback loop enables the system to continuously improve its accuracy and provide better store opening strategies.

[1028] Specific examples

[1029] For example, consider the case where a user wants to open a new cafe. Using a terminal, the user inputs the area where they want to open the cafe and the conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map and identifies the optimal location for opening a cafe. Finally, the user provides feedback on the proposed location and the usability of the system, and the system uses this feedback to make future improvements.

[1030] Prompt Sentence Examples

[1031] To optimize the location of a store in the city center, please find the best location within a 10-minute walk from a major station with a maximum rent of XXX yen. Please collect and analyze the necessary demographics, traffic volume, accessibility, lifestyle data, and property status data. Please visually display the best store location and the reasons for it.

[1032] In this way, the system of the present invention can help users to create rational location strategies based on data, and significantly reduce the risks of opening a store.

[1033] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1034] Step 1: Data collection

[1035] The server collects local population data, age group data, traffic volume data, accessibility data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc. based on the area and conditions specified by the user. Specifically, it obtains these datasets through API endpoints. It receives the specific area and conditions as input and obtains the collected, unprocessed raw data as output.

[1036] Step 2: Data Preprocessing

[1037] The server organizes the raw data collected in step 1, fills in missing data, and corrects outliers. Specifically, if the latest traffic volume data or new property information is lacking, it fills in the missing data from the internet or cloud services. It also uses numpy to clip extreme values ​​to values ​​closer to the average. It receives raw data as input and obtains preprocessed data as output.

[1038] Step 3: Data analysis

[1039] The server analyzes the data preprocessed in step 2 and evaluates the suitability of locations. Specifically, it uses a machine learning algorithm (e.g., KMeans clustering) to calculate a suitability score for each location. It receives the preprocessed data as input and obtains the suitability evaluation score and analysis results as output.

[1040] Step 4: Presenting the results

[1041] The terminal receives the analysis results provided by the server and presents them to the user via a user interface. Specifically, it uses matplotlib to visually display a store opening guide map and show the suitability score for each location. The terminal receives the analysis results from the server as input and obtains a visually displayed store opening guide map as output.

[1042] Step 5: Gather feedback

[1043] Users provide feedback on the displayed store opening guide map and proposals. Specifically, they input their evaluations and opinions and send them from their terminals to the server. The system receives user feedback as input and obtains collected feedback data as output.

[1044] Step 6: Feedback analysis and system improvement

[1045] The server analyzes the feedback collected in step 5 and uses it for future improvements to the system. Specifically, it analyzes the feedback data and uses it to retrain the machine learning model to improve the accuracy of the system. It receives the feedback data as input and obtains an improved analytical model as output.

[1046] Specific examples

[1047] For example, a user who wants to open a new cafe uses a terminal to input the conditions for opening a cafe (city center, within 10 minutes' walk from a major station, maximum rent, etc.). Based on these conditions, the server collects the necessary data, performs preprocessing, and then analyzes it using a machine learning algorithm. The generated cafe opening guide map is presented to the user, who provides feedback, which the server analyzes and improves the system.

[1048] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1049] This invention combines an emotion engine with an AI system for optimizing store opening strategies. This system combines the methods of data collection, data preprocessing, data analysis, result presentation, and feedback collection, and is equipped with an emotion engine that recognizes user emotions and adjusts the analysis results and recommendations to determine the optimal store location. Each of the main components and their processing are explained below.

[1050] The server first collects regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data collection is done via the Internet, public databases, cloud services, etc. For example, population data includes the population size and age distribution of a specified area, traffic volume data includes the flow of people at major stations and bus stops, access status data includes the usage status of public transportation, lifestyle data includes residents' hobbies and purchasing power, and property status data includes the area, rent, and availability of properties.

[1051] The server then preprocesses the collected data: it cleans it up, fills in gaps, and corrects or removes outliers. For example, outdated traffic data is updated with more recent data, and extremely high rents are adjusted to be closer to the average.

[1052] The server then analyzes the preprocessed data and evaluates the suitability of the location using statistical analysis and machine learning algorithms (regression analysis, clustering, deep learning, etc.). For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable." Based on this, a store opening guide map showing the optimal store locations is generated.

[1053] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface. The user can click on a potential location on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The terminal also provides interactive functions, allowing the user to zoom in on the information they need and click to view detailed information.

[1054] Users provide feedback on the presented store opening guide map and the usability of the system. This feedback is sent from the terminal to the server. The server analyzes the collected feedback and uses it to improve the system. This feedback loop allows the system to continuously improve its accuracy.

[1055] Furthermore, the system of the present invention is equipped with an emotion engine. This emotion engine has the function of recognizing the user's emotions from the user's input data and feedback, and adjusting the analysis results and the proposed contents of the store opening guide map based on the user's emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's text input. It is also possible to recognize emotions in real time by analyzing the user's facial expressions and voice through the user interface.

[1056] As a concrete example, consider the case of someone wanting to open a new cafe. Using a terminal, the user inputs the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). Based on this, the server collects the necessary data, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting guide map for opening a cafe and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal location for opening a cafe, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[1057] In this way, the system of the present invention allows users to create rational location strategies based on data and emotions, significantly reducing the risks of opening a store.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] Users access the system using a terminal. They input the type of business they wish to open (e.g., cafe), the desired area, and other conditions (e.g., city center, within 10 minutes' walk from a major station, maximum rent, etc.). The terminal then sends this input data to the server.

[1061] Step 2:

[1062] The server receives the desired store type and conditions sent by the user, and collects necessary information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from the Internet, public databases, cloud services, etc.

[1063] Step 3:

[1064] The server preprocesses the collected data. First, it organizes the data and fills in any missing parts. If there are any outliers, it corrects or removes them. For example, old traffic volume data is updated with the latest data, and extremely high rents are corrected to values ​​closer to the average.

[1065] Step 4:

[1066] The server performs data analysis using the preprocessed data. Statistical analysis and machine learning algorithms (e.g., regression analysis, clustering, deep learning, etc.) are used to evaluate the suitability of the location. For example, a location with high traffic volume and few nearby competing stores is rated as "highly suitable."

[1067] Step 5:

[1068] The server generates a store opening guide map based on the analysis results. This store opening guide map plots the optimal candidate locations for store openings on a map and includes detailed information about each location (such as demographics, traffic volume, rent, and competitive situation).

[1069] Step 6:

[1070] The server sends the generated store opening guide map to the terminal. The terminal displays the received store opening guide map to the user via a user interface. The user can view detailed information by clicking on a candidate site on the map. The terminal also provides interactive functions, allowing the user to zoom in on the information they need and display more detailed information.

[1071] Step 7:

[1072] The user provides feedback on the presented store opening guide map and the usability of the system. The terminal sends the user's feedback to the server.

[1073] Step 8:

[1074] The server analyzes the collected feedback, and based on the feedback, the system and data analysis algorithms are improved. This feedback loop allows the system to continuously improve its accuracy.

[1075] Step 9:

[1076] The server sends the user's input data and feedback to the emotion engine, which uses natural language processing technology to analyze emotions from the user's text input. The device also analyzes the user's facial expressions and voice through the user interface and sends the emotional data to the server.

[1077] Step 10:

[1078] The server receives the analysis results from the emotion engine and dynamically adjusts the analysis results and the store opening guide map suggestions based on the user's emotions. For example, if the user is feeling stressed, the server will prioritize suggestions to open a store in a relaxing location.

[1079] Step 11:

[1080] The device then presents the adjusted store opening guide map and suggestions to the user again, allowing the user to select the optimal store opening location that suits their emotional state.

[1081] Through these steps, the system of the present invention can help users develop rational location strategies based on data, and can also provide more personalized suggestions by taking into account the user's emotional state.

[1082] Example 2

[1083] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1084] Conventional store opening strategy support systems simply provide analysis results based on collected data, and do not provide enough information that takes into account user emotions and feedback. This makes it difficult to create a more effective store opening strategy that reflects user emotions and intuition.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1086] In this invention, the server includes an information collection means, a data preprocessing means, a data analysis means, a presentation means, a feedback collection means, and a sentiment analysis means, which makes it possible to propose a store opening strategy that takes into account the sentiments of users.

[1087] "Information collection means" is a function that collects information such as local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from various data sources.

[1088] "Data preprocessing means" refers to a function for organizing collected data, supplementing missing data, and correcting or removing outliers.

[1089] "Data analysis means" is a function that analyzes pre-processed data, evaluates the suitability of the location using statistical analysis and machine learning algorithms, and generates a store opening guide map based on the analysis results.

[1090] The "presentation means" is a function for providing the generated store opening guide map to the user and allowing the user to provide feedback.

[1091] The "feedback collection means" is a function for collecting feedback from users and using it to improve the system.

[1092] The "emotion analysis means" is a function for analyzing the user's emotions from the user's input data, feedback, facial expressions, voice, etc., and adjusting the analysis results and proposal content.

[1093] "Communication means" is a function for transmitting input data to a server and receiving a store opening guide map from the server.

[1094] The present invention is a system for optimizing store opening strategies, and includes information collection means, data preprocessing means, data analysis means, presentation means, feedback collection means, and sentiment analysis means. This system collects various local data, processes and analyzes it, and suggests optimal store locations to users. Furthermore, the system is continuously improved by analyzing user feedback and sentiment data.

[1095] First, the server uses information collection methods to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. The hardware used for collection uses a general web server and cloud service, and data is automatically acquired using Python libraries (such as requests and BeautifulSoup).

[1096] The server then uses data preprocessing tools to organize the collected data, impute missing data, and correct or remove outliers. Specifically, it creates a data frame using pandas or NumPy, imputes missing data values ​​with the mean or median, and replaces outliers with the mean.

[1097] The server then analyzes the preprocessed data using data analysis tools. It applies statistical analysis and machine learning algorithms (such as scikit-learn regression models, KMeans clustering, and TensorFlow neural network models) to evaluate the suitability of the location. For example, it evaluates locations with high traffic volume and few competing stores as highly suitable and generates a store opening guide map showing the optimal store locations.

[1098] The terminal uses the presentation means to receive the store opening guide map provided by the server and presents it to the user through the user interface. A specific example is to display an interactive map on a web browser, allowing the user to click on a candidate site on the map to view detailed information (such as demographics, traffic volume, rent, and competitive situation). The map is drawn using a JavaScript library (such as Leaflet.js or Google Maps API).

[1099] Users provide feedback on the presented store opening guide map and the usability of the system. The terminal collects this feedback and sends it to the server via a feedback collection means. The server analyzes the collected feedback and uses it to improve the system. The feedback content is analyzed using text analysis tools (such as NLTK and spaCy) to extract specific keywords and emotions.

[1100] Furthermore, the server analyzes the user's emotions using emotion analysis. The server uses natural language processing technology to recognize emotions from the user's input data and feedback. Specifically, it analyzes emotions using an API (e.g., Google Cloud Natural Language API or IBM Watson's emotion analysis API) and adjusts the analysis results and suggestions based on the user's emotions. The device can also use a webcam and microphone to analyze the user's facial expressions and voice in real time and recognize emotions in real time.

[1101] As a specific example of use, consider a user who wants to open a new cafe. The user uses a terminal to input the desired location and conditions (e.g., city center, within a 10-minute walk from a major station, maximum rent, etc.). The server collects the necessary data based on this, performs preprocessing, and then analyzes it using statistical analysis and machine learning. The terminal receives the resulting cafe guide map and presents it to the user. The user refers to the guide map, and in the process of identifying the optimal cafe location, the emotion engine analyzes the user's emotions and adjusts the suggestions. Finally, the user provides feedback on the proposed location and the usability of the system, and the system is improved based on that feedback.

[1102] Prompt Sentence Examples

[1103] "Please explain the specific steps for data collection, preprocessing, and data analysis for optimizing store openings using Python's pandas, NumPy, scikit-learn, and TensorFlow. Also, please provide an example of how to collect user feedback and use it to improve the system. Furthermore, please specifically demonstrate how to use an emotion engine to recognize user emotions and adjust the suggestions."

[1104] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1105] Step 1: Data collection

[1106] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data from public databases and cloud services available on the Internet.

[1107] Input: local keywords or queries

[1108] Processing: We use Python's requests library to collect data from the internet, parse HTML with BeautifulSoup to extract the necessary information, and use cloud service APIs to obtain more detailed data.

[1109] Output: A data frame containing the various regional data collected.

[1110] Step 2: Data Preprocessing

[1111] The server organizes the collected data using data pre-processing means, fills in missing data, and corrects or removes outliers.

[1112] Input: Collected data frame

[1113] Processing: Create a data frame using the pandas library, impute missing values ​​(mean, median, etc.), detect and correct outliers, standardize the data format, and normalize and scale the data.

[1114] Output: Preprocessed data frame

[1115] Step 3: Data analysis

[1116] The server analyzes the pre-processed data using data analysis tools, specifically statistical analysis and machine learning algorithms, to evaluate the suitability of the location.

[1117] Input: Preprocessed data frame

[1118] Processing: Regression analysis using scikit-learn, KMeans clustering, and neural networks using TensorFlow are applied. Factors such as traffic volume, number of stores, and competitive situation are evaluated to calculate a suitability score.

[1119] Output: A store opening guide map generated along with suitability scores

[1120] Step 4: Presenting the results

[1121] The terminal receives the store opening guide map provided by the server and presents it to the user through a user interface.

[1122] Input: Store opening guide map and suitability score

[1123] Processing: Use JavaScript libraries (Leaflet.js and Google Maps API) to display an interactive map and allow users to click on potential locations on the map to view more information.

[1124] Output: An interactive store opening guide map displayed on the user interface

[1125] Step 5: Gather feedback

[1126] Users provide feedback on the presented store opening guide map and the usability of the system.

[1127] Input: User feedback (text, selection, rating, etc.)

[1128] Processing: The terminal collects input from the feedback form and sends it to the server.

[1129] Output: Feedback data sent to the server

[1130] Step 6: Analyze feedback and improve the system

[1131] The server analyzes the collected feedback and uses it to improve the system.

[1132] Input: Feedback data from users

[1133] Processing: Analyze the feedback content using text analysis tools (NLTK or spaCy) to extract specific keywords and sentiments. Also, analyze the sentiment of the feedback using sentiment analysis API.

[1134] Output: System improvement instructions based on analysis results

[1135] Step 7: Perform sentiment analysis

[1136] The server uses emotion analysis means to recognize emotions from the user's input data and feedback.

[1137] Input: User text input, facial expression data, voice data

[1138] Processing: Natural language processing technology (Google Cloud Natural Language API and IBM Watson Sentiment Analysis API) is used to analyze emotions from text and adjust the content displayed and suggestions on the user interface as appropriate.

[1139] Output: Sentiment analysis results and tailored recommendations

[1140] (Application example 2)

[1141] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1142] Conventional store opening strategy optimization systems propose store locations based on regional data analysis, but do not take user emotions into consideration when making proposals. As a result, the proposed store locations may not match the user's wishes or emotions, resulting in low user satisfaction. Furthermore, there was a need for a system that could recognize user emotions in real time and instantly adjust the analysis results and proposals.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1144] In this invention, the server includes a data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data, a data preprocessing means for organizing the collected data, supplementing missing data, and correcting outliers, a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results, and an emotion analysis means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and proposal content. This makes it possible to make proposals that take the user's emotions into consideration, thereby improving user satisfaction.

[1145] "Data collection means" refers to means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data.

[1146] "Data preprocessing means" refers to means for organizing collected data, supplementing missing data, and correcting outliers.

[1147] The "data analysis means" is a means for analyzing pre-processed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results.

[1148] The "result presentation means" is a means for providing the generated shop opening guide map to the user and allowing the user to provide feedback.

[1149] "Feedback collection means" refers to a means for collecting feedback from users and using it to improve the system.

[1150] The "emotion analysis means" is a means for analyzing the user's input data, facial expressions, and voice, recognizing emotions, and adjusting the analysis results and suggestions.

[1151] The "input means" is a means for inputting the desired business type and desired conditions for opening a store.

[1152] The "transmission and reception means" is a means for transmitting input data to a server and receiving a store opening guide map from the server.

[1153] "Statistical analysis" is a method of analyzing data using statistical techniques in data analysis means.

[1154] A "machine learning algorithm" is an algorithm that analyzes data using machine learning technology in a data analysis means.

[1155] This invention combines an emotion analysis method with an AI system for optimizing store opening strategies. This system makes it possible to propose optimal store locations that take user emotions into consideration. The system configuration and processing are explained below.

[1156] System Configuration

[1157] The system is centered around a server and is composed of data collection means, data preprocessing means, data analysis means, result presentation means, feedback collection means, sentiment analysis means, input means, and transmission / reception means.

[1158] Hardware and software used

[1159] Hardware: A head-mounted display (HMD) is used as the device used by the user.

[1160] Software: TensorFlow (machine learning), NLTK (natural language processing), OpenCV (facial expression recognition), Google Maps API (map display)

[1161] Data collection methods

[1162] The server collects local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. This data is obtained from the Internet, public databases, cloud services, etc. For example, the Google Maps API can be used to obtain demographics and traffic volume of key locations.

[1163] Data preprocessing measures

[1164] The collected data is then processed by data pre-processing methods, where missing data is filled in and outliers are corrected or removed, for example, old data is updated to the latest data, and extremely high outliers are corrected to the average value.

[1165] Data Analysis Methods

[1166] Statistical analysis and machine learning algorithms are used to analyze the pre-processed data and evaluate the suitability of locations. For example, TensorFlow is used to build predictive models based on traffic and population data to evaluate optimal store locations.

[1167] Results presentation means

[1168] The terminal (HMD) receives the store opening guide map provided by the server. The user can interactively view the map through the HMD and check detailed information about the candidate site (such as demographics, traffic volume, rent, and competitive situation).

[1169] Feedback collection methods

[1170] Users provide feedback on the guide map and the usability of the system. This feedback is sent to the server and used to improve the system. This feedback loop allows the system to continually improve its accuracy.

[1171] Emotion analysis means

[1172] The emotion analysis means analyzes the user's input data, facial expressions, and voice to recognize emotions. Emotions are analyzed from the user's text input using NLTK, and the user's facial expressions are analyzed using OpenCV. Based on the obtained emotional information, the analysis results and the proposed content of the store opening guide map are adjusted.

[1173] Specific processing flow

[1174] For example, if a user inputs "I want to open a new cafe," this input is analyzed using NLTK to recognize emotions. Furthermore, the user's facial image is captured using an HMD and facial expression analysis is performed using OpenCV. The system then comprehensively evaluates the user's emotions, such as whether they are excited or anxious, and adjusts the suggestions accordingly.

[1175] Example prompts for generative AI models

[1176] "Please provide the coordinates of the area where you would like to open a store and your desired conditions (e.g., maximum rent, distance from major stations, etc.). We will recommend potential locations using AI models and natural language analysis, and adjust the suggestions based on user sentiment."

[1177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1178] Step 1:

[1179] The user puts on the HMD and inputs the desired conditions for opening a store, such as "I want to open a new cafe." Using the input means, the user inputs the conditions (desired business type, city center location, within 10 minutes' walk from a major station, maximum rent, etc.). The input data is sent to the server via the sending and receiving means. The desired business type and conditions are obtained as input data and sent to the server as output.

[1180] Step 2:

[1181] The server uses data collection means to collect local population data, age group data, traffic volume data, access status data, lifestyle data, and property status data. These data are obtained from the Internet, public databases, cloud services, etc. Various data related to the local area are obtained as input data, and the collected data is obtained as output.

[1182] Step 3:

[1183] The server preprocesses the collected data using a data preprocessing means. It organizes the collected data, fills in missing data, and corrects or removes outliers. For example, it updates the traffic volume data to the latest one, and corrects extremely high rents to values ​​closer to the average. The collected data is obtained as input data, and the preprocessed data is obtained as output data.

[1184] Step 4:

[1185] The server analyzes the preprocessed data using data analysis tools. It evaluates the suitability of the location using statistical analysis and machine learning algorithms. For example, it builds a model using TensorFlow and inputs traffic volume and population data to evaluate the optimal store location. The preprocessed data is obtained as input data, and the analysis results are output.

[1186] Step 5:

[1187] The server generates a store opening guide map based on the generated analysis results and sends it to the terminal via the results presentation means. The user can view the map interactively through the HMD and check detailed information about the candidate site (demographic composition, traffic volume, rent, competitive situation, etc.). The analysis results are obtained as input data, and the store opening guide map is obtained as output.

[1188] Step 6:

[1189] Users provide feedback on the store opening guide map and the usability of the system. The feedback is sent from the terminal to the server and collected by the feedback collection means. The user feedback is obtained as input data, and the feedback data is saved in the server as output.

[1190] Step 7:

[1191] The server uses emotion analysis means to analyze the user's input data, facial expressions, and voice to recognize the user's emotions. For example, OpenCV is used to analyze facial expressions from the user's facial video, and NLTK is used to analyze emotions from text input. Based on the obtained emotional information, the server adjusts the analysis results and the proposed content of the store opening guide map. The user's text input and facial expression data are obtained as input data, and the recognized emotional data is obtained as output.

[1192] Step 8:

[1193] Finally, the proposal content that takes into account the emotional information is provided to the user. The proposal content adjusted based on the user's emotions can improve the user's satisfaction. The proposal content before adjustment is obtained as input data, and the final proposal content that reflects the emotional information is displayed on the terminal as output.

[1194] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1195] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1196] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1197] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1198] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1199] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1200] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1201] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1202] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1203] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1204] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1205] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1206] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1207] 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.

[1208] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1209] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1210] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1211] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1212] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1213] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1214] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1215] The following is further disclosed regarding the above embodiment.

[1216] (Claim 1)

[1217] A data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data;

[1218] a data preprocessing means for organizing the collected data, filling in missing data, and correcting outliers;

[1219] a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results;

[1220] A result presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback;

[1221] a feedback collection means for collecting feedback from users and using it to improve the system;

[1222] A system including:

[1223] (Claim 2)

[1224] An input means for inputting the desired business type and desired conditions for opening a store;

[1225] 2. The system according to claim 1, further comprising a transmitting / receiving means for transmitting the input data to a server and receiving the store opening guide map from the server.

[1226] (Claim 3)

[1227] 2. The system according to claim 1, wherein the data analysis means evaluates the suitability of a location using statistical analysis or a machine learning algorithm.

[1228] "Example 1"

[1229] (Claim 1)

[1230] An information gathering means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data;

[1231] a data preprocessing means for organizing the collected data into a unified format, supplementing missing data from external data sources, and correcting outliers;

[1232] An information analysis means for analyzing the preprocessed data using machine learning algorithms and statistical analysis, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results;

[1233] A result presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback;

[1234] a feedback collection means for collecting feedback from users and using it to improve the system;

[1235] A system including:

[1236] (Claim 2)

[1237] An input means for inputting the desired business type and desired conditions for opening a store;

[1238] 2. The system according to claim 1, further comprising a communication means for transmitting input data to a server and receiving a store opening guide map from the server.

[1239] (Claim 3)

[1240] The system according to claim 1, wherein the data analysis means evaluates the suitability of a location using machine learning algorithms such as regression analysis, clustering, and deep learning.

[1241] "Application Example 1"

[1242] Claims based on the subject matter of a new invention

[1243] (Claim 1)

[1244] A data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data;

[1245] a data preprocessing means for organizing the collected data, filling in missing data, and correcting outliers;

[1246] a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results;

[1247] A result presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback;

[1248] a feedback collection means for collecting feedback from users and using it to improve the system;

[1249] a visualization means for visually displaying the scoring of the potential store location;

[1250] A system including:

[1251] (Claim 2)

[1252] An input means for inputting the desired business type and desired conditions for opening a store;

[1253] 2. The system according to claim 1, further comprising a transmitting / receiving means for transmitting the input data to a server and receiving the store opening guide map from the server.

[1254] (Claim 3)

[1255] The data analysis method uses statistical analysis and machine learning algorithms to evaluate the suitability of the location;

[1256] 10. The system of claim 1, further comprising a prompt generator that collects and analyzes data based on user-specified conditions.

[1257] "Example 2: Combining Emotion Engines"

[1258] (Claim 1)

[1259] An information gathering means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data;

[1260] a data preprocessing means for organizing the collected data, filling in missing data, and correcting outliers;

[1261] a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results;

[1262] A presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback;

[1263] a feedback collection means for collecting feedback from users and using it to improve the system;

[1264] emotion analysis means for analyzing the user's emotions and adjusting the analysis results;

[1265] A system including:

[1266] (Claim 2)

[1267] An input means;

[1268] 2. The system according to claim 1, further comprising a communication means for transmitting input data to a server and receiving a store opening guide map from the server.

[1269] (Claim 3)

[1270] 2. The system according to claim 1, wherein the data analysis means evaluates the suitability of a location using statistical analysis or a machine learning algorithm.

[1271] "Application example 2 when combining emotion engines"

[1272] (Claim 1)

[1273] A data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data;

[1274] a data preprocessing means for organizing the collected data, filling in missing data, and correcting outliers;

[1275] a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results;

[1276] A result presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback;

[1277] a feedback collection means for collecting feedback from users and using it to improve the system;

[1278] emotion analysis means for analyzing input data, facial expressions, and voice of a user, recognizing emotions, and adjusting analysis results and proposal contents;

[1279] A system including:

[1280] (Claim 2)

[1281] An input means for inputting the desired business type and desired conditions for opening a store;

[1282] 2. The system according to claim 1, further comprising a transmitting / receiving means for transmitting the input data to a server and receiving the store opening guide map from the server.

[1283] (Claim 3)

[1284] 10. The system of claim 1, further comprising a data analysis means for evaluating the suitability of a location using statistical analysis and machine learning algorithms. [Explanation of symbols]

[1285] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A data collection means for collecting regional population data, age group data, traffic volume data, access status data, lifestyle data, and property status data; a data preprocessing means for organizing the collected data, filling in missing data, and correcting outliers; a data analysis means for analyzing the preprocessed data, evaluating the suitability of the location, and generating a store opening guide map based on the analysis results; A result presentation means for providing the generated store opening guide map to a user and allowing the user to provide feedback; a feedback collection means for collecting feedback from users and using it to improve the system; A system including:

2. An input means for inputting the desired business type and desired conditions for opening a store; 2. The system according to claim 1, further comprising a transmitting / receiving means for transmitting the input data to a server and receiving the store opening guide map from the server.

3. The system according to claim 1, wherein the data analysis means evaluates the suitability of a location using statistical analysis or a machine learning algorithm.

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