system

Abstract

Provide a system. 【Solution means】 Means for collecting demographic data, Means for collecting location information of competing facilities, Means for collecting floating population data, Means for analyzing the collected demographic data, the location information of competing facilities, and the floating population data, Means for identifying an appropriate site based on the analysis result, Means for calculating rent and operating costs based on the selected site, Means for proposing an optimal operation model, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For the new opening and operation of commercial facilities, there is a problem that the site selection has a great impact on profitability. However, with the conventional site evaluation methods, it is difficult to comprehensively evaluate various factors such as demographics, the layout of competing facilities, and the trends of floating population, and as a result, there is a possibility of overlooking the optimal store opening candidates. Furthermore, regarding the prediction of operating costs and the optimization of the layout with competing facilities, it has been difficult to formulate an efficient strategy because the dispersed data is analyzed individually. There is a need for a system that solves these problems and maximizes the profitability of commercial facilities.

Means for Solving the Problems

[0005] The present invention solves the above problems by the following means: providing a system that includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, the location information of competing facilities, and the circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, and means for proposing an optimal operating model. Furthermore, by including means for integrating the collected demographic data, the location information of competing facilities, and the circulating population data to calculate predicted revenue and means for proposing an optimal competitive placement strategy, the invention realizes efficient and profitable store opening and operation.

[0006] "Demographic data" refers to statistical information such as the age, gender, income, and education level of residents in a specific region.

[0007] "Location information of competing facilities" refers to information indicating the geographical location of other commercial facilities or competing stores located within a specific area.

[0008] "Population mobility data" refers to information about people's movement and stay in specific areas and time periods.

[0009] "Analysis" refers to the process of analyzing collected data using statistical or machine learning methods to derive useful information.

[0010] "Location" refers to the geographical area or place where a new commercial facility is to be opened.

[0011] "Rent" refers to the monetary compensation paid for renting a commercial facility.

[0012] "Operating costs" refer to the total operating expenses necessary for running a commercial facility, including personnel costs, utility costs, equipment costs, and other related expenses.

[0013] An "operational model" refers to the specific strategies and methods for operating a commercial facility.

[0014] "Expected revenue" refers to the revenue that is expected to be obtained in the future based on a specific location and operation model.

[0015] "Competitive layout strategy" refers to the strategy for ensuring competitive superiority by maintaining the positional relationship with competing facilities for commercial facilities.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and based on this data identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy.

[0038] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, and competitive placement strategy means. The processing of each means will be explained below with specific examples.

[0039] 1. Data Collection

[0040] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[0041] Specific example: The server retrieves demographic data such as age distribution and average income for a specific region from a census database. It also uses the Google® Maps API to collect location information for competing stores in that region. Furthermore, it obtains population movement data for a specific area through traffic data and social media APIs.

[0042] 2. Data Analysis

[0043] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[0044] Specific example: The server uses analytical tools such as Python and R to process demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS (Geographic Information System) and analyzes the density of competition. Furthermore, it analyzes traffic volume and social media data to extract peak times for population movement and event information.

[0045] 3. Location Evaluation

[0046] Based on the analysis results, the server identifies the most suitable location for opening a store.

[0047] Specific example: The server integrates demographic, competitor, and traffic data to evaluate the advantages and disadvantages of each candidate location. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime traffic volume, making it suitable for an apparel store."

[0048] 4. Proposed Operating Model

[0049] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[0050] Specific example: The server retrieves the average rent for a given location from a database and uses that information to calculate the balance with operating costs (e.g., personnel costs, management fees). For example, it might propose an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0051] 5. Revenue forecast

[0052] The server predicts future revenue based on integrated data.

[0053] Specific example: The server builds a sales forecasting model and calculates expected revenue for each location. This uses statistical and machine learning models based on current data to provide specific predictions, such as "expected annual revenue for this location will be 5 million yen."

[0054] 6. Competitive Placement Strategy

[0055] The server optimizes its placement relative to nearby competing facilities and proposes strategies to enhance competitiveness.

[0056] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "Opening a store in this area can be expected to create synergistic effects with competing facilities."

[0057] Examples of use

[0058] Users (for example, operators of commercial facilities) access a dashboard provided by the server to view detailed information about potential store locations.

[0059] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. They can also update the data in real time and re-evaluate their choices based on new conditions and circumstances.

[0060] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This, in turn, leads to improved profitability and optimized operational efficiency.

[0061] The following describes the processing flow.

[0062] Step 1:

[0063] Data collection

[0064] The server accesses APIs and databases to collect the necessary data.

[0065] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains data on the mobile population through traffic measurement systems and social media APIs.

[0066] Step 2:

[0067] Data Analysis

[0068] The server analyzes the collected data and extracts useful information.

[0069] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. Simultaneously, it uses GIS software to plot the location data of competing stores on a map and calculate competition density. Furthermore, it analyzes traffic data and social media posts to understand pedestrian flow patterns during specific times of day or events.

[0070] Step 3:

[0071] Location evaluation

[0072] The server identifies the optimal location for a store based on the analysis results.

[0073] Specific operation: The server integrates analyzed demographic data, competitor store information, and pedestrian traffic data to rank multiple potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0074] Step 4:

[0075] Proposed Operating Model

[0076] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0077] Specific operation: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0078] Step 5:

[0079] Revenue forecast

[0080] The server predicts future revenue based on an integrated database.

[0081] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0082] Step 6:

[0083] Proposal for competitive placement strategy

[0084] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0085] Specific operation: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it may propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[0086] Through the above processing steps, the system enables efficient and profitable store opening and operation.

[0087] (Example 1)

[0088] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] To ensure the opening of new commercial facilities, improve operational efficiency, and increase profitability, it is necessary to collect and analyze various data and identify appropriate locations based on the results. However, traditional methods were time-consuming and inefficient in collecting and analyzing the necessary data, and had limitations in accuracy, making it difficult to formulate appropriate store opening strategies. Furthermore, it was difficult to develop strategies that took into account the relationship with competing facilities.

[0090] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0091] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for calculating predicted revenue, and means for proposing a location strategy that takes into account synergies with competing facilities. This makes it possible to efficiently collect and analyze various types of data and formulate a highly accurate store opening strategy. Furthermore, by proposing a location strategy that takes into account synergies with competitors, it becomes possible to formulate a competitive store opening plan.

[0092] "Demographic data" refers to statistical information about the population in a specific region, such as age, gender, number of households, average income, and occupational distribution.

[0093] "Competitor location information" refers to geographical information that indicates the location of competing stores and services in the same industry within a commercial or service facility.

[0094] "Population flow data" refers to information about the movement and retention of people in a specific region or area over a certain period of time, and includes traffic volume, visitor numbers, and the flow of people by time of day.

[0095] "Analysis results" refer to the output of analysis and interpretation based on collected data, and are indicators or visualized data that show the characteristics and trends of a specific location.

[0096] "Appropriate location" refers to the most suitable place for opening or operating a commercial or service facility, based on collected and analyzed data.

[0097] "Rent" refers to the cost required to rent land or a building in a specific location.

[0098] "Operating costs" refer to the expenses incurred in running commercial or service facilities, including personnel costs, management fees, and utility costs.

[0099] An "operational model" is a plan that outlines the optimal operating methods and strategies based on collected and analyzed data.

[0100] "Projected revenue" refers to numerical values ​​or indicators that show the expected future sales and profits, derived from collected and analyzed data.

[0101] "Synergy with competing facilities" refers to the phenomenon where opening a store in a specific location creates cooperation or competition with nearby competing facilities, leading to increased revenue and visitor numbers for both parties.

[0102] A "location strategy" is a set of policies and methods used to determine the optimal location for competing facilities and one's own facilities, based on collected and analyzed data.

[0103] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes various data to formulate effective store opening strategies and enhance the competitiveness of commercial facilities, thereby identifying optimal locations and proposing operational models.

[0104] System Configuration

[0105] The main components of this system include the following means:

[0106] Data acquisition methods

[0107] Data analysis means

[0108] Location evaluation methods

[0109] Operating Model Proposal Methods

[0110] Revenue forecasting methods

[0111] Competitive Deployment Strategies

[0112] These methods are server-centric and perform advanced analysis based on data collected from users and terminals. Furthermore, these methods are primarily implemented using the following specific software and hardware.

[0113] Specific software and hardware to be used

[0114] Databases: Census database, Competitor information database

[0115] APIs: Google Maps API, Traffic Sensor API, Social Media API

[0116] Analysis tools: Python, R, GIS (Geographic Information System)

[0117] Server: High-performance server (e.g., cloud-based server)

[0118] Specific example of processing

[0119] Data collection

[0120] The server uses APIs and databases to collect demographic data, location information of competing facilities, and population flow data.

[0121] Specific example: The server uses the Census API to obtain data on age distribution and average income in a specific area. It uses the Google Maps API to collect location information for competitor stores and uses traffic sensor APIs and social media APIs to gather population movement data for a specific area.

[0122] Data Analysis

[0123] The server uses analytical tools to analyze the collected data, examining regional characteristics, competitive landscape, and population movement trends.

[0124] Specific example: The server uses Python and R to process collected demographic data and understand the characteristics of local residents. It also uses GIS to visualize the locations of competing facilities and analyze the density of competition. Furthermore, it analyzes traffic volume data and social media data to understand the trends of the mobile population.

[0125] Location evaluation

[0126] The server identifies suitable locations for store openings based on the analysis results.

[0127] Specific example: The server integrates demographic data, location information of competing facilities, and pedestrian traffic data to evaluate potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an apparel store."

[0128] Proposed Operating Model

[0129] The server calculates operating costs based on the selected location and proposes the optimal operating model.

[0130] Specific example: The server retrieves the average rent for a location from a database and calculates the balance with operating costs (personnel costs, management fees, etc.). It then proposes an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0131] Revenue forecast

[0132] The server predicts future revenue based on integrated data.

[0133] Specific example: The server builds a sales forecasting model and calculates the expected revenue for each location. For example, it provides a specific forecast such as, "The expected annual revenue for this location will be 5 million yen."

[0134] Competitive Placement Strategy

[0135] The server proposes a placement strategy in relation to nearby competing facilities.

[0136] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "If we open a store in this area, we can expect synergistic effects with competing facilities."

[0137] Examples of prompt statements

[0138] Example: "Obtain the population distribution of people in their 20s and 30s in a specific region, and use this data to propose potential locations for apparel stores."

[0139] Specific example: "Please provide an operational model and revenue forecast for a potential new store location. Evaluate it based on collected demographic data, location information of competing facilities, and population flow data."

[0140] This invention enables commercial facility operators (users) to formulate efficient and precise store opening strategies. This leads to improved profitability and optimized operational efficiency.

[0141] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0142] Step 1:

[0143] Data collection

[0144] Input: Regional information (e.g., coordinates of the target area), data collection conditions (e.g., age distribution, number of competitors)

[0145] The server accesses APIs and databases to collect necessary demographic data, location information for competing facilities, and population flow data.

[0146] Specific actions:

[0147] Demographic data collection: Send requests to the Census API to obtain data on age distribution and average income for a specific region.

[0148] Gathering location information for competitor facilities: Use the Google Maps API to obtain location information for competitor facilities within the area.

[0149] Collection of population flow data: Use traffic sensor APIs and social media APIs to collect population flow data for specific areas.

[0150] Output: Collected demographic data, location information of competing facilities, and population flow data.

[0151] Step 2:

[0152] Data Analysis

[0153] Input: Collected demographic data, location information of competing facilities, and population flow data.

[0154] The server processes the collected data using analytical tools to analyze regional characteristics, competitive landscape, and population movement trends.

[0155] Specific actions:

[0156] Demographic Data Analysis: Using Python or R, collect demographic data is statistically analyzed to understand the characteristics of local residents.

[0157] Competitive facility situation analysis: Use GIS to visualize the location information of competing facilities and analyze the density and influence of competition.

[0158] Analysis of population flow data: Analyze traffic volume data and social media data to extract peak times and patterns of population flow.

[0159] Output: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[0160] Step 3:

[0161] Location evaluation

[0162] Input: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[0163] The server identifies suitable locations for store openings based on the analysis results.

[0164] Specific actions:

[0165] Data Integration: The collected and analyzed data is integrated to evaluate each candidate site.

[0166] Generating evaluation results: Generate specific evaluation results such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime foot traffic, making it suitable for apparel stores."

[0167] Output: List of suitable store locations, evaluation results for each location

[0168] Step 4:

[0169] Proposed Operating Model

[0170] Input: List of suitable potential store locations, evaluation results for each location

[0171] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[0172] Specific actions:

[0173] Acquiring rental data: Obtain rental market rates for the relevant area from the database.

[0174] Cost calculation: Calculate by balancing operating costs (e.g., personnel costs, administrative costs, etc.).

[0175] Proposed operating model: We propose an operating model that states, "The monthly rent in this area is 200,000 yen, labor costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0176] Output: Optimal operating model, detailed operating costs

[0177] Step 5:

[0178] Revenue forecast

[0179] Input: List of suitable store locations, evaluation results for each location, optimal operating model, and detailed operating costs.

[0180] The server predicts future revenue based on integrated data.

[0181] Specific actions:

[0182] Building Predictive Models: Build revenue prediction models using machine learning models and statistical models.

[0183] Revenue forecast: Calculates expected revenue for each location. Provides specific forecasts such as, "Expected annual revenue for this location is 5 million yen."

[0184] Output: Predicted revenue for each location

[0185] Step 6:

[0186] Competitive Placement Strategy

[0187] Input: List of suitable store locations, evaluation results for each location, optimal operating model, detailed operating costs, and projected revenue for each location.

[0188] The server proposes a placement strategy that takes into account synergies with competing facilities.

[0189] Specific actions:

[0190] Competitive data simulation: Simulations are performed based on the location data of competing facilities.

[0191] Strategic proposal: Develop a specific location strategy, such as "Opening a store in this area is expected to create synergistic effects with competing facilities."

[0192] Output: Optimal competitive placement strategy

[0193] (Application Example 1)

[0194] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] In modern commercial facility development and operation, effectively collecting and analyzing large amounts of data to make quick and accurate decisions is essential for appropriate location selection and profitability improvement. Therefore, features such as real-time data provision and visualization, and voice assistant guidance are required. Conventional systems fail to adequately meet these needs, resulting in challenges in optimizing operational efficiency and profitability.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0197] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, the location information of competing facilities, and the circulating population data, means for identifying a suitable location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for providing data in real time via a user interface, means for guiding the user with a voice assistant based on the collected data, and means for visualizing the collected data using augmented reality technology. This enables commercial facility operators to support their decision-making regarding new store openings in real time.

[0198] "Demographic data" refers to statistical information about the population of a specific region or group, such as age, sex, occupation, and income.

[0199] "Competitor facility location information" refers to information indicating the geographical location of competing commercial facilities located within a specific area.

[0200] "Population mobility data" refers to information about the number of people moving within a specific region within a certain period of time, as well as their movement routes.

[0201] "Analysis means" refers to methods or devices used to analyze specific conditions and trends based on collected data and derive results.

[0202] "Means for determining location" refers to methods or devices for determining an appropriate store location based on the results of the analysis.

[0203] "Means for calculating rent and operating costs" refers to methods or devices for calculating the market rent and operating costs for a selected location.

[0204] "Means of proposing an operating model" refers to methods and devices for demonstrating the optimal management method based on calculated rent and operating costs.

[0205] "Means of providing data in real time" refers to methods or devices for immediately presenting collected and analyzed data to users via a user interface.

[0206] "Means of guiding users with voice assistants" refers to methods and devices for conveying analysis results and important information to users by voice.

[0207] "Means of visualizing collected data using augmented reality technology" refers to methods and devices for visually presenting collected data to users using technology that overlays it onto a three-dimensional space.

[0208] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data. Based on this data, it identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy. In particular, it features support functions that enable stress-free and immediate decision-making by providing real-time data, visualization, and guidance via voice assistants.

[0209] Required hardware and software

[0210] Hardware:

[0211] Smart glasses: A device that collects and visualizes data in real time using a camera.

[0212] Server: A computer system that functions as the central hub for data collection, analysis, and storage.

[0213] GPS sensor: A device for accurately acquiring location information.

[0214] software:

[0215] Google Cloud Vision API: A service for image processing and label detection.

[0216] Geopy: A library for obtaining location information.

[0217] Pandas: A Python library for data analysis and manipulation.

[0218] pyttsx3: A library for audio output.

[0219] arpy: A library for visualizing data using augmented reality technology.

[0220] System operation

[0221] 1. Data collection:

[0222] The server uses the Google Cloud Vision API to acquire data on competing facilities and traffic volume based on images obtained from smart glasses. Location information is collected using a GPS sensor and the Geopy library.

[0223] 2. Data Analysis:

[0224] The server uses Pandas to analyze the acquired location information, demographic data, and population movement data of competing facilities to gain a detailed understanding of regional characteristics.

[0225] 3. Data provided by:

[0226] When a user wears smart glasses and speaks a voice command such as "Check the profitability of this area," the server provides analysis results in real time and guides the user via voice using pyttsx3. Furthermore, visualized data is overlaid on the smart glasses' display using augmented reality (AR) technology.

[0227] Specific example

[0228] For example, when considering the possibility of opening a store in a shopping area, a user can wear smart glasses, take photos of their surroundings, and inquire via voice command, "Check the profitability of this area." The server immediately begins analysis, notifying the user of the results via voice while visualizing the data and displaying it on the smart glasses' screen. This allows the user to grasp detailed location information and profit forecasts on the spot, enabling efficient decision-making.

[0229] Example of a prompt

[0230] "Analyze the following image and evaluate the profitability of the store. Calculate a score based on data on the density of competing stores and the foot traffic."

[0231] "Please collect demographic data, traffic data, and competitor location information to calculate the profitability of opening a store at this location."

[0232] This system allows commercial facility operators to receive real-time support in decision-making regarding new store openings, enabling them to formulate more precise store opening strategies.

[0233] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0234] Step 1:

[0235] The server receives image data acquired from the smart glasses. The user wears the smart glasses and takes pictures of a specific area. This image data is sent to the server. The input is the captured image data, and the output is the image data stored on the server.

[0236] Step 2:

[0237] The server uses the Google Cloud Vision API to analyze the received image data. Specifically, it performs label detection to identify competing facilities and traffic conditions within the image. The input is image data stored on the server, and the output is label data indicating the location information of competing facilities and traffic conditions.

[0238] Step 3:

[0239] The server uses the Geopy library to collect location information obtained from the GPS sensor of the smart glasses. The input is location data from the GPS sensor of the smart glasses, and the output is precise geographical location information for a specific area.

[0240] Step 4:

[0241] The server analyzes this location information, demographic data, and population flow data using Pandas. Specifically, it formats this data and analyzes regional characteristics, competitive landscape, and population flow trends. The input is location information, demographic data, and population flow data of competing facilities, and the output is data on regional characteristics, competitive landscape, and population flow trends as a result of the analysis.

[0242] Step 5:

[0243] The user speaks a voice command to the smart glasses, saying "Check the profitability of this area," and sends this command to the server. The input is a voice command, and the output is an analysis request to the server.

[0244] Step 6:

[0245] After receiving a voice command, the server uses the pyttsx3 library to provide the user with the analysis results via voice. The input is the analysis result data, and the output is a voice notification of the analysis results. Specifically, it provides voice guidance on profitability scores, competitive landscape, and other information.

[0246] Step 7:

[0247] The server simultaneously uses the arpy library to visualize the collected data in augmented reality and overlays the results onto the smart glasses' field of view. The input is the analyzed data, and the output is the visualized data on the smart glasses' display. Specifically, regional characteristics, the locations of competing facilities, and the movement of the circulating population are visually displayed.

[0248] This allows users to access detailed location information and revenue forecasts in real time, enabling them to make more efficient decisions.

[0249] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0250] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. Furthermore, by combining it with an emotion engine that recognizes user emotions, it enables the construction of more accurate store opening strategies. This system utilizes user emotion data in addition to collecting and analyzing demographic data, location information of competing facilities, and population flow data.

[0251] The main components of the system and their operation

[0252] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, competitive placement strategy means, and an emotion engine. The processing of each means will be explained below with specific examples.

[0253] 1. Data Collection

[0254] The server accesses APIs and databases to collect the necessary data.

[0255] Specific example: The server calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. Furthermore, it obtains population flow data through traffic measurement systems and social media APIs.

[0256] 2. Data Analysis

[0257] The server analyzes the collected data to understand regional characteristics, competitive landscape, and population movement trends.

[0258] Specific example: The server uses analytical tools such as Python and R to analyze demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS and calculates competition density. Furthermore, it analyzes traffic volume data and social media posts to extract peak times and event information for the mobile population.

[0259] 3. Location Evaluation

[0260] The server identifies the optimal location for a store based on the analysis results.

[0261] Specific example: The server integrates analyzed demographic data, competitor information, and population flow data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime population flow, making it suitable for an entertainment shop."

[0262] 4. Proposed Operating Model

[0263] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0264] Specific example: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0265] 5. Revenue forecast

[0266] The server predicts future revenue based on an integrated database.

[0267] Specific example: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0268] 6. Proposed competitive placement strategy

[0269] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0270] Specific example: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it might propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[0271] 7. Introduction of an emotional engine

[0272] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[0273] Specific example: The emotion engine installed in the device analyzes user feedback and social media posts to understand the user's emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[0274] 8. Adjusting location and operational models based on emotional data

[0275] The server dynamically adjusts its location and operating model based on emotional data obtained from the emotion engine.

[0276] Specific example: The server analyzes user sentiment data and, based on information such as "users in this area have a strong interest in a particular service," changes the location of the store and the content of the services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added as a candidate for opening a cafe.

[0277] Examples of use

[0278] Users (for example, commercial facility operators) can access a dashboard provided by the server to view detailed information about potential store locations. They can also review user sentiment data collected by the sentiment engine, allowing them to refine their store opening strategies with greater precision.

[0279] The user examines the operation model, revenue prediction, and competitive placement strategy proposed by the server and selects the most suitable store location. It is also possible to update data in real time and re-evaluate according to new conditions and situations.

[0280] According to the present invention, decision-making related to the opening and operation of commercial facilities can be made more efficiently and accurately. As a result, not only can profitability be improved and operation efficiency be optimized, but by utilizing the user's emotional data, further improvement in customer satisfaction can also be expected.

[0281] The following describes the process flow.

[0282] Step 1:

[0283] Data collection

[0284] The server accesses APIs and databases to collect demographic data, location information of competing facilities, and floating population data.

[0285] Specific operations: The server calls the API of the national census to obtain demographic data such as age distribution, household income, and education level for each region. Next, it uses the Google Maps API to collect the location information of competing stores within the specified area. Also, it obtains the floating population data of a specific area through a traffic volume measurement system and social media APIs.

[0286] Step 2:

[0287] Data analysis

[0288] The server analyzes the collected data to analyze the characteristics of the region, the competitive situation, and the trends of the floating population.

[0289] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. It uses GIS software to plot the location data of competing stores on a map and calculates competition density. It also analyzes traffic volume data and social media posts to understand pedestrian flow patterns during specific times of day and events.

[0290] Step 3:

[0291] Location evaluation

[0292] The server identifies the optimal location for a store based on the analysis results.

[0293] Specific operation: The server integrates analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0294] Step 4:

[0295] Proposed Operating Model

[0296] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0297] Specific operation: The server retrieves rental market rates for each region from a real estate database and calculates operating costs (e.g., personnel costs, utility costs) based on that information. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0298] Step 5:

[0299] Revenue forecast

[0300] The server predicts future revenue based on integrated data.

[0301] Specific actions: The server makes predictions from historical data using a machine learning model and calculates the expected revenue for each candidate location. For example, it provides revenue predictions such as "The expected annual revenue at this location will be 5 million yen."

[0302] Step 6:

[0303] Proposed competitive layout strategy

[0304] The server formulates a strategy to strengthen competitiveness based on the layout information of competing facilities.

[0305] Specific actions: The server analyzes the collected location information of competing facilities and formulates strategies such as "Opening a store in this area will maximize the synergy with existing competing facilities." For example, it proposes measures to enhance the customer attraction of its own store by opening a store in a place with few competing stores of the same business type and high foot traffic.

[0306] Step 7:

[0307] Data collection for the emotion engine

[0308] The terminal collects users' emotion data using the emotion engine.

[0309] Specific actions: The terminal analyzes feedback provided by the user and social media posts, etc., to recognize the user's emotions. For example, it extracts positive emotions or dissatisfaction towards a specific store.

[0310] Step 8:

[0311] Analysis of emotion data

[0312] The server analyzes the users' emotion data obtained from the emotion engine.

[0313] Specific operation: The server statistically analyzes the collected sentiment data to understand which services and products users have positive or negative feelings towards. For example, it might derive information such as, "There are many positive reactions to cafes in this area."

[0314] Step 9:

[0315] Strategy adjustment based on emotional data

[0316] The server dynamically adjusts its location and operational model based on the analyzed sentiment data.

[0317] Specific operation: The server changes the location of the store and the services offered based on sentiment data. For example, if there is a lot of positive feedback for the cafe in a particular area, that area will be added as a candidate for opening a cafe, and the operating model will be adapted accordingly.

[0318] The specific processing steps described above enable the system to achieve efficient and profitable store openings and operations. Furthermore, by combining it with an emotion engine, it is possible to build strategies tailored to user needs and emotions, thereby improving customer satisfaction.

[0319] (Example 2)

[0320] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0321] In opening and operating new commercial facilities, there is a need to achieve increased efficiency and profitability. Conventional systems were limited to collecting and analyzing demographic data, location information of competing facilities, and population flow data, and strategies were often formulated without considering user sentiment data, resulting in a lack of accuracy. Furthermore, dynamic adjustments were difficult when identifying optimal locations and proposing operating models.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for collecting and analyzing sentiment data, and means for adjusting the location and operating model based on the sentiment data. As a result, a highly accurate store opening strategy can be formulated based on the collected data, and user satisfaction can be improved by utilizing sentiment data.

[0324] "Demographic data" refers to statistical information such as population composition, age distribution, household composition, and income levels for each region.

[0325] "Location information of competing facilities" refers to the geographical information of where commercial facilities are located and the attribute information of those stores.

[0326] "Population mobility data" refers to information on the movement and stay of people within a specific area over a certain period of time.

[0327] "Means of analysis" refers to methods and tools for processing and analyzing collected data and extracting useful information.

[0328] "Means of identifying suitable locations" refers to methods and tools for determining the most advantageous store locations based on analytical data.

[0329] "Means for calculating rent and operating costs" refers to methods and tools for calculating the rent and various operating expenses necessary for real estate in a specific location.

[0330] "Means of proposing an operating model" refers to methods and tools for proposing the optimal operating method and revenue model based on calculated costs.

[0331] "Emotional data" refers to information obtained by analyzing users' emotions, opinions, feedback, and other psychological responses.

[0332] "Means for collecting and analyzing emotional data" refers to methods and tools for measuring users' emotions, analyzing them, and converting them into valuable data.

[0333] "Means of adjusting location and operating models based on emotional data" refers to methods and tools for dynamically modifying and optimizing potential store locations and operating methods by taking emotional data into consideration.

[0334] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and further combines this with an emotion engine to enable the construction of more accurate store opening strategies.

[0335] System components and their operation

[0336] 1. Data Collection

[0337] The server accesses APIs and databases to collect necessary data. Specifically, it calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. It also obtains population movement data through traffic measurement systems and social media APIs.

[0338] 2. Data Analysis

[0339] The server analyzes collected data to understand regional characteristics, competitive landscape, and population movement trends. Using analytical tools such as Python and R, it analyzes demographic data to understand the characteristics of local residents. Furthermore, it visualizes the locations of competing facilities using GIS and calculates competition density. Traffic data and social media posts are analyzed to extract peak times for population movement and event information.

[0340] 3. Location Evaluation

[0341] The server identifies the optimal location for a store based on the analysis results. It integrates the analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0342] 4. Proposed Operating Model

[0343] The server calculates rent and operating costs based on identified candidate locations and proposes an operating model. It obtains rental market rates for each region from a real estate database and combines them with data on necessary operating costs (personnel costs, utilities, etc.) to calculate the total cost. For example, it might suggest, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0344] 5. Revenue forecast

[0345] The server predicts future revenue based on integrated data. Using machine learning models, it makes predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides specific revenue predictions such as, "This location will have an expected annual revenue of 5 million yen."

[0346] 6. Proposed competitive placement strategy

[0347] The server develops strategies to strengthen competitiveness based on the location information of competing facilities. It analyzes the collected location information of competing facilities and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." It proposes measures to increase the store's ability to attract customers by opening stores in areas with few competing stores of the same type and high foot traffic.

[0348] 7. Introduction of an emotional engine

[0349] The server uses an emotion engine to collect user emotion data and uses it for analysis. The emotion engine analyzes user feedback and social media posts to understand user emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[0350] 8. Adjusting location and operational models based on emotional data

[0351] The server dynamically adjusts location and operating model based on emotional data obtained from the emotion engine. By analyzing user emotional data and using information such as "users in this area have a strong interest in a particular service," it changes the location of stores and the content of services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added to the list of potential cafe locations.

[0352] Example of a prompt

[0353] "Please suggest potential locations for an entertainment shop in an area with a high concentration of young people in their 20s and 30s, and a high nighttime foot traffic."

[0354] "Based on the collected data, please propose an operational model to optimize the café opening strategy in a specific area."

[0355] This system will enable more efficient and accurate decision-making regarding the opening and operation of new stores in commercial facilities. Furthermore, by utilizing emotional data, it is expected to improve customer satisfaction.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] Data collection

[0359] The server collects the data necessary for opening a store in a commercial facility.

[0360] Input: Data from the Census API, Google Maps API, traffic measurement systems, and social media APIs.

[0361] Output: Demographic data, location information of competing facilities, and population flow data.

[0362] Specific operation: The server uses the requests library to call the census API to obtain demographic data in JSON format. Similarly, it uses the Google Maps API to obtain location information for competing stores in each region and accesses the traffic volume measurement system API to collect population flow data.

[0363] Step 2:

[0364] Data Analysis

[0365] The server analyzes the collected data.

[0366] Input: Collected demographic data, location information of competing facilities, and population flow data.

[0367] Output: Analysis results regarding regional characteristics, competitive landscape, and trends in population movement.

[0368] Specific operation: The server uses the Pandas library to convert demographic data into a dataframe and analyze the characteristics of local residents. It also uses GeoPandas and Shapely to calculate competition density and visualize it on a map. For traffic data, time series analysis is performed to extract peak times and event information.

[0369] Step 3:

[0370] Location evaluation

[0371] The server identifies the optimal location for a store based on the analysis results.

[0372] Input: Analysis results (regional characteristics, competitive situation, trends in population movement).

[0373] Output: Ranking results for multiple potential store locations.

[0374] Specific operation: The server analyzes the data, scores it, and ranks each candidate location. For example, areas with a large young population and high nighttime foot traffic will receive a high score as potential locations for entertainment shops. This result is displayed on a dashboard.

[0375] Step 4:

[0376] Proposed Operating Model

[0377] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0378] Input: Information on identified potential store locations, rental data obtained from a real estate database, and pre-set operating costs.

[0379] Output: Total operating costs and proposed operating models for each candidate site.

[0380] Specific operation: The server uses a real estate database API to retrieve rental data and calculates the total cost by adding it to operating costs such as personnel expenses and utilities. Based on the results, it presents an operating model such as, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0381] Step 5:

[0382] Revenue forecast

[0383] The server predicts future revenue based on the integrated data.

[0384] Input: Integrated data (demographic data, competitor information, population flow data).

[0385] Output: Expected earnings.

[0386] Specific operation: The server uses the Scikit-learn library to train a machine learning model and build a predictive model based on the collected data. Using that model, it predicts future revenue and provides specific revenue forecasts such as "Expected annual revenue at this location is 5 million yen."

[0387] Step 6:

[0388] Proposal for competitive placement strategy

[0389] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0390] Input: Location information of competing facilities.

[0391] Output: Proposal of competitive placement strategy.

[0392] Specific operation: The server analyzes the location information of competing facilities on a GIS and formulates the optimal strategy for strengthening competitiveness. For example, it plots a strategy such as "opening a store in this area will maximize synergies with existing competing facilities" on a dashboard and presents it visually to the user.

[0393] Step 7:

[0394] Introducing an emotional engine

[0395] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[0396] Input: User feedback, social media posts.

[0397] Output: User sentiment data.

[0398] Specific operation: The emotion engine analyzes user feedback and tags emotions. It also analyzes text data from social media posts and extracts emotion trends.

[0399] Step 8:

[0400] Adjusting location and operating models based on emotional data

[0401] The server dynamically adjusts its location and operating model based on sentiment data.

[0402] Input: Sentiment data.

[0403] Output: Adjusted location and operational model.

[0404] Specific operation: The server statistically analyzes sentiment data to identify user trends in specific regions. Based on this, it re-evaluates potential store locations and the services offered, and displays the updated results on a dashboard. For example, if there is a high level of positive feedback towards cafes in a particular region, that region will be added as a potential location for a cafe.

[0405] Through these steps, the system can comprehensively analyze data and efficiently and accurately support decision-making regarding the opening and operation of commercial facilities. Furthermore, by utilizing user sentiment data, it can also improve customer satisfaction.

[0406] (Application Example 2)

[0407] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0408] To improve the efficiency and profitability of new store openings and operations in commercial facilities, store opening strategies must consider not only demographic and population flow data, but also user sentiment data. However, conventional systems have made it difficult to develop concrete store opening strategies that utilize sentiment data, resulting in challenges in optimizing operations and maximizing profits.

[0409] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for collecting sentiment data, means for analyzing the collected demographic data, location information of competing facilities, circulating population data, and sentiment data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, and means for proposing an optimal operating model. This makes it possible to construct a highly accurate store opening strategy utilizing sentiment data.

[0410] "Demographic data" refers to statistical information about the distribution and attributes of the population in a specific region.

[0411] "Location information of competing facilities" refers to geographical information that shows where competing facilities such as commercial facilities and stores are located.

[0412] "Population mobility data" refers to information about the number of people moving within a specific region and their movement patterns.

[0413] "Sentimental data" refers to information collected from users regarding their feelings and evaluations of specific services or facilities.

[0414] "Means of analysis" refers to methods and tools for analyzing collected data using statistical analysis and machine learning to extract meaningful information.

[0415] "Means of identifying a location" refers to methods and tools for selecting the optimal location for a commercial facility based on analysis results.

[0416] "Means for calculating rent and operating costs" refers to methods or tools for calculating rent and operating costs at a selected location.

[0417] "Means of proposing an operating model" refers to methods and tools for proposing the optimal business operation methods and strategies for a specific location.

[0418] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities, and in particular, to a system that enables the construction of more accurate store opening strategies by utilizing user sentiment data.

[0419] The system consists of the following hardware and software.

[0420] 1. Hardware

[0421] Server: Performs data collection, analysis, and proposes operational models.

[0422] Smartphone or tablet: A device used by store operators to check data and formulate store opening strategies.

[0423] 2. Software

[0424] Data collection APIs (e.g., Google Maps API, traffic volume measurement API, etc.)

[0425] Sentiment analysis engine (e.g., Microsoft® Azure® Cognitive Services)

[0426] Data analysis tools (e.g., Python, R, GIS software)

[0427] System operation

[0428] The system operates using the following steps:

[0429] 1. Data Collection

[0430] The server uses the Google Maps API to collect location information for competing facilities. It also obtains demographic data through the Census API and collects population movement data using the traffic volume measurement API.

[0431] Emotional data is obtained from social media and user feedback and analyzed by an emotion analysis engine.

[0432] 2. Data Analysis

[0433] The server integrates and analyzes collected demographic data, location information of competing facilities, population movement data, and sentiment data. Based on the analysis results, it understands regional characteristics, competitive landscape, and user sentiment trends.

[0434] 3. Location identification and proposal of an operational model

[0435] Based on the analysis results, the server identifies the optimal locations for store openings. The identified locations are presented in a ranking format, along with location evaluations, projected revenues, and operating costs.

[0436] Based on the data displayed on the device, users can consider the optimal operating model.

[0437] Specific example

[0438] For example, when a user considers opening a new store, data is collected and analyzed using the following procedure.

[0439] Prompt message example 1: Obtain location information of competitor stores

[0440] "Please use the Google Maps API to retrieve store information for locations within a 5-kilometer radius of the proposed store location."

[0441] Prompt example 2: Analysis of sentiment data

[0442] "Collect social media posts and analyze the sentiment score for this region."

[0443] Prompt message example 3: Suggestion of the optimal store location

[0444] "Based on demographic data and competitive landscape in this area, please rank the most suitable locations for opening a store."

[0445] This allows users to develop highly accurate store opening strategies based on real-time data. Furthermore, by incorporating emotional data, it is expected to contribute to improved customer satisfaction.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The server collects demographic data, location information of competitor facilities, and population flow data using various APIs. Specifically, it obtains location information of competitor facilities using the Google Maps API and collects demographic data through the Census API. It also obtains population flow data using the Traffic Volume Measurement API. The input for data collection is the API endpoints and parameters, and the output is the data obtained from the APIs.

[0449] Step 2:

[0450] The server collects and analyzes emotional data using an emotion analysis engine. Specifically, it obtains text data from social media and user feedback and inputs it into the emotion analysis engine. This engine analyzes the text data and outputs an emotion score such as positive, negative, or neutral. The input is text data, and the output is an emotion score.

[0451] Step 3:

[0452] The server integrates collected and analyzed demographic data, location information of competing facilities, population movement data, and sentiment data, and performs analysis using data analysis tools. Specifically, it uses Python and R to integrate each dataset and understand regional characteristics, competitive situations, and population movement trends. The input is various datasets, and the output is the analysis results.

[0453] Step 4:

[0454] The server identifies suitable location candidates based on the analysis results and presents them in a ranked format. Specifically, it uses GIS software to perform geographical visualization and ranks the location candidates according to evaluation criteria. Users can view this information on their smartphones or tablets. The input is the analysis results, and the output is a list of ranked location candidates.

[0455] Step 5:

[0456] The server calculates rent and operating costs based on the selected location. Specifically, it retrieves rental market rates for each region from a real estate database and calculates the costs necessary for operation (personnel costs, utilities, etc.). The inputs are location information and real estate data, and the outputs are rent and operating costs.

[0457] Step 6:

[0458] The server proposes the optimal operating model. Based on analysis results and projected revenue data, it presents specific operating models and strategies. Users can review the operating model and make decisions on their smartphones or tablets. Inputs are rent, operating costs, and projected revenue data, and output is a proposed operating model.

[0459] Step 7:

[0460] Based on information provided by the server, users consider potential store locations and make adjustments by incorporating sentiment data. Specifically, they prioritize favorable areas identified by sentiment data and formulate their final store location strategy. The input is the server's suggestions, and the output is the final store location strategy.

[0461] This will enable users to develop highly accurate store opening strategies in real time using emotional data, which is expected to improve customer satisfaction and maximize profitability.

[0462] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0463] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0464] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0465] [Second Embodiment]

[0466] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0467] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0468] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0469] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0470] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0471] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0472] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0473] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0474] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0475] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0476] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0477] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0478] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and based on this data identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy.

[0479] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, and competitive placement strategy means. The processing of each means will be explained below with specific examples.

[0480] 1. Data Collection

[0481] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[0482] Specific example: The server retrieves demographic data such as age distribution and average income for a specific region from a census database. It also uses the Google Maps API to collect location information for competing stores in that region. Furthermore, it obtains population movement data for a specific area through traffic data and social media APIs.

[0483] 2. Data Analysis

[0484] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[0485] Specific example: The server uses analytical tools such as Python and R to process demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS (Geographic Information System) and analyzes the density of competition. Furthermore, it analyzes traffic volume and social media data to extract peak times for population movement and event information.

[0486] 3. Location Evaluation

[0487] Based on the analysis results, the server identifies the most suitable location for opening a store.

[0488] Specific example: The server integrates demographic, competitor, and traffic data to evaluate the advantages and disadvantages of each candidate location. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime traffic volume, making it suitable for an apparel store."

[0489] 4. Proposed Operating Model

[0490] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[0491] Specific example: The server retrieves the average rent for a given location from a database and uses that information to calculate the balance with operating costs (e.g., personnel costs, management fees). For example, it might propose an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0492] 5. Revenue forecast

[0493] The server predicts future revenue based on integrated data.

[0494] Specific example: The server builds a sales forecasting model and calculates expected revenue for each location. This uses statistical and machine learning models based on current data to provide specific predictions, such as "expected annual revenue for this location will be 5 million yen."

[0495] 6. Competitive Placement Strategy

[0496] The server optimizes its placement relative to nearby competing facilities and proposes strategies to enhance its competitive edge.

[0497] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "Opening a store in this area can be expected to create synergistic effects with competing facilities."

[0498] Examples of use

[0499] Users (for example, operators of commercial facilities) access a dashboard provided by the server to view detailed information about potential store locations.

[0500] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. They can also update the data in real time and re-evaluate their choices based on new conditions and circumstances.

[0501] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This, in turn, leads to improved profitability and optimized operational efficiency.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] Data collection

[0505] The server accesses APIs and databases to collect the necessary data.

[0506] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains data on the mobile population through traffic measurement systems and social media APIs.

[0507] Step 2:

[0508] Data Analysis

[0509] The server analyzes the collected data and extracts useful information.

[0510] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. Simultaneously, it uses GIS software to plot the location data of competing stores on a map and calculate competition density. Furthermore, it analyzes traffic data and social media posts to understand pedestrian flow patterns during specific times of day or events.

[0511] Step 3:

[0512] Location evaluation

[0513] The server identifies the optimal location for a store based on the analysis results.

[0514] Specific operation: The server integrates analyzed demographic data, competitor store information, and pedestrian traffic data to rank multiple potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0515] Step 4:

[0516] Proposed Operating Model

[0517] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0518] Specific operation: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0519] Step 5:

[0520] Revenue forecast

[0521] The server predicts future revenue based on an integrated database.

[0522] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0523] Step 6:

[0524] Proposal for competitive placement strategy

[0525] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0526] Specific operation: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it may propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[0527] Through the above processing steps, the system enables efficient and profitable store opening and operation.

[0528] (Example 1)

[0529] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0530] To ensure the opening of new commercial facilities, improve operational efficiency, and increase profitability, it is necessary to collect and analyze various data and identify appropriate locations based on the results. However, traditional methods were time-consuming and inefficient in collecting and analyzing the necessary data, and had limitations in accuracy, making it difficult to formulate appropriate store opening strategies. Furthermore, it was difficult to develop strategies that took into account the relationship with competing facilities.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0532] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for calculating predicted revenue, and means for proposing a location strategy that takes into account synergies with competing facilities. This makes it possible to efficiently collect and analyze various types of data and formulate a highly accurate store opening strategy. Furthermore, by proposing a location strategy that takes into account synergies with competitors, it becomes possible to formulate a competitive store opening plan.

[0533] "Demographic data" refers to statistical information about the population in a specific region, such as age, gender, number of households, average income, and occupational distribution.

[0534] "Competitor location information" refers to geographical information that indicates the location of competing stores and services in the same industry within a commercial or service facility.

[0535] "Population flow data" refers to information about the movement and retention of people in a specific region or area over a certain period of time, and includes traffic volume, visitor numbers, and the flow of people by time of day.

[0536] "Analysis results" refer to the output of analysis and interpretation based on collected data, and are indicators or visualized data that show the characteristics and trends of a specific location.

[0537] "Appropriate location" refers to the most suitable place for opening or operating a commercial or service facility, based on collected and analyzed data.

[0538] "Rent" refers to the cost required to rent land or a building in a specific location.

[0539] "Operating costs" refer to the expenses incurred in running commercial or service facilities, including personnel costs, management fees, and utility costs.

[0540] An "operational model" is a plan that outlines the optimal operating methods and strategies based on collected and analyzed data.

[0541] "Projected revenue" refers to numerical values ​​or indicators that show the expected future sales and profits, derived from collected and analyzed data.

[0542] "Synergy with competing facilities" refers to the phenomenon where opening a store in a specific location creates cooperation or competition with nearby competing facilities, leading to increased revenue and visitor numbers for both parties.

[0543] A "location strategy" is a set of policies and methods used to determine the optimal location for competing facilities and one's own facilities, based on collected and analyzed data.

[0544] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes various data to formulate effective store opening strategies and enhance the competitiveness of commercial facilities, thereby identifying optimal locations and proposing operational models.

[0545] System Configuration

[0546] The main components of this system include the following means:

[0547] Data acquisition methods

[0548] Data analysis means

[0549] Location evaluation methods

[0550] Operating Model Proposal Methods

[0551] Revenue forecasting methods

[0552] Competitive Deployment Strategies

[0553] These methods are server-centric and perform advanced analysis based on data collected from users and terminals. Furthermore, these methods are primarily implemented using the following specific software and hardware.

[0554] Specific software and hardware to be used

[0555] Databases: Census database, Competitor information database

[0556] APIs: Google Maps API, Traffic Sensor API, Social Media API

[0557] Analysis tools: Python, R, GIS (Geographic Information System)

[0558] Server: High-performance server (e.g., cloud-based server)

[0559] Specific example of processing

[0560] Data collection

[0561] The server uses APIs and databases to collect demographic data, location information of competing facilities, and population flow data.

[0562] Specific example: The server uses the Census API to obtain data on age distribution and average income in a specific area. It uses the Google Maps API to collect location information for competitor stores and uses traffic sensor APIs and social media APIs to gather population movement data for a specific area.

[0563] Data Analysis

[0564] The server uses analytical tools to analyze the collected data, examining regional characteristics, competitive landscape, and population movement trends.

[0565] Specific example: The server uses Python and R to process collected demographic data and understand the characteristics of local residents. It also uses GIS to visualize the locations of competing facilities and analyze the density of competition. Furthermore, it analyzes traffic volume data and social media data to understand the trends of the mobile population.

[0566] Location evaluation

[0567] The server identifies suitable locations for store openings based on the analysis results.

[0568] Specific example: The server integrates demographic data, location information of competing facilities, and pedestrian traffic data to evaluate potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an apparel store."

[0569] Proposed Operating Model

[0570] The server calculates operating costs based on the selected location and proposes the optimal operating model.

[0571] Specific example: The server retrieves the average rent for a location from a database and calculates the balance with operating costs (personnel costs, management fees, etc.). It then proposes an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0572] Revenue forecast

[0573] The server predicts future revenue based on integrated data.

[0574] Specific example: The server builds a sales forecasting model and calculates the expected revenue for each location. For example, it provides a specific forecast such as, "The expected annual revenue for this location will be 5 million yen."

[0575] Competitive Placement Strategy

[0576] The server proposes a placement strategy in relation to nearby competing facilities.

[0577] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "If we open a store in this area, we can expect synergistic effects with competing facilities."

[0578] Examples of prompt statements

[0579] Example: "Obtain the population distribution of people in their 20s and 30s in a specific region, and use this data to propose potential locations for apparel stores."

[0580] Specific example: "Please provide an operational model and revenue forecast for a potential new store location. Evaluate it based on collected demographic data, location information of competing facilities, and population flow data."

[0581] This invention enables commercial facility operators (users) to formulate efficient and precise store opening strategies. This leads to improved profitability and optimized operational efficiency.

[0582] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0583] Step 1:

[0584] Data collection

[0585] Input: Regional information (e.g., coordinates of the target area), data collection conditions (e.g., age distribution, number of competitors)

[0586] The server accesses APIs and databases to collect necessary demographic data, location information for competing facilities, and population flow data.

[0587] Specific actions:

[0588] Demographic data collection: Send requests to the Census API to obtain data on age distribution and average income for a specific region.

[0589] Gathering location information for competitor facilities: Use the Google Maps API to obtain location information for competitor facilities within the area.

[0590] Collection of population flow data: Use traffic sensor APIs and social media APIs to collect population flow data for specific areas.

[0591] Output: Collected demographic data, location information of competing facilities, and population flow data.

[0592] Step 2:

[0593] Data Analysis

[0594] Input: Collected demographic data, location information of competing facilities, and population flow data.

[0595] The server processes the collected data using analytical tools to analyze regional characteristics, competitive landscape, and population movement trends.

[0596] Specific actions:

[0597] Demographic Data Analysis: Using Python or R, collect demographic data is statistically analyzed to understand the characteristics of local residents.

[0598] Competitive facility situation analysis: Use GIS to visualize the location information of competing facilities and analyze the density and influence of competition.

[0599] Analysis of population flow data: Analyze traffic volume data and social media data to extract peak times and patterns of population flow.

[0600] Output: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[0601] Step 3:

[0602] Location evaluation

[0603] Input: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[0604] The server identifies suitable locations for store openings based on the analysis results.

[0605] Specific actions:

[0606] Data Integration: The collected and analyzed data is integrated to evaluate each candidate site.

[0607] Generating evaluation results: Generate specific evaluation results such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime foot traffic, making it suitable for apparel stores."

[0608] Output: List of suitable store locations, evaluation results for each location

[0609] Step 4:

[0610] Proposed Operating Model

[0611] Input: List of suitable potential store locations, evaluation results for each location

[0612] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[0613] Specific actions:

[0614] Acquiring rental data: Obtain rental market rates for the relevant area from the database.

[0615] Cost calculation: Calculate by balancing operating costs (e.g., personnel costs, administrative costs, etc.).

[0616] Proposed operating model: We propose an operating model that states, "The monthly rent in this area is 200,000 yen, labor costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0617] Output: Optimal operating model, detailed operating costs

[0618] Step 5:

[0619] Revenue forecast

[0620] Input: List of suitable store locations, evaluation results for each location, optimal operating model, and detailed operating costs.

[0621] The server predicts future revenue based on integrated data.

[0622] Specific actions:

[0623] Building Predictive Models: Build revenue prediction models using machine learning models and statistical models.

[0624] Revenue forecast: Calculates expected revenue for each location. Provides specific forecasts such as, "Expected annual revenue for this location is 5 million yen."

[0625] Output: Predicted revenue for each location

[0626] Step 6:

[0627] Competitive Placement Strategy

[0628] Input: List of suitable store locations, evaluation results for each location, optimal operating model, detailed operating costs, and projected revenue for each location.

[0629] The server proposes a placement strategy that takes into account synergies with competing facilities.

[0630] Specific actions:

[0631] Simulation using competitive data: The simulation is performed based on the location data of competing facilities.

[0632] Strategic proposal: Develop a specific location strategy, such as "Opening a store in this area is expected to create synergistic effects with competing facilities."

[0633] Output: Optimal competitive placement strategy

[0634] (Application Example 1)

[0635] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0636] In modern commercial facility development and operation, effectively collecting and analyzing large amounts of data to make quick and accurate decisions is essential for appropriate location selection and profitability improvement. Therefore, features such as real-time data provision and visualization, and voice assistant guidance are required. Conventional systems fail to adequately meet these needs, resulting in challenges in optimizing operational efficiency and profitability.

[0637] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0638] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, the location information of competing facilities, and the circulating population data, means for identifying a suitable location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for providing data in real time via a user interface, means for guiding the user with a voice assistant based on the collected data, and means for visualizing the collected data using augmented reality technology. This enables commercial facility operators to support their decision-making regarding new store openings in real time.

[0639] "Demographic data" refers to statistical information about the population of a specific region or group, such as age, sex, occupation, and income.

[0640] "Competitor facility location information" refers to information indicating the geographical location of competing commercial facilities located within a specific area.

[0641] "Population mobility data" refers to information about the number of people moving within a specific region within a certain period of time, as well as their movement routes.

[0642] "Analysis means" refers to methods or devices used to analyze specific conditions and trends based on collected data and derive results.

[0643] "Means for determining location" refers to methods or devices for determining an appropriate store location based on the results of the analysis.

[0644] "Means for calculating rent and operating costs" refers to methods or devices for calculating the market rent and operating costs for a selected location.

[0645] "Means of proposing an operating model" refers to methods and devices for demonstrating the optimal management method based on calculated rent and operating costs.

[0646] "Means of providing data in real time" refers to methods or devices for immediately presenting collected and analyzed data to users via a user interface.

[0647] "Means of guiding users with voice assistants" refers to methods and devices for conveying analysis results and important information to users by voice.

[0648] "Means of visualizing collected data using augmented reality technology" refers to methods and devices for visually presenting collected data to users using technology that overlays it onto a three-dimensional space.

[0649] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data. Based on this data, it identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy. In particular, it features support functions that enable stress-free and immediate decision-making by providing real-time data, visualization, and guidance via voice assistants.

[0650] Required hardware and software

[0651] Hardware:

[0652] Smart glasses: A device that collects and visualizes data in real time using a camera.

[0653] Server: A computer system that functions as the central hub for data collection, analysis, and storage.

[0654] GPS sensor: A device for accurately acquiring location information.

[0655] software:

[0656] Google Cloud Vision API: A service for image processing and label detection.

[0657] Geopy: A library for obtaining location information.

[0658] Pandas: A Python library for data analysis and manipulation.

[0659] pyttsx3: A library for audio output.

[0660] arpy: A library for visualizing data using augmented reality technology.

[0661] System operation

[0662] 1. Data collection:

[0663] The server uses the Google Cloud Vision API to acquire data on competing facilities and traffic volume based on images obtained from smart glasses. Location information is collected using a GPS sensor and the Geopy library.

[0664] 2. Data Analysis:

[0665] The server uses Pandas to analyze the acquired location information, demographic data, and population movement data of competing facilities to gain a detailed understanding of regional characteristics.

[0666] 3. Data provided by:

[0667] When a user wears smart glasses and speaks a voice command such as "Check the profitability of this area," the server provides analysis results in real time and guides the user via voice using pyttsx3. Furthermore, visualized data is overlaid on the smart glasses' display using augmented reality (AR) technology.

[0668] Specific example

[0669] For example, when considering the possibility of opening a store in a shopping area, a user can wear smart glasses, take photos of their surroundings, and inquire via voice command, "Check the profitability of this area." The server immediately begins analysis, notifying the user of the results via voice while visualizing the data and displaying it on the smart glasses' screen. This allows the user to grasp detailed location information and profit forecasts on the spot, enabling efficient decision-making.

[0670] Example of a prompt

[0671] "Analyze the following image and evaluate the profitability of the store. Calculate a score based on data on the density of competing stores and the foot traffic."

[0672] "Please collect demographic data, traffic data, and competitor location information to calculate the profitability of opening a store at this location."

[0673] This system allows commercial facility operators to receive real-time support in decision-making regarding new store openings, enabling them to formulate more precise store opening strategies.

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] The server receives image data acquired from the smart glasses. The user wears the smart glasses and takes pictures of a specific area. This image data is sent to the server. The input is the captured image data, and the output is the image data stored on the server.

[0677] Step 2:

[0678] The server uses the Google Cloud Vision API to analyze the received image data. Specifically, it performs label detection to identify competing facilities and traffic conditions within the image. The input is image data stored on the server, and the output is label data indicating the location information of competing facilities and traffic conditions.

[0679] Step 3:

[0680] The server uses the Geopy library to collect location information obtained from the GPS sensor of the smart glasses. The input is location data from the GPS sensor of the smart glasses, and the output is precise geographical location information for a specific area.

[0681] Step 4:

[0682] The server analyzes this location information, demographic data, and population flow data using Pandas. Specifically, it formats this data and analyzes regional characteristics, competitive landscape, and population flow trends. The input is location information, demographic data, and population flow data of competing facilities, and the output is data on regional characteristics, competitive landscape, and population flow trends as a result of the analysis.

[0683] Step 5:

[0684] The user speaks a voice command to the smart glasses, saying "Check the profitability of this area," and sends this command to the server. The input is a voice command, and the output is an analysis request to the server.

[0685] Step 6:

[0686] After receiving a voice command, the server uses the pyttsx3 library to provide the user with the analysis results via voice. The input is the analysis result data, and the output is a voice notification of the analysis results. Specifically, it provides voice guidance on profitability scores, competitive landscape, and other information.

[0687] Step 7:

[0688] The server simultaneously uses the arpy library to visualize the collected data in augmented reality and overlays the results onto the smart glasses' field of view. The input is the analyzed data, and the output is the visualized data on the smart glasses' display. Specifically, regional characteristics, the locations of competing facilities, and the movement of the circulating population are visually displayed.

[0689] This allows users to access detailed location information and revenue forecasts in real time, enabling them to make more efficient decisions.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. Furthermore, by combining it with an emotion engine that recognizes user emotions, it enables the construction of more accurate store opening strategies. This system utilizes user emotion data in addition to collecting and analyzing demographic data, location information of competing facilities, and population flow data.

[0692] The main components of the system and their operation

[0693] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, competitive placement strategy means, and an emotion engine. The processing of each means will be explained below with specific examples.

[0694] 1. Data Collection

[0695] The server accesses APIs and databases to collect the necessary data.

[0696] Specific example: The server calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. Furthermore, it obtains population flow data through traffic measurement systems and social media APIs.

[0697] 2. Data Analysis

[0698] The server analyzes the collected data to understand regional characteristics, competitive landscape, and population movement trends.

[0699] Specific example: The server uses analytical tools such as Python and R to analyze demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS and calculates competition density. Furthermore, it analyzes traffic volume data and social media posts to extract peak times and event information for the mobile population.

[0700] 3. Location Evaluation

[0701] The server identifies the optimal location for a store based on the analysis results.

[0702] Specific example: The server integrates analyzed demographic data, competitor information, and population flow data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime population flow, making it suitable for an entertainment shop."

[0703] 4. Proposed Operating Model

[0704] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0705] Specific example: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0706] 5. Revenue forecast

[0707] The server predicts future revenue based on an integrated database.

[0708] Specific example: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0709] 6. Proposed competitive placement strategy

[0710] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0711] Specific example: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it might propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[0712] 7. Introduction of an emotional engine

[0713] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[0714] Specific example: The emotion engine installed in the device analyzes user feedback and social media posts to understand the user's emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[0715] 8. Adjusting location and operational models based on emotional data

[0716] The server dynamically adjusts its location and operating model based on emotional data obtained from the emotion engine.

[0717] Specific example: The server analyzes user sentiment data and, based on information such as "users in this area have a strong interest in a particular service," changes the location of the store and the content of the services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added as a candidate for opening a cafe.

[0718] Examples of use

[0719] Users (for example, commercial facility operators) can access a dashboard provided by the server to view detailed information about potential store locations. They can also review user sentiment data collected by the sentiment engine, allowing them to refine their store opening strategies with greater precision.

[0720] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. The system can update data in real time, allowing for re-evaluation based on new conditions and circumstances.

[0721] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This not only improves profitability and optimizes operational efficiency, but also allows for further improvements in customer satisfaction by utilizing user sentiment data.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] Data collection

[0725] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[0726] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains population flow data for a specific area through traffic measurement systems and social media APIs.

[0727] Step 2:

[0728] Data Analysis

[0729] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[0730] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. It uses GIS software to plot the location data of competing stores on a map and calculates competition density. It also analyzes traffic volume data and social media posts to understand pedestrian flow patterns during specific times of day and events.

[0731] Step 3:

[0732] Location evaluation

[0733] The server identifies the optimal location for a store based on the analysis results.

[0734] Specific operation: The server integrates analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0735] Step 4:

[0736] Proposed Operating Model

[0737] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0738] Specific operation: The server retrieves rental market rates for each region from a real estate database and calculates operating costs (e.g., personnel costs, utility costs) based on that information. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0739] Step 5:

[0740] Revenue forecast

[0741] The server predicts future revenue based on integrated data.

[0742] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0743] Step 6:

[0744] Proposal for competitive placement strategy

[0745] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0746] Specific operation: The server analyzes the location information of collected competitor facilities and creates strategies such as "opening a store in this area will maximize synergies with existing competitor facilities." For example, it might suggest measures to increase the store's ability to attract customers by opening a store in an area with few competitors of the same type and high foot traffic.

[0747] Step 7:

[0748] Emotion Engine Data Collection

[0749] The device uses an emotion engine to collect user emotion data.

[0750] Specific operation: The device analyzes user feedback and social media posts to recognize user emotions. For example, it extracts positive or negative feelings towards a particular store.

[0751] Step 8:

[0752] Analysis of emotional data

[0753] The server analyzes the user's emotional data obtained from the emotion engine.

[0754] Specific operation: The server statistically analyzes the collected sentiment data to understand which services and products users have positive or negative feelings towards. For example, it might derive information such as, "There are many positive reactions to cafes in this area."

[0755] Step 9:

[0756] Strategy adjustment based on emotional data

[0757] The server dynamically adjusts its location and operational model based on the analyzed sentiment data.

[0758] Specific operation: The server changes the location of the store and the services offered based on sentiment data. For example, if there is a lot of positive feedback for the cafe in a particular area, that area will be added as a candidate for opening a cafe, and the operating model will be adapted accordingly.

[0759] The specific processing steps described above enable the system to achieve efficient and profitable store openings and operations. Furthermore, by combining it with an emotion engine, it is possible to build strategies tailored to user needs and emotions, thereby improving customer satisfaction.

[0760] (Example 2)

[0761] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0762] In opening and operating new commercial facilities, there is a need to achieve increased efficiency and profitability. Conventional systems were limited to collecting and analyzing demographic data, location information of competing facilities, and population flow data, and strategies were often formulated without considering user sentiment data, resulting in a lack of accuracy. Furthermore, dynamic adjustments were difficult when identifying optimal locations and proposing operating models.

[0763] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0764] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for collecting and analyzing sentiment data, and means for adjusting the location and operating model based on the sentiment data. As a result, a highly accurate store opening strategy can be formulated based on the collected data, and user satisfaction can be improved by utilizing sentiment data.

[0765] "Demographic data" refers to statistical information such as population composition, age distribution, household composition, and income levels for each region.

[0766] "Location information of competing facilities" refers to the geographical information of where commercial facilities are located and the attribute information of those stores.

[0767] "Population mobility data" refers to information on the movement and stay of people within a specific area over a certain period of time.

[0768] "Means of analysis" refers to methods and tools for processing and analyzing collected data and extracting useful information.

[0769] "Means of identifying suitable locations" refers to methods and tools for determining the most advantageous store locations based on analytical data.

[0770] "Means for calculating rent and operating costs" refers to methods and tools for calculating the rent and various operating expenses necessary for real estate in a specific location.

[0771] "Means of proposing an operating model" refers to methods and tools for proposing the optimal operating method and revenue model based on calculated costs.

[0772] "Emotional data" refers to information obtained by analyzing users' emotions, opinions, feedback, and other psychological responses.

[0773] "Means for collecting and analyzing emotional data" refers to methods and tools for measuring users' emotions, analyzing them, and converting them into valuable data.

[0774] "Means of adjusting location and operating models based on emotional data" refers to methods and tools for dynamically modifying and optimizing potential store locations and operating methods by taking emotional data into consideration.

[0775] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and further combines this with an emotion engine to enable the construction of more accurate store opening strategies.

[0776] System components and their operation

[0777] 1. Data Collection

[0778] The server accesses APIs and databases to collect necessary data. Specifically, it calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. It also obtains population movement data through traffic measurement systems and social media APIs.

[0779] 2. Data Analysis

[0780] The server analyzes collected data to understand regional characteristics, competitive landscape, and population movement trends. Using analytical tools such as Python and R, it analyzes demographic data to understand the characteristics of local residents. Furthermore, it visualizes the locations of competing facilities using GIS and calculates competition density. Traffic data and social media posts are analyzed to extract peak times for population movement and event information.

[0781] 3. Location Evaluation

[0782] The server identifies the optimal location for a store based on the analysis results. It integrates the analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0783] 4. Proposed Operating Model

[0784] The server calculates rent and operating costs based on identified candidate locations and proposes an operating model. It obtains rental market rates for each region from a real estate database and combines them with data on necessary operating costs (personnel costs, utilities, etc.) to calculate the total cost. For example, it might suggest, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0785] 5. Revenue forecast

[0786] The server predicts future revenue based on integrated data. Using machine learning models, it makes predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides specific revenue forecasts such as, "This location will generate an expected annual revenue of 5 million yen."

[0787] 6. Proposed competitive placement strategy

[0788] The server develops strategies to strengthen competitiveness based on the location information of competing facilities. It analyzes the collected location information of competing facilities and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." It proposes measures to increase the store's ability to attract customers by opening stores in areas with few competing stores of the same type and high foot traffic.

[0789] 7. Introduction of an emotional engine

[0790] The server uses an emotion engine to collect user emotion data and uses it for analysis. The emotion engine analyzes user feedback and social media posts to understand user emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[0791] 8. Adjusting location and operational models based on emotional data

[0792] The server dynamically adjusts location and operating model based on emotional data obtained from the emotion engine. By analyzing user emotional data and using information such as "users in this area have a strong interest in a particular service," it changes the location of stores and the content of services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added to the list of potential cafe locations.

[0793] Example of a prompt

[0794] "Please suggest potential locations for an entertainment shop in an area with a high concentration of young people in their 20s and 30s, and a high nighttime foot traffic."

[0795] "Based on the collected data, please propose an operational model to optimize the café opening strategy in a specific area."

[0796] This system will enable more efficient and accurate decision-making regarding the opening and operation of new stores in commercial facilities. Furthermore, by utilizing emotional data, it is expected to improve customer satisfaction.

[0797] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0798] Step 1:

[0799] Data collection

[0800] The server collects the data necessary for opening a store in a commercial facility.

[0801] Input: Data from the Census API, Google Maps API, traffic measurement systems, and social media APIs.

[0802] Output: Demographic data, location information of competing facilities, and population flow data.

[0803] Specific operation: The server uses the requests library to call the census API to obtain demographic data in JSON format. Similarly, it uses the Google Maps API to obtain location information for competing stores in each region and accesses the traffic volume measurement system API to collect population flow data.

[0804] Step 2:

[0805] Data Analysis

[0806] The server analyzes the collected data.

[0807] Input: Collected demographic data, location information of competing facilities, and population flow data.

[0808] Output: Analysis results regarding regional characteristics, competitive landscape, and trends in population movement.

[0809] Specific operation: The server uses the Pandas library to convert demographic data into a dataframe and analyze the characteristics of local residents. It also uses GeoPandas and Shapely to calculate competition density and visualize it on a map. For traffic data, time series analysis is performed to extract peak times and event information.

[0810] Step 3:

[0811] Location evaluation

[0812] The server identifies the optimal location for a store based on the analysis results.

[0813] Input: Analysis results (regional characteristics, competitive situation, trends in population movement).

[0814] Output: Ranking results for multiple potential store locations.

[0815] Specific operation: The server analyzes the data, scores it, and ranks each candidate location. For example, areas with a large young population and high nighttime foot traffic will receive a high score as potential locations for entertainment shops. This result is displayed on a dashboard.

[0816] Step 4:

[0817] Proposed Operating Model

[0818] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0819] Input: Information on identified potential store locations, rental data obtained from a real estate database, and pre-set operating costs.

[0820] Output: Total operating costs and proposed operating models for each candidate site.

[0821] Specific operation: The server uses a real estate database API to retrieve rental data and calculates the total cost by adding it to operating costs such as personnel expenses and utilities. Based on the results, it presents an operating model such as, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0822] Step 5:

[0823] Revenue forecast

[0824] The server predicts future revenue based on the integrated data.

[0825] Input: Integrated data (demographic data, competitor information, population flow data).

[0826] Output: Expected earnings.

[0827] Specific operation: The server uses the Scikit-learn library to train a machine learning model and build a predictive model based on the collected data. Using that model, it predicts future revenue and provides specific revenue forecasts such as "Expected annual revenue at this location is 5 million yen."

[0828] Step 6:

[0829] Proposal for competitive placement strategy

[0830] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0831] Input: Location information of competing facilities.

[0832] Output: Proposal of competitive placement strategy.

[0833] Specific operation: The server analyzes the location information of competing facilities on a GIS and formulates the optimal strategy for strengthening competitiveness. For example, it plots a strategy such as "opening a store in this area will maximize synergies with existing competing facilities" on a dashboard and presents it visually to the user.

[0834] Step 7:

[0835] Introducing an emotional engine

[0836] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[0837] Input: User feedback, social media posts.

[0838] Output: User sentiment data.

[0839] Specific operation: The emotion engine analyzes user feedback and tags emotions. It also analyzes text data from social media posts and extracts emotion trends.

[0840] Step 8:

[0841] Adjusting location and operating models based on emotional data

[0842] The server dynamically adjusts its location and operating model based on sentiment data.

[0843] Input: Sentiment data.

[0844] Output: Adjusted location and operational model.

[0845] Specific operation: The server statistically analyzes sentiment data to identify user trends in specific regions. Based on this, it re-evaluates potential store locations and the services offered, and displays the updated results on a dashboard. For example, if there is a high level of positive feedback towards cafes in a particular region, that region will be added as a potential location for a cafe.

[0846] Through these steps, the system can comprehensively analyze data and efficiently and accurately support decision-making regarding the opening and operation of commercial facilities. Furthermore, by utilizing user sentiment data, it can also improve customer satisfaction.

[0847] (Application Example 2)

[0848] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0849] To improve the efficiency and profitability of new store openings and operations in commercial facilities, store opening strategies must consider not only demographic and population flow data, but also user sentiment data. However, conventional systems have made it difficult to develop concrete store opening strategies that utilize sentiment data, resulting in challenges in optimizing operations and maximizing profits.

[0850] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for collecting sentiment data, means for analyzing the collected demographic data, location information of competing facilities, circulating population data, and sentiment data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, and means for proposing an optimal operating model. This makes it possible to construct a highly accurate store opening strategy utilizing sentiment data.

[0851] "Demographic data" refers to statistical information about the distribution and attributes of the population in a specific region.

[0852] "Location information of competing facilities" refers to geographical information that shows where competing facilities such as commercial facilities and stores are located.

[0853] "Population mobility data" refers to information about the number of people moving within a specific region and their movement patterns.

[0854] "Sentimental data" refers to information collected from users regarding their feelings and evaluations of specific services or facilities.

[0855] "Means of analysis" refers to methods and tools for analyzing collected data using statistical analysis and machine learning to extract meaningful information.

[0856] "Means of identifying a location" refers to methods and tools for selecting the optimal location for a commercial facility based on analysis results.

[0857] "Means for calculating rent and operating costs" refers to methods or tools for calculating rent and operating costs at a selected location.

[0858] "Means of proposing an operating model" refers to methods and tools for proposing the optimal business operation methods and strategies for a specific location.

[0859] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities, and in particular, to a system that enables the construction of more accurate store opening strategies by utilizing user sentiment data.

[0860] The system consists of the following hardware and software.

[0861] 1. Hardware

[0862] Server: Performs data collection, analysis, and proposes operational models.

[0863] Smartphone or tablet: A device used by store operators to check data and formulate store opening strategies.

[0864] 2. Software

[0865] Data collection APIs (e.g., Google Maps API, traffic volume measurement API, etc.)

[0866] Sentiment analysis engine (e.g., Microsoft Azure Cognitive Services)

[0867] Data analysis tools (e.g., Python, R, GIS software)

[0868] System operation

[0869] The system operates using the following steps:

[0870] 1. Data Collection

[0871] The server uses the Google Maps API to collect location information for competing facilities. It also obtains demographic data through the Census API and collects population movement data using the traffic volume measurement API.

[0872] Emotional data is obtained from social media and user feedback and analyzed by an emotion analysis engine.

[0873] 2. Data Analysis

[0874] The server integrates and analyzes collected demographic data, location information of competing facilities, population movement data, and sentiment data. Based on the analysis results, it understands regional characteristics, competitive landscape, and user sentiment trends.

[0875] 3. Location identification and proposal of an operational model

[0876] Based on the analysis results, the server identifies the optimal locations for store openings. The identified locations are presented in a ranking format along with location evaluations, projected revenues, and operating costs.

[0877] Based on the data displayed on the device, users can consider the optimal operating model.

[0878] Specific example

[0879] For example, when a user considers opening a new store, data is collected and analyzed using the following procedure.

[0880] Prompt message example 1: Obtain location information of competitor stores

[0881] "Please use the Google Maps API to retrieve store information for locations within a 5-kilometer radius of the proposed store location."

[0882] Prompt example 2: Analysis of sentiment data

[0883] "Collect social media posts and analyze the sentiment score for this region."

[0884] Prompt message example 3: Suggestion of the optimal store location

[0885] "Based on demographic data and competitive landscape in this area, please rank the most suitable locations for opening a store."

[0886] This allows users to develop highly accurate store opening strategies based on real-time data. Furthermore, by incorporating emotional data, it is expected to contribute to improved customer satisfaction.

[0887] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0888] Step 1:

[0889] The server collects demographic data, location information of competitor facilities, and population flow data using various APIs. Specifically, it obtains location information of competitor facilities using the Google Maps API and collects demographic data through the Census API. It also obtains population flow data using the Traffic Volume Measurement API. The input for data collection is the API endpoints and parameters, and the output is the data obtained from the APIs.

[0890] Step 2:

[0891] The server collects and analyzes emotional data using an emotion analysis engine. Specifically, it obtains text data from social media and user feedback and inputs it into the emotion analysis engine. This engine analyzes the text data and outputs an emotion score such as positive, negative, or neutral. The input is text data, and the output is an emotion score.

[0892] Step 3:

[0893] The server integrates collected and analyzed demographic data, location information of competing facilities, population movement data, and sentiment data, and performs analysis using data analysis tools. Specifically, it uses Python and R to integrate each dataset and understand regional characteristics, competitive situations, and population movement trends. The input is various datasets, and the output is the analysis results.

[0894] Step 4:

[0895] The server identifies suitable location candidates based on the analysis results and presents them in a ranked format. Specifically, it uses GIS software to perform geographical visualization and ranks the location candidates according to evaluation criteria. Users can view this information on their smartphones or tablets. The input is the analysis results, and the output is a list of ranked location candidates.

[0896] Step 5:

[0897] The server calculates rent and operating costs based on the selected location. Specifically, it retrieves rental market rates for each region from a real estate database and calculates the costs necessary for operation (personnel costs, utilities, etc.). The inputs are location information and real estate data, and the outputs are rent and operating costs.

[0898] Step 6:

[0899] The server proposes the optimal operating model. Based on analysis results and projected revenue data, it presents specific operating models and strategies. Users can review the operating model and make decisions on their smartphones or tablets. Inputs are rent, operating costs, and projected revenue data, and output is a proposed operating model.

[0900] Step 7:

[0901] Based on information provided by the server, users consider potential store locations and make adjustments by incorporating sentiment data. Specifically, they prioritize favorable areas identified by sentiment data and formulate their final store location strategy. The input is the server's suggestions, and the output is the final store location strategy.

[0902] This will enable users to develop highly accurate store opening strategies in real time using emotional data, which is expected to improve customer satisfaction and maximize profitability.

[0903] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0904] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0905] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0906] [Third Embodiment]

[0907] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0908] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0909] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0910] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0911] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0912] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0913] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0914] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0915] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0916] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0917] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0918] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0919] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and based on this data identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy.

[0920] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, and competitive placement strategy means. The processing of each means will be explained below with specific examples.

[0921] 1. Data Collection

[0922] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[0923] Specific example: The server retrieves demographic data such as age distribution and average income for a specific region from a census database. It also uses the Google Maps API to collect location information for competing stores in that region. Furthermore, it obtains population movement data for a specific area through traffic data and social media APIs.

[0924] 2. Data Analysis

[0925] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[0926] Specific example: The server uses analytical tools such as Python and R to process demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS (Geographic Information System) and analyzes the density of competition. Furthermore, it analyzes traffic volume and social media data to extract peak times for population movement and event information.

[0927] 3. Location Evaluation

[0928] Based on the analysis results, the server identifies the most suitable location for opening a store.

[0929] Specific example: The server integrates demographic, competitor, and traffic data to evaluate the advantages and disadvantages of each candidate location. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime traffic volume, making it suitable for an apparel store."

[0930] 4. Proposed Operating Model

[0931] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[0932] Specific example: The server retrieves the average rent for a given location from a database and uses that information to calculate the balance with operating costs (e.g., personnel costs, management fees). For example, it might propose an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[0933] 5. Revenue forecast

[0934] The server predicts future revenue based on integrated data.

[0935] Specific example: The server builds a sales forecasting model and calculates expected revenue for each location. This uses statistical and machine learning models based on current data to provide specific predictions, such as "expected annual revenue for this location will be 5 million yen."

[0936] 6. Competitive Placement Strategy

[0937] The server optimizes its placement relative to nearby competing facilities and proposes strategies to enhance its competitive edge.

[0938] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "Opening a store in this area can be expected to create synergistic effects with competing facilities."

[0939] Examples of use

[0940] Users (for example, operators of commercial facilities) access a dashboard provided by the server to view detailed information about potential store locations.

[0941] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. They can also update the data in real time and re-evaluate their choices based on new conditions and circumstances.

[0942] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This, in turn, leads to improved profitability and optimized operational efficiency.

[0943] The following describes the processing flow.

[0944] Step 1:

[0945] Data collection

[0946] The server accesses APIs and databases to collect the necessary data.

[0947] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains data on the mobile population through traffic measurement systems and social media APIs.

[0948] Step 2:

[0949] Data Analysis

[0950] The server analyzes the collected data and extracts useful information.

[0951] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. Simultaneously, it uses GIS software to plot the location data of competing stores on a map and calculate competition density. Furthermore, it analyzes traffic data and social media posts to understand pedestrian flow patterns during specific times of day or events.

[0952] Step 3:

[0953] Location evaluation

[0954] The server identifies the optimal location for a store based on the analysis results.

[0955] Specific operation: The server integrates analyzed demographic data, competitor store information, and pedestrian traffic data to rank multiple potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[0956] Step 4:

[0957] Proposed Operating Model

[0958] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[0959] Specific operation: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[0960] Step 5:

[0961] Revenue forecast

[0962] The server predicts future revenue based on an integrated database.

[0963] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[0964] Step 6:

[0965] Proposal for competitive placement strategy

[0966] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[0967] Specific operation: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it may propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[0968] Through the above processing steps, the system enables efficient and profitable store opening and operation.

[0969] (Example 1)

[0970] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0971] To ensure the opening of new commercial facilities, improve operational efficiency, and increase profitability, it is necessary to collect and analyze various data and identify appropriate locations based on the results. However, traditional methods were time-consuming and inefficient in collecting and analyzing the necessary data, and had limitations in accuracy, making it difficult to formulate appropriate store opening strategies. Furthermore, it was difficult to develop strategies that took into account the relationship with competing facilities.

[0972] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0973] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for calculating predicted revenue, and means for proposing a location strategy that takes into account synergies with competing facilities. This makes it possible to efficiently collect and analyze various types of data and formulate a highly accurate store opening strategy. Furthermore, by proposing a location strategy that takes into account synergies with competitors, it becomes possible to formulate a competitive store opening plan.

[0974] "Demographic data" refers to statistical information about the population in a specific region, such as age, gender, number of households, average income, and occupational distribution.

[0975] "Competitor location information" refers to geographical information that indicates the location of competing stores and services in the same industry within a commercial or service facility.

[0976] "Population flow data" refers to information about the movement and retention of people in a specific region or area over a certain period of time, and includes traffic volume, visitor numbers, and the flow of people by time of day.

[0977] "Analysis results" refer to the output of analysis and interpretation based on collected data, and are indicators or visualized data that show the characteristics and trends of a specific location.

[0978] "Appropriate location" refers to the most suitable place for opening or operating a commercial or service facility, based on collected and analyzed data.

[0979] "Rent" refers to the cost required to rent land or a building in a specific location.

[0980] "Operating costs" refer to the expenses incurred in running commercial or service facilities, including personnel costs, management fees, and utility costs.

[0981] An "operational model" is a plan that outlines the optimal operating methods and strategies based on collected and analyzed data.

[0982] "Projected revenue" refers to numerical values ​​or indicators that show the expected future sales and profits, derived from collected and analyzed data.

[0983] "Synergy with competing facilities" refers to the phenomenon where opening a store in a specific location creates cooperation or competition with nearby competing facilities, leading to increased revenue and visitor numbers for both parties.

[0984] A "location strategy" is a set of policies and methods used to determine the optimal location for competing facilities and one's own facilities, based on collected and analyzed data.

[0985] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes various data to formulate effective store opening strategies and enhance the competitiveness of commercial facilities, thereby identifying optimal locations and proposing operational models.

[0986] System Configuration

[0987] The main components of this system include the following means:

[0988] Data acquisition methods

[0989] Data analysis means

[0990] Location evaluation methods

[0991] Operating Model Proposal Methods

[0992] Revenue forecasting methods

[0993] Competitive Deployment Strategies

[0994] These methods are server-centric and perform advanced analysis based on data collected from users and terminals. Furthermore, these methods are primarily implemented using the following specific software and hardware.

[0995] Specific software and hardware to be used

[0996] Databases: Census database, Competitor information database

[0997] APIs: Google Maps API, Traffic Sensor API, Social Media API

[0998] Analysis tools: Python, R, GIS (Geographic Information System)

[0999] Server: High-performance server (e.g., cloud-based server)

[1000] Specific example of processing

[1001] Data collection

[1002] The server uses APIs and databases to collect demographic data, location information of competing facilities, and population flow data.

[1003] Specific example: The server uses the Census API to obtain data on age distribution and average income in a specific area. It uses the Google Maps API to collect location information for competitor stores and uses traffic sensor APIs and social media APIs to gather population movement data for a specific area.

[1004] Data Analysis

[1005] The server uses analytical tools to analyze the collected data, examining regional characteristics, competitive landscape, and population movement trends.

[1006] Specific example: The server uses Python and R to process collected demographic data and understand the characteristics of local residents. It also uses GIS to visualize the locations of competing facilities and analyze the density of competition. Furthermore, it analyzes traffic volume data and social media data to understand the trends of the mobile population.

[1007] Location evaluation

[1008] The server identifies suitable locations for store openings based on the analysis results.

[1009] Specific example: The server integrates demographic data, location information of competing facilities, and pedestrian traffic data to evaluate potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an apparel store."

[1010] Proposed Operating Model

[1011] The server calculates operating costs based on the selected location and proposes the optimal operating model.

[1012] Specific example: The server retrieves the average rent for a location from a database and calculates the balance with operating costs (personnel costs, management fees, etc.). It then proposes an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[1013] Revenue forecast

[1014] The server predicts future revenue based on integrated data.

[1015] Specific example: The server builds a sales forecasting model and calculates the expected revenue for each location. For example, it provides a specific forecast such as, "The expected annual revenue for this location will be 5 million yen."

[1016] Competitive Placement Strategy

[1017] The server proposes a placement strategy in relation to nearby competing facilities.

[1018] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "If we open a store in this area, we can expect synergistic effects with competing facilities."

[1019] Examples of prompt statements

[1020] Example: "Obtain the population distribution of people in their 20s and 30s in a specific region, and use this data to propose potential locations for apparel stores."

[1021] Specific example: "Please provide an operational model and revenue forecast for a potential new store location. Evaluate it based on collected demographic data, location information of competing facilities, and population flow data."

[1022] This invention enables commercial facility operators (users) to formulate efficient and precise store opening strategies. This leads to improved profitability and optimized operational efficiency.

[1023] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1024] Step 1:

[1025] Data collection

[1026] Input: Regional information (e.g., coordinates of the target area), data collection conditions (e.g., age distribution, number of competitors)

[1027] The server accesses APIs and databases to collect necessary demographic data, location information for competing facilities, and population flow data.

[1028] Specific actions:

[1029] Demographic data collection: Send requests to the Census API to obtain data on age distribution and average income for a specific region.

[1030] Gathering location information for competitor facilities: Use the Google Maps API to obtain location information for competitor facilities within the area.

[1031] Collection of population flow data: Use traffic sensor APIs and social media APIs to collect population flow data for specific areas.

[1032] Output: Collected demographic data, location information of competing facilities, and population flow data.

[1033] Step 2:

[1034] Data Analysis

[1035] Input: Collected demographic data, location information of competing facilities, and population flow data.

[1036] The server processes the collected data using analytical tools to analyze regional characteristics, competitive landscape, and population movement trends.

[1037] Specific actions:

[1038] Demographic Data Analysis: Using Python or R, collect demographic data is statistically analyzed to understand the characteristics of local residents.

[1039] Competitive facility situation analysis: Use GIS to visualize the location information of competing facilities and analyze the density and influence of competition.

[1040] Analysis of population flow data: Analyze traffic volume data and social media data to extract peak times and patterns of population flow.

[1041] Output: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[1042] Step 3:

[1043] Location evaluation

[1044] Input: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[1045] The server identifies suitable locations for store openings based on the analysis results.

[1046] Specific actions:

[1047] Data Integration: The collected and analyzed data is integrated to evaluate each candidate site.

[1048] Generating evaluation results: Generate specific evaluation results such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime foot traffic, making it suitable for apparel stores."

[1049] Output: List of suitable store locations, evaluation results for each location

[1050] Step 4:

[1051] Proposed Operating Model

[1052] Input: List of suitable potential store locations, evaluation results for each location

[1053] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[1054] Specific actions:

[1055] Acquiring rental data: Obtain rental market rates for the relevant area from the database.

[1056] Cost calculation: Calculate by balancing operating costs (e.g., personnel costs, administrative costs, etc.).

[1057] Proposed operating model: We propose an operating model that states, "The monthly rent in this area is 200,000 yen, labor costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[1058] Output: Optimal operating model, detailed operating costs

[1059] Step 5:

[1060] Revenue forecast

[1061] Input: List of suitable store locations, evaluation results for each location, optimal operating model, and detailed operating costs.

[1062] The server predicts future revenue based on integrated data.

[1063] Specific actions:

[1064] Building Predictive Models: Build revenue prediction models using machine learning models and statistical models.

[1065] Revenue forecast: Calculates expected revenue for each location. Provides specific forecasts such as, "Expected annual revenue for this location is 5 million yen."

[1066] Output: Predicted revenue for each location

[1067] Step 6:

[1068] Competitive Placement Strategy

[1069] Input: List of suitable store locations, evaluation results for each location, optimal operating model, detailed operating costs, and projected revenue for each location.

[1070] The server proposes a placement strategy that takes into account synergies with competing facilities.

[1071] Specific actions:

[1072] Simulation using competitive data: The simulation is performed based on the location data of competing facilities.

[1073] Strategic proposal: Develop a specific location strategy, such as "Opening a store in this area is expected to create synergistic effects with competing facilities."

[1074] Output: Optimal competitive placement strategy

[1075] (Application Example 1)

[1076] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1077] In modern commercial facility development and operation, effectively collecting and analyzing large amounts of data to make quick and accurate decisions is essential for appropriate location selection and profitability improvement. Therefore, features such as real-time data provision and visualization, and voice assistant guidance are required. Conventional systems fail to adequately meet these needs, resulting in challenges in optimizing operational efficiency and profitability.

[1078] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1079] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, the location information of competing facilities, and the circulating population data, means for identifying a suitable location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for providing data in real time via a user interface, means for guiding the user with a voice assistant based on the collected data, and means for visualizing the collected data using augmented reality technology. This enables commercial facility operators to support their decision-making regarding new store openings in real time.

[1080] "Demographic data" refers to statistical information about the population of a specific region or group, such as age, sex, occupation, and income.

[1081] "Competitor facility location information" refers to information indicating the geographical location of competing commercial facilities located within a specific area.

[1082] "Population mobility data" refers to information about the number of people moving within a specific region within a certain period of time, as well as their movement routes.

[1083] "Analysis means" refers to methods or devices used to analyze specific conditions and trends based on collected data and derive results.

[1084] "Means for determining location" refers to methods or devices for determining an appropriate store location based on the results of the analysis.

[1085] "Means for calculating rent and operating costs" refers to methods or devices for calculating the market rent and operating costs for a selected location.

[1086] "Means of proposing an operating model" refers to methods and devices for demonstrating the optimal management method based on calculated rent and operating costs.

[1087] "Means of providing data in real time" refers to methods or devices for immediately presenting collected and analyzed data to users via a user interface.

[1088] "Means of guiding users with voice assistants" refers to methods and devices for conveying analysis results and important information to users by voice.

[1089] "Means of visualizing collected data using augmented reality technology" refers to methods and devices for visually presenting collected data to users using technology that overlays it onto a three-dimensional space.

[1090] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data. Based on this data, it identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy. In particular, it features support functions that enable stress-free and immediate decision-making by providing real-time data, visualization, and guidance via voice assistants.

[1091] Required hardware and software

[1092] Hardware:

[1093] Smart glasses: A device that collects and visualizes data in real time using a camera.

[1094] Server: A computer system that functions as the central hub for data collection, analysis, and storage.

[1095] GPS sensor: A device for accurately acquiring location information.

[1096] software:

[1097] Google Cloud Vision API: A service for image processing and label detection.

[1098] Geopy: A library for obtaining location information.

[1099] Pandas: A Python library for data analysis and manipulation.

[1100] pyttsx3: A library for audio output.

[1101] arpy: A library for visualizing data using augmented reality technology.

[1102] System operation

[1103] 1. Data collection:

[1104] The server uses the Google Cloud Vision API to acquire data on competing facilities and traffic volume based on images obtained from smart glasses. Location information is collected using a GPS sensor and the Geopy library.

[1105] 2. Data Analysis:

[1106] The server uses Pandas to analyze the acquired location information, demographic data, and population movement data of competing facilities to gain a detailed understanding of regional characteristics.

[1107] 3. Data provided by:

[1108] When a user wears smart glasses and speaks a voice command such as "Check the profitability of this area," the server provides analysis results in real time and guides the user via voice using pyttsx3. Furthermore, visualized data is overlaid on the smart glasses' display using augmented reality (AR) technology.

[1109] Specific example

[1110] For example, when considering the possibility of opening a store in a shopping area, a user can wear smart glasses, take photos of their surroundings, and inquire via voice command, "Check the profitability of this area." The server immediately begins analysis, notifying the user of the results via voice while visualizing the data and displaying it on the smart glasses' screen. This allows the user to grasp detailed location information and profit forecasts on the spot, enabling efficient decision-making.

[1111] Example of a prompt

[1112] "Analyze the following image and evaluate the profitability of the store. Calculate a score based on data on the density of competing stores and the foot traffic."

[1113] "Please collect demographic data, traffic data, and competitor location information to calculate the profitability of opening a store at this location."

[1114] This system allows commercial facility operators to receive real-time support in decision-making regarding new store openings, enabling them to formulate more precise store opening strategies.

[1115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1116] Step 1:

[1117] The server receives image data acquired from the smart glasses. The user wears the smart glasses and takes pictures of a specific area. This image data is sent to the server. The input is the captured image data, and the output is the image data stored on the server.

[1118] Step 2:

[1119] The server uses the Google Cloud Vision API to analyze the received image data. Specifically, it performs label detection to identify competing facilities and traffic conditions within the image. The input is image data stored on the server, and the output is label data indicating the location information of competing facilities and traffic conditions.

[1120] Step 3:

[1121] The server uses the Geopy library to collect location information obtained from the GPS sensor of the smart glasses. The input is location data from the GPS sensor of the smart glasses, and the output is precise geographical location information for a specific area.

[1122] Step 4:

[1123] The server analyzes this location information, demographic data, and population flow data using Pandas. Specifically, it formats this data and analyzes regional characteristics, competitive landscape, and population flow trends. The input is location information, demographic data, and population flow data of competing facilities, and the output is data on regional characteristics, competitive landscape, and population flow trends as a result of the analysis.

[1124] Step 5:

[1125] The user speaks a voice command to the smart glasses, saying "Check the profitability of this area," and sends this command to the server. The input is a voice command, and the output is an analysis request to the server.

[1126] Step 6:

[1127] After receiving a voice command, the server uses the pyttsx3 library to provide the user with the analysis results via voice. The input is the analysis result data, and the output is a voice notification of the analysis results. Specifically, it provides voice guidance on profitability scores, competitive landscape, and other information.

[1128] Step 7:

[1129] The server simultaneously uses the arpy library to visualize the collected data in augmented reality and overlays the results onto the smart glasses' field of view. The input is the analyzed data, and the output is the visualized data on the smart glasses' display. Specifically, regional characteristics, the locations of competing facilities, and the movement of the circulating population are visually displayed.

[1130] This allows users to access detailed location information and revenue forecasts in real time, enabling them to make more efficient decisions.

[1131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1132] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. Furthermore, by combining it with an emotion engine that recognizes user emotions, it enables the construction of more accurate store opening strategies. This system utilizes user emotion data in addition to collecting and analyzing demographic data, location information of competing facilities, and population flow data.

[1133] The main components of the system and their operation

[1134] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, competitive placement strategy means, and an emotion engine. The processing of each means will be explained below with specific examples.

[1135] 1. Data Collection

[1136] The server accesses APIs and databases to collect the necessary data.

[1137] Specific example: The server calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. Furthermore, it obtains population flow data through traffic measurement systems and social media APIs.

[1138] 2. Data Analysis

[1139] The server analyzes the collected data to understand regional characteristics, competitive landscape, and population movement trends.

[1140] Specific example: The server uses analytical tools such as Python and R to analyze demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS and calculates competition density. Furthermore, it analyzes traffic volume data and social media posts to extract peak times and event information for the mobile population.

[1141] 3. Location Evaluation

[1142] The server identifies the optimal location for a store based on the analysis results.

[1143] Specific example: The server integrates analyzed demographic data, competitor information, and population flow data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime population flow, making it suitable for an entertainment shop."

[1144] 4. Proposed Operating Model

[1145] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1146] Specific example: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1147] 5. Revenue forecast

[1148] The server predicts future revenue based on an integrated database.

[1149] Specific example: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[1150] 6. Proposed competitive placement strategy

[1151] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1152] Specific example: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it might propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[1153] 7. Introduction of an emotional engine

[1154] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[1155] Specific example: The emotion engine installed in the device analyzes user feedback and social media posts to understand the user's emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[1156] 8. Adjusting location and operational models based on emotional data

[1157] The server dynamically adjusts its location and operating model based on emotional data obtained from the emotion engine.

[1158] Specific example: The server analyzes user sentiment data and, based on information such as "users in this area have a strong interest in a particular service," changes the location of the store and the content of the services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added as a candidate for opening a cafe.

[1159] Examples of use

[1160] Users (for example, commercial facility operators) can access a dashboard provided by the server to view detailed information about potential store locations. They can also review user sentiment data collected by the sentiment engine, allowing them to refine their store opening strategies with greater precision.

[1161] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. The system can update data in real time, allowing for re-evaluation based on new conditions and circumstances.

[1162] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This not only improves profitability and optimizes operational efficiency, but also allows for further improvements in customer satisfaction by utilizing user sentiment data.

[1163] The following describes the processing flow.

[1164] Step 1:

[1165] Data collection

[1166] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[1167] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains population flow data for a specific area through traffic measurement systems and social media APIs.

[1168] Step 2:

[1169] Data Analysis

[1170] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[1171] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. It uses GIS software to plot the location data of competing stores on a map and calculates competition density. It also analyzes traffic volume data and social media posts to understand pedestrian flow patterns during specific times of day and events.

[1172] Step 3:

[1173] Location evaluation

[1174] The server identifies the optimal location for a store based on the analysis results.

[1175] Specific operation: The server integrates analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[1176] Step 4:

[1177] Proposed Operating Model

[1178] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1179] Specific operation: The server retrieves rental market rates for each region from a real estate database and calculates operating costs (e.g., personnel costs, utility costs) based on that information. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1180] Step 5:

[1181] Revenue forecast

[1182] The server predicts future revenue based on integrated data.

[1183] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[1184] Step 6:

[1185] Proposal for competitive placement strategy

[1186] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1187] Specific operation: The server analyzes the location information of collected competitor facilities and creates strategies such as "opening a store in this area will maximize synergies with existing competitor facilities." For example, it might suggest measures to increase the store's ability to attract customers by opening a store in an area with few competitors of the same type and high foot traffic.

[1188] Step 7:

[1189] Emotion Engine Data Collection

[1190] The device uses an emotion engine to collect user emotion data.

[1191] Specific operation: The device analyzes user feedback and social media posts to recognize user emotions. For example, it extracts positive or negative feelings towards a particular store.

[1192] Step 8:

[1193] Analysis of emotional data

[1194] The server analyzes the user's emotional data obtained from the emotion engine.

[1195] Specific operation: The server statistically analyzes the collected sentiment data to understand which services and products users have positive or negative feelings towards. For example, it might derive information such as, "There are many positive reactions to cafes in this area."

[1196] Step 9:

[1197] Strategy adjustment based on emotional data

[1198] The server dynamically adjusts its location and operational model based on the analyzed sentiment data.

[1199] Specific operation: The server changes the location of the store and the services offered based on sentiment data. For example, if there is a lot of positive feedback for the cafe in a particular area, that area will be added as a candidate for opening a cafe, and the operating model will be adapted accordingly.

[1200] The specific processing steps described above enable the system to achieve efficient and profitable store openings and operations. Furthermore, by combining it with an emotion engine, it is possible to build strategies tailored to user needs and emotions, thereby improving customer satisfaction.

[1201] (Example 2)

[1202] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1203] In opening and operating new commercial facilities, there is a need to achieve increased efficiency and profitability. Conventional systems were limited to collecting and analyzing demographic data, location information of competing facilities, and population flow data, and strategies were often formulated without considering user sentiment data, resulting in a lack of accuracy. Furthermore, dynamic adjustments were difficult when identifying optimal locations and proposing operating models.

[1204] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1205] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for collecting and analyzing sentiment data, and means for adjusting the location and operating model based on the sentiment data. As a result, a highly accurate store opening strategy can be formulated based on the collected data, and user satisfaction can be improved by utilizing sentiment data.

[1206] "Demographic data" refers to statistical information such as population composition, age distribution, household composition, and income levels for each region.

[1207] "Location information of competing facilities" refers to the geographical information of where commercial facilities are located and the attribute information of those stores.

[1208] "Population mobility data" refers to information on the movement and stay of people within a specific area over a certain period of time.

[1209] "Means of analysis" refers to methods and tools for processing and analyzing collected data and extracting useful information.

[1210] "Means of identifying suitable locations" refers to methods and tools for determining the most advantageous store locations based on analytical data.

[1211] "Means for calculating rent and operating costs" refers to methods and tools for calculating the rent and various operating expenses necessary for real estate in a specific location.

[1212] "Means of proposing an operating model" refers to methods and tools for proposing the optimal operating method and revenue model based on calculated costs.

[1213] "Emotional data" refers to information obtained by analyzing users' emotions, opinions, feedback, and other psychological responses.

[1214] "Means for collecting and analyzing emotional data" refers to methods and tools for measuring users' emotions, analyzing them, and converting them into valuable data.

[1215] "Means of adjusting location and operating models based on emotional data" refers to methods and tools for dynamically modifying and optimizing potential store locations and operating methods by taking emotional data into consideration.

[1216] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and further combines this with an emotion engine to enable the construction of more accurate store opening strategies.

[1217] System components and their operation

[1218] 1. Data Collection

[1219] The server accesses APIs and databases to collect necessary data. Specifically, it calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. It also obtains population movement data through traffic measurement systems and social media APIs.

[1220] 2. Data Analysis

[1221] The server analyzes collected data to understand regional characteristics, competitive landscape, and population movement trends. Using analytical tools such as Python and R, it analyzes demographic data to understand the characteristics of local residents. Furthermore, it visualizes the locations of competing facilities using GIS and calculates competition density. Traffic data and social media posts are analyzed to extract peak times for population movement and event information.

[1222] 3. Location Evaluation

[1223] The server identifies the optimal location for a store based on the analysis results. It integrates the analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[1224] 4. Proposed Operating Model

[1225] The server calculates rent and operating costs based on identified candidate locations and proposes an operating model. It obtains rental market rates for each region from a real estate database and combines them with data on necessary operating costs (personnel costs, utilities, etc.) to calculate the total cost. For example, it might suggest, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1226] 5. Revenue forecast

[1227] The server predicts future revenue based on integrated data. Using machine learning models, it makes predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides specific revenue forecasts such as, "This location will generate an expected annual revenue of 5 million yen."

[1228] 6. Proposed competitive placement strategy

[1229] The server develops strategies to strengthen competitiveness based on the location information of competing facilities. It analyzes the collected location information of competing facilities and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." It proposes measures to increase the store's ability to attract customers by opening stores in areas with few competing stores of the same type and high foot traffic.

[1230] 7. Introduction of an emotional engine

[1231] The server uses an emotion engine to collect user emotion data and uses it for analysis. The emotion engine analyzes user feedback and social media posts to understand user emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[1232] 8. Adjusting location and operational models based on emotional data

[1233] The server dynamically adjusts location and operating model based on emotional data obtained from the emotion engine. By analyzing user emotional data and using information such as "users in this area have a strong interest in a particular service," it changes the location of stores and the content of services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added to the list of potential cafe locations.

[1234] Example of a prompt

[1235] "Please suggest potential locations for an entertainment shop in an area with a high concentration of young people in their 20s and 30s, and a high nighttime foot traffic."

[1236] "Based on the collected data, please propose an operational model to optimize the café opening strategy in a specific area."

[1237] This system will enable more efficient and accurate decision-making regarding the opening and operation of new stores in commercial facilities. Furthermore, by utilizing emotional data, it is expected to improve customer satisfaction.

[1238] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1239] Step 1:

[1240] Data collection

[1241] The server collects the data necessary for opening a store in a commercial facility.

[1242] Input: Data from the Census API, Google Maps API, traffic measurement systems, and social media APIs.

[1243] Output: Demographic data, location information of competing facilities, and population flow data.

[1244] Specific operation: The server uses the requests library to call the census API to obtain demographic data in JSON format. Similarly, it uses the Google Maps API to obtain location information for competing stores in each region and accesses the traffic volume measurement system API to collect population flow data.

[1245] Step 2:

[1246] Data Analysis

[1247] The server analyzes the collected data.

[1248] Input: Collected demographic data, location information of competing facilities, and population flow data.

[1249] Output: Analysis results regarding regional characteristics, competitive landscape, and trends in population movement.

[1250] Specific operation: The server uses the Pandas library to convert demographic data into a dataframe and analyze the characteristics of local residents. It also uses GeoPandas and Shapely to calculate competition density and visualize it on a map. For traffic data, time series analysis is performed to extract peak times and event information.

[1251] Step 3:

[1252] Location evaluation

[1253] The server identifies the optimal location for a store based on the analysis results.

[1254] Input: Analysis results (regional characteristics, competitive situation, trends in population movement).

[1255] Output: Ranking results for multiple potential store locations.

[1256] Specific operation: The server analyzes the data, scores it, and ranks each candidate location. For example, areas with a large young population and high nighttime foot traffic will receive a high score as potential locations for entertainment shops. This result is displayed on a dashboard.

[1257] Step 4:

[1258] Proposed Operating Model

[1259] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1260] Input: Information on identified potential store locations, rental data obtained from a real estate database, and pre-set operating costs.

[1261] Output: Total operating costs and proposed operating models for each candidate site.

[1262] Specific operation: The server uses a real estate database API to retrieve rental data and calculates the total cost by adding it to operating costs such as personnel expenses and utilities. Based on the results, it presents an operating model such as, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1263] Step 5:

[1264] Revenue forecast

[1265] The server predicts future revenue based on the integrated data.

[1266] Input: Integrated data (demographic data, competitor information, population flow data).

[1267] Output: Expected earnings.

[1268] Specific operation: The server uses the Scikit-learn library to train a machine learning model and build a predictive model based on the collected data. Using that model, it predicts future revenue and provides specific revenue forecasts such as "Expected annual revenue at this location is 5 million yen."

[1269] Step 6:

[1270] Proposal for competitive placement strategy

[1271] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1272] Input: Location information of competing facilities.

[1273] Output: Proposal of competitive placement strategy.

[1274] Specific operation: The server analyzes the location information of competing facilities on a GIS and formulates the optimal strategy for strengthening competitiveness. For example, it plots a strategy such as "opening a store in this area will maximize synergies with existing competing facilities" on a dashboard and presents it visually to the user.

[1275] Step 7:

[1276] Introducing an emotional engine

[1277] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[1278] Input: User feedback, social media posts.

[1279] Output: User sentiment data.

[1280] Specific operation: The emotion engine analyzes user feedback and tags emotions. It also analyzes text data from social media posts and extracts emotion trends.

[1281] Step 8:

[1282] Adjusting location and operating models based on emotional data

[1283] The server dynamically adjusts its location and operating model based on sentiment data.

[1284] Input: Sentiment data.

[1285] Output: Adjusted location and operational model.

[1286] Specific operation: The server statistically analyzes sentiment data to identify user trends in specific regions. Based on this, it re-evaluates potential store locations and the services offered, and displays the updated results on a dashboard. For example, if there is a high level of positive feedback towards cafes in a particular region, that region will be added as a potential location for a cafe.

[1287] Through these steps, the system can comprehensively analyze data and efficiently and accurately support decision-making regarding the opening and operation of commercial facilities. Furthermore, by utilizing user sentiment data, it can also improve customer satisfaction.

[1288] (Application Example 2)

[1289] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1290] To improve the efficiency and profitability of new store openings and operations in commercial facilities, store opening strategies must consider not only demographic and population flow data, but also user sentiment data. However, conventional systems have made it difficult to develop concrete store opening strategies that utilize sentiment data, resulting in challenges in optimizing operations and maximizing profits.

[1291] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for collecting sentiment data, means for analyzing the collected demographic data, location information of competing facilities, circulating population data, and sentiment data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, and means for proposing an optimal operating model. This makes it possible to construct a highly accurate store opening strategy utilizing sentiment data.

[1292] "Demographic data" refers to statistical information about the distribution and attributes of the population in a specific region.

[1293] "Location information of competing facilities" refers to geographical information that shows where competing facilities such as commercial facilities and stores are located.

[1294] "Population mobility data" refers to information about the number of people moving within a specific region and their movement patterns.

[1295] "Sentimental data" refers to information collected from users regarding their feelings and evaluations of specific services or facilities.

[1296] "Means of analysis" refers to methods and tools for analyzing collected data using statistical analysis and machine learning to extract meaningful information.

[1297] "Means of identifying a location" refers to methods and tools for selecting the optimal location for a commercial facility based on analysis results.

[1298] "Means for calculating rent and operating costs" refers to methods or tools for calculating rent and operating costs at a selected location.

[1299] "Means of proposing an operating model" refers to methods and tools for proposing the optimal business operation methods and strategies for a specific location.

[1300] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities, and in particular, to a system that enables the construction of more accurate store opening strategies by utilizing user sentiment data.

[1301] The system consists of the following hardware and software.

[1302] 1. Hardware

[1303] Server: Performs data collection, analysis, and proposes operational models.

[1304] Smartphone or tablet: A device used by store operators to check data and formulate store opening strategies.

[1305] 2. Software

[1306] Data collection APIs (e.g., Google Maps API, traffic volume measurement API, etc.)

[1307] Sentiment analysis engine (e.g., Microsoft Azure Cognitive Services)

[1308] Data analysis tools (e.g., Python, R, GIS software)

[1309] System operation

[1310] The system operates using the following steps:

[1311] 1. Data Collection

[1312] The server uses the Google Maps API to collect location information for competing facilities. It also obtains demographic data through the Census API and collects population movement data using the traffic volume measurement API.

[1313] Emotional data is obtained from social media and user feedback and analyzed by an emotion analysis engine.

[1314] 2. Data Analysis

[1315] The server integrates and analyzes collected demographic data, location information of competing facilities, population movement data, and sentiment data. Based on the analysis results, it understands regional characteristics, competitive landscape, and user sentiment trends.

[1316] 3. Location identification and proposal of an operational model

[1317] Based on the analysis results, the server identifies the optimal locations for store openings. The identified locations are presented in a ranking format along with location evaluations, projected revenues, and operating costs.

[1318] Based on the data displayed on the device, users can consider the optimal operating model.

[1319] Specific example

[1320] For example, when a user considers opening a new store, data is collected and analyzed using the following procedure.

[1321] Prompt message example 1: Obtain location information of competitor stores

[1322] "Please use the Google Maps API to retrieve store information for locations within a 5-kilometer radius of the proposed store location."

[1323] Prompt example 2: Analysis of sentiment data

[1324] "Collect social media posts and analyze the sentiment score for this region."

[1325] Prompt message example 3: Suggestion of the optimal store location

[1326] "Based on demographic data and competitive landscape in this area, please rank the most suitable locations for opening a store."

[1327] This allows users to develop highly accurate store opening strategies based on real-time data. Furthermore, by incorporating emotional data, it is expected to contribute to improved customer satisfaction.

[1328] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1329] Step 1:

[1330] The server collects demographic data, location information of competitor facilities, and population flow data using various APIs. Specifically, it obtains location information of competitor facilities using the Google Maps API and collects demographic data through the Census API. It also obtains population flow data using the Traffic Volume Measurement API. The input for data collection is the API endpoints and parameters, and the output is the data obtained from the APIs.

[1331] Step 2:

[1332] The server collects and analyzes emotional data using an emotion analysis engine. Specifically, it obtains text data from social media and user feedback and inputs it into the emotion analysis engine. This engine analyzes the text data and outputs an emotion score such as positive, negative, or neutral. The input is text data, and the output is an emotion score.

[1333] Step 3:

[1334] The server integrates collected and analyzed demographic data, location information of competing facilities, population movement data, and sentiment data, and performs analysis using data analysis tools. Specifically, it uses Python and R to integrate each dataset and understand regional characteristics, competitive situations, and population movement trends. The input is various datasets, and the output is the analysis results.

[1335] Step 4:

[1336] The server identifies suitable location candidates based on the analysis results and presents them in a ranked format. Specifically, it uses GIS software to perform geographical visualization and ranks the location candidates according to evaluation criteria. Users can view this information on their smartphones or tablets. The input is the analysis results, and the output is a list of ranked location candidates.

[1337] Step 5:

[1338] The server calculates rent and operating costs based on the selected location. Specifically, it retrieves rental market rates for each region from a real estate database and calculates the costs necessary for operation (personnel costs, utilities, etc.). The inputs are location information and real estate data, and the outputs are rent and operating costs.

[1339] Step 6:

[1340] The server proposes the optimal operating model. Based on analysis results and projected revenue data, it presents specific operating models and strategies. Users can review the operating model and make decisions on their smartphones or tablets. Inputs are rent, operating costs, and projected revenue data, and output is a proposed operating model.

[1341] Step 7:

[1342] Based on information provided by the server, users consider potential store locations and make adjustments by incorporating sentiment data. Specifically, they prioritize favorable areas identified by sentiment data and formulate their final store location strategy. The input is the server's suggestions, and the output is the final store location strategy.

[1343] This will enable users to develop highly accurate store opening strategies in real time using emotional data, which is expected to improve customer satisfaction and maximize profitability.

[1344] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1345] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1346] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1347] [Fourth Embodiment]

[1348] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1349] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1350] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1351] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1352] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1353] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1354] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1355] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1356] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1357] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1358] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1359] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1360] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1361] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and based on this data identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy.

[1362] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, and competitive placement strategy means. The processing of each means will be explained below with specific examples.

[1363] 1. Data Collection

[1364] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[1365] Specific example: The server retrieves demographic data such as age distribution and average income for a specific region from a census database. It also uses the Google Maps API to collect location information for competing stores in that region. Furthermore, it obtains population movement data for a specific area through traffic data and social media APIs.

[1366] 2. Data Analysis

[1367] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[1368] Specific example: The server uses analytical tools such as Python and R to process demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS (Geographic Information System) and analyzes the density of competition. Furthermore, it analyzes traffic volume and social media data to extract peak times for population movement and event information.

[1369] 3. Location Evaluation

[1370] Based on the analysis results, the server identifies the most suitable location for opening a store.

[1371] Specific example: The server integrates demographic, competitor, and traffic data to evaluate the advantages and disadvantages of each candidate location. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime traffic volume, making it suitable for an apparel store."

[1372] 4. Proposed Operating Model

[1373] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[1374] Specific example: The server retrieves the average rent for a given location from a database and uses that information to calculate the balance with operating costs (e.g., personnel costs, management fees). For example, it might propose an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[1375] 5. Revenue forecast

[1376] The server predicts future revenue based on integrated data.

[1377] Specific example: The server builds a sales forecasting model and calculates expected revenue for each location. This uses statistical and machine learning models based on current data to provide specific predictions, such as "expected annual revenue for this location will be 5 million yen."

[1378] 6. Competitive Placement Strategy

[1379] The server optimizes its placement relative to nearby competing facilities and proposes strategies to enhance its competitive edge.

[1380] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "Opening a store in this area can be expected to create synergistic effects with competing facilities."

[1381] Examples of use

[1382] Users (for example, operators of commercial facilities) access a dashboard provided by the server to view detailed information about potential store locations.

[1383] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. They can also update the data in real time and re-evaluate their choices based on new conditions and circumstances.

[1384] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This, in turn, leads to improved profitability and optimized operational efficiency.

[1385] The following describes the processing flow.

[1386] Step 1:

[1387] Data collection

[1388] The server accesses APIs and databases to collect the necessary data.

[1389] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains data on the mobile population through traffic measurement systems and social media APIs.

[1390] Step 2:

[1391] Data Analysis

[1392] The server analyzes the collected data and extracts useful information.

[1393] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. Simultaneously, it uses GIS software to plot the location data of competing stores on a map and calculate competition density. Furthermore, it analyzes traffic data and social media posts to understand pedestrian flow patterns during specific times of day or events.

[1394] Step 3:

[1395] Location evaluation

[1396] The server identifies the optimal location for a store based on the analysis results.

[1397] Specific operation: The server integrates analyzed demographic data, competitor store information, and pedestrian traffic data to rank multiple potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[1398] Step 4:

[1399] Proposed Operating Model

[1400] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1401] Specific operation: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1402] Step 5:

[1403] Revenue forecast

[1404] The server predicts future revenue based on an integrated database.

[1405] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[1406] Step 6:

[1407] Proposal for competitive placement strategy

[1408] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1409] Specific operation: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it may propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[1410] Through the above processing steps, the system enables efficient and profitable store opening and operation.

[1411] (Example 1)

[1412] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1413] To ensure the opening of new commercial facilities, improve operational efficiency, and increase profitability, it is necessary to collect and analyze various data and identify appropriate locations based on the results. However, traditional methods were time-consuming and inefficient in collecting and analyzing the necessary data, and had limitations in accuracy, making it difficult to formulate appropriate store opening strategies. Furthermore, it was difficult to develop strategies that took into account the relationship with competing facilities.

[1414] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1415] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for calculating predicted revenue, and means for proposing a location strategy that takes into account synergies with competing facilities. This makes it possible to efficiently collect and analyze various types of data and formulate a highly accurate store opening strategy. Furthermore, by proposing a location strategy that takes into account synergies with competitors, it becomes possible to formulate a competitive store opening plan.

[1416] "Demographic data" refers to statistical information about the population in a specific region, such as age, gender, number of households, average income, and occupational distribution.

[1417] "Competitor location information" refers to geographical information that indicates the location of competing stores and services in the same industry within a commercial or service facility.

[1418] "Population flow data" refers to information about the movement and retention of people in a specific region or area over a certain period of time, and includes traffic volume, visitor numbers, and the flow of people by time of day.

[1419] "Analysis results" refer to the output of analysis and interpretation based on collected data, and are indicators or visualized data that show the characteristics and trends of a specific location.

[1420] "Appropriate location" refers to the most suitable place for opening or operating a commercial or service facility, based on collected and analyzed data.

[1421] "Rent" refers to the cost required to rent land or a building in a specific location.

[1422] "Operating costs" refer to the expenses incurred in running commercial or service facilities, including personnel costs, management fees, and utility costs.

[1423] An "operational model" is a plan that outlines the optimal operating methods and strategies based on collected and analyzed data.

[1424] "Projected revenue" refers to numerical values ​​or indicators that show the expected future sales and profits, derived from collected and analyzed data.

[1425] "Synergy with competing facilities" refers to the phenomenon where opening a store in a specific location creates cooperation or competition with nearby competing facilities, leading to increased revenue and visitor numbers for both parties.

[1426] A "location strategy" is a set of policies and methods used to determine the optimal location for competing facilities and one's own facilities, based on collected and analyzed data.

[1427] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes various data to formulate effective store opening strategies and enhance the competitiveness of commercial facilities, thereby identifying optimal locations and proposing operational models.

[1428] System Configuration

[1429] The main components of this system include the following means:

[1430] Data acquisition methods

[1431] Data analysis means

[1432] Location evaluation methods

[1433] Operating Model Proposal Methods

[1434] Revenue forecasting methods

[1435] Competitive Deployment Strategies

[1436] These methods are server-centric and perform advanced analysis based on data collected from users and terminals. Furthermore, these methods are primarily implemented using the following specific software and hardware.

[1437] Specific software and hardware to be used

[1438] Databases: Census database, Competitor information database

[1439] APIs: Google Maps API, Traffic Sensor API, Social Media API

[1440] Analysis tools: Python, R, GIS (Geographic Information System)

[1441] Server: High-performance server (e.g., cloud-based server)

[1442] Specific example of processing

[1443] Data collection

[1444] The server uses APIs and databases to collect demographic data, location information of competing facilities, and population flow data.

[1445] Specific example: The server uses the Census API to obtain data on age distribution and average income in a specific area. It uses the Google Maps API to collect location information for competitor stores and uses traffic sensor APIs and social media APIs to gather population movement data for a specific area.

[1446] Data Analysis

[1447] The server uses analytical tools to analyze the collected data, examining regional characteristics, competitive landscape, and population movement trends.

[1448] Specific example: The server uses Python and R to process collected demographic data and understand the characteristics of local residents. It also uses GIS to visualize the locations of competing facilities and analyze the density of competition. Furthermore, it analyzes traffic volume data and social media data to understand the trends of the mobile population.

[1449] Location evaluation

[1450] The server identifies suitable locations for store openings based on the analysis results.

[1451] Specific example: The server integrates demographic data, location information of competing facilities, and pedestrian traffic data to evaluate potential store locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an apparel store."

[1452] Proposed Operating Model

[1453] The server calculates operating costs based on the selected location and proposes the optimal operating model.

[1454] Specific example: The server retrieves the average rent for a location from a database and calculates the balance with operating costs (personnel costs, management fees, etc.). It then proposes an operating model such as, "The monthly rent in this area is 200,000 yen, personnel costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[1455] Revenue forecast

[1456] The server predicts future revenue based on integrated data.

[1457] Specific example: The server builds a sales forecasting model and calculates the expected revenue for each location. For example, it provides a specific forecast such as, "The expected annual revenue for this location will be 5 million yen."

[1458] Competitive Placement Strategy

[1459] The server proposes a placement strategy in relation to nearby competing facilities.

[1460] Specific example: The server performs simulations based on location data of competing facilities and formulates competitive placement strategies such as, "If we open a store in this area, we can expect synergistic effects with competing facilities."

[1461] Examples of prompt statements

[1462] Example: "Obtain the population distribution of people in their 20s and 30s in a specific region, and use this data to propose potential locations for apparel stores."

[1463] Specific example: "Please provide an operational model and revenue forecast for a potential new store location. Evaluate it based on collected demographic data, location information of competing facilities, and population flow data."

[1464] This invention enables commercial facility operators (users) to formulate efficient and precise store opening strategies. This leads to improved profitability and optimized operational efficiency.

[1465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1466] Step 1:

[1467] Data collection

[1468] Input: Regional information (e.g., coordinates of the target area), data collection conditions (e.g., age distribution, number of competitors)

[1469] The server accesses APIs and databases to collect necessary demographic data, location information for competing facilities, and population flow data.

[1470] Specific actions:

[1471] Demographic data collection: Send requests to the Census API to obtain data on age distribution and average income for a specific region.

[1472] Gathering location information for competitor facilities: Use the Google Maps API to obtain location information for competitor facilities within the area.

[1473] Collection of population flow data: Use traffic sensor APIs and social media APIs to collect population flow data for specific areas.

[1474] Output: Collected demographic data, location information of competing facilities, and population flow data.

[1475] Step 2:

[1476] Data Analysis

[1477] Input: Collected demographic data, location information of competing facilities, and population flow data.

[1478] The server processes the collected data using analytical tools to analyze regional characteristics, competitive landscape, and population movement trends.

[1479] Specific actions:

[1480] Demographic Data Analysis: Using Python or R, collect demographic data is statistically analyzed to understand the characteristics of local residents.

[1481] Competitive facility situation analysis: Use GIS to visualize the location information of competing facilities and analyze the density and influence of competition.

[1482] Analysis of population flow data: Analyze traffic volume data and social media data to extract peak times and patterns of population flow.

[1483] Output: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[1484] Step 3:

[1485] Location evaluation

[1486] Input: Regional characteristics analysis results, competitive situation analysis results, population mobility analysis results

[1487] The server identifies suitable locations for store openings based on the analysis results.

[1488] Specific actions:

[1489] Data Integration: The collected and analyzed data is integrated to evaluate each candidate site.

[1490] Generating evaluation results: Generate specific evaluation results such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime foot traffic, making it suitable for apparel stores."

[1491] Output: List of suitable store locations, evaluation results for each location

[1492] Step 4:

[1493] Proposed Operating Model

[1494] Input: List of suitable potential store locations, evaluation results for each location

[1495] The server calculates rent and operating costs based on the selected location and proposes the optimal operating model.

[1496] Specific actions:

[1497] Acquiring rental data: Obtain rental market rates for the relevant area from the database.

[1498] Cost calculation: Calculate by balancing operating costs (e.g., personnel costs, administrative costs, etc.).

[1499] Proposed operating model: We propose an operating model that states, "The monthly rent in this area is 200,000 yen, labor costs are 300,000 yen, and estimated monthly sales are 600,000 yen, therefore this location is highly profitable."

[1500] Output: Optimal operating model, detailed operating costs

[1501] Step 5:

[1502] Revenue forecast

[1503] Input: List of suitable store locations, evaluation results for each location, optimal operating model, and detailed operating costs.

[1504] The server predicts future revenue based on integrated data.

[1505] Specific actions:

[1506] Building Predictive Models: Build revenue prediction models using machine learning models and statistical models.

[1507] Revenue forecast: Calculates expected revenue for each location. Provides specific forecasts such as, "Expected annual revenue for this location is 5 million yen."

[1508] Output: Predicted revenue for each location

[1509] Step 6:

[1510] Competitive Placement Strategy

[1511] Input: List of suitable store locations, evaluation results for each location, optimal operating model, detailed operating costs, and projected revenue for each location.

[1512] The server proposes a placement strategy that takes into account synergies with competing facilities.

[1513] Specific actions:

[1514] Simulation using competitive data: The simulation is performed based on the location data of competing facilities.

[1515] Strategic proposal: Develop a specific location strategy, such as "Opening a store in this area is expected to create synergistic effects with competing facilities."

[1516] Output: Optimal competitive placement strategy

[1517] (Application Example 1)

[1518] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1519] In modern commercial facility development and operation, effectively collecting and analyzing large amounts of data to make quick and accurate decisions is essential for appropriate location selection and profitability improvement. Therefore, features such as real-time data provision and visualization, and voice assistant guidance are required. Conventional systems fail to adequately meet these needs, resulting in challenges in optimizing operational efficiency and profitability.

[1520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1521] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, the location information of competing facilities, and the circulating population data, means for identifying a suitable location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for providing data in real time via a user interface, means for guiding the user with a voice assistant based on the collected data, and means for visualizing the collected data using augmented reality technology. This enables commercial facility operators to support their decision-making regarding new store openings in real time.

[1522] "Demographic data" refers to statistical information about the population of a specific region or group, such as age, sex, occupation, and income.

[1523] "Competitor facility location information" refers to information indicating the geographical location of competing commercial facilities located within a specific area.

[1524] "Population mobility data" refers to information about the number of people moving within a specific region within a certain period of time, as well as their movement routes.

[1525] "Analysis means" refers to methods or devices used to analyze specific conditions and trends based on collected data and derive results.

[1526] "Means for determining location" refers to methods or devices for determining an appropriate store location based on the results of the analysis.

[1527] "Means for calculating rent and operating costs" refers to methods or devices for calculating the market rent and operating costs for a selected location.

[1528] "Means of proposing an operating model" refers to methods and devices for demonstrating the optimal management method based on calculated rent and operating costs.

[1529] "Means of providing data in real time" refers to methods or devices for immediately presenting collected and analyzed data to users via a user interface.

[1530] "Means of guiding users with voice assistants" refers to methods and devices for conveying analysis results and important information to users by voice.

[1531] "Means of visualizing collected data using augmented reality technology" refers to methods and devices for visually presenting collected data to users using technology that overlays it onto a three-dimensional space.

[1532] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data. Based on this data, it identifies suitable locations and proposes operational models, thereby providing a more profitable store opening strategy. In particular, it features support functions that enable stress-free and immediate decision-making by providing real-time data, visualization, and guidance via voice assistants.

[1533] Required hardware and software

[1534] Hardware:

[1535] Smart glasses: A device that collects and visualizes data in real time using a camera.

[1536] Server: A computer system that functions as the central hub for data collection, analysis, and storage.

[1537] GPS sensor: A device for accurately acquiring location information.

[1538] software:

[1539] Google Cloud Vision API: A service for image processing and label detection.

[1540] Geopy: A library for obtaining location information.

[1541] Pandas: A Python library for data analysis and manipulation.

[1542] pyttsx3: A library for audio output.

[1543] arpy: A library for visualizing data using augmented reality technology.

[1544] System operation

[1545] 1. Data collection:

[1546] The server uses the Google Cloud Vision API to acquire data on competing facilities and traffic volume based on images obtained from smart glasses. Location information is collected using a GPS sensor and the Geopy library.

[1547] 2. Data Analysis:

[1548] The server uses Pandas to analyze the acquired location information, demographic data, and population movement data of competing facilities to gain a detailed understanding of regional characteristics.

[1549] 3. Data provided by:

[1550] When a user wears smart glasses and speaks a voice command such as "Check the profitability of this area," the server provides analysis results in real time and guides the user via voice using pyttsx3. Furthermore, visualized data is overlaid on the smart glasses' display using augmented reality (AR) technology.

[1551] Specific example

[1552] For example, when considering the possibility of opening a store in a shopping area, a user can wear smart glasses, take photos of their surroundings, and inquire via voice command, "Check the profitability of this area." The server immediately begins analysis, notifying the user of the results via voice while visualizing the data and displaying it on the smart glasses' screen. This allows the user to grasp detailed location information and profit forecasts on the spot, enabling efficient decision-making.

[1553] Example of a prompt

[1554] "Analyze the following image and evaluate the profitability of the store. Calculate a score based on data on the density of competing stores and the foot traffic."

[1555] "Please collect demographic data, traffic data, and competitor location information to calculate the profitability of opening a store at this location."

[1556] This system allows commercial facility operators to receive real-time support in decision-making regarding new store openings, enabling them to formulate more precise store opening strategies.

[1557] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1558] Step 1:

[1559] The server receives image data acquired from the smart glasses. The user wears the smart glasses and takes pictures of a specific area. This image data is sent to the server. The input is the captured image data, and the output is the image data stored on the server.

[1560] Step 2:

[1561] The server uses the Google Cloud Vision API to analyze the received image data. Specifically, it performs label detection to identify competing facilities and traffic conditions within the image. The input is image data stored on the server, and the output is label data indicating the location information of competing facilities and traffic conditions.

[1562] Step 3:

[1563] The server uses the Geopy library to collect location information obtained from the GPS sensor of the smart glasses. The input is location data from the GPS sensor of the smart glasses, and the output is precise geographical location information for a specific area.

[1564] Step 4:

[1565] The server analyzes this location information, demographic data, and population flow data using Pandas. Specifically, it formats this data and analyzes regional characteristics, competitive landscape, and population flow trends. The input is location information, demographic data, and population flow data of competing facilities, and the output is data on regional characteristics, competitive landscape, and population flow trends as a result of the analysis.

[1566] Step 5:

[1567] The user speaks a voice command to the smart glasses, saying "Check the profitability of this area," and sends this command to the server. The input is a voice command, and the output is an analysis request to the server.

[1568] Step 6:

[1569] After receiving a voice command, the server uses the pyttsx3 library to provide the user with the analysis results via voice. The input is the analysis result data, and the output is a voice notification of the analysis results. Specifically, it provides voice guidance on profitability scores, competitive landscape, and other information.

[1570] Step 7:

[1571] The server simultaneously uses the arpy library to visualize the collected data in augmented reality and overlays the results onto the smart glasses' field of view. The input is the analyzed data, and the output is the visualized data on the smart glasses' display. Specifically, regional characteristics, the locations of competing facilities, and the movement of the circulating population are visually displayed.

[1572] This allows users to access detailed location information and revenue forecasts in real time, enabling them to make more efficient decisions.

[1573] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1574] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. Furthermore, by combining it with an emotion engine that recognizes user emotions, it enables the construction of more accurate store opening strategies. This system utilizes user emotion data in addition to collecting and analyzing demographic data, location information of competing facilities, and population flow data.

[1575] The main components of the system and their operation

[1576] The main components of the system include data collection means, data analysis means, location evaluation means, operational model proposal means, revenue forecasting means, competitive placement strategy means, and an emotion engine. The processing of each means will be explained below with specific examples.

[1577] 1. Data Collection

[1578] The server accesses APIs and databases to collect the necessary data.

[1579] Specific example: The server calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. Furthermore, it obtains population flow data through traffic measurement systems and social media APIs.

[1580] 2. Data Analysis

[1581] The server analyzes the collected data to understand regional characteristics, competitive landscape, and population movement trends.

[1582] Specific example: The server uses analytical tools such as Python and R to analyze demographic data and understand the characteristics of local residents. It visualizes the location information of competing facilities using GIS and calculates competition density. Furthermore, it analyzes traffic volume data and social media posts to extract peak times and event information for the mobile population.

[1583] 3. Location Evaluation

[1584] The server identifies the optimal location for a store based on the analysis results.

[1585] Specific example: The server integrates analyzed demographic data, competitor information, and population flow data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime population flow, making it suitable for an entertainment shop."

[1586] 4. Proposed Operating Model

[1587] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1588] Specific example: The server retrieves rental market rates for each region from a real estate database and combines them with data on operating costs (personnel costs, utilities, etc.) to calculate total costs. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1589] 5. Revenue forecast

[1590] The server predicts future revenue based on an integrated database.

[1591] Specific example: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a specific revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[1592] 6. Proposed competitive placement strategy

[1593] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1594] Specific example: The server analyzes the location information of competing facilities that it has collected and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." For example, it might propose measures to increase the store's ability to attract customers by opening a store in an area with few competing stores of the same type and high foot traffic.

[1595] 7. Introduction of an emotional engine

[1596] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[1597] Specific example: The emotion engine installed in the device analyzes user feedback and social media posts to understand the user's emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[1598] 8. Adjusting location and operational models based on emotional data

[1599] The server dynamically adjusts its location and operating model based on emotional data obtained from the emotion engine.

[1600] Specific example: The server analyzes user sentiment data and, based on information such as "users in this area have a strong interest in a particular service," changes the location of the store and the content of the services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added as a candidate for opening a cafe.

[1601] Examples of use

[1602] Users (for example, commercial facility operators) can access a dashboard provided by the server to view detailed information about potential store locations. They can also review user sentiment data collected by the sentiment engine, allowing them to refine their store opening strategies with greater precision.

[1603] Users review the operating models, revenue forecasts, and competitive placement strategies proposed by the server and select the most suitable location for their store. The system can update data in real time, allowing for re-evaluation based on new conditions and circumstances.

[1604] This invention enables more efficient and accurate decision-making regarding the opening and operation of new commercial facilities. This not only improves profitability and optimizes operational efficiency, but also allows for further improvements in customer satisfaction by utilizing user sentiment data.

[1605] The following describes the processing flow.

[1606] Step 1:

[1607] Data collection

[1608] The server accesses APIs and databases to collect demographic data, location information for competing facilities, and population flow data.

[1609] Specific operation: The server calls the Census API to obtain demographic data such as age distribution, household income, and education level by region. Next, it uses the Google Maps API to collect location information of competing stores within a specified area. It also obtains population flow data for a specific area through traffic measurement systems and social media APIs.

[1610] Step 2:

[1611] Data Analysis

[1612] The server analyzes the collected data to determine regional characteristics, competitive landscape, and trends in population movement.

[1613] Specific operation: The server uses the Python pandas library to load collected data into a dataframe and analyzes age distribution and average income. It uses GIS software to plot the location data of competing stores on a map and calculates competition density. It also analyzes traffic volume data and social media posts to understand pedestrian flow patterns during specific times of day and events.

[1614] Step 3:

[1615] Location evaluation

[1616] The server identifies the optimal location for a store based on the analysis results.

[1617] Specific operation: The server integrates analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations for a store. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[1618] Step 4:

[1619] Proposed Operating Model

[1620] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1621] Specific operation: The server retrieves rental market rates for each region from a real estate database and calculates operating costs (e.g., personnel costs, utility costs) based on that information. For example, it might present an operating model such as, "The monthly rent in this region is 200,000 yen, and the estimated personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1622] Step 5:

[1623] Revenue forecast

[1624] The server predicts future revenue based on integrated data.

[1625] Specific operation: The server uses a machine learning model to make predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides a revenue forecast such as, "This location will have an expected annual revenue of 5 million yen."

[1626] Step 6:

[1627] Proposal for competitive placement strategy

[1628] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1629] Specific operation: The server analyzes the location information of collected competitor facilities and creates strategies such as "opening a store in this area will maximize synergies with existing competitor facilities." For example, it might suggest measures to increase the store's ability to attract customers by opening a store in an area with few competitors of the same type and high foot traffic.

[1630] Step 7:

[1631] Emotion Engine Data Collection

[1632] The device uses an emotion engine to collect user emotion data.

[1633] Specific operation: The device analyzes user feedback and social media posts to recognize user emotions. For example, it extracts positive or negative feelings towards a particular store.

[1634] Step 8:

[1635] Analysis of emotional data

[1636] The server analyzes the user's emotional data obtained from the emotion engine.

[1637] Specific operation: The server statistically analyzes the collected sentiment data to understand which services and products users have positive or negative feelings towards. For example, it might derive information such as, "There are many positive reactions to cafes in this area."

[1638] Step 9:

[1639] Strategy adjustment based on emotional data

[1640] The server dynamically adjusts its location and operational model based on the analyzed sentiment data.

[1641] Specific operation: The server changes the location of the store and the services offered based on sentiment data. For example, if there is a lot of positive feedback for the cafe in a particular area, that area will be added as a candidate for opening a cafe, and the operating model will be adapted accordingly.

[1642] The specific processing steps described above enable the system to achieve efficient and profitable store openings and operations. Furthermore, by combining it with an emotion engine, it is possible to build strategies tailored to user needs and emotions, thereby improving customer satisfaction.

[1643] (Example 2)

[1644] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1645] In opening and operating new commercial facilities, there is a need to achieve increased efficiency and profitability. Conventional systems were limited to collecting and analyzing demographic data, location information of competing facilities, and population flow data, and strategies were often formulated without considering user sentiment data, resulting in a lack of accuracy. Furthermore, dynamic adjustments were difficult when identifying optimal locations and proposing operating models.

[1646] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1647] In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for analyzing the collected demographic data, location information of competing facilities, and circulating population data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, means for proposing an optimal operating model, means for collecting and analyzing sentiment data, and means for adjusting the location and operating model based on the sentiment data. As a result, a highly accurate store opening strategy can be formulated based on the collected data, and user satisfaction can be improved by utilizing sentiment data.

[1648] "Demographic data" refers to statistical information such as population composition, age distribution, household composition, and income levels for each region.

[1649] "Location information of competing facilities" refers to the geographical information of where commercial facilities are located and the attribute information of those stores.

[1650] "Population mobility data" refers to information on the movement and stay of people within a specific area over a certain period of time.

[1651] "Means of analysis" refers to methods and tools for processing and analyzing collected data and extracting useful information.

[1652] "Means of identifying suitable locations" refers to methods and tools for determining the most advantageous store locations based on analytical data.

[1653] "Means for calculating rent and operating costs" refers to methods and tools for calculating the rent and various operating expenses necessary for real estate in a specific location.

[1654] "Means of proposing an operating model" refers to methods and tools for proposing the optimal operating method and revenue model based on calculated costs.

[1655] "Emotional data" refers to information obtained by analyzing users' emotions, opinions, feedback, and other psychological responses.

[1656] "Means for collecting and analyzing emotional data" refers to methods and tools for measuring users' emotions, analyzing them, and converting them into valuable data.

[1657] "Means of adjusting location and operating models based on emotional data" refers to methods and tools for dynamically modifying and optimizing potential store locations and operating methods by taking emotional data into consideration.

[1658] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities. This system collects and analyzes demographic data, location information of competing facilities, and population flow data, and further combines this with an emotion engine to enable the construction of more accurate store opening strategies.

[1659] System components and their operation

[1660] 1. Data Collection

[1661] The server accesses APIs and databases to collect necessary data. Specifically, it calls the Census API to obtain demographic data for each region and uses the Google Maps API to collect location information for competing stores. It also obtains population movement data through traffic measurement systems and social media APIs.

[1662] 2. Data Analysis

[1663] The server analyzes collected data to understand regional characteristics, competitive landscape, and population movement trends. Using analytical tools such as Python and R, it analyzes demographic data to understand the characteristics of local residents. Furthermore, it visualizes the locations of competing facilities using GIS and calculates competition density. Traffic data and social media posts are analyzed to extract peak times for population movement and event information.

[1664] 3. Location Evaluation

[1665] The server identifies the optimal location for a store based on the analysis results. It integrates the analyzed demographic data, competitor information, and pedestrian traffic data to rank multiple potential locations. For example, it might make an evaluation such as, "This area has a large population of young people in their 20s and 30s, and a high nighttime pedestrian traffic, making it suitable for an entertainment shop."

[1666] 4. Proposed Operating Model

[1667] The server calculates rent and operating costs based on identified candidate locations and proposes an operating model. It obtains rental market rates for each region from a real estate database and combines them with data on necessary operating costs (personnel costs, utilities, etc.) to calculate the total cost. For example, it might suggest, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1668] 5. Revenue forecast

[1669] The server predicts future revenue based on integrated data. Using machine learning models, it makes predictions based on historical data and calculates the expected revenue for each candidate location. For example, it provides specific revenue forecasts such as, "This location will generate an expected annual revenue of 5 million yen."

[1670] 6. Proposed competitive placement strategy

[1671] The server develops strategies to strengthen competitiveness based on the location information of competing facilities. It analyzes the collected location information of competing facilities and creates strategies such as, "Opening a store in this area will maximize synergies with existing competing facilities." It proposes measures to increase the store's ability to attract customers by opening stores in areas with few competing stores of the same type and high foot traffic.

[1672] 7. Introduction of an emotional engine

[1673] The server uses an emotion engine to collect user emotion data and uses it for analysis. The emotion engine analyzes user feedback and social media posts to understand user emotions. For example, it collects information such as "the user has positive feelings towards a particular store."

[1674] 8. Adjusting location and operational models based on emotional data

[1675] The server dynamically adjusts location and operating model based on emotional data obtained from the emotion engine. By analyzing user emotional data and using information such as "users in this area have a strong interest in a particular service," it changes the location of stores and the content of services offered. For example, if there is a lot of positive feedback about cafes in a particular area, that area will be added to the list of potential cafe locations.

[1676] Example of a prompt

[1677] "Please suggest potential locations for an entertainment shop in an area with a high concentration of young people in their 20s and 30s, and a high nighttime foot traffic."

[1678] "Based on the collected data, please propose an operational model to optimize the café opening strategy in a specific area."

[1679] This system will enable more efficient and accurate decision-making regarding the opening and operation of new stores in commercial facilities. Furthermore, by utilizing emotional data, it is expected to improve customer satisfaction.

[1680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1681] Step 1:

[1682] Data collection

[1683] The server collects the data necessary for opening a store in a commercial facility.

[1684] Input: Data from the Census API, Google Maps API, traffic measurement systems, and social media APIs.

[1685] Output: Demographic data, location information of competing facilities, and population flow data.

[1686] Specific operation: The server uses the requests library to call the census API to obtain demographic data in JSON format. Similarly, it uses the Google Maps API to obtain location information for competing stores in each region and accesses the traffic volume measurement system API to collect population flow data.

[1687] Step 2:

[1688] Data Analysis

[1689] The server analyzes the collected data.

[1690] Input: Collected demographic data, location information of competing facilities, and population flow data.

[1691] Output: Analysis results regarding regional characteristics, competitive landscape, and trends in population movement.

[1692] Specific operation: The server uses the Pandas library to convert demographic data into a dataframe and analyze the characteristics of local residents. It also uses GeoPandas and Shapely to calculate competition density and visualize it on a map. For traffic data, time series analysis is performed to extract peak times and event information.

[1693] Step 3:

[1694] Location evaluation

[1695] The server identifies the optimal location for a store based on the analysis results.

[1696] Input: Analysis results (regional characteristics, competitive situation, trends in population movement).

[1697] Output: Ranking results for multiple potential store locations.

[1698] Specific operation: The server analyzes the data, scores it, and ranks each candidate location. For example, areas with a large young population and high nighttime foot traffic will receive a high score as potential locations for entertainment shops. This result is displayed on a dashboard.

[1699] Step 4:

[1700] Proposed Operating Model

[1701] Based on the identified candidate locations, the server calculates rent and operating costs and proposes an operating model.

[1702] Input: Information on identified potential store locations, rental data obtained from a real estate database, and pre-set operating costs.

[1703] Output: Total operating costs and proposed operating models for each candidate site.

[1704] Specific operation: The server uses a real estate database API to retrieve rental data and calculates the total cost by adding it to operating costs such as personnel expenses and utilities. Based on the results, it presents an operating model such as, "The monthly rent in this area is 200,000 yen, and personnel costs are 300,000 yen, so a minimum monthly revenue of 500,000 yen is required."

[1705] Step 5:

[1706] Revenue forecast

[1707] The server predicts future revenue based on the integrated data.

[1708] Input: Integrated data (demographic data, competitor information, population flow data).

[1709] Output: Expected earnings.

[1710] Specific operation: The server uses the Scikit-learn library to train a machine learning model and build a predictive model based on the collected data. Using that model, it predicts future revenue and provides specific revenue forecasts such as "Expected annual revenue at this location is 5 million yen."

[1711] Step 6:

[1712] Proposal for competitive placement strategy

[1713] The server develops strategies to strengthen its competitive position based on information about the locations of competing facilities.

[1714] Input: Location information of competing facilities.

[1715] Output: Proposal of competitive placement strategy.

[1716] Specific operation: The server analyzes the location information of competing facilities on a GIS and formulates the optimal strategy for strengthening competitiveness. For example, it plots a strategy such as "opening a store in this area will maximize synergies with existing competing facilities" on a dashboard and presents it visually to the user.

[1717] Step 7:

[1718] Introducing an emotional engine

[1719] The server uses an emotion engine to collect user emotion data and uses it for analysis.

[1720] Input: User feedback, social media posts.

[1721] Output: User sentiment data.

[1722] Specific operation: The emotion engine analyzes user feedback and tags emotions. It also analyzes text data from social media posts and extracts emotion trends.

[1723] Step 8:

[1724] Adjusting location and operating models based on emotional data

[1725] The server dynamically adjusts its location and operating model based on sentiment data.

[1726] Input: Sentiment data.

[1727] Output: Adjusted location and operational model.

[1728] Specific operation: The server statistically analyzes sentiment data to identify user trends in specific regions. Based on this, it re-evaluates potential store locations and the services offered, and displays the updated results on a dashboard. For example, if there is a high level of positive feedback towards cafes in a particular region, that region will be added as a potential location for a cafe.

[1729] Through these steps, the system can comprehensively analyze data and efficiently and accurately support decision-making regarding the opening and operation of commercial facilities. Furthermore, by utilizing user sentiment data, it can also improve customer satisfaction.

[1730] (Application Example 2)

[1731] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1732] To improve the efficiency and profitability of new store openings and operations in commercial facilities, store opening strategies must consider not only demographic and population flow data, but also user sentiment data. However, conventional systems have made it difficult to develop concrete store opening strategies that utilize sentiment data, resulting in challenges in optimizing operations and maximizing profits.

[1733] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting demographic data, means for collecting location information of competing facilities, means for collecting circulating population data, means for collecting sentiment data, means for analyzing the collected demographic data, location information of competing facilities, circulating population data, and sentiment data, means for identifying an appropriate location based on the analysis results, means for calculating rent and operating costs based on the selected location, and means for proposing an optimal operating model. This makes it possible to construct a highly accurate store opening strategy utilizing sentiment data.

[1734] "Demographic data" refers to statistical information about the distribution and attributes of the population in a specific region.

[1735] "Location information of competing facilities" refers to geographical information that shows where competing facilities such as commercial facilities and stores are located.

[1736] "Population mobility data" refers to information about the number of people moving within a specific region and their movement patterns.

[1737] "Sentimental data" refers to information collected from users regarding their feelings and evaluations of specific services or facilities.

[1738] "Means of analysis" refers to methods and tools for analyzing collected data using statistical analysis and machine learning to extract meaningful information.

[1739] "Means of identifying a location" refers to methods and tools for selecting the optimal location for a commercial facility based on analysis results.

[1740] "Means for calculating rent and operating costs" refers to methods or tools for calculating rent and operating costs at a selected location.

[1741] "Means of proposing an operating model" refers to methods and tools for proposing the optimal business operation methods and strategies for a specific location.

[1742] This invention relates to a system for improving the efficiency and profitability of new store openings and operations in commercial facilities, and in particular, to a system that enables the construction of more accurate store opening strategies by utilizing user sentiment data.

[1743] The system consists of the following hardware and software.

[1744] 1. Hardware

[1745] Server: Performs data collection, analysis, and proposes operational models.

[1746] Smartphone or tablet: A device used by store operators to check data and formulate store opening strategies.

[1747] 2. Software

[1748] Data collection APIs (e.g., Google Maps API, traffic volume measurement API, etc.)

[1749] Sentiment analysis engine (e.g., Microsoft Azure Cognitive Services)

[1750] Data analysis tools (e.g., Python, R, GIS software)

[1751] System operation

[1752] The system operates using the following steps:

[1753] 1. Data Collection

[1754] The server uses the Google Maps API to collect location information for competing facilities. It also obtains demographic data through the Census API and collects population movement data using the traffic volume measurement API.

[1755] Emotional data is obtained from social media and user feedback and analyzed by an emotion analysis engine.

[1756] 2. Data Analysis

[1757] The server integrates and analyzes collected demographic data, location information of competing facilities, population movement data, and sentiment data. Based on the analysis results, it understands regional characteristics, competitive landscape, and user sentiment trends.

[1758] 3. Location identification and proposal of an operational model

[1759] Based on the analysis results, the server identifies the optimal locations for store openings. The identified locations are presented in a ranking format along with location evaluations, projected revenues, and operating costs.

[1760] Based on the data displayed on the device, users can consider the optimal operating model.

[1761] Specific example

[1762] For example, when a user considers opening a new store, data is collected and analyzed using the following procedure.

[1763] Prompt message example 1: Obtain location information of competitor stores

[1764] "Please use the Google Maps API to retrieve store information for locations within a 5-kilometer radius of the proposed store location."

[1765] Prompt example 2: Analysis of sentiment data

[1766] "Collect social media posts and analyze the sentiment score for this region."

[1767] Prompt message example 3: Suggestion of the optimal store location

[1768] "Based on demographic data and competitive landscape in this area, please rank the most suitable locations for opening a store."

[1769] This allows users to develop highly accurate store opening strategies based on real-time data. Furthermore, by incorporating emotional data, it is expected to contribute to improved customer satisfaction.

[1770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1771] Step 1:

[1772] The server collects demographic data, location information of competitor facilities, and population flow data using various APIs. Specifically, it obtains location information of competitor facilities using the Google Maps API and collects demographic data through the Census API. It also obtains population flow data using the Traffic Volume Measurement API. The input for data collection is the API endpoints and parameters, and the output is the data obtained from the APIs.

[1773] Step 2:

[1774] The server collects and analyzes emotional data using an emotion analysis engine. Specifically, it obtains text data from social media and user feedback and inputs it into the emotion analysis engine. This engine analyzes the text data and outputs an emotion score such as positive, negative, or neutral. The input is text data, and the output is an emotion score.

[1775] Step 3:

[1776] The server integrates collected and analyzed demographic data, location information of competing facilities, population movement data, and sentiment data, and performs analysis using data analysis tools. Specifically, it uses Python and R to integrate each dataset and understand regional characteristics, competitive situations, and population movement trends. The input is various datasets, and the output is the analysis results.

[1777] Step 4:

[1778] The server identifies suitable location candidates based on the analysis results and presents them in a ranked format. Specifically, it uses GIS software to perform geographical visualization and ranks the location candidates according to evaluation criteria. Users can view this information on their smartphones or tablets. The input is the analysis results, and the output is a list of ranked location candidates.

[1779] Step 5...

Claims

1. Means of collecting demographic data, Means for collecting location information of competing facilities, Means of collecting population movement data, means for analyzing the collected demographic data, location information of the competing facilities, and population flow data, A means for identifying an appropriate location based on the analysis results, A method for calculating rent and operating costs based on the selected location, A means of proposing the optimal operating model, A system that includes this.

2. The system according to claim 1, further comprising means for integrating the collected demographic data, the location information of the competing facilities, and the flow of population data to calculate predicted revenue.

3. The system according to claim 1, further comprising means for proposing an optimal competitive placement strategy.

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