System

Abstract

To provide a system for achieving efficient and effective arrangement of communication service providing stores.SOLUTION: A means for acquiring demographic data, a means for acquiring geographic information data, a means for acquiring market demand data, a means for integrating the acquired pieces of data, a means for normalizing the integrated data and complementing missing values, a means for training a prediction model and predicting future demographic data and market demand, a means for proposing store consolidation and new store opening based on the prediction data, a means for creating a store opening strategy in consideration of digital divide elimination, a means for generating a report in which the content of the proposal is described in detail, and a means for collecting feedback from a user; Means for re-evaluating and updating the proposed content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, with social changes such as population decline, an aging population with a very low birthrate, and increased migration to rural areas, optimal location of telecommunications service outlets has become an important issue. In particular, eliminating the digital divide is essential for spreading telecommunications infrastructure and ensuring equal access to information throughout local communities. However, accurately predicting these social changes and strategically implementing appropriate store consolidation and new store openings is extremely difficult. This invention provides a system that solves these problems and enables efficient and effective location of telecommunications service outlets. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides a system including the following means.

[0006] First, the system includes a means for acquiring demographic data, geographic information data, and market demand data. It also includes a means for integrating this data and normalizing and imputing missing values. It provides a means for training a predictive model based on this data to forecast future demographic trends and market demand. It also includes a means for proposing store consolidation and new store openings based on the predicted data, and a means for formulating a store opening strategy that takes into account, in particular, the elimination of the digital divide. It also includes a means for generating a report detailing the proposals and a means for collecting user feedback and reevaluating and updating the proposals. This configuration makes it possible to achieve optimal placement of communication service providers and contribute to the elimination of the digital divide.

[0007] "Demographic data" refers to statistical information such as population growth and decline, age structure, birth rate, death rate, and migration rate in a particular area.

[0008] "Geographic information data" refers to spatial data such as geographical features of a specific area, map information, land use status, latitude and longitude, etc.

[0009] "Market demand data" refers to data related to economic activity, such as consumer needs, purchasing behavior, sales performance, and customer satisfaction in a specific region.

[0010] "Normalization" refers to the process of converting acquired data into a unified format and units to ensure consistency.

[0011] "Missing value imputation" refers to the process of filling in missing values ​​in a dataset to maintain data integrity.

[0012] A "predictive model" refers to a mathematical or statistical method or algorithm for predicting future trends based on past data.

[0013] "Consolidation" refers to measures to streamline and integrate existing stores in order to operate them more efficiently.

[0014] "New store openings" refers to the opening of new stores.

[0015] The "digital divide" refers to disparities between regions and social classes in access to the Internet and digital technology.

[0016] "Feedback" refers to information on opinions and evaluations from users regarding the proposed content, and refers to the process of reevaluating and updating the proposed content based on that information. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0039] This system involves a series of processes primarily performed by the server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report with the results. Users review this report, rate it, and send feedback to the server. The server then reevaluates the data based on the feedback and updates its strategy as necessary.

[0040] A natural language description of the program's operation

[0041] Data collection and integration

[0042] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0043] Data Preprocessing

[0044] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0045] Training and predicting a predictive model

[0046] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0047] Proposals for consolidation and new store openings

[0048] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0049] Report generation and distribution

[0050] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0051] Gathering feedback and updating strategy

[0052] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0053] Specific examples

[0054] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0055] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0056] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0057] The processing flow will be explained below.

[0058] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0059] A natural language description of the program's operation

[0060] Data collection and integration

[0061] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0062] Data Preprocessing

[0063] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0064] Training and predicting a predictive model

[0065] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0066] Proposals for consolidation and new store openings

[0067] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0068] Report generation and distribution

[0069] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0070] Gathering feedback and updating strategy

[0071] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0072] Specific examples

[0073] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0074] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0075] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0076] Processing step details

[0077] Step 1: Data collection

[0078] The server retrieves demographic data from the government's public API.

[0079] The server retrieves geographic information data from a GIS system.

[0080] The server extracts market demand data from the company's internal database.

[0081] Step 2: Data Integration

[0082] The server consolidates the collected data using a common key (regional code) and stores it in a database.

[0083] Step 3: Data Preprocessing

[0084] The server normalizes the integrated data (e.g., population is standardized to "people" and sales to "yen").

[0085] The server uses linear interpolation to fill in the missing values.

[0086] Step 4: Training the predictive model

[0087] The server uses historical data to train a machine learning model (e.g., a random forest regression model).

[0088] The server uses the trained model to predict future demographic trends and market demand.

[0089] Step 5: Generate proposals

[0090] Based on the prediction results, the server creates a list of candidates for consolidation, closure, and new store openings.

[0091] The server takes into consideration the elimination of the digital divide and proposes specific regions as priority candidates.

[0092] Step 6: Generate reports

[0093] The server generates a report detailing the proposal.

[0094] The server delivers the report to the terminal.

[0095] Step 7: Gather feedback

[0096] The user checks the report and gives an evaluation.

[0097] The user sends feedback to the server.

[0098] Step 8: Reassess and update your strategy

[0099] The server will reevaluate its proposals based on the feedback and update its strategy as needed.

[0100] The server generates the updated strategy again as a report and delivers it to the terminal.

[0101] As described above, the present invention provides a system for realizing efficient and effective allocation of communication service providing stores through a series of steps of data collection, integration, pre-processing, prediction, proposal and evaluation.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] In modern society, rapid changes in demographics and market demands require optimal location of telecommunications service outlets. However, conventional methods lack centralized data collection and integration, predictive model training, and store opening strategy formulation, resulting in a high level of manual work and inefficiency. Furthermore, they do not adequately address the digital divide, making it difficult to realize store location that meets residents' needs. A system that can solve these issues and provide efficient and accurate store location proposals is needed.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidations and new store openings based on the predicted data, means for using a machine learning model as the prediction model, means for generating a report detailing the proposals, means for sending the report to a user's terminal, means for collecting feedback from users, reevaluating and updating the proposals, and means for updating the strategy based on the feedback. This automates a series of processes, from data collection to prediction, proposal, feedback collection, and strategy updating, enabling efficient and highly accurate store placement proposals.

[0107] "Demographic data" refers to data that shows the size, composition, and trends of population growth and decline in a particular area.

[0108] "Geographic information data" is data that indicates the geographical features and location information of a specific area.

[0109] "Market demand data" refers to data that indicates the amount and trends of demand for a product or service in a particular market.

[0110] "Integration" refers to the process of matching data from multiple different data sources into a single data set.

[0111] "Normalization" is a process for standardizing the units and scale of data to ensure consistency.

[0112] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate way.

[0113] A "predictive model" is a statistical or machine learning model that estimates future data based on past data.

[0114] A "machine learning model" is an algorithm that learns patterns and rules from data and makes predictions and classifications for unseen data.

[0115] "Consolidation" refers to combining multiple stores or locations into one in order to eliminate overlapping functions and roles.

[0116] "New store openings" refer to the opening of new stores in new regions or markets.

[0117] The "digital divide" refers to disparities in access to and use of information technology, and indicates differences in the environment for using the Internet and digital services.

[0118] "Strategic update" refers to reviewing and improving existing plans and proposals based on collected feedback and new information.

[0119] A "report" is a document detailing the analysis results and recommendations, and is provided in a format that is easy for users to understand.

[0120] "Feedback" refers to evaluations, opinions, and comments provided by users, and is information that is used to improve and optimize the system.

[0121] A "terminal" is a device used by a user to receive or input information, and includes a computer, smartphone, etc.

[0122] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0123] The system mainly involves a series of processes executed by a server. The server obtains demographic data and geographic information data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal database of the telecommunications service provider. This data is integrated using a common key (e.g., area code) and compiled into a single database. The hardware used includes a general-purpose server computer, and the software used includes Python, Pandas, Scikit-learn, and TENSORFLOW (registered trademark).

[0124] Next, the server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also performs linear interpolation to fill in missing data values ​​and estimates missing values ​​based on past trends. This is done using functions such as the "interpolate" function in Pandas.

[0125] The server then uses historical data to train a machine learning model. For example, it learns patterns of population decline and rural migration and builds a model to predict future trends. Specifically, it uses Scikit-learn's Random Forest and TensorFlow. It also uses Python libraries to verify the accuracy of the predictions using appropriate evaluation methods.

[0126] Using a trained model, the system predicts future demographic trends and market demand for each region, and based on those predictions, it recommends which stores should be consolidated or closed, and which areas should be opened. The server lists stores in areas experiencing rapid population decline as candidates for closure, and suggests new store openings in areas where migration to rural areas is progressing. It also identifies areas with particularly low digital access and prioritizes opening stores there.

[0127] The server generates a report detailing the proposal. This report is provided in a format that is easy for users to understand, detailing forecast data for each region and the reasons for the proposal. The generated report can be provided in PDF format or as a web-based dashboard and sent to the user's device. The user reviews the report and sends their evaluation and comments as feedback to the server. The server uses this information to reevaluate the proposal and update the strategy.

[0128] As a concrete example, consider a case where a server analyzes two cities, City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, and its population is predicted to grow by 20% over the next 10 years. As a result, the server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. It also proposes opening new stores in new areas of City B that are attracting new residents. Furthermore, it identifies areas with particularly low digital access and recommends prioritizing opening stores there.

[0129] The following are examples of prompt sentences:

[0130] 1. Prompts for collecting demographic data

[0131] "Please retrieve the past 10 years of demographic data for Tokyo from the API."

[0132] "Please use the API to obtain geographic information data for Tokyo."

[0133] 2. Prompt for data normalization

[0134] "Please normalize population data to a single unit of people."

[0135] "Please standardize the sales data to yen."

[0136] 3. Prompts about predictive models

[0137] "Train a population decline prediction model using TensorFlow based on historical data."

[0138] "Use Scikit-learn to predict population growth and decline over the next five years."

[0139] 4. Report Generation and Delivery Prompts

[0140] "Generate a PDF report with proposals for new store openings."

[0141] Send the generated report to the user's email address.

[0142] The above is a detailed description of the mode for carrying out the invention. This process enables efficient and highly accurate store layout proposals.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1: Collect data

[0145] The server collects demographic and geographic data from public government APIs and geographic information systems (GIS), while extracting market demand data from the internal databases of telecommunications service providers.

[0146] Input: API endpoints (e.g., public government APIs), GIS data sources, corporate databases

[0147] Output: Demographic data, geographic information data, market demand data

[0148] Specific operation: The server sends an API request and retrieves the response data. It also downloads GIS data and executes queries against the company's internal SQL database to extract market demand data. For example, it retrieves data from "https: / / api.example.com / population" and executes the SQL query "SELECT FROM market_demand WHERE region_code = 'ABC'".

[0149] Step 2: Data integration and normalization

[0150] The server integrates the collected data using the region code as a key, and then normalizes the data to unify its units and ensure consistency.

[0151] Input: Demographic data, geographic information data, market demand data

[0152] Output: Integrated dataset (integrated + normalized)

[0153] Specific operation: The server uses the Python Pandas library to merge data using the "merge" function, and the "apply" function to unify population and sales data into people and circles. Example: df.merge(population_data, on='region_code'), df['population'] = df['population'].apply(lambda x: x 1000).

[0154] Step 3: Data preprocessing and missing value imputation

[0155] The server preprocesses the aggregated data, imputes missing values ​​using linear interpolation, and detects and appropriately corrects or removes outliers and values.

[0156] Input: Integrated dataset

[0157] Output: Preprocessed and imputed dataset

[0158] Specific operation: The server executes "df.interpolate(method='linear')" to fill in missing values. Outliers are detected with "df[(df['sales'] < 0) | (df['sales'] > 1e7)]" and corrected or deleted. Example: Executes df.interpolate(method='linear').

[0159] Step 4: Training the predictive model

[0160] The server uses historical data to train a machine learning model (e.g., Scikit-learn's Random Forest), which learns patterns of population decline and market demand and builds a model to predict future trends.

[0161] Input: Preprocessed dataset

[0162] Output: A trained predictive model

[0163] Specific operation: The server imports "from sklearn.ensemble import RandomForestRegressor" and initializes the model. Next, it runs "model.fit(X_train, y_train)" to train it. Example: Run model.fit(X_train, y_train).

[0164] Step 5: Perform future predictions

[0165] The server uses the trained model to predict future demographic trends and market demand.

[0166] Input: trained predictive model, new input data

[0167] Output: Future demographic forecast data, future market demand forecast data

[0168] Specific operation: The server executes "predictions = model.predict(X_test)" and obtains the prediction results. The prediction results are recorded in the database and saved in a data frame. Example: Execute predictions = model.predict(X_test).

[0169] Step 6: Propose store layout

[0170] Based on the prediction results, the server proposes optimal store locations, considering store consolidation and closure in areas experiencing rapid population decline, and proposing new store openings in areas where population growth is expected.

[0171] Input: Future demographic forecast data, market demand forecast data

[0172] Output: Proposal for store consolidation and new store openings

[0173] Specific operation: The server lists candidates for closure using "df[df['predicted_population'] < threshold]", and selects areas for new store openings where "df[df['predicted_population'] > threshold]". Example: df[df['predicted_population'] < threshold].

[0174] Step 7: Generate and distribute reports

[0175] The server generates a detailed report detailing the recommendations, which is then sent to the user's device in PDF format or as a web-based dashboard.

[0176] Input: Store layout proposal

[0177] Output: Report (PDF format, web dashboard)

[0178] Specific operation: The server generates a PDF using "from fpdf import FPDF" and saves it using "pdf.output('report.pdf')". Then it sends it using "send_email(to='user@example.com', attachment='report.pdf')". Example: pdf.output('report.pdf').

[0179] Step 8: Gather feedback and update your strategy

[0180] Users review the reports and send their ratings and comments as feedback to the server, which then reevaluates the proposals and updates the strategy.

[0181] Input: User feedback

[0182] Output: Updated proposals and strategies

[0183] Specific behavior: A user enters a comment in the feedback form and sends it to the server. The server executes "feedback = get_feedback()" and updates the strategy with "update_strategy(feedback)". Example: feedback = get_feedback().

[0184] (Application example 1)

[0185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] When determining the optimal locations of telecommunications service outlets, companies face challenges in efficiently managing demographic changes, geographic information, and market demand, while also bridging the digital divide. Furthermore, the current system lacks real-time capabilities, making it difficult for users to review proposal results, provide feedback, and quickly reevaluate and update strategies. A system that efficiently resolves these challenges and provides an easy-to-use user experience is needed.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0188] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for displaying predictions and store layout proposals using the data to the user via a smartphone application. This enables real-time predictions and proposals, allowing users to provide rapid feedback and efficiently reevaluate and update strategies based on the results.

[0189] "Demographic data" refers to data that shows information on the composition, changes, and movement of the population in a particular region or group.

[0190] "Geographic information data" is data that includes information about physical features, topography, infrastructure, land use, and other aspects of a particular geographic space.

[0191] "Market demand data" refers to data that indicates information regarding the demand for a product in a particular market, consumer purchasing behavior, sales, etc.

[0192] "Integration methods" are methods for centralizing different types of data and compiling them into a single data set for analysis and prediction.

[0193] "Normalization" is the process of standardizing the units and scale of data, a process that is performed to make analysis and modeling easier.

[0194] A "means of imputing missing values" is a means of filling in gaps or missing data in a dataset by interpolation or estimation.

[0195] A "predictive model" is a mathematical or statistical model that uses historical data to predict future events or trends.

[0196] "Store consolidation" is the process of integrating or closing existing stores to improve efficiency and reduce costs.

[0197] "New store opening" means opening a new store in a new location.

[0198] The "digital divide" is a term that refers to disparities between regions and classes in access to and use of information technology.

[0199] A "store opening strategy" is a plan or policy for determining optimal store placement based on market research and forecasts.

[0200] "Report generation" is the process of organizing analysis results and proposals and outputting them as written or visual materials.

[0201] "Means for collecting feedback" refers to methods for efficiently collecting ratings and comments from users and incorporating them as data.

[0202] "Reevaluation and updating" is the process of reviewing initial proposals and models based on collected feedback and making modifications and improvements as needed.

[0203] A "smartphone application" is a software program that runs on a smartphone and provides specific functions or services.

[0204] "Real-time" is a term that refers to a state in which various data and information are processed and provided immediately without any time delay.

[0205] Specific embodiments for carrying out the present invention will be described below.

[0206] The present invention is a system for optimizing the location of telecommunications service outlets by using demographic data, geographic information data, and market demand data. The system includes a server, a database, and a smartphone application that interfaces with users. It also has the ability to make predictions using machine learning models and collect feedback to reevaluate strategies.

[0207] Program Overview

[0208] Data collection and integration

[0209] The server collects data by the following means:

[0210] Use government-provided APIs to obtain demographic data.

[0211] Obtain geographic information data from a geographic information system (GIS).

[0212] Extract market demand data from a company's internal database.

[0213] These data are compiled into a single integrated database using the area code as a common key.

[0214] Data Preprocessing

[0215] The server normalizes the integrated data and fills in missing values. For example, it standardizes the population unit to "people" and the sales unit to "yen." If there are missing values, it fills them in using methods such as linear interpolation.

[0216] Training and predicting a predictive model

[0217] The historical data is then used to train a machine learning model (e.g., a linear regression model). This model is then used to predict future demographics and market demand. The trained model can then process new data and make predictions in real time.

[0218] Proposals for store consolidation and new store openings

[0219] Based on the prediction results, the server will propose store consolidation and new store openings. For example, it will propose store closures in areas with declining populations and new store openings in areas with growing populations. Taking into account the elimination of the digital divide, it will also identify areas with insufficient digital access and prioritize new store openings there.

[0220] Report Generation and User Interface

[0221] A report detailing the recommendations is automatically generated and provided to the user, who can view it through a smartphone application that displays the prediction results along with visualized data fragments in an easy-to-understand interface.

[0222] Specific examples

[0223] For example, consider a case where a server is analyzing two cities, A and B. City A has seen a significant decline in population over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and the server predicts a 20% population increase over the next 10 years.

[0224] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0225] Prompt Sentence Examples

[0226] "City A is expected to experience a 30% population decline over the past five years. The server uses this data to suggest which stores should be closed and identify cities where new stores should be opened."

[0227] This system will enable telecommunications service providers to efficiently position their stores in a way that reflects the needs of residents and market demand, thereby contributing to bridging the digital divide.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] Inputs: Data from government APIs, geographic information systems, and internal corporate databases

[0231] Operation:

[0232] The server retrieves current demographic data using government-provided APIs, then collects geographic information from geographic information systems (GIS) and extracts market demand data from companies' internal databases.

[0233] Output: Demographic data, geographic information data, market demand data

[0234] Step 2:

[0235] Input: Demographic data, geographic information data, market demand data

[0236] Operation:

[0237] The server integrates each piece of data it has acquired using the region code as a key. In this integration process, data with common attributes are compiled into a single database.

[0238] Output: Integrated database

[0239] Step 3:

[0240] Input: Integrated database

[0241] Operation:

[0242] The server normalizes the integrated data. Specifically, it standardizes the population unit to "people" and the sales unit to "yen." In addition, if there are missing values, it fills them in using linear interpolation.

[0243] Output: Normalized and imputed data

[0244] Step 4:

[0245] Input: Normalized and imputed data

[0246] Operation:

[0247] The server uses this data to train machine learning models, such as linear regression models, which are used to predict future demographic trends and market demand.

[0248] Output: A trained machine learning model

[0249] Step 5:

[0250] Input: trained machine learning model, integrated database

[0251] Operation:

[0252] The server uses the trained model to predict future demographics and market demand, which in turn informs decisions about new store openings and store consolidation.

[0253] Output: Future demographic forecast data, market demand forecast data

[0254] Step 6:

[0255] Input: Future demographic forecast data, market demand forecast data

[0256] Operation:

[0257] Based on the prediction results, the server will suggest which stores should be consolidated or closed, and which areas to open new stores in. Furthermore, taking into consideration the elimination of the digital divide, it will identify areas with insufficient digital access and suggest prioritizing new store openings in those areas.

[0258] Output: Proposal data for consolidation and new store openings

[0259] Step 7:

[0260] Input: Proposal data regarding consolidation and new store openings

[0261] Operation:

[0262] The server generates a report detailing the recommendations, including visual graphs and maps, that users can view in a smartphone application.

[0263] Output: A report detailing the proposal

[0264] Step 8:

[0265] Input: Report detailing proposals, user feedback

[0266] Operation:

[0267] Users can review the generated reports and provide feedback via a smartphone application, which the server collects and uses to reevaluate and update strategies and recommendations.

[0268] Output: Updated strategy and proposal data

[0269] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0270] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[0271] This system involves a series of processes primarily performed by a server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report of the proposals. Users review and rate the report, then send feedback to the server. The server uses an emotion engine to analyze the sentiment of the feedback and reevaluate and update the proposals based on this sentiment.

[0272] A natural language description of the program's operation

[0273] Data collection and integration

[0274] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0275] Data Preprocessing

[0276] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0277] Training and predicting a predictive model

[0278] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0279] Proposals for consolidation and new store openings

[0280] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0281] Report generation and distribution

[0282] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0283] Feedback collection and sentiment analysis

[0284] The user checks the report and sends their rating and comments as feedback to the server. The server uses an emotion engine to analyze the emotion from the user's feedback text. This emotion analysis allows us to measure whether the user's feedback is positive or negative, and its intensity.

[0285] Reassess and update your strategy

[0286] The server takes into account the analysis results of the emotion engine and reevaluates the content of the suggestions. For example, if there is a lot of negative feedback, it determines that there is a problem with the prediction model or the content of the suggestions, and takes measures such as collecting data again. Based on the analysis results of the emotion engine, the priority of the suggestions is adjusted to make suggestions that are more in line with the user's emotions.

[0287] Specific examples

[0288] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0289] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0290] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[0291] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0292] The processing flow will be explained below.

[0293] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[0294] A natural language description of the program's operation

[0295] Step 1: Data collection

[0296] The server retrieves demographic data from the government's public API, accesses the government's statistical database, and collects data on population growth and decline by region and age distribution.

[0297] The server retrieves geographic information data from the GIS system, queries the GIS system, and collects geographical features and land use information for the target area.

[0298] The server extracts market demand data from the company's internal database, as well as market research data, sales data from existing stores, and customer satisfaction data.

[0299] Step 2: Data Integration

[0300] The server unifies the collected data using a common key (regional code) and stores it in an integrated database, allowing information from different data sources to be managed centrally.

[0301] Step 3: Data Preprocessing

[0302] The server normalizes the integrated data, standardizing the units of each data item, for example, standardizing the population unit to "people" and the sales unit to "yen."

[0303] The server uses linear interpolation to fill in missing values, and if there are missing data points, it fills them in based on nearby data points.

[0304] Step 4: Training the predictive model

[0305] The server uses historical data to train a machine learning model, using a random forest regression model to learn patterns of population decline and rural migration.

[0306] The server uses the trained model to forecast future demographic trends and market demand, predicting population and demand changes in each region over the next 10 years.

[0307] Step 5: Generate proposals

[0308] Based on the prediction results, the server creates a list of candidates for consolidation and new store openings. It lists stores in areas with a significant population decline as candidates for closure, and suggests opening new stores in areas where population growth is expected.

[0309] Taking into consideration the need to eliminate the digital divide, Sarver will prioritize areas lacking infrastructure as potential store locations.

[0310] Step 6: Generate reports

[0311] The server generates a report detailing the proposal, including forecast data for each region, the rationale for the proposal, and recommendations for consolidation and new store openings.

[0312] The server sends the generated report to the terminal, and distributes it in PDF format to the email address of the person in charge or to the company's internal portal.

[0313] Step 7: Gather feedback

[0314] The user checks the received report and sends their evaluation and comments to the server as feedback. Specifically, they enter comments such as whether they agree or disagree with the proposals in the report, or if they would like to make additions.

[0315] Step 8: Sentiment Analysis

[0316] The server analyzes the emotions from the user's feedback using an emotion engine. It uses natural language processing techniques to extract emotions (positive, negative, neutral) from the feedback text.

[0317] The server evaluates the intensity of the emotion and determines that special action should be taken if the feedback contains extremely strong negative emotions.

[0318] Step 9: Reassess and update your strategy

[0319] The server re-evaluates the suggestions based on the analysis results, and dynamically adjusts the priority of suggestions according to the user's actual emotions, taking into account the results of the sentiment analysis.

[0320] The server will reflect user feedback and the results of sentiment analysis, and take appropriate action such as recollecting data and retraining the predictive model as necessary.

[0321] The server generates the updated strategy again as a report and delivers it to the terminal.

[0322] Specific examples

[0323] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0324] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0325] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[0326] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional strategies for the placement of telecommunications service stores have limitations in the accuracy of aggregation and forecasting of demographic data, geographic information data, and market demand data, making it difficult to accurately locate stores. Furthermore, it has been difficult to fully utilize user feedback, making it difficult to build an optimal strategy that reflects user emotions and needs. This has resulted in delays in decisions regarding new store openings and consolidation, making it difficult to adapt to market trends. Furthermore, there has been a lack of means to provide more flexible and emotionally sensitive proposals by analyzing the emotions of feedback.

[0330] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0331] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the forecast data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for analyzing the sentiment of the user feedback and reevaluating and updating the proposals. This enables an optimal strategy for locating communication service providing stores that reflects user sentiment and feedback.

[0332] "Demographic data" refers to data that includes information on population changes and composition, birth and death rates, migration patterns, etc. in a particular area.

[0333] "Geographic information data" is data that includes information about geography, such as geographic location, topography, land use, and infrastructure.

[0334] "Market demand data" refers to data that includes information such as trends in demand for products and services in a particular market, consumer behavior, and purchasing history.

[0335] "Means of integration" refers to methods or techniques for combining multiple datasets based on common keys or items and consolidating them into a single dataset.

[0336] "Normalization" refers to the process of standardizing data formats and units to ensure consistency among data.

[0337] "Methods for imputing missing values" refers to methods that use statistical techniques or algorithms to fill in missing values ​​in a dataset.

[0338] "Predictive model" refers to a mathematical or statistical model that uses historical data to predict future fluctuations or trends.

[0339] "Store consolidation" refers to reorganization of store operations, such as closing existing stores or merging multiple stores into one.

[0340] "New store opening" refers to the establishment of a new store in a new location.

[0341] "Closing the digital divide" refers to reducing disparities in access to and use of information and communications technology and providing digital services equally in all regions.

[0342] "Means for generating reports" refers to the methods and techniques for compiling analysis results and proposals in document or graph format and creating a report.

[0343] "User feedback" refers to the act of a user providing evaluations or opinions on provided reports or proposals, and the information provided.

[0344] "Means for analyzing emotions" refers to methods and technologies for evaluating and analyzing the positive and negative emotions from user feedback.

[0345] "Reevaluation and updating" refers to the process of reviewing existing proposals and developing revisions or new proposals as needed.

[0346] This invention combines a system that realizes optimal placement of communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This system improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs.

[0347] The present system includes a series of processes that are mainly executed by a server. The following describes in detail an embodiment of the present system.

[0348] 1. Data Collection

[0349] The server obtains demographic data using the government's public API, downloads geographic information data from a geographic information system (GIS), and extracts market demand data from the internal database of a telecommunications service provider. Various analyses are performed based on this data.

[0350] 2. Data Integration

[0351] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a region code, thereby creating a unified data set.

[0352] 3. Data Preprocessing

[0353] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[0354] 4. Training the predictive model

[0355] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[0356] 5. Prediction and Recommendations

[0357] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[0358] 6. Report Generation and Delivery

[0359] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[0360] 7. Gathering Feedback

[0361] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[0362] 8. Emotion analysis

[0363] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[0364] 9. Reassessment and Updates

[0365] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[0366] Specific examples

[0367] For example, consider a scenario in which a server analyzes two cities: City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, with a predicted 20% population growth over the next 10 years. The server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. At the same time, the server suggests opening a new store in a new migration area in City B. Furthermore, the server identifies areas of City B that lack digital access and suggests prioritizing opening a store there. If a user reviews the report and submits negative feedback about the proposed store closure in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the proposed store closure in City A and considers other options.

[0368] Examples of prompt statements

[0369] Example 1:

[0370] "Analyze demographic data from the past five years and identify areas most likely to experience population decline over the next decade."

[0371] Example 2:

[0372] "Please integrate market demand data and geographic information data to suggest areas where new stores should be opened within the next two years."

[0373] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0375] Step 1: Data collection

[0376] The server retrieves demographic data using public government APIs, downloads geographic information data from geographic information systems (GIS), and extracts market demand data from the internal databases of telecommunications service providers.

[0377] Input: API key or query

[0378] Output: Population data in JSON format, GIS data, market demand data extracted from SQL database

[0379] Specific operations: The server uses the API key to send requests to government sites, obtain population data in JSON format, download geographic information data from GIS, and extract market demand data from the company's internal database using SQL queries.

[0380] Step 2: Data Integration

[0381] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a regional code.

[0382] Input: Demographic data, geographic information data, market demand data

[0383] Output: Unified dataset

[0384] Specific operation: The server uses a data frame to combine each dataset with a common region code to build a single integrated dataset.

[0385] Step 3: Data Preprocessing

[0386] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[0387] Input: Integrated dataset

[0388] Output: Standardized and imputed data

[0389] Specific operation: The server uses the Pandas library to unify the population unit to "person", change the sales unit to "yen", and perform linear interpolation for missing values.

[0390] Step 4: Training the predictive model

[0391] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[0392] Input: Standardized and imputed data

[0393] Output: A trained predictive model

[0394] Specific operation: The server uses the scikit-learn library to train algorithms such as random forest models and evaluate their accuracy through cross-validation.

[0395] Step 5: Prediction execution and recommendations

[0396] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[0397] Input: trained model, latest data

[0398] Output: List of store opening and consolidation proposals

[0399] How it works: The server supplies the latest input data to the model and outputs prediction results. Based on the results, it creates a list of proposals for consolidation and new store openings and identifies areas with a digital divide.

[0400] Step 6: Report generation and distribution

[0401] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[0402] Input: List of store opening and consolidation proposals

[0403] Output: Proposal report (PDF, etc.)

[0404] Specific operation: The server generates a report using a Markdown template, exports it to PDF format, and sends it to the user's device.

[0405] Step 7: Gather feedback

[0406] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[0407] Input: User rating and comments

[0408] Output: User feedback data

[0409] Specific behavior: A user views a report through a web application, enters ratings and comments in a feedback form, and submits it.

[0410] Step 8: Sentiment Analysis

[0411] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[0412] Input: User feedback data

[0413] Output: Emotion analysis results

[0414] What it does: The server uses an NLP library to score the positivity and negativity of the feedback text.

[0415] Step 9: Reassess and update

[0416] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[0417] Input: Sentiment analysis results

[0418] Output: Updated proposal

[0419] Specific operation: The server recollects data from areas with a lot of negative feedback, retunes the parameters of the predictive model, and updates the suggestions.

[0420] (Application example 2)

[0421] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0422] Traditional store location strategies for telecommunications service providers have primarily been based on demographic data, geographic information data, and market demand data. However, the integration of these data and proposals based on predictive models have been limited, and proposals often do not meet users' actual needs because they do not fully consider user sentiment and feedback. Furthermore, it has been difficult to formulate store opening strategies that take the digital divide into account, resulting in many areas where telecommunications services are inadequate. Furthermore, there has been insufficient customization of services provided in virtual stores that effectively utilizes real-time user feedback.

[0423] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, means for displaying service provision locations in a virtual store using a smart device, means for collecting user feedback via voice and analyzing it using a sentiment analysis engine, and means for customizing service content based on the analysis results. This makes it possible to formulate an optimal location strategy for communication service providing stores while taking user emotions into consideration, and to customize service provision in the virtual store to be more user-centric.

[0424] "Demographic data" is data that shows trends and changes related to population, such as human migration, births, deaths, and marriages.

[0425] "Geographic information data" refers to data relating to geographical elements such as topography, geology, climate, and land use patterns.

[0426] "Market demand data" refers to data that indicates consumer demand, purchasing behavior, sales volume, etc. in a particular market.

[0427] "Synthesis" is the process of combining multiple datasets into one coherent dataset.

[0428] "Normalization" is the process of standardizing the format and scale of data to make it consistent.

[0429] "Missing values" refer to missing or incomplete data points in a dataset.

[0430] A "predictive model" is a mathematical model or machine learning model that predicts future trends based on past data.

[0431] "Consolidation" is the process of combining or abolishing multiple stores or service locations.

[0432] "New store openings" refers to the opening of new stores or service locations.

[0433] The "digital divide" is a concept that refers to differences in access to and opportunities to use information technology.

[0434] A "store opening strategy" refers to plans and policies regarding how to locate new stores and service locations.

[0435] A "report" refers to a document that summarizes the analysis results and proposals.

[0436] "Feedback" refers to reaction information such as ratings and comments from users.

[0437] An "emotion analysis engine" is a software system for automatically analyzing emotions from text and voice data.

[0438] "Smart device" is a general term for electronic devices that are connected to the Internet and can collect and communicate data.

[0439] "Virtual store" refers to a virtual store operated on the Internet.

[0440] "Service Point" refers to a service provided at a specific location or base.

[0441] "Customization" refers to tailoring an offering to meet the needs and desires of a particular user.

[0442] This invention includes a system that uses demographic data, geographic information data, and market demand data to optimize the placement of telecommunications service providers, and combines it with an emotion engine. The system collects, integrates, and preprocesses data, forecasts future trends using predictive models, and proposes store consolidation and new store openings. It also collects and analyzes user feedback to update its proposals.

[0443] This system uses a server as its main processing center and provides services in a virtual store using smart devices. The server obtains demographic and geographic data from public government APIs and geographic information systems, and market demand data from the internal databases of telecommunications service providers. It then integrates the acquired data into a single, consistent database. This integrated data is normalized using software tools such as Python and numpy, and missing values ​​are filled in using linear interpolation.

[0444] After preprocessing the integrated data, the server uses TensorFlow to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes optimal store consolidation and new store openings, and also formulates a store opening strategy that takes into account the elimination of the digital divide. A report detailing the proposals is then generated and provided to the user.

[0445] When a user visits a virtual store using a smart device, specifically smart glasses, the server displays suggestions visually in real time. Furthermore, the server collects user voice feedback and performs sentiment analysis using natural language processing libraries (such as Spacy and NLTK). Based on this sentiment analysis, the service content can be customized and updated to better meet the user's needs.

[0446] For example, in an analysis of cities A and B, city A's population has declined significantly in recent years, with further decline predicted over the next 10 years. Meanwhile, city B has seen active migration to rural areas in recent years and is predicted to experience population growth. Based on this, the server proposes the consolidation and closure of stores in city A and the opening of a new store in city B. If a user checks the proposal through smart glasses and provides voice feedback such as "It would be a shame if the store closed," the server's emotion analysis engine detects negative emotion and evaluates its strength. If the feedback is strongly negative, the server reevaluates the proposal and considers other options.

[0447] Examples of prompts include:

[0448] "User provided feedback:

[0449] "It's a shame the store is closing. Recently, more people have been living in the area and they use this store all the time."

[0450] System suggestions for this feedback:

[0451] We will reevaluate the proposed store closure in City A and consider other options."

[0452] In this way, the system of the present invention not only uses demographic data, geographic information data, and market demand data to develop an optimal store placement strategy, but also performs feedback analysis that takes user emotions into account, allowing for more user-centric proposals.

[0453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0454] Step 1: Collect data

[0455] The server obtains demographic and geographic data from the government's public API and geographic information system (GIS), and extracts market demand data from the internal databases of telecommunications service providers. The input data is the API data from the government and GIS, and the market demand data from the company's database. This allows various data to be collected.

[0456] Step 2: Integrate the data

[0457] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key (such as a region code). The input is the integrated data from various datasets, and the output is the data compiled into a single, consistent database.

[0458] Step 3: Preprocessing the data

[0459] The server normalizes the integrated data. For example, it standardizes the unit of population to "person" and sales to "yen." If there are missing values, it fills them in using linear interpolation. The input is the integrated data, and the output is the normalized data with missing values ​​filled in.

[0460] Step 4: Training the predictive model

[0461] The server uses TensorFlow to train a predictive model based on historical data, for example, learning patterns of population decline or trends in market demand. The input is the preprocessed data, and the output is the trained predictive model. This builds a model for forecasting future trends.

[0462] Step 5: Forecast market trends

[0463] The server uses the trained model to predict future demographic trends and market demand. The inputs are the trained model and current data, and the output is predicted future demographic trends and market demand. Based on this forecast data, a foundation is laid for store consolidation and new store openings.

[0464] Step 6: Proposal for consolidation and new store openings

[0465] The server then makes proposals for store consolidation and new store openings based on the forecast data. For example, it may propose closure of stores in areas where population decline is predicted, and propose new store openings in areas where population growth is predicted. The input is the forecast data, and the output is a report detailing the proposals.

[0466] Step 7: Formulating a store opening strategy that takes into account bridging the digital divide

[0467] The server formulates a store opening strategy to ensure digital access in a specific area, taking into account the digital divide situation. The inputs are forecast data and digital divide information, and the output is a store opening strategy that takes into account eliminating the digital divide.

[0468] Step 8: Generate and distribute reports

[0469] The server generates a report detailing the proposal and sends it to the user's terminal. The input is the proposal, and the output is the generated report, allowing the user to check the proposal.

[0470] Step 9: Gather feedback

[0471] The user checks the report using a smart device (smart glasses) and provides feedback by voice. The input is the user's voice feedback, which is sent to the server.

[0472] Step 10: Sentiment analysis and feedback evaluation

[0473] The server receives the voice feedback and performs sentiment analysis using a natural language processing library (such as spacy or NLTK). The input is the user's feedback text, and the output is the sentiment evaluation result of the feedback. Based on this result, the server reevaluates and updates the suggestions.

[0474] Step 11: Update the proposal based on user feedback

[0475] The server reevaluates the proposals based on the results of the sentiment analysis and makes corrections or updates as necessary. The input is the sentiment analysis results, and the output is the updated proposals.

[0476] As a concrete example, if a user sends feedback saying, "It would be a shame if the store closed," the server will analyze the sentiment as negative and reevaluate the proposal. An example of a prompt sentence is the following: "User-provided feedback: 'It would be a shame if the store closed. Recently, more and more people have been living in this area, and I regularly visit this store.' System suggestion in response to this feedback: Reevaluate the store closure proposal in City A and consider other options."

[0477] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0478] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0479] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0483] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0484] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0485] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0486] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0487] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0488] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0489] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0490] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0491] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0492] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0493] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0494] This system involves a series of processes primarily performed by the server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report with the results. Users review this report, rate it, and send feedback to the server. The server then reevaluates the data based on the feedback and updates its strategy as necessary.

[0495] A natural language description of the program's operation

[0496] Data collection and integration

[0497] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0498] Data Preprocessing

[0499] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0500] Training and predicting a predictive model

[0501] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0502] Proposals for consolidation and new store openings

[0503] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0504] Report generation and distribution

[0505] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0506] Gathering feedback and updating strategy

[0507] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0508] Specific examples

[0509] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0510] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0511] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0512] The processing flow will be explained below.

[0513] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0514] A natural language description of the program's operation

[0515] Data collection and integration

[0516] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0517] Data Preprocessing

[0518] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0519] Training and predicting a predictive model

[0520] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0521] Proposals for consolidation and new store openings

[0522] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0523] Report generation and distribution

[0524] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0525] Gathering feedback and updating strategy

[0526] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0527] Specific examples

[0528] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0529] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0530] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0531] Processing step details

[0532] Step 1: Data collection

[0533] The server retrieves demographic data from the government's public API.

[0534] The server retrieves geographic information data from a GIS system.

[0535] The server extracts market demand data from the company's internal database.

[0536] Step 2: Data Integration

[0537] The server consolidates the collected data using a common key (regional code) and stores it in a database.

[0538] Step 3: Data Preprocessing

[0539] The server normalizes the integrated data (e.g., population is standardized to "people" and sales to "yen").

[0540] The server uses linear interpolation to fill in the missing values.

[0541] Step 4: Training the predictive model

[0542] The server uses historical data to train a machine learning model (e.g., a random forest regression model).

[0543] The server uses the trained model to predict future demographic trends and market demand.

[0544] Step 5: Generate proposals

[0545] Based on the prediction results, the server creates a list of candidates for consolidation, closure, and new store openings.

[0546] The server takes into consideration the elimination of the digital divide and proposes specific regions as priority candidates.

[0547] Step 6: Generate reports

[0548] The server generates a report detailing the proposal.

[0549] The server delivers the report to the terminal.

[0550] Step 7: Gather feedback

[0551] The user checks the report and gives an evaluation.

[0552] The user sends feedback to the server.

[0553] Step 8: Reassess and update your strategy

[0554] The server will reevaluate its proposals based on the feedback and update its strategy as needed.

[0555] The server generates the updated strategy again as a report and delivers it to the terminal.

[0556] As described above, the present invention provides a system for realizing efficient and effective allocation of communication service providing stores through a series of steps of data collection, integration, pre-processing, prediction, proposal and evaluation.

[0557] Example 1

[0558] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0559] In modern society, rapid changes in demographics and market demands require optimal location of telecommunications service outlets. However, conventional methods lack centralized data collection and integration, predictive model training, and store opening strategy formulation, resulting in a high level of manual work and inefficiency. Furthermore, they do not adequately address the digital divide, making it difficult to realize store location that meets residents' needs. A system that can solve these issues and provide efficient and accurate store location proposals is needed.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0561] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidations and new store openings based on the predicted data, means for using a machine learning model as the prediction model, means for generating a report detailing the proposals, means for sending the report to a user's terminal, means for collecting feedback from users, reevaluating and updating the proposals, and means for updating the strategy based on the feedback. This automates a series of processes, from data collection to prediction, proposal, feedback collection, and strategy updating, enabling efficient and highly accurate store placement proposals.

[0562] "Demographic data" refers to data that shows the size, composition, and trends of population growth and decline in a particular area.

[0563] "Geographic information data" is data that indicates the geographical features and location information of a specific area.

[0564] "Market demand data" refers to data that indicates the amount and trends of demand for a product or service in a particular market.

[0565] "Integration" refers to the process of matching data from multiple different data sources into a single data set.

[0566] "Normalization" is a process for standardizing the units and scale of data to ensure consistency.

[0567] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate way.

[0568] A "predictive model" is a statistical or machine learning model that estimates future data based on past data.

[0569] A "machine learning model" is an algorithm that learns patterns and rules from data and makes predictions and classifications for unseen data.

[0570] "Consolidation" refers to combining multiple stores or locations into one in order to eliminate overlapping functions and roles.

[0571] "New store openings" refer to the opening of new stores in new regions or markets.

[0572] The "digital divide" refers to disparities in access to and use of information technology, and indicates differences in the environment for using the Internet and digital services.

[0573] "Strategic update" refers to reviewing and improving existing plans and proposals based on collected feedback and new information.

[0574] A "report" is a document detailing the analysis results and recommendations, and is provided in a format that is easy for users to understand.

[0575] "Feedback" refers to evaluations, opinions, and comments provided by users, and is information that is used to improve and optimize the system.

[0576] A "terminal" is a device used by a user to receive or input information, and includes a computer, smartphone, etc.

[0577] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0578] The system mainly involves a series of processes executed by a server. The server obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the telecommunications service provider's internal database. This data is integrated using a common key (e.g., area code) and compiled into a single database. The hardware used includes a general-purpose server computer, and the software used includes Python, Pandas, Scikit-learn, and TensorFlow.

[0579] Next, the server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also performs linear interpolation to fill in missing data values ​​and estimates missing values ​​based on past trends. This is done using functions such as the "interpolate" function in Pandas.

[0580] The server then uses historical data to train a machine learning model. For example, it learns patterns of population decline and rural migration and builds a model to predict future trends. Specifically, it uses Scikit-learn's Random Forest and TensorFlow. It also uses Python libraries to verify the accuracy of the predictions using appropriate evaluation methods.

[0581] Using a trained model, the system predicts future demographic trends and market demand for each region, and based on those predictions, it recommends which stores should be consolidated or closed, and which areas should be opened. The server lists stores in areas experiencing rapid population decline as candidates for closure, and suggests new store openings in areas where migration to rural areas is progressing. It also identifies areas with particularly low digital access and prioritizes opening stores there.

[0582] The server generates a report detailing the proposal. This report is provided in a format that is easy for users to understand, detailing forecast data for each region and the reasons for the proposal. The generated report can be provided in PDF format or as a web-based dashboard and sent to the user's device. The user reviews the report and sends their evaluation and comments as feedback to the server. The server uses this information to reevaluate the proposal and update the strategy.

[0583] As a concrete example, consider a case where a server analyzes two cities, City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, and its population is predicted to grow by 20% over the next 10 years. As a result, the server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. It also proposes opening new stores in new areas of City B that are attracting new residents. Furthermore, it identifies areas with particularly low digital access and recommends prioritizing opening stores there.

[0584] The following are examples of prompt sentences:

[0585] 1. Prompts for collecting demographic data

[0586] "Please retrieve the past 10 years of demographic data for Tokyo from the API."

[0587] "Please use the API to obtain geographic information data for Tokyo."

[0588] 2. Prompt for data normalization

[0589] "Please normalize population data to a single unit of people."

[0590] "Please standardize the sales data to yen."

[0591] 3. Prompts about predictive models

[0592] "Train a population decline prediction model using TensorFlow based on historical data."

[0593] "Use Scikit-learn to predict population growth and decline over the next five years."

[0594] 4. Report Generation and Delivery Prompts

[0595] "Generate a PDF report with proposals for new store openings."

[0596] Send the generated report to the user's email address.

[0597] The above is a detailed description of the mode for carrying out the invention. This process enables efficient and highly accurate store layout proposals.

[0598] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0599] Step 1: Collect data

[0600] The server collects demographic and geographic data from public government APIs and geographic information systems (GIS), while extracting market demand data from the internal databases of telecommunications service providers.

[0601] Input: API endpoints (e.g., public government APIs), GIS data sources, corporate databases

[0602] Output: Demographic data, geographic information data, market demand data

[0603] Specific operation: The server sends an API request and retrieves the response data. It also downloads GIS data and executes queries against the company's internal SQL database to extract market demand data. For example, it retrieves data from "https: / / api.example.com / population" and executes the SQL query "SELECT FROM market_demand WHERE region_code = 'ABC'".

[0604] Step 2: Data integration and normalization

[0605] The server integrates the collected data using the region code as a key, and then normalizes the data to unify its units and ensure consistency.

[0606] Input: Demographic data, geographic information data, market demand data

[0607] Output: Integrated dataset (integrated + normalized)

[0608] Specific operation: The server uses the Python Pandas library to merge data using the "merge" function, and the "apply" function to unify population and sales data into people and circles. Example: df.merge(population_data, on='region_code'), df['population'] = df['population'].apply(lambda x: x 1000).

[0609] Step 3: Data preprocessing and missing value imputation

[0610] The server preprocesses the aggregated data, imputes missing values ​​using linear interpolation, and detects and appropriately corrects or removes outliers and values.

[0611] Input: Integrated dataset

[0612] Output: Preprocessed and imputed dataset

[0613] Specific operation: The server executes "df.interpolate(method='linear')" to fill in missing values. Outliers are detected with "df[(df['sales'] < 0) | (df['sales'] > 1e7)]" and corrected or deleted. Example: Executes df.interpolate(method='linear').

[0614] Step 4: Training the predictive model

[0615] The server uses historical data to train a machine learning model (e.g., Scikit-learn's Random Forest), which learns patterns of population decline and market demand and builds a model to predict future trends.

[0616] Input: Preprocessed dataset

[0617] Output: A trained predictive model

[0618] Specific operation: The server imports "from sklearn.ensemble import RandomForestRegressor" and initializes the model. Next, it runs "model.fit(X_train, y_train)" to train it. Example: Run model.fit(X_train, y_train).

[0619] Step 5: Perform future predictions

[0620] The server uses the trained model to predict future demographic trends and market demand.

[0621] Input: trained predictive model, new input data

[0622] Output: Future demographic forecast data, future market demand forecast data

[0623] Specific operation: The server executes "predictions = model.predict(X_test)" and obtains the prediction results. The prediction results are recorded in the database and saved in a data frame. Example: Execute predictions = model.predict(X_test).

[0624] Step 6: Propose store layout

[0625] Based on the prediction results, the server proposes optimal store locations, considering store consolidation and closure in areas experiencing rapid population decline, and proposing new store openings in areas where population growth is expected.

[0626] Input: Future demographic forecast data, market demand forecast data

[0627] Output: Proposal for store consolidation and new store openings

[0628] Specific operation: The server lists candidates for closure using "df[df['predicted_population'] < threshold]", and selects areas for new store openings where "df[df['predicted_population'] > threshold]". Example: df[df['predicted_population'] < threshold].

[0629] Step 7: Generate and distribute reports

[0630] The server generates a detailed report detailing the recommendations, which is then sent to the user's device in PDF format or as a web-based dashboard.

[0631] Input: Store layout proposal

[0632] Output: Report (PDF format, web dashboard)

[0633] Specific operation: The server generates a PDF using "from fpdf import FPDF" and saves it using "pdf.output('report.pdf')". Then it sends it using "send_email(to='user@example.com', attachment='report.pdf')". Example: pdf.output('report.pdf').

[0634] Step 8: Gather feedback and update your strategy

[0635] Users review the reports and send their ratings and comments as feedback to the server, which then reevaluates the proposals and updates the strategy.

[0636] Input: User feedback

[0637] Output: Updated proposals and strategies

[0638] Specific behavior: A user enters a comment in the feedback form and sends it to the server. The server executes "feedback = get_feedback()" and updates the strategy with "update_strategy(feedback)". Example: feedback = get_feedback().

[0639] (Application example 1)

[0640] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] When determining the optimal locations of telecommunications service outlets, companies face challenges in efficiently managing demographic changes, geographic information, and market demand, while also bridging the digital divide. Furthermore, the current system lacks real-time capabilities, making it difficult for users to review proposal results, provide feedback, and quickly reevaluate and update strategies. A system that efficiently resolves these challenges and provides an easy-to-use user experience is needed.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0643] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for displaying predictions and store layout proposals using the data to the user via a smartphone application. This enables real-time predictions and proposals, allowing users to provide rapid feedback and efficiently reevaluate and update strategies based on the results.

[0644] "Demographic data" refers to data that shows information on the composition, changes, and movement of the population in a particular region or group.

[0645] "Geographic information data" is data that includes information about physical features, topography, infrastructure, land use, and other aspects of a particular geographic space.

[0646] "Market demand data" refers to data that indicates information regarding the demand for a product in a particular market, consumer purchasing behavior, sales, etc.

[0647] "Integration methods" are methods for centralizing different types of data and compiling them into a single data set for analysis and prediction.

[0648] "Normalization" is the process of standardizing the units and scale of data, a process that is performed to make analysis and modeling easier.

[0649] A "means of imputing missing values" is a means of filling in gaps or missing data in a dataset by interpolation or estimation.

[0650] A "predictive model" is a mathematical or statistical model that uses historical data to predict future events or trends.

[0651] "Store consolidation" is the process of integrating or closing existing stores to improve efficiency and reduce costs.

[0652] "New store opening" means opening a new store in a new location.

[0653] The "digital divide" is a term that refers to disparities between regions and classes in access to and use of information technology.

[0654] A "store opening strategy" is a plan or policy for determining optimal store placement based on market research and forecasts.

[0655] "Report generation" is the process of organizing analysis results and proposals and outputting them as written or visual materials.

[0656] "Means for collecting feedback" refers to methods for efficiently collecting ratings and comments from users and incorporating them as data.

[0657] "Reevaluation and updating" is the process of reviewing initial proposals and models based on collected feedback and making modifications and improvements as needed.

[0658] A "smartphone application" is a software program that runs on a smartphone and provides specific functions or services.

[0659] "Real-time" is a term that refers to a state in which various data and information are processed and provided immediately without any time delay.

[0660] Specific embodiments for carrying out the present invention will be described below.

[0661] The present invention is a system for optimizing the location of telecommunications service outlets by using demographic data, geographic information data, and market demand data. The system includes a server, a database, and a smartphone application that interfaces with users. It also has the ability to make predictions using machine learning models and collect feedback to reevaluate strategies.

[0662] Program Overview

[0663] Data collection and integration

[0664] The server collects data by the following means:

[0665] Use government-provided APIs to obtain demographic data.

[0666] Obtain geographic information data from a geographic information system (GIS).

[0667] Extract market demand data from a company's internal database.

[0668] These data are compiled into a single integrated database using the area code as a common key.

[0669] Data Preprocessing

[0670] The server normalizes the integrated data and fills in missing values. For example, it standardizes the population unit to "people" and the sales unit to "yen." If there are missing values, it fills them in using methods such as linear interpolation.

[0671] Training and predicting a predictive model

[0672] The historical data is then used to train a machine learning model (e.g., a linear regression model). This model is then used to predict future demographics and market demand. The trained model can then process new data and make predictions in real time.

[0673] Proposals for store consolidation and new store openings

[0674] Based on the prediction results, the server will propose store consolidation and new store openings. For example, it will propose store closures in areas with declining populations and new store openings in areas with growing populations. Taking into account the elimination of the digital divide, it will also identify areas with insufficient digital access and prioritize new store openings there.

[0675] Report Generation and User Interface

[0676] A report detailing the recommendations is automatically generated and provided to the user, who can view it through a smartphone application that displays the prediction results along with visualized data fragments in an easy-to-understand interface.

[0677] Specific examples

[0678] For example, consider a case where a server is analyzing two cities, A and B. City A has seen a significant decline in population over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and the server predicts a 20% population increase over the next 10 years.

[0679] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0680] Prompt Sentence Examples

[0681] "City A is expected to experience a 30% population decline over the past five years. The server uses this data to suggest which stores should be closed and identify cities where new stores should be opened."

[0682] This system will enable telecommunications service providers to efficiently position their stores in a way that reflects the needs of residents and market demand, thereby contributing to bridging the digital divide.

[0683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0684] Step 1:

[0685] Inputs: Data from government APIs, geographic information systems, and internal corporate databases

[0686] Operation:

[0687] The server retrieves current demographic data using government-provided APIs, then collects geographic information from geographic information systems (GIS) and extracts market demand data from companies' internal databases.

[0688] Output: Demographic data, geographic information data, market demand data

[0689] Step 2:

[0690] Input: Demographic data, geographic information data, market demand data

[0691] Operation:

[0692] The server integrates each piece of data it has acquired using the region code as a key. In this integration process, data with common attributes are compiled into a single database.

[0693] Output: Integrated database

[0694] Step 3:

[0695] Input: Integrated database

[0696] Operation:

[0697] The server normalizes the integrated data. Specifically, it standardizes the population unit to "people" and the sales unit to "yen." In addition, if there are missing values, it fills them in using linear interpolation.

[0698] Output: Normalized and imputed data

[0699] Step 4:

[0700] Input: Normalized and imputed data

[0701] Operation:

[0702] The server uses this data to train machine learning models, such as linear regression models, which are used to predict future demographic trends and market demand.

[0703] Output: A trained machine learning model

[0704] Step 5:

[0705] Input: trained machine learning model, integrated database

[0706] Operation:

[0707] The server uses the trained model to predict future demographics and market demand, which in turn informs decisions about new store openings and store consolidation.

[0708] Output: Future demographic forecast data, market demand forecast data

[0709] Step 6:

[0710] Input: Future demographic forecast data, market demand forecast data

[0711] Operation:

[0712] Based on the prediction results, the server will suggest which stores should be consolidated or closed, and which areas to open new stores in. Furthermore, taking into consideration the elimination of the digital divide, it will identify areas with insufficient digital access and suggest prioritizing new store openings in those areas.

[0713] Output: Proposal data for consolidation and new store openings

[0714] Step 7:

[0715] Input: Proposal data regarding consolidation and new store openings

[0716] Operation:

[0717] The server generates a report detailing the recommendations, including visual graphs and maps, that users can view in a smartphone application.

[0718] Output: A report detailing the proposal

[0719] Step 8:

[0720] Input: Report detailing proposals, user feedback

[0721] Operation:

[0722] Users can review the generated reports and provide feedback via a smartphone application, which the server collects and uses to reevaluate and update strategies and recommendations.

[0723] Output: Updated strategy and proposal data

[0724] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0725] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[0726] This system involves a series of processes primarily performed by a server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report of the proposals. Users review and rate the report, then send feedback to the server. The server uses an emotion engine to analyze the sentiment of the feedback and reevaluate and update the proposals based on this sentiment.

[0727] A natural language description of the program's operation

[0728] Data collection and integration

[0729] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0730] Data Preprocessing

[0731] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0732] Training and predicting a predictive model

[0733] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0734] Proposals for consolidation and new store openings

[0735] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0736] Report generation and distribution

[0737] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0738] Feedback collection and sentiment analysis

[0739] The user checks the report and sends their rating and comments as feedback to the server. The server uses an emotion engine to analyze the emotion from the user's feedback text. This emotion analysis allows us to measure whether the user's feedback is positive or negative, and its intensity.

[0740] Reassess and update your strategy

[0741] The server takes into account the analysis results of the emotion engine and reevaluates the content of the suggestions. For example, if there is a lot of negative feedback, it determines that there is a problem with the prediction model or the content of the suggestions, and takes measures such as collecting data again. Based on the analysis results of the emotion engine, the priority of the suggestions is adjusted to make suggestions that are more in line with the user's emotions.

[0742] Specific examples

[0743] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0744] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0745] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[0746] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0747] The processing flow will be explained below.

[0748] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[0749] A natural language description of the program's operation

[0750] Step 1: Data collection

[0751] The server retrieves demographic data from the government's public API, accesses the government's statistical database, and collects data on population growth and decline by region and age distribution.

[0752] The server retrieves geographic information data from the GIS system, queries the GIS system, and collects geographical features and land use information for the target area.

[0753] The server extracts market demand data from the company's internal database, as well as market research data, sales data from existing stores, and customer satisfaction data.

[0754] Step 2: Data Integration

[0755] The server unifies the collected data using a common key (regional code) and stores it in an integrated database, allowing information from different data sources to be managed centrally.

[0756] Step 3: Data Preprocessing

[0757] The server normalizes the integrated data, standardizing the units of each data item, for example, standardizing the population unit to "people" and the sales unit to "yen."

[0758] The server uses linear interpolation to fill in missing values, and if there are missing data points, it fills them in based on nearby data points.

[0759] Step 4: Training the predictive model

[0760] The server uses historical data to train a machine learning model, using a random forest regression model to learn patterns of population decline and rural migration.

[0761] The server uses the trained model to forecast future demographic trends and market demand, predicting population and demand changes in each region over the next 10 years.

[0762] Step 5: Generate proposals

[0763] Based on the prediction results, the server creates a list of candidates for consolidation and new store openings. It lists stores in areas with a significant population decline as candidates for closure, and suggests opening new stores in areas where population growth is expected.

[0764] Taking into consideration the need to eliminate the digital divide, Sarver will prioritize areas lacking infrastructure as potential store locations.

[0765] Step 6: Generate reports

[0766] The server generates a report detailing the proposal, including forecast data for each region, the rationale for the proposal, and recommendations for consolidation and new store openings.

[0767] The server sends the generated report to the terminal, and distributes it in PDF format to the email address of the person in charge or to the company's internal portal.

[0768] Step 7: Gather feedback

[0769] The user checks the received report and sends their evaluation and comments to the server as feedback. Specifically, they enter comments such as whether they agree or disagree with the proposals in the report, or if they would like to make additions.

[0770] Step 8: Sentiment Analysis

[0771] The server analyzes the emotions from the user's feedback using an emotion engine. It uses natural language processing techniques to extract emotions (positive, negative, neutral) from the feedback text.

[0772] The server evaluates the intensity of the emotion and determines that special action should be taken if the feedback contains extremely strong negative emotions.

[0773] Step 9: Reassess and update your strategy

[0774] The server re-evaluates the suggestions based on the analysis results, and dynamically adjusts the priority of suggestions according to the user's actual emotions, taking into account the results of the sentiment analysis.

[0775] The server will reflect user feedback and the results of sentiment analysis, and take appropriate action such as recollecting data and retraining the predictive model as necessary.

[0776] The server generates the updated strategy again as a report and delivers it to the terminal.

[0777] Specific examples

[0778] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0779] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0780] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[0781] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0782] Example 2

[0783] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0784] Conventional strategies for the placement of telecommunications service stores have limitations in the accuracy of aggregation and forecasting of demographic data, geographic information data, and market demand data, making it difficult to accurately locate stores. Furthermore, it has been difficult to fully utilize user feedback, making it difficult to build an optimal strategy that reflects user emotions and needs. This has resulted in delays in decisions regarding new store openings and consolidation, making it difficult to adapt to market trends. Furthermore, there has been a lack of means to provide more flexible and emotionally sensitive proposals by analyzing the emotions of feedback.

[0785] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0786] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the forecast data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for analyzing the sentiment of the user feedback and reevaluating and updating the proposals. This enables an optimal strategy for locating communication service providing stores that reflects user sentiment and feedback.

[0787] "Demographic data" refers to data that includes information on population changes and composition, birth and death rates, migration patterns, etc. in a particular area.

[0788] "Geographic information data" is data that includes information about geography, such as geographic location, topography, land use, and infrastructure.

[0789] "Market demand data" refers to data that includes information such as trends in demand for products and services in a particular market, consumer behavior, and purchasing history.

[0790] "Means of integration" refers to methods or techniques for combining multiple datasets based on common keys or items and consolidating them into a single dataset.

[0791] "Normalization" refers to the process of standardizing data formats and units to ensure consistency among data.

[0792] "Methods for imputing missing values" refers to methods that use statistical techniques or algorithms to fill in missing values ​​in a dataset.

[0793] "Predictive model" refers to a mathematical or statistical model that uses historical data to predict future fluctuations or trends.

[0794] "Store consolidation" refers to reorganization of store operations, such as closing existing stores or merging multiple stores into one.

[0795] "New store opening" refers to the establishment of a new store in a new location.

[0796] "Closing the digital divide" refers to reducing disparities in access to and use of information and communications technology and providing digital services equally in all regions.

[0797] "Means for generating reports" refers to the methods and techniques for compiling analysis results and proposals in document or graph format and creating a report.

[0798] "User feedback" refers to the act of a user providing evaluations or opinions on provided reports or proposals, and the information provided.

[0799] "Means for analyzing emotions" refers to methods and technologies for evaluating and analyzing the positive and negative emotions from user feedback.

[0800] "Reevaluation and updating" refers to the process of reviewing existing proposals and developing revisions or new proposals as needed.

[0801] This invention combines a system that realizes optimal placement of communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This system improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs.

[0802] The present system includes a series of processes that are mainly executed by a server. The following describes in detail an embodiment of the present system.

[0803] 1. Data Collection

[0804] The server obtains demographic data using the government's public API, downloads geographic information data from a geographic information system (GIS), and extracts market demand data from the internal database of a telecommunications service provider. Various analyses are performed based on this data.

[0805] 2. Data Integration

[0806] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a region code, thereby creating a unified data set.

[0807] 3. Data Preprocessing

[0808] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[0809] 4. Training the predictive model

[0810] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[0811] 5. Prediction and Recommendations

[0812] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[0813] 6. Report Generation and Delivery

[0814] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[0815] 7. Gathering Feedback

[0816] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[0817] 8. Emotion analysis

[0818] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[0819] 9. Reassessment and Updates

[0820] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[0821] Specific examples

[0822] For example, consider a scenario in which a server analyzes two cities: City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, with a predicted 20% population growth over the next 10 years. The server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. At the same time, the server suggests opening a new store in a new migration area in City B. Furthermore, the server identifies areas of City B that lack digital access and suggests prioritizing opening a store there. If a user reviews the report and submits negative feedback about the proposed store closure in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the proposed store closure in City A and considers other options.

[0823] Examples of prompt statements

[0824] Example 1:

[0825] "Analyze demographic data from the past five years and identify areas most likely to experience population decline over the next decade."

[0826] Example 2:

[0827] "Please integrate market demand data and geographic information data to suggest areas where new stores should be opened within the next two years."

[0828] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[0829] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0830] Step 1: Data collection

[0831] The server retrieves demographic data using public government APIs, downloads geographic information data from geographic information systems (GIS), and extracts market demand data from the internal databases of telecommunications service providers.

[0832] Input: API key or query

[0833] Output: Population data in JSON format, GIS data, market demand data extracted from SQL database

[0834] Specific operations: The server uses the API key to send requests to government sites, obtain population data in JSON format, download geographic information data from GIS, and extract market demand data from the company's internal database using SQL queries.

[0835] Step 2: Data Integration

[0836] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a regional code.

[0837] Input: Demographic data, geographic information data, market demand data

[0838] Output: Unified dataset

[0839] Specific operation: The server uses a data frame to combine each dataset with a common region code to build a single integrated dataset.

[0840] Step 3: Data Preprocessing

[0841] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[0842] Input: Integrated dataset

[0843] Output: Standardized and imputed data

[0844] Specific operation: The server uses the Pandas library to unify the population unit to "person", change the sales unit to "yen", and perform linear interpolation for missing values.

[0845] Step 4: Training the predictive model

[0846] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[0847] Input: Standardized and imputed data

[0848] Output: A trained predictive model

[0849] Specific operation: The server uses the scikit-learn library to train algorithms such as random forest models and evaluate their accuracy through cross-validation.

[0850] Step 5: Prediction execution and recommendations

[0851] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[0852] Input: trained model, latest data

[0853] Output: List of store opening and consolidation proposals

[0854] How it works: The server supplies the latest input data to the model and outputs prediction results. Based on the results, it creates a list of proposals for consolidation and new store openings and identifies areas with a digital divide.

[0855] Step 6: Report generation and distribution

[0856] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[0857] Input: List of store opening and consolidation proposals

[0858] Output: Proposal report (PDF, etc.)

[0859] Specific operation: The server generates a report using a Markdown template, exports it to PDF format, and sends it to the user's device.

[0860] Step 7: Gather feedback

[0861] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[0862] Input: User rating and comments

[0863] Output: User feedback data

[0864] Specific behavior: A user views a report through a web application, enters ratings and comments in a feedback form, and submits it.

[0865] Step 8: Sentiment Analysis

[0866] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[0867] Input: User feedback data

[0868] Output: Emotion analysis results

[0869] What it does: The server uses an NLP library to score the positivity and negativity of the feedback text.

[0870] Step 9: Reassess and update

[0871] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[0872] Input: Sentiment analysis results

[0873] Output: Updated proposal

[0874] Specific operation: The server recollects data from areas with a lot of negative feedback, retunes the parameters of the predictive model, and updates the suggestions.

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] Traditional store location strategies for telecommunications service providers have primarily been based on demographic data, geographic information data, and market demand data. However, the integration of these data and proposals based on predictive models have been limited, and proposals often do not meet users' actual needs because they do not fully consider user sentiment and feedback. Furthermore, it has been difficult to formulate store opening strategies that take the digital divide into account, resulting in many areas where telecommunications services are inadequate. Furthermore, there has been insufficient customization of services provided in virtual stores that effectively utilizes real-time user feedback.

[0878] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, means for displaying service provision locations in a virtual store using a smart device, means for collecting user feedback via voice and analyzing it using a sentiment analysis engine, and means for customizing service content based on the analysis results. This makes it possible to formulate an optimal location strategy for communication service providing stores while taking user emotions into consideration, and to customize service provision in the virtual store to be more user-centric.

[0879] "Demographic data" is data that shows trends and changes related to population, such as human migration, births, deaths, and marriages.

[0880] "Geographic information data" refers to data relating to geographical elements such as topography, geology, climate, and land use patterns.

[0881] "Market demand data" refers to data that indicates consumer demand, purchasing behavior, sales volume, etc. in a particular market.

[0882] "Synthesis" is the process of combining multiple datasets into one coherent dataset.

[0883] "Normalization" is the process of standardizing the format and scale of data to make it consistent.

[0884] "Missing values" refer to missing or incomplete data points in a dataset.

[0885] A "predictive model" is a mathematical model or machine learning model that predicts future trends based on past data.

[0886] "Consolidation" is the process of combining or abolishing multiple stores or service locations.

[0887] "New store openings" refers to the opening of new stores or service locations.

[0888] The "digital divide" is a concept that refers to differences in access to and opportunities to use information technology.

[0889] A "store opening strategy" refers to plans and policies regarding how to locate new stores and service locations.

[0890] A "report" refers to a document that summarizes the analysis results and proposals.

[0891] "Feedback" refers to reaction information such as ratings and comments from users.

[0892] An "emotion analysis engine" is a software system for automatically analyzing emotions from text and voice data.

[0893] "Smart device" is a general term for electronic devices that are connected to the Internet and can collect and communicate data.

[0894] "Virtual store" refers to a virtual store operated on the Internet.

[0895] "Service Point" refers to a service provided at a specific location or base.

[0896] "Customization" refers to tailoring an offering to meet the needs and desires of a particular user.

[0897] This invention includes a system that uses demographic data, geographic information data, and market demand data to optimize the placement of telecommunications service providers, and combines it with an emotion engine. The system collects, integrates, and preprocesses data, forecasts future trends using predictive models, and proposes store consolidation and new store openings. It also collects and analyzes user feedback to update its proposals.

[0898] This system uses a server as its main processing center and provides services in a virtual store using smart devices. The server obtains demographic and geographic data from public government APIs and geographic information systems, and market demand data from the internal databases of telecommunications service providers. It then integrates the acquired data into a single, consistent database. This integrated data is normalized using software tools such as Python and numpy, and missing values ​​are filled in using linear interpolation.

[0899] After preprocessing the integrated data, the server uses TensorFlow to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes optimal store consolidation and new store openings, and also formulates a store opening strategy that takes into account the elimination of the digital divide. A report detailing the proposals is then generated and provided to the user.

[0900] When a user visits a virtual store using a smart device, specifically smart glasses, the server displays suggestions visually in real time. Furthermore, the server collects user voice feedback and performs sentiment analysis using natural language processing libraries (such as Spacy and NLTK). Based on this sentiment analysis, the service content can be customized and updated to better meet the user's needs.

[0901] For example, in an analysis of cities A and B, city A's population has declined significantly in recent years, with further decline predicted over the next 10 years. Meanwhile, city B has seen active migration to rural areas in recent years and is predicted to experience population growth. Based on this, the server proposes the consolidation and closure of stores in city A and the opening of a new store in city B. If a user checks the proposal through smart glasses and provides voice feedback such as "It would be a shame if the store closed," the server's emotion analysis engine detects negative emotion and evaluates its strength. If the feedback is strongly negative, the server reevaluates the proposal and considers other options.

[0902] Examples of prompts include:

[0903] "User provided feedback:

[0904] "It's a shame the store is closing. Recently, more people have been living in the area and they use this store all the time."

[0905] System suggestions for this feedback:

[0906] We will reevaluate the proposed store closure in City A and consider other options."

[0907] In this way, the system of the present invention not only uses demographic data, geographic information data, and market demand data to develop an optimal store placement strategy, but also performs feedback analysis that takes user emotions into account, allowing for more user-centric proposals.

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Step 1: Collect data

[0910] The server obtains demographic and geographic data from the government's public API and geographic information system (GIS), and extracts market demand data from the internal databases of telecommunications service providers. The input data is the API data from the government and GIS, and the market demand data from the company's database. This allows various data to be collected.

[0911] Step 2: Integrate the data

[0912] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key (such as a region code). The input is the integrated data from various datasets, and the output is the data compiled into a single, consistent database.

[0913] Step 3: Preprocessing the data

[0914] The server normalizes the integrated data. For example, it standardizes the unit of population to "person" and sales to "yen." If there are missing values, it fills them in using linear interpolation. The input is the integrated data, and the output is the normalized data with missing values ​​filled in.

[0915] Step 4: Training the predictive model

[0916] The server uses TensorFlow to train a predictive model based on historical data, for example, learning patterns of population decline or trends in market demand. The input is the preprocessed data, and the output is the trained predictive model. This builds a model for forecasting future trends.

[0917] Step 5: Forecast market trends

[0918] The server uses the trained model to predict future demographic trends and market demand. The inputs are the trained model and current data, and the output is predicted future demographic trends and market demand. Based on this forecast data, a foundation is laid for store consolidation and new store openings.

[0919] Step 6: Proposal for consolidation and new store openings

[0920] The server then makes proposals for store consolidation and new store openings based on the forecast data. For example, it may propose closure of stores in areas where population decline is predicted, and propose new store openings in areas where population growth is predicted. The input is the forecast data, and the output is a report detailing the proposals.

[0921] Step 7: Formulating a store opening strategy that takes into account bridging the digital divide

[0922] The server formulates a store opening strategy to ensure digital access in a specific area, taking into account the digital divide situation. The inputs are forecast data and digital divide information, and the output is a store opening strategy that takes into account eliminating the digital divide.

[0923] Step 8: Generate and distribute reports

[0924] The server generates a report detailing the proposal and sends it to the user's terminal. The input is the proposal, and the output is the generated report, allowing the user to check the proposal.

[0925] Step 9: Gather feedback

[0926] The user checks the report using a smart device (smart glasses) and provides feedback by voice. The input is the user's voice feedback, which is sent to the server.

[0927] Step 10: Sentiment analysis and feedback evaluation

[0928] The server receives the voice feedback and performs sentiment analysis using a natural language processing library (such as spacy or NLTK). The input is the user's feedback text, and the output is the sentiment evaluation result of the feedback. Based on this result, the server reevaluates and updates the suggestions.

[0929] Step 11: Update the proposal based on user feedback

[0930] The server reevaluates the proposals based on the results of the sentiment analysis and makes corrections or updates as necessary. The input is the sentiment analysis results, and the output is the updated proposals.

[0931] As a concrete example, if a user sends feedback saying, "It would be a shame if the store closed," the server will analyze the sentiment as negative and reevaluate the proposal. An example of a prompt sentence is the following: "User-provided feedback: 'It would be a shame if the store closed. Recently, more and more people have been living in this area, and I regularly visit this store.' System suggestion in response to this feedback: Reevaluate the store closure proposal in City A and consider other options."

[0932] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0933] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0934] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0935] [Third embodiment]

[0936] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0937] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0938] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0939] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0940] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0941] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0942] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0943] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0944] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0945] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0946] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0947] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0948] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0949] This system involves a series of processes primarily performed by the server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report with the results. Users review this report, rate it, and send feedback to the server. The server then reevaluates the data based on the feedback and updates its strategy as necessary.

[0950] A natural language description of the program's operation

[0951] Data collection and integration

[0952] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0953] Data Preprocessing

[0954] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0955] Training and predicting a predictive model

[0956] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0957] Proposals for consolidation and new store openings

[0958] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0959] Report generation and distribution

[0960] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0961] Gathering feedback and updating strategy

[0962] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0963] Specific examples

[0964] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0965] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0966] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0967] The processing flow will be explained below.

[0968] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[0969] A natural language description of the program's operation

[0970] Data collection and integration

[0971] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[0972] Data Preprocessing

[0973] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[0974] Training and predicting a predictive model

[0975] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[0976] Proposals for consolidation and new store openings

[0977] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[0978] Report generation and distribution

[0979] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[0980] Gathering feedback and updating strategy

[0981] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[0982] Specific examples

[0983] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[0984] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[0985] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[0986] Processing step details

[0987] Step 1: Data collection

[0988] The server retrieves demographic data from the government's public API.

[0989] The server retrieves geographic information data from a GIS system.

[0990] The server extracts market demand data from the company's internal database.

[0991] Step 2: Data Integration

[0992] The server consolidates the collected data using a common key (regional code) and stores it in a database.

[0993] Step 3: Data Preprocessing

[0994] The server normalizes the integrated data (e.g., population is standardized to "people" and sales to "yen").

[0995] The server uses linear interpolation to fill in the missing values.

[0996] Step 4: Training the predictive model

[0997] The server uses historical data to train a machine learning model (e.g., a random forest regression model).

[0998] The server uses the trained model to predict future demographic trends and market demand.

[0999] Step 5: Generate proposals

[1000] Based on the prediction results, the server creates a list of candidates for consolidation, closure, and new store openings.

[1001] The server takes into consideration the elimination of the digital divide and proposes specific regions as priority candidates.

[1002] Step 6: Generate reports

[1003] The server generates a report detailing the proposal.

[1004] The server delivers the report to the terminal.

[1005] Step 7: Gather feedback

[1006] The user checks the report and gives an evaluation.

[1007] The user sends feedback to the server.

[1008] Step 8: Reassess and update your strategy

[1009] The server will reevaluate its proposals based on the feedback and update its strategy as needed.

[1010] The server generates the updated strategy again as a report and delivers it to the terminal.

[1011] As described above, the present invention provides a system for realizing efficient and effective allocation of communication service providing stores through a series of steps of data collection, integration, pre-processing, prediction, proposal and evaluation.

[1012] Example 1

[1013] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1014] In modern society, rapid changes in demographics and market demands require optimal location of telecommunications service outlets. However, conventional methods lack centralized data collection and integration, predictive model training, and store opening strategy formulation, resulting in a high level of manual work and inefficiency. Furthermore, they do not adequately address the digital divide, making it difficult to realize store location that meets residents' needs. A system that can solve these issues and provide efficient and accurate store location proposals is needed.

[1015] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1016] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidations and new store openings based on the predicted data, means for using a machine learning model as the prediction model, means for generating a report detailing the proposals, means for sending the report to a user's terminal, means for collecting feedback from users, reevaluating and updating the proposals, and means for updating the strategy based on the feedback. This automates a series of processes, from data collection to prediction, proposal, feedback collection, and strategy updating, enabling efficient and highly accurate store placement proposals.

[1017] "Demographic data" refers to data that shows the size, composition, and trends of population growth and decline in a particular area.

[1018] "Geographic information data" is data that indicates the geographical features and location information of a specific area.

[1019] "Market demand data" refers to data that indicates the amount and trends of demand for a product or service in a particular market.

[1020] "Integration" refers to the process of matching data from multiple different data sources into a single data set.

[1021] "Normalization" is a process for standardizing the units and scale of data to ensure consistency.

[1022] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate way.

[1023] A "predictive model" is a statistical or machine learning model that estimates future data based on past data.

[1024] A "machine learning model" is an algorithm that learns patterns and rules from data and makes predictions and classifications for unseen data.

[1025] "Consolidation" refers to combining multiple stores or locations into one in order to eliminate overlapping functions and roles.

[1026] "New store openings" refer to the opening of new stores in new regions or markets.

[1027] The "digital divide" refers to disparities in access to and use of information technology, and indicates differences in the environment for using the Internet and digital services.

[1028] "Strategic update" refers to reviewing and improving existing plans and proposals based on collected feedback and new information.

[1029] A "report" is a document detailing the analysis results and recommendations, and is provided in a format that is easy for users to understand.

[1030] "Feedback" refers to evaluations, opinions, and comments provided by users, and is information that is used to improve and optimize the system.

[1031] A "terminal" is a device used by a user to receive or input information, and includes a computer, smartphone, etc.

[1032] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[1033] The system mainly involves a series of processes executed by a server. The server obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the telecommunications service provider's internal database. This data is integrated using a common key (e.g., area code) and compiled into a single database. The hardware used includes a general-purpose server computer, and the software used includes Python, Pandas, Scikit-learn, and TensorFlow.

[1034] Next, the server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also performs linear interpolation to fill in missing data values ​​and estimates missing values ​​based on past trends. This is done using functions such as the "interpolate" function in Pandas.

[1035] The server then uses historical data to train a machine learning model. For example, it learns patterns of population decline and rural migration and builds a model to predict future trends. Specifically, it uses Scikit-learn's Random Forest and TensorFlow. It also uses Python libraries to verify the accuracy of the predictions using appropriate evaluation methods.

[1036] Using a trained model, the system predicts future demographic trends and market demand for each region, and based on those predictions, it recommends which stores should be consolidated or closed, and which areas should be opened. The server lists stores in areas experiencing rapid population decline as candidates for closure, and suggests new store openings in areas where migration to rural areas is progressing. It also identifies areas with particularly low digital access and prioritizes opening stores there.

[1037] The server generates a report detailing the proposal. This report is provided in a format that is easy for users to understand, detailing forecast data for each region and the reasons for the proposal. The generated report can be provided in PDF format or as a web-based dashboard and sent to the user's device. The user reviews the report and sends their evaluation and comments as feedback to the server. The server uses this information to reevaluate the proposal and update the strategy.

[1038] As a concrete example, consider a case where a server analyzes two cities, City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, and its population is predicted to grow by 20% over the next 10 years. As a result, the server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. It also proposes opening new stores in new areas of City B that are attracting new residents. Furthermore, it identifies areas with particularly low digital access and recommends prioritizing opening stores there.

[1039] The following are examples of prompt sentences:

[1040] 1. Prompts for collecting demographic data

[1041] "Please retrieve the past 10 years of demographic data for Tokyo from the API."

[1042] "Please use the API to obtain geographic information data for Tokyo."

[1043] 2. Prompt for data normalization

[1044] "Please normalize population data to a single unit of people."

[1045] "Please standardize the sales data to yen."

[1046] 3. Prompts about predictive models

[1047] "Train a population decline prediction model using TensorFlow based on historical data."

[1048] "Use Scikit-learn to predict population growth and decline over the next five years."

[1049] 4. Report Generation and Delivery Prompts

[1050] "Generate a PDF report with proposals for new store openings."

[1051] Send the generated report to the user's email address.

[1052] The above is a detailed description of the mode for carrying out the invention. This process enables efficient and highly accurate store layout proposals.

[1053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1054] Step 1: Collect data

[1055] The server collects demographic and geographic data from public government APIs and geographic information systems (GIS), while extracting market demand data from the internal databases of telecommunications service providers.

[1056] Input: API endpoints (e.g., public government APIs), GIS data sources, corporate databases

[1057] Output: Demographic data, geographic information data, market demand data

[1058] Specific operation: The server sends an API request and retrieves the response data. It also downloads GIS data and executes queries against the company's internal SQL database to extract market demand data. For example, it retrieves data from "https: / / api.example.com / population" and executes the SQL query "SELECT FROM market_demand WHERE region_code = 'ABC'".

[1059] Step 2: Data integration and normalization

[1060] The server integrates the collected data using the region code as a key, and then normalizes the data to unify its units and ensure consistency.

[1061] Input: Demographic data, geographic information data, market demand data

[1062] Output: Integrated dataset (integrated + normalized)

[1063] Specific operation: The server uses the Python Pandas library to merge data using the "merge" function, and the "apply" function to unify population and sales data into people and circles. Example: df.merge(population_data, on='region_code'), df['population'] = df['population'].apply(lambda x: x 1000).

[1064] Step 3: Data preprocessing and missing value imputation

[1065] The server preprocesses the aggregated data, imputes missing values ​​using linear interpolation, and detects and appropriately corrects or removes outliers and values.

[1066] Input: Integrated dataset

[1067] Output: Preprocessed and imputed dataset

[1068] Specific operation: The server executes "df.interpolate(method='linear')" to fill in missing values. Outliers are detected with "df[(df['sales'] < 0) | (df['sales'] > 1e7)]" and corrected or deleted. Example: Executes df.interpolate(method='linear').

[1069] Step 4: Training the predictive model

[1070] The server uses historical data to train a machine learning model (e.g., Scikit-learn's Random Forest), which learns patterns of population decline and market demand and builds a model to predict future trends.

[1071] Input: Preprocessed dataset

[1072] Output: A trained predictive model

[1073] Specific operation: The server imports "from sklearn.ensemble import RandomForestRegressor" and initializes the model. Next, it runs "model.fit(X_train, y_train)" to train it. Example: Run model.fit(X_train, y_train).

[1074] Step 5: Perform future predictions

[1075] The server uses the trained model to predict future demographic trends and market demand.

[1076] Input: trained predictive model, new input data

[1077] Output: Future demographic forecast data, future market demand forecast data

[1078] Specific operation: The server executes "predictions = model.predict(X_test)" and obtains the prediction results. The prediction results are recorded in the database and saved in a data frame. Example: Execute predictions = model.predict(X_test).

[1079] Step 6: Propose store layout

[1080] Based on the prediction results, the server proposes optimal store locations, considering store consolidation and closure in areas experiencing rapid population decline, and proposing new store openings in areas where population growth is expected.

[1081] Input: Future demographic forecast data, market demand forecast data

[1082] Output: Proposal for store consolidation and new store openings

[1083] Specific operation: The server lists candidates for closure using "df[df['predicted_population'] < threshold]", and selects areas for new store openings where "df[df['predicted_population'] > threshold]". Example: df[df['predicted_population'] < threshold].

[1084] Step 7: Generate and distribute reports

[1085] The server generates a detailed report detailing the recommendations, which is then sent to the user's device in PDF format or as a web-based dashboard.

[1086] Input: Store layout proposal

[1087] Output: Report (PDF format, web dashboard)

[1088] Specific operation: The server generates a PDF using "from fpdf import FPDF" and saves it using "pdf.output('report.pdf')". Then it sends it using "send_email(to='user@example.com', attachment='report.pdf')". Example: pdf.output('report.pdf').

[1089] Step 8: Gather feedback and update your strategy

[1090] Users review the reports and send their ratings and comments as feedback to the server, which then reevaluates the proposals and updates the strategy.

[1091] Input: User feedback

[1092] Output: Updated proposals and strategies

[1093] Specific behavior: A user enters a comment in the feedback form and sends it to the server. The server executes "feedback = get_feedback()" and updates the strategy with "update_strategy(feedback)". Example: feedback = get_feedback().

[1094] (Application example 1)

[1095] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1096] When determining the optimal locations of telecommunications service outlets, companies face challenges in efficiently managing demographic changes, geographic information, and market demand, while also bridging the digital divide. Furthermore, the current system lacks real-time capabilities, making it difficult for users to review proposal results, provide feedback, and quickly reevaluate and update strategies. A system that efficiently resolves these challenges and provides an easy-to-use user experience is needed.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1098] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for displaying predictions and store layout proposals using the data to the user via a smartphone application. This enables real-time predictions and proposals, allowing users to provide rapid feedback and efficiently reevaluate and update strategies based on the results.

[1099] "Demographic data" refers to data that shows information on the composition, changes, and movement of the population in a particular region or group.

[1100] "Geographic information data" is data that includes information about physical features, topography, infrastructure, land use, and other aspects of a particular geographic space.

[1101] "Market demand data" refers to data that indicates information regarding the demand for a product in a particular market, consumer purchasing behavior, sales, etc.

[1102] "Integration methods" are methods for centralizing different types of data and compiling them into a single data set for analysis and prediction.

[1103] "Normalization" is the process of standardizing the units and scale of data, a process that is performed to make analysis and modeling easier.

[1104] A "means of imputing missing values" is a means of filling in gaps or missing data in a dataset by interpolation or estimation.

[1105] A "predictive model" is a mathematical or statistical model that uses historical data to predict future events or trends.

[1106] "Store consolidation" is the process of integrating or closing existing stores to improve efficiency and reduce costs.

[1107] "New store opening" means opening a new store in a new location.

[1108] The "digital divide" is a term that refers to disparities between regions and classes in access to and use of information technology.

[1109] A "store opening strategy" is a plan or policy for determining optimal store placement based on market research and forecasts.

[1110] "Report generation" is the process of organizing analysis results and proposals and outputting them as written or visual materials.

[1111] "Means for collecting feedback" refers to methods for efficiently collecting ratings and comments from users and incorporating them as data.

[1112] "Reevaluation and updating" is the process of reviewing initial proposals and models based on collected feedback and making modifications and improvements as needed.

[1113] A "smartphone application" is a software program that runs on a smartphone and provides specific functions or services.

[1114] "Real-time" is a term that refers to a state in which various data and information are processed and provided immediately without any time delay.

[1115] Specific embodiments for carrying out the present invention will be described below.

[1116] The present invention is a system for optimizing the location of telecommunications service outlets by using demographic data, geographic information data, and market demand data. The system includes a server, a database, and a smartphone application that interfaces with users. It also has the ability to make predictions using machine learning models and collect feedback to reevaluate strategies.

[1117] Program Overview

[1118] Data collection and integration

[1119] The server collects data by the following means:

[1120] Use government-provided APIs to obtain demographic data.

[1121] Obtain geographic information data from a geographic information system (GIS).

[1122] Extract market demand data from a company's internal database.

[1123] These data are compiled into a single integrated database using the area code as a common key.

[1124] Data Preprocessing

[1125] The server normalizes the integrated data and fills in missing values. For example, it standardizes the population unit to "people" and the sales unit to "yen." If there are missing values, it fills them in using methods such as linear interpolation.

[1126] Training and predicting a predictive model

[1127] The historical data is then used to train a machine learning model (e.g., a linear regression model). This model is then used to predict future demographics and market demand. The trained model can then process new data and make predictions in real time.

[1128] Proposals for store consolidation and new store openings

[1129] Based on the prediction results, the server will propose store consolidation and new store openings. For example, it will propose store closures in areas with declining populations and new store openings in areas with growing populations. Taking into account the elimination of the digital divide, it will also identify areas with insufficient digital access and prioritize new store openings there.

[1130] Report Generation and User Interface

[1131] A report detailing the recommendations is automatically generated and provided to the user, who can view it through a smartphone application that displays the prediction results along with visualized data fragments in an easy-to-understand interface.

[1132] Specific examples

[1133] For example, consider a case where a server is analyzing two cities, A and B. City A has seen a significant decline in population over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and the server predicts a 20% population increase over the next 10 years.

[1134] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1135] Prompt Sentence Examples

[1136] "City A is expected to experience a 30% population decline over the past five years. The server uses this data to suggest which stores should be closed and identify cities where new stores should be opened."

[1137] This system will enable telecommunications service providers to efficiently position their stores in a way that reflects the needs of residents and market demand, thereby contributing to bridging the digital divide.

[1138] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1139] Step 1:

[1140] Inputs: Data from government APIs, geographic information systems, and internal corporate databases

[1141] Operation:

[1142] The server retrieves current demographic data using government-provided APIs, then collects geographic information from geographic information systems (GIS) and extracts market demand data from companies' internal databases.

[1143] Output: Demographic data, geographic information data, market demand data

[1144] Step 2:

[1145] Input: Demographic data, geographic information data, market demand data

[1146] Operation:

[1147] The server integrates each piece of data it has acquired using the region code as a key. In this integration process, data with common attributes are compiled into a single database.

[1148] Output: Integrated database

[1149] Step 3:

[1150] Input: Integrated database

[1151] Operation:

[1152] The server normalizes the integrated data. Specifically, it standardizes the population unit to "people" and the sales unit to "yen." In addition, if there are missing values, it fills them in using linear interpolation.

[1153] Output: Normalized and imputed data

[1154] Step 4:

[1155] Input: Normalized and imputed data

[1156] Operation:

[1157] The server uses this data to train machine learning models, such as linear regression models, which are used to predict future demographic trends and market demand.

[1158] Output: A trained machine learning model

[1159] Step 5:

[1160] Input: trained machine learning model, integrated database

[1161] Operation:

[1162] The server uses the trained model to predict future demographics and market demand, which in turn informs decisions about new store openings and store consolidation.

[1163] Output: Future demographic forecast data, market demand forecast data

[1164] Step 6:

[1165] Input: Future demographic forecast data, market demand forecast data

[1166] Operation:

[1167] Based on the prediction results, the server will suggest which stores should be consolidated or closed, and which areas to open new stores in. Furthermore, taking into consideration the elimination of the digital divide, it will identify areas with insufficient digital access and suggest prioritizing new store openings in those areas.

[1168] Output: Proposal data for consolidation and new store openings

[1169] Step 7:

[1170] Input: Proposal data regarding consolidation and new store openings

[1171] Operation:

[1172] The server generates a report detailing the recommendations, including visual graphs and maps, that users can view in a smartphone application.

[1173] Output: A report detailing the proposal

[1174] Step 8:

[1175] Input: Report detailing proposals, user feedback

[1176] Operation:

[1177] Users can review the generated reports and provide feedback via a smartphone application, which the server collects and uses to reevaluate and update strategies and recommendations.

[1178] Output: Updated strategy and proposal data

[1179] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1180] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[1181] This system involves a series of processes primarily performed by a server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report of the proposals. Users review and rate the report, then send feedback to the server. The server uses an emotion engine to analyze the sentiment of the feedback and reevaluate and update the proposals based on this sentiment.

[1182] A natural language description of the program's operation

[1183] Data collection and integration

[1184] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[1185] Data Preprocessing

[1186] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[1187] Training and predicting a predictive model

[1188] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[1189] Proposals for consolidation and new store openings

[1190] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[1191] Report generation and distribution

[1192] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[1193] Feedback collection and sentiment analysis

[1194] The user checks the report and sends their rating and comments as feedback to the server. The server uses an emotion engine to analyze the emotion from the user's feedback text. This emotion analysis allows us to measure whether the user's feedback is positive or negative, and its intensity.

[1195] Reassess and update your strategy

[1196] The server takes into account the analysis results of the emotion engine and reevaluates the content of the suggestions. For example, if there is a lot of negative feedback, it determines that there is a problem with the prediction model or the content of the suggestions, and takes measures such as collecting data again. Based on the analysis results of the emotion engine, the priority of the suggestions is adjusted to make suggestions that are more in line with the user's emotions.

[1197] Specific examples

[1198] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1199] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1200] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[1201] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[1202] The processing flow will be explained below.

[1203] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[1204] A natural language description of the program's operation

[1205] Step 1: Data collection

[1206] The server retrieves demographic data from the government's public API, accesses the government's statistical database, and collects data on population growth and decline by region and age distribution.

[1207] The server retrieves geographic information data from the GIS system, queries the GIS system, and collects geographical features and land use information for the target area.

[1208] The server extracts market demand data from the company's internal database, as well as market research data, sales data from existing stores, and customer satisfaction data.

[1209] Step 2: Data Integration

[1210] The server unifies the collected data using a common key (regional code) and stores it in an integrated database, allowing information from different data sources to be managed centrally.

[1211] Step 3: Data Preprocessing

[1212] The server normalizes the integrated data, standardizing the units of each data item, for example, standardizing the population unit to "people" and the sales unit to "yen."

[1213] The server uses linear interpolation to fill in missing values, and if there are missing data points, it fills them in based on nearby data points.

[1214] Step 4: Training the predictive model

[1215] The server uses historical data to train a machine learning model, using a random forest regression model to learn patterns of population decline and rural migration.

[1216] The server uses the trained model to forecast future demographic trends and market demand, predicting population and demand changes in each region over the next 10 years.

[1217] Step 5: Generate proposals

[1218] Based on the prediction results, the server creates a list of candidates for consolidation and new store openings. It lists stores in areas with a significant population decline as candidates for closure, and suggests opening new stores in areas where population growth is expected.

[1219] Taking into consideration the need to eliminate the digital divide, Sarver will prioritize areas lacking infrastructure as potential store locations.

[1220] Step 6: Generate reports

[1221] The server generates a report detailing the proposal, including forecast data for each region, the rationale for the proposal, and recommendations for consolidation and new store openings.

[1222] The server sends the generated report to the terminal, and distributes it in PDF format to the email address of the person in charge or to the company's internal portal.

[1223] Step 7: Gather feedback

[1224] The user checks the received report and sends their evaluation and comments to the server as feedback. Specifically, they enter comments such as whether they agree or disagree with the proposals in the report, or if they would like to make additions.

[1225] Step 8: Sentiment Analysis

[1226] The server analyzes the emotions from the user's feedback using an emotion engine. It uses natural language processing techniques to extract emotions (positive, negative, neutral) from the feedback text.

[1227] The server evaluates the intensity of the emotion and determines that special action should be taken if the feedback contains extremely strong negative emotions.

[1228] Step 9: Reassess and update your strategy

[1229] The server re-evaluates the suggestions based on the analysis results, and dynamically adjusts the priority of suggestions according to the user's actual emotions, taking into account the results of the sentiment analysis.

[1230] The server will reflect user feedback and the results of sentiment analysis, and take appropriate action such as recollecting data and retraining the predictive model as necessary.

[1231] The server generates the updated strategy again as a report and delivers it to the terminal.

[1232] Specific examples

[1233] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1234] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1235] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[1236] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[1237] Example 2

[1238] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1239] Conventional strategies for the placement of telecommunications service stores have limitations in the accuracy of aggregation and forecasting of demographic data, geographic information data, and market demand data, making it difficult to accurately locate stores. Furthermore, it has been difficult to fully utilize user feedback, making it difficult to build an optimal strategy that reflects user emotions and needs. This has resulted in delays in decisions regarding new store openings and consolidation, making it difficult to adapt to market trends. Furthermore, there has been a lack of means to provide more flexible and emotionally sensitive proposals by analyzing the emotions of feedback.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1241] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the forecast data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for analyzing the sentiment of the user feedback and reevaluating and updating the proposals. This enables an optimal strategy for locating communication service providing stores that reflects user sentiment and feedback.

[1242] "Demographic data" refers to data that includes information on population changes and composition, birth and death rates, migration patterns, etc. in a particular area.

[1243] "Geographic information data" is data that includes information about geography, such as geographic location, topography, land use, and infrastructure.

[1244] "Market demand data" refers to data that includes information such as trends in demand for products and services in a particular market, consumer behavior, and purchasing history.

[1245] "Means of integration" refers to methods or techniques for combining multiple datasets based on common keys or items and consolidating them into a single dataset.

[1246] "Normalization" refers to the process of standardizing data formats and units to ensure consistency among data.

[1247] "Methods for imputing missing values" refers to methods that use statistical techniques or algorithms to fill in missing values ​​in a dataset.

[1248] "Predictive model" refers to a mathematical or statistical model that uses historical data to predict future fluctuations or trends.

[1249] "Store consolidation" refers to reorganization of store operations, such as closing existing stores or merging multiple stores into one.

[1250] "New store opening" refers to the establishment of a new store in a new location.

[1251] "Closing the digital divide" refers to reducing disparities in access to and use of information and communications technology and providing digital services equally in all regions.

[1252] "Means for generating reports" refers to the methods and techniques for compiling analysis results and proposals in document or graph format and creating a report.

[1253] "User feedback" refers to the act of a user providing evaluations or opinions on provided reports or proposals, and the information provided.

[1254] "Means for analyzing emotions" refers to methods and technologies for evaluating and analyzing the positive and negative emotions from user feedback.

[1255] "Reevaluation and updating" refers to the process of reviewing existing proposals and developing revisions or new proposals as needed.

[1256] This invention combines a system that realizes optimal placement of communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This system improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs.

[1257] The present system includes a series of processes that are mainly executed by a server. The following describes in detail an embodiment of the present system.

[1258] 1. Data Collection

[1259] The server obtains demographic data using the government's public API, downloads geographic information data from a geographic information system (GIS), and extracts market demand data from the internal database of a telecommunications service provider. Various analyses are performed based on this data.

[1260] 2. Data Integration

[1261] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a region code, thereby creating a unified data set.

[1262] 3. Data Preprocessing

[1263] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[1264] 4. Training the predictive model

[1265] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[1266] 5. Prediction and Recommendations

[1267] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[1268] 6. Report Generation and Delivery

[1269] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[1270] 7. Gathering Feedback

[1271] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[1272] 8. Emotion analysis

[1273] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[1274] 9. Reassessment and Updates

[1275] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[1276] Specific examples

[1277] For example, consider a scenario in which a server analyzes two cities: City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, with a predicted 20% population growth over the next 10 years. The server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. At the same time, the server suggests opening a new store in a new migration area in City B. Furthermore, the server identifies areas of City B that lack digital access and suggests prioritizing opening a store there. If a user reviews the report and submits negative feedback about the proposed store closure in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the proposed store closure in City A and considers other options.

[1278] Examples of prompt statements

[1279] Example 1:

[1280] "Analyze demographic data from the past five years and identify areas most likely to experience population decline over the next decade."

[1281] Example 2:

[1282] "Please integrate market demand data and geographic information data to suggest areas where new stores should be opened within the next two years."

[1283] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[1284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1285] Step 1: Data collection

[1286] The server retrieves demographic data using public government APIs, downloads geographic information data from geographic information systems (GIS), and extracts market demand data from the internal databases of telecommunications service providers.

[1287] Input: API key or query

[1288] Output: Population data in JSON format, GIS data, market demand data extracted from SQL database

[1289] Specific operations: The server uses the API key to send requests to government sites, obtain population data in JSON format, download geographic information data from GIS, and extract market demand data from the company's internal database using SQL queries.

[1290] Step 2: Data Integration

[1291] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key such as a regional code.

[1292] Input: Demographic data, geographic information data, market demand data

[1293] Output: Unified dataset

[1294] Specific operation: The server uses a data frame to combine each dataset with a common region code to build a single integrated dataset.

[1295] Step 3: Data Preprocessing

[1296] The server standardizes the integrated data and fills in missing values. For example, it standardizes population counts to "people" and sales to "yen." It also uses linear interpolation for missing values.

[1297] Input: Integrated dataset

[1298] Output: Standardized and imputed data

[1299] Specific operation: The server uses the Pandas library to unify the population unit to "person", change the sales unit to "yen", and perform linear interpolation for missing values.

[1300] Step 4: Training the predictive model

[1301] The server uses historical data to train machine learning models, using data from the past five years to build models to forecast demographic trends and market demand for the next 10 years.

[1302] Input: Standardized and imputed data

[1303] Output: A trained predictive model

[1304] Specific operation: The server uses the scikit-learn library to train algorithms such as random forest models and evaluate their accuracy through cross-validation.

[1305] Step 5: Prediction execution and recommendations

[1306] The server uses the trained model to predict future demographic trends and market demand. Based on the predictions, it recommends which stores should be consolidated or closed, and which new stores should be opened, taking into account bridging the digital divide.

[1307] Input: trained model, latest data

[1308] Output: List of store opening and consolidation proposals

[1309] How it works: The server supplies the latest input data to the model and outputs prediction results. Based on the results, it creates a list of proposals for consolidation and new store openings and identifies areas with a digital divide.

[1310] Step 6: Report generation and distribution

[1311] The server generates a detailed report of the proposal and sends it to the user's device, including forecast data for each region and the reasons for the proposal.

[1312] Input: List of store opening and consolidation proposals

[1313] Output: Proposal report (PDF, etc.)

[1314] Specific operation: The server generates a report using a Markdown template, exports it to PDF format, and sends it to the user's device.

[1315] Step 7: Gather feedback

[1316] The user checks the report on the device and sends their evaluation and comments to the server as feedback.

[1317] Input: User rating and comments

[1318] Output: User feedback data

[1319] Specific behavior: A user views a report through a web application, enters ratings and comments in a feedback form, and submits it.

[1320] Step 8: Sentiment Analysis

[1321] The server uses an emotion engine to analyze the emotion of the user's feedback text, thereby assessing whether the feedback is positive or negative.

[1322] Input: User feedback data

[1323] Output: Emotion analysis results

[1324] What it does: The server uses an NLP library to score the positivity and negativity of the feedback text.

[1325] Step 9: Reassess and update

[1326] The server reevaluates and updates the content of the suggestions based on the results of the sentiment analysis, enabling flexible suggestions that reflect the user's emotions and opinions.

[1327] Input: Sentiment analysis results

[1328] Output: Updated proposal

[1329] Specific operation: The server recollects data from areas with a lot of negative feedback, retunes the parameters of the predictive model, and updates the suggestions.

[1330] (Application example 2)

[1331] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1332] Traditional store location strategies for telecommunications service providers have primarily been based on demographic data, geographic information data, and market demand data. However, the integration of these data and proposals based on predictive models have been limited, and proposals often do not meet users' actual needs because they do not fully consider user sentiment and feedback. Furthermore, it has been difficult to formulate store opening strategies that take the digital divide into account, resulting in many areas where telecommunications services are inadequate. Furthermore, there has been insufficient customization of services provided in virtual stores that effectively utilizes real-time user feedback.

[1333] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, means for displaying service provision locations in a virtual store using a smart device, means for collecting user feedback via voice and analyzing it using a sentiment analysis engine, and means for customizing service content based on the analysis results. This makes it possible to formulate an optimal location strategy for communication service providing stores while taking user emotions into consideration, and to customize service provision in the virtual store to be more user-centric.

[1334] "Demographic data" is data that shows trends and changes related to population, such as human migration, births, deaths, and marriages.

[1335] "Geographic information data" refers to data relating to geographical elements such as topography, geology, climate, and land use patterns.

[1336] "Market demand data" refers to data that indicates consumer demand, purchasing behavior, sales volume, etc. in a particular market.

[1337] "Synthesis" is the process of combining multiple datasets into one coherent dataset.

[1338] "Normalization" is the process of standardizing the format and scale of data to make it consistent.

[1339] "Missing values" refer to missing or incomplete data points in a dataset.

[1340] A "predictive model" is a mathematical model or machine learning model that predicts future trends based on past data.

[1341] "Consolidation" is the process of combining or abolishing multiple stores or service locations.

[1342] "New store openings" refers to the opening of new stores or service locations.

[1343] The "digital divide" is a concept that refers to differences in access to and opportunities to use information technology.

[1344] A "store opening strategy" refers to plans and policies regarding how to locate new stores and service locations.

[1345] A "report" refers to a document that summarizes the analysis results and proposals.

[1346] "Feedback" refers to reaction information such as ratings and comments from users.

[1347] An "emotion analysis engine" is a software system for automatically analyzing emotions from text and voice data.

[1348] "Smart device" is a general term for electronic devices that are connected to the Internet and can collect and communicate data.

[1349] "Virtual store" refers to a virtual store operated on the Internet.

[1350] "Service Point" refers to a service provided at a specific location or base.

[1351] "Customization" refers to tailoring an offering to meet the needs and desires of a particular user.

[1352] This invention includes a system that uses demographic data, geographic information data, and market demand data to optimize the placement of telecommunications service providers, and combines it with an emotion engine. The system collects, integrates, and preprocesses data, forecasts future trends using predictive models, and proposes store consolidation and new store openings. It also collects and analyzes user feedback to update its proposals.

[1353] This system uses a server as its main processing center and provides services in a virtual store using smart devices. The server obtains demographic and geographic data from public government APIs and geographic information systems, and market demand data from the internal databases of telecommunications service providers. It then integrates the acquired data into a single, consistent database. This integrated data is normalized using software tools such as Python and numpy, and missing values ​​are filled in using linear interpolation.

[1354] After preprocessing the integrated data, the server uses TensorFlow to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes optimal store consolidation and new store openings, and also formulates a store opening strategy that takes into account the elimination of the digital divide. A report detailing the proposals is then generated and provided to the user.

[1355] When a user visits a virtual store using a smart device, specifically smart glasses, the server displays suggestions visually in real time. Furthermore, the server collects user voice feedback and performs sentiment analysis using natural language processing libraries (such as Spacy and NLTK). Based on this sentiment analysis, the service content can be customized and updated to better meet the user's needs.

[1356] For example, in an analysis of cities A and B, city A's population has declined significantly in recent years, with further decline predicted over the next 10 years. Meanwhile, city B has seen active migration to rural areas in recent years and is predicted to experience population growth. Based on this, the server proposes the consolidation and closure of stores in city A and the opening of a new store in city B. If a user checks the proposal through smart glasses and provides voice feedback such as "It would be a shame if the store closed," the server's emotion analysis engine detects negative emotion and evaluates its strength. If the feedback is strongly negative, the server reevaluates the proposal and considers other options.

[1357] Examples of prompts include:

[1358] "User provided feedback:

[1359] "It's a shame the store is closing. Recently, more people have been living in the area and they use this store all the time."

[1360] System suggestions for this feedback:

[1361] We will reevaluate the proposed store closure in City A and consider other options."

[1362] In this way, the system of the present invention not only uses demographic data, geographic information data, and market demand data to develop an optimal store placement strategy, but also performs feedback analysis that takes user emotions into account, allowing for more user-centric proposals.

[1363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1364] Step 1: Collect data

[1365] The server obtains demographic and geographic data from the government's public API and geographic information system (GIS), and extracts market demand data from the internal databases of telecommunications service providers. The input data is the API data from the government and GIS, and the market demand data from the company's database. This allows various data to be collected.

[1366] Step 2: Integrate the data

[1367] The server integrates the acquired demographic data, geographic information data, and market demand data using a common key (such as a region code). The input is the integrated data from various datasets, and the output is the data compiled into a single, consistent database.

[1368] Step 3: Preprocessing the data

[1369] The server normalizes the integrated data. For example, it standardizes the unit of population to "person" and sales to "yen." If there are missing values, it fills them in using linear interpolation. The input is the integrated data, and the output is the normalized data with missing values ​​filled in.

[1370] Step 4: Training the predictive model

[1371] The server uses TensorFlow to train a predictive model based on historical data, for example, learning patterns of population decline or trends in market demand. The input is the preprocessed data, and the output is the trained predictive model. This builds a model for forecasting future trends.

[1372] Step 5: Forecast market trends

[1373] The server uses the trained model to predict future demographic trends and market demand. The inputs are the trained model and current data, and the output is predicted future demographic trends and market demand. Based on this forecast data, a foundation is laid for store consolidation and new store openings.

[1374] Step 6: Proposal for consolidation and new store openings

[1375] The server then makes proposals for store consolidation and new store openings based on the forecast data. For example, it may propose closure of stores in areas where population decline is predicted, and propose new store openings in areas where population growth is predicted. The input is the forecast data, and the output is a report detailing the proposals.

[1376] Step 7: Formulating a store opening strategy that takes into account bridging the digital divide

[1377] The server formulates a store opening strategy to ensure digital access in a specific area, taking into account the digital divide situation. The inputs are forecast data and digital divide information, and the output is a store opening strategy that takes into account eliminating the digital divide.

[1378] Step 8: Generate and distribute reports

[1379] The server generates a report detailing the proposal and sends it to the user's terminal. The input is the proposal, and the output is the generated report, allowing the user to check the proposal.

[1380] Step 9: Gather feedback

[1381] The user checks the report using a smart device (smart glasses) and provides feedback by voice. The input is the user's voice feedback, which is sent to the server.

[1382] Step 10: Sentiment analysis and feedback evaluation

[1383] The server receives the voice feedback and performs sentiment analysis using a natural language processing library (such as spacy or NLTK). The input is the user's feedback text, and the output is the sentiment evaluation result of the feedback. Based on this result, the server reevaluates and updates the suggestions.

[1384] Step 11: Update the proposal based on user feedback

[1385] The server reevaluates the proposals based on the results of the sentiment analysis and makes corrections or updates as necessary. The input is the sentiment analysis results, and the output is the updated proposals.

[1386] As a concrete example, if a user sends feedback saying, "It would be a shame if the store closed," the server will analyze the sentiment as negative and reevaluate the proposal. An example of a prompt sentence is the following: "User-provided feedback: 'It would be a shame if the store closed. Recently, more and more people have been living in this area, and I regularly visit this store.' System suggestion in response to this feedback: Reevaluate the store closure proposal in City A and consider other options."

[1387] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1388] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1389] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1390] [Fourth embodiment]

[1391] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1392] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1393] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1394] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1395] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1397] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1398] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1399] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1400] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1401] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1402] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1403] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1404] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[1405] This system involves a series of processes primarily performed by the server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report with the results. Users review this report, rate it, and send feedback to the server. The server then reevaluates the data based on the feedback and updates its strategy as necessary.

[1406] A natural language description of the program's operation

[1407] Data collection and integration

[1408] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[1409] Data Preprocessing

[1410] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[1411] Training and predicting a predictive model

[1412] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[1413] Proposals for consolidation and new store openings

[1414] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[1415] Report generation and distribution

[1416] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[1417] Gathering feedback and updating strategy

[1418] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[1419] Specific examples

[1420] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1421] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1422] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[1423] The processing flow will be explained below.

[1424] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[1425] A natural language description of the program's operation

[1426] Data collection and integration

[1427] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[1428] Data Preprocessing

[1429] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[1430] Training and predicting a predictive model

[1431] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[1432] Proposals for consolidation and new store openings

[1433] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[1434] Report generation and distribution

[1435] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[1436] Gathering feedback and updating strategy

[1437] The user reviews the report and sends their ratings and comments as feedback to the server. The server then reevaluates the proposal based on this feedback and updates the strategy as necessary. By incorporating new information gained through the feedback and making new proposals, the accuracy of the strategy can be improved.

[1438] Specific examples

[1439] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1440] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1441] Through the above process, the server will be able to realize store locations that accurately reflect the needs of residents and local characteristics, and will also contribute to eliminating the digital divide.

[1442] Processing step details

[1443] Step 1: Data collection

[1444] The server retrieves demographic data from the government's public API.

[1445] The server retrieves geographic information data from a GIS system.

[1446] The server extracts market demand data from the company's internal database.

[1447] Step 2: Data Integration

[1448] The server consolidates the collected data using a common key (regional code) and stores it in a database.

[1449] Step 3: Data Preprocessing

[1450] The server normalizes the integrated data (e.g., population is standardized to "people" and sales to "yen").

[1451] The server uses linear interpolation to fill in the missing values.

[1452] Step 4: Training the predictive model

[1453] The server uses historical data to train a machine learning model (e.g., a random forest regression model).

[1454] The server uses the trained model to predict future demographic trends and market demand.

[1455] Step 5: Generate proposals

[1456] Based on the prediction results, the server creates a list of candidates for consolidation, closure, and new store openings.

[1457] The server takes into consideration the elimination of the digital divide and proposes specific regions as priority candidates.

[1458] Step 6: Generate reports

[1459] The server generates a report detailing the proposal.

[1460] The server delivers the report to the terminal.

[1461] Step 7: Gather feedback

[1462] The user checks the report and gives an evaluation.

[1463] The user sends feedback to the server.

[1464] Step 8: Reassess and update your strategy

[1465] The server will reevaluate its proposals based on the feedback and update its strategy as needed.

[1466] The server generates the updated strategy again as a report and delivers it to the terminal.

[1467] As described above, the present invention provides a system for realizing efficient and effective allocation of communication service providing stores through a series of steps of data collection, integration, pre-processing, prediction, proposal and evaluation.

[1468] Example 1

[1469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1470] In modern society, rapid changes in demographics and market demands require optimal location of telecommunications service outlets. However, conventional methods lack centralized data collection and integration, predictive model training, and store opening strategy formulation, resulting in a high level of manual work and inefficiency. Furthermore, they do not adequately address the digital divide, making it difficult to realize store location that meets residents' needs. A system that can solve these issues and provide efficient and accurate store location proposals is needed.

[1471] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1472] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to predict future demographic trends and market demand, means for proposing store consolidations and new store openings based on the predicted data, means for using a machine learning model as the prediction model, means for generating a report detailing the proposals, means for sending the report to a user's terminal, means for collecting feedback from users, reevaluating and updating the proposals, and means for updating the strategy based on the feedback. This automates a series of processes, from data collection to prediction, proposal, feedback collection, and strategy updating, enabling efficient and highly accurate store placement proposals.

[1473] "Demographic data" refers to data that shows the size, composition, and trends of population growth and decline in a particular area.

[1474] "Geographic information data" is data that indicates the geographical features and location information of a specific area.

[1475] "Market demand data" refers to data that indicates the amount and trends of demand for a product or service in a particular market.

[1476] "Integration" refers to the process of matching data from multiple different data sources into a single data set.

[1477] "Normalization" is a process for standardizing the units and scale of data to ensure consistency.

[1478] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate way.

[1479] A "predictive model" is a statistical or machine learning model that estimates future data based on past data.

[1480] A "machine learning model" is an algorithm that learns patterns and rules from data and makes predictions and classifications for unseen data.

[1481] "Consolidation" refers to combining multiple stores or locations into one in order to eliminate overlapping functions and roles.

[1482] "New store openings" refer to the opening of new stores in new regions or markets.

[1483] The "digital divide" refers to disparities in access to and use of information technology, and indicates differences in the environment for using the Internet and digital services.

[1484] "Strategic update" refers to reviewing and improving existing plans and proposals based on collected feedback and new information.

[1485] A "report" is a document detailing the analysis results and recommendations, and is provided in a format that is easy for users to understand.

[1486] "Feedback" refers to evaluations, opinions, and comments provided by users, and is information that is used to improve and optimize the system.

[1487] A "terminal" is a device used by a user to receive or input information, and includes a computer, smartphone, etc.

[1488] The present invention is a system for realizing the optimal location of communication service providers using demographic data, geographic information data, and market demand data. Hereinafter, an embodiment of the system will be described in detail.

[1489] The system mainly involves a series of processes executed by a server. The server obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the telecommunications service provider's internal database. This data is integrated using a common key (e.g., area code) and compiled into a single database. The hardware used includes a general-purpose server computer, and the software used includes Python, Pandas, Scikit-learn, and TensorFlow.

[1490] Next, the server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also performs linear interpolation to fill in missing data values ​​and estimates missing values ​​based on past trends. This is done using functions such as the "interpolate" function in Pandas.

[1491] The server then uses historical data to train a machine learning model. For example, it learns patterns of population decline and rural migration and builds a model to predict future trends. Specifically, it uses Scikit-learn's Random Forest and TensorFlow. It also uses Python libraries to verify the accuracy of the predictions using appropriate evaluation methods.

[1492] Using a trained model, the system predicts future demographic trends and market demand for each region, and based on those predictions, it recommends which stores should be consolidated or closed, and which areas should be opened. The server lists stores in areas experiencing rapid population decline as candidates for closure, and suggests new store openings in areas where migration to rural areas is progressing. It also identifies areas with particularly low digital access and prioritizes opening stores there.

[1493] The server generates a report detailing the proposal. This report is provided in a format that is easy for users to understand, detailing forecast data for each region and the reasons for the proposal. The generated report can be provided in PDF format or as a web-based dashboard and sent to the user's device. The user reviews the report and sends their evaluation and comments as feedback to the server. The server uses this information to reevaluate the proposal and update the strategy.

[1494] As a concrete example, consider a case where a server analyzes two cities, City A and City B. City A's population has declined significantly over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, City B is experiencing a rapid migration to rural areas, and its population is predicted to grow by 20% over the next 10 years. As a result, the server proposes the consolidation of two existing stores in City A and lists these stores as candidates for closure. It also proposes opening new stores in new areas of City B that are attracting new residents. Furthermore, it identifies areas with particularly low digital access and recommends prioritizing opening stores there.

[1495] The following are examples of prompt sentences:

[1496] 1. Prompts for collecting demographic data

[1497] "Please retrieve the past 10 years of demographic data for Tokyo from the API."

[1498] "Please use the API to obtain geographic information data for Tokyo."

[1499] 2. Prompt for data normalization

[1500] "Please normalize population data to a single unit of people."

[1501] "Please standardize the sales data to yen."

[1502] 3. Prompts about predictive models

[1503] "Train a population decline prediction model using TensorFlow based on historical data."

[1504] "Use Scikit-learn to predict population growth and decline over the next five years."

[1505] 4. Report Generation and Delivery Prompts

[1506] "Generate a PDF report with proposals for new store openings."

[1507] Send the generated report to the user's email address.

[1508] The above is a detailed description of the mode for carrying out the invention. This process enables efficient and highly accurate store layout proposals.

[1509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1510] Step 1: Collect data

[1511] The server collects demographic and geographic data from public government APIs and geographic information systems (GIS), while extracting market demand data from the internal databases of telecommunications service providers.

[1512] Input: API endpoints (e.g., public government APIs), GIS data sources, corporate databases

[1513] Output: Demographic data, geographic information data, market demand data

[1514] Specific operation: The server sends an API request and retrieves the response data. It also downloads GIS data and executes queries against the company's internal SQL database to extract market demand data. For example, it retrieves data from "https: / / api.example.com / population" and executes the SQL query "SELECT FROM market_demand WHERE region_code = 'ABC'".

[1515] Step 2: Data integration and normalization

[1516] The server integrates the collected data using the region code as a key, and then normalizes the data to unify its units and ensure consistency.

[1517] Input: Demographic data, geographic information data, market demand data

[1518] Output: Integrated dataset (integrated + normalized)

[1519] Specific operation: The server uses the Python Pandas library to merge data using the "merge" function, and the "apply" function to unify population and sales data into people and circles. Example: df.merge(population_data, on='region_code'), df['population'] = df['population'].apply(lambda x: x 1000).

[1520] Step 3: Data preprocessing and missing value imputation

[1521] The server preprocesses the aggregated data, imputes missing values ​​using linear interpolation, and detects and appropriately corrects or removes outliers and values.

[1522] Input: Integrated dataset

[1523] Output: Preprocessed and imputed dataset

[1524] Specific operation: The server executes "df.interpolate(method='linear')" to fill in missing values. Outliers are detected with "df[(df['sales'] < 0) | (df['sales'] > 1e7)]" and corrected or deleted. Example: Executes df.interpolate(method='linear').

[1525] Step 4: Training the predictive model

[1526] The server uses historical data to train a machine learning model (e.g., Scikit-learn's Random Forest), which learns patterns of population decline and market demand and builds a model to predict future trends.

[1527] Input: Preprocessed dataset

[1528] Output: A trained predictive model

[1529] Specific operation: The server imports "from sklearn.ensemble import RandomForestRegressor" and initializes the model. Next, it runs "model.fit(X_train, y_train)" to train it. Example: Run model.fit(X_train, y_train).

[1530] Step 5: Perform future predictions

[1531] The server uses the trained model to predict future demographic trends and market demand.

[1532] Input: trained predictive model, new input data

[1533] Output: Future demographic forecast data, future market demand forecast data

[1534] Specific operation: The server executes "predictions = model.predict(X_test)" and obtains the prediction results. The prediction results are recorded in the database and saved in a data frame. Example: Execute predictions = model.predict(X_test).

[1535] Step 6: Propose store layout

[1536] Based on the prediction results, the server proposes optimal store locations, considering store consolidation and closure in areas experiencing rapid population decline, and proposing new store openings in areas where population growth is expected.

[1537] Input: Future demographic forecast data, market demand forecast data

[1538] Output: Proposal for store consolidation and new store openings

[1539] Specific operation: The server lists candidates for closure using "df[df['predicted_population'] < threshold]", and selects areas for new store openings where "df[df['predicted_population'] > threshold]". Example: df[df['predicted_population'] < threshold].

[1540] Step 7: Generate and distribute reports

[1541] The server generates a detailed report detailing the recommendations, which is then sent to the user's device in PDF format or as a web-based dashboard.

[1542] Input: Store layout proposal

[1543] Output: Report (PDF format, web dashboard)

[1544] Specific operation: The server generates a PDF using "from fpdf import FPDF" and saves it using "pdf.output('report.pdf')". Then it sends it using "send_email(to='user@example.com', attachment='report.pdf')". Example: pdf.output('report.pdf').

[1545] Step 8: Gather feedback and update your strategy

[1546] Users review the reports and send their ratings and comments as feedback to the server, which then reevaluates the proposals and updates the strategy.

[1547] Input: User feedback

[1548] Output: Updated proposals and strategies

[1549] Specific behavior: A user enters a comment in the feedback form and sends it to the server. The server executes "feedback = get_feedback()" and updates the strategy with "update_strategy(feedback)". Example: feedback = get_feedback().

[1550] (Application example 1)

[1551] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1552] When determining the optimal locations of telecommunications service outlets, companies face challenges in efficiently managing demographic changes, geographic information, and market demand, while also bridging the digital divide. Furthermore, the current system lacks real-time capabilities, making it difficult for users to review proposal results, provide feedback, and quickly reevaluate and update strategies. A system that efficiently resolves these challenges and provides an easy-to-use user experience is needed.

[1553] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1554] In this invention, the server includes means for acquiring demographic data, means for acquiring geographic information data, means for acquiring market demand data, means for integrating the acquired data, means for normalizing the integrated data and imputing missing values, means for training a prediction model to forecast future demographic trends and market demand, means for proposing store consolidation and new store openings based on the predicted data, means for formulating a store opening strategy that takes into account bridging the digital divide, means for generating a report detailing the proposals, means for collecting user feedback and reevaluating and updating the proposals, and means for displaying predictions and store layout proposals using the data to the user via a smartphone application. This enables real-time predictions and proposals, allowing users to provide rapid feedback and efficiently reevaluate and update strategies based on the results.

[1555] "Demographic data" refers to data that shows information on the composition, changes, and movement of the population in a particular region or group.

[1556] "Geographic information data" is data that includes information about physical features, topography, infrastructure, land use, and other aspects of a particular geographic space.

[1557] "Market demand data" refers to data that indicates information regarding the demand for a product in a particular market, consumer purchasing behavior, sales, etc.

[1558] "Integration methods" are methods for centralizing different types of data and compiling them into a single data set for analysis and prediction.

[1559] "Normalization" is the process of standardizing the units and scale of data, a process that is performed to make analysis and modeling easier.

[1560] A "means of imputing missing values" is a means of filling in gaps or missing data in a dataset by interpolation or estimation.

[1561] A "predictive model" is a mathematical or statistical model that uses historical data to predict future events or trends.

[1562] "Store consolidation" is the process of integrating or closing existing stores to improve efficiency and reduce costs.

[1563] "New store opening" means opening a new store in a new location.

[1564] The "digital divide" is a term that refers to disparities between regions and classes in access to and use of information technology.

[1565] A "store opening strategy" is a plan or policy for determining optimal store placement based on market research and forecasts.

[1566] "Report generation" is the process of organizing analysis results and proposals and outputting them as written or visual materials.

[1567] "Means for collecting feedback" refers to methods for efficiently collecting ratings and comments from users and incorporating them as data.

[1568] "Reevaluation and updating" is the process of reviewing initial proposals and models based on collected feedback and making modifications and improvements as needed.

[1569] A "smartphone application" is a software program that runs on a smartphone and provides specific functions or services.

[1570] "Real-time" is a term that refers to a state in which various data and information are processed and provided immediately without any time delay.

[1571] Specific embodiments for carrying out the present invention will be described below.

[1572] The present invention is a system for optimizing the location of telecommunications service outlets by using demographic data, geographic information data, and market demand data. The system includes a server, a database, and a smartphone application that interfaces with users. It also has the ability to make predictions using machine learning models and collect feedback to reevaluate strategies.

[1573] Program Overview

[1574] Data collection and integration

[1575] The server collects data by the following means:

[1576] Use government-provided APIs to obtain demographic data.

[1577] Obtain geographic information data from a geographic information system (GIS).

[1578] Extract market demand data from a company's internal database.

[1579] These data are compiled into a single integrated database using the area code as a common key.

[1580] Data Preprocessing

[1581] The server normalizes the integrated data and fills in missing values. For example, it standardizes the population unit to "people" and the sales unit to "yen." If there are missing values, it fills them in using methods such as linear interpolation.

[1582] Training and predicting a predictive model

[1583] The historical data is then used to train a machine learning model (e.g., a linear regression model). This model is then used to predict future demographics and market demand. The trained model can then process new data and make predictions in real time.

[1584] Proposals for store consolidation and new store openings

[1585] Based on the prediction results, the server will propose store consolidation and new store openings. For example, it will propose store closures in areas with declining populations and new store openings in areas with growing populations. Taking into account the elimination of the digital divide, it will also identify areas with insufficient digital access and prioritize new store openings there.

[1586] Report Generation and User Interface

[1587] A report detailing the recommendations is automatically generated and provided to the user, who can view it through a smartphone application that displays the prediction results along with visualized data fragments in an easy-to-understand interface.

[1588] Specific examples

[1589] For example, consider a case where a server is analyzing two cities, A and B. City A has seen a significant decline in population over the past five years, and the server predicts a further 30% decline over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and the server predicts a 20% population increase over the next 10 years.

[1590] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1591] Prompt Sentence Examples

[1592] "City A is expected to experience a 30% population decline over the past five years. The server uses this data to suggest which stores should be closed and identify cities where new stores should be opened."

[1593] This system will enable telecommunications service providers to efficiently position their stores in a way that reflects the needs of residents and market demand, thereby contributing to bridging the digital divide.

[1594] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1595] Step 1:

[1596] Inputs: Data from government APIs, geographic information systems, and internal corporate databases

[1597] Operation:

[1598] The server retrieves current demographic data using government-provided APIs, then collects geographic information from geographic information systems (GIS) and extracts market demand data from companies' internal databases.

[1599] Output: Demographic data, geographic information data, market demand data

[1600] Step 2:

[1601] Input: Demographic data, geographic information data, market demand data

[1602] Operation:

[1603] The server integrates each piece of data it has acquired using the region code as a key. In this integration process, data with common attributes are compiled into a single database.

[1604] Output: Integrated database

[1605] Step 3:

[1606] Input: Integrated database

[1607] Operation:

[1608] The server normalizes the integrated data. Specifically, it standardizes the population unit to "people" and the sales unit to "yen." In addition, if there are missing values, it fills them in using linear interpolation.

[1609] Output: Normalized and imputed data

[1610] Step 4:

[1611] Input: Normalized and imputed data

[1612] Operation:

[1613] The server uses this data to train machine learning models, such as linear regression models, which are used to predict future demographic trends and market demand.

[1614] Output: A trained machine learning model

[1615] Step 5:

[1616] Input: trained machine learning model, integrated database

[1617] Operation:

[1618] The server uses the trained model to predict future demographics and market demand, which in turn informs decisions about new store openings and store consolidation.

[1619] Output: Future demographic forecast data, market demand forecast data

[1620] Step 6:

[1621] Input: Future demographic forecast data, market demand forecast data

[1622] Operation:

[1623] Based on the prediction results, the server will suggest which stores should be consolidated or closed, and which areas to open new stores in. Furthermore, taking into consideration the elimination of the digital divide, it will identify areas with insufficient digital access and suggest prioritizing new store openings in those areas.

[1624] Output: Proposal data for consolidation and new store openings

[1625] Step 7:

[1626] Input: Proposal data regarding consolidation and new store openings

[1627] Operation:

[1628] The server generates a report detailing the recommendations, including visual graphs and maps, that users can view in a smartphone application.

[1629] Output: A report detailing the proposal

[1630] Step 8:

[1631] Input: Report detailing proposals, user feedback

[1632] Operation:

[1633] Users can review the generated reports and provide feedback via a smartphone application, which the server collects and uses to reevaluate and update strategies and recommendations.

[1634] Output: Updated strategy and proposal data

[1635] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1636] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[1637] This system involves a series of processes primarily performed by a server. The server acquires demographic data, geographic information data, and market demand data, and integrates, normalizes, and imputes missing values ​​from this data. The server then uses this data to train a predictive model to forecast future demographic trends and market demand. Based on the prediction results, it proposes store consolidations and new store openings and generates a report of the proposals. Users review and rate the report, then send feedback to the server. The server uses an emotion engine to analyze the sentiment of the feedback and reevaluate and update the proposals based on this sentiment.

[1638] A natural language description of the program's operation

[1639] Data collection and integration

[1640] The server first obtains demographic and geographic data from the government's public API and geographic information system. At the same time, it extracts market demand data from the internal databases of telecommunications service providers. This data is then integrated into a single database using common keys such as area codes.

[1641] Data Preprocessing

[1642] The server normalizes the integrated data. For example, it standardizes the population unit to "people" and the sales unit to "yen." It also fills in missing data. If there is missing data, it performs linear interpolation, for example, to fill in the value based on past trends.

[1643] Training and predicting a predictive model

[1644] The server then uses the historical data to train machine learning models, such as those studying patterns of population decline and rural migration, to build models to predict future trends.The trained models are then used to forecast future demographics and market demand for each region.

[1645] Proposals for consolidation and new store openings

[1646] Based on the prediction results, the server will propose which stores should be consolidated or closed, and in which areas new stores should be opened. For example, stores in areas with rapid population decline will be listed as candidates for closure, and new stores will be proposed in areas where people are moving to rural areas. Furthermore, from the perspective of eliminating the digital divide, areas with particularly low digital access will be identified and prioritized for opening stores there.

[1647] Report generation and distribution

[1648] The server generates a report detailing the proposal. This report includes detailed forecast data for each region and the reasons for the proposal, and is presented in a format that is easy for users to understand. The generated report is sent to the terminal so that the user can view it.

[1649] Feedback collection and sentiment analysis

[1650] The user checks the report and sends their rating and comments as feedback to the server. The server uses an emotion engine to analyze the emotion from the user's feedback text. This emotion analysis allows us to measure whether the user's feedback is positive or negative, and its intensity.

[1651] Reassess and update your strategy

[1652] The server takes into account the analysis results of the emotion engine and reevaluates the content of the suggestions. For example, if there is a lot of negative feedback, it determines that there is a problem with the prediction model or the content of the suggestions, and takes measures such as collecting data again. Based on the analysis results of the emotion engine, the priority of the suggestions is adjusted to make suggestions that are more in line with the user's emotions.

[1653] Specific examples

[1654] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1655] The server proposes the consolidation of two existing stores in City A and lists them as candidates for closure. At the same time, it proposes opening a new store in a new migration area of ​​City B. It also identifies areas of City B that lack digital access and suggests prioritizing opening a store there.

[1656] If a user reviews the report and submits negative feedback about the store closure proposal in City A, the server's sentiment engine extracts negative sentiment from the feedback and evaluates the strength of the feedback. If the feedback is strongly negative, the server reevaluates the store closure proposal in City A and considers other options.

[1657] In this way, by combining the emotion engine, the system of the present invention can analyze feedback that takes user emotions into account and provide more suitable suggestions to users. This will improve the accuracy of the placement strategy of communication service providers and contribute to bridging the digital divide.

[1658] The processing flow will be explained below.

[1659] The present invention is a system that combines a system for optimally locating communication service providers using demographic data, geographic information data, and market demand data with an emotion engine that recognizes user emotions. This improves the accuracy of evaluation and re-evaluation of proposal content, enabling customization to meet user needs. The following describes in detail an embodiment of the system.

[1660] A natural language description of the program's operation

[1661] Step 1: Data collection

[1662] The server retrieves demographic data from the government's public API, accesses the government's statistical database, and collects data on population growth and decline by region and age distribution.

[1663] The server retrieves geographic information data from the GIS system, queries the GIS system, and collects geographical features and land use information for the target area.

[1664] The server extracts market demand data from the company's internal database, as well as market research data, sales data from existing stores, and customer satisfaction data.

[1665] Step 2: Data Integration

[1666] The server unifies the collected data using a common key (regional code) and stores it in an integrated database, allowing information from different data sources to be managed centrally.

[1667] Step 3: Data Preprocessing

[1668] The server normalizes the integrated data, standardizing the units of each data item, for example, standardizing the population unit to "people" and the sales unit to "yen."

[1669] The server uses linear interpolation to fill in missing values, and if there are missing data points, it fills them in based on nearby data points.

[1670] Step 4: Training the predictive model

[1671] The server uses historical data to train a machine learning model, using a random forest regression model to learn patterns of population decline and rural migration.

[1672] The server uses the trained model to forecast future demographic trends and market demand, predicting population and demand changes in each region over the next 10 years.

[1673] Step 5: Generate proposals

[1674] Based on the prediction results, the server creates a list of candidates for consolidation and new store openings. It lists stores in areas with a significant population decline as candidates for closure, and suggests opening new stores in areas where population growth is expected.

[1675] Taking into consideration the need to eliminate the digital divide, Sarver will prioritize areas lacking infrastructure as potential store locations.

[1676] Step 6: Generate reports

[1677] The server generates a report detailing the proposal, including forecast data for each region, the rationale for the proposal, and recommendations for consolidation and new store openings.

[1678] The server sends the generated report to the terminal, and distributes it in PDF format to the email address of the person in charge or to the company's internal portal.

[1679] Step 7: Gather feedback

[1680] The user checks the received report and sends their evaluation and comments to the server as feedback. Specifically, they enter comments such as whether they agree or disagree with the proposals in the report, or if they would like to make additions.

[1681] Step 8: Sentiment Analysis

[1682] The server analyzes the emotions from the user's feedback using an emotion engine. It uses natural language processing techniques to extract emotions (positive, negative, neutral) from the feedback text.

[1683] The server evaluates the intensity of the emotion and determines that special action should be taken if the feedback contains extremely strong negative emotions.

[1684] Step 9: Reassess and update your strategy

[1685] The server re-evaluates the suggestions based on the analysis results, and dynamically adjusts the priority of suggestions according to the user's actual emotions, taking into account the results of the sentiment analysis.

[1686] The server will reflect user feedback and the results of sentiment analysis, and take appropriate action such as recollecting data and retraining the predictive model as necessary.

[1687] The server generates the updated strategy again as a report and delivers it to the terminal.

[1688] Specific examples

[1689] For example, consider the case where two cities, A and B, both of which have servers, are analyzed. City A has seen a significant decline in population over the past five years, and the server is predicted to decline by another 30% over the next 10 years. Meanwhile, city B is experiencing a rapid migration to rural areas, and its population is predicted to increase by 20% over the next 10 years.

[1690] The server proposes the consolidation of two existing stores in City ...

Claims

1. a means of obtaining demographic data; A means for obtaining geographic information data; a means for obtaining market demand data; A means for integrating each of the acquired data; A means of normalizing the combined data and imputing missing values; A means to train predictive models to forecast future demographic trends and market demand; A method for proposing store consolidation and closures and new store openings based on forecast data; A means of formulating a store opening strategy that takes into consideration bridging the digital divide, a means of generating a report detailing the proposal; A means to gather user feedback and reassess and update the proposal; A system including:

2. The system of claim 1 , wherein missing values ​​in the data are filled by linear interpolation.

3. The system of claim 1 , wherein the prediction results are combined with geographic information to create visualized data fragments.

Images