System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting human flow information, a means for acquiring settlement information, a means for integrating the collected human flow information and the acquired settlement information, an AI means for analyzing the integrated information, a means for generating a management improvement proposal based on the analysis result, and a means for providing the generated proposal in real time.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] The shrinking retail market and intensifying competition have made it particularly difficult for small and medium-sized retailers to adapt to complex market trends. This has made it difficult to plan sales improvement measures and allocate resources appropriately, resulting in tight management. Another problem is the lack of financial resources to utilize external consulting services. The object of this invention is to provide such small and medium-sized retailers with a system that supports management improvement efficiently and at low cost. [Means for solving the problem]

[0005] The present invention is a system that includes a means for collecting people flow data, a means for acquiring payment data, a means for integrating the collected people flow data and the acquired payment data, an AI means for analyzing the integrated data, a means for generating management improvement proposals based on the analysis results, and a means for providing the generated proposals in real time. People flow data is collected using mobile phone base stations, and payment data is acquired through a payment system API. This data is integrated and analyzed to generate specific and practical management improvement measures, such as future demand forecasts and product proposals tailored to target demographics. These proposals are provided in real time via chatbots, etc., enabling immediate responses.

[0006] "People flow data" is data that indicates information on the movement of people in a specific area.

[0007] "Payment data" refers to data related to payments, including the date and time of the transaction, the transaction amount, and the location of the transaction.

[0008] "Integrated data" is a dataset that combines collected people flow data and acquired payment data.

[0009] "AI means" refers to artificial intelligence systems that perform machine learning and data analysis.

[0010] "Management improvement proposals" refer to specific strategies and measures aimed at increasing sales and efficiency at retail stores, based on the results of data analysis.

[0011] "Means of providing in real time" refers to a method of instantly notifying users of affiliated stores of suggestions and feedback.

[0012] A "mobile phone base station" is a communication facility that makes up a mobile phone network, and here refers to the source of people flow data.

[0013] "Payment system API" refers to an interface that allows a payment system to communicate with external parties, and is used here as a means of obtaining payment data.

[0014] A "chatbot" is an automated response system operated by AI that provides information and suggestions through dialogue with users. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that collects and analyzes people flow data and payment data, and provides effective management improvement proposals to retailers. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time.

[0037] Program processing

[0038] Data collection

[0039] The server collects people flow data from mobile phone towers, which includes information on people's movements in a specific area. The server also obtains payment data through the payment system API, which includes transaction date and time, transaction amount, transaction location, etc.

[0040] Data Integration and Preprocessing

[0041] The server integrates the collected people flow data and the acquired payment data into a single dataset, where it performs preprocessing such as correcting missing values ​​and removing outliers.

[0042] Data analysis

[0043] The AI ​​tools within the server analyze the integrated data, including analyzing foot traffic patterns using machine learning algorithms, analyzing sales patterns based on payment data, and forecasting future demand.

[0044] Generate business improvement proposals

[0045] Based on the analysis results, the server generates specific management improvement proposals for retailers, including optimal store layout, new store opening areas, product lineup optimization, marketing strategies for target demographics, and limited-time sales.

[0046] Real-time feedback

[0047] The generated business improvement proposals are provided to the user in real time by the server, via chatbots and other notification methods, allowing for immediate response.

[0048] Specific examples

[0049] Example 1: Reviewing store locations

[0050] This section explains the case where a proposal is made to consider opening a new store based on people flow data in a certain area. The server analyzes the people flow data to detect an increase in the number of people in a specific area. Based on this result, the server proposes to the user to open a new store in the same area.

[0051] Example 2: Distributing Target-exclusive coupons

[0052] Let's say you analyze payment data and determine that the customers who visit on weekends are students. The server uses this information to create special coupons for students and propose them to users in real time.

[0053] Example 3: Running a Limited-Time Sale

[0054] Let's consider a case where a decrease in the number of customers during a specific time period is analyzed and a time sale is held during that time period. The server analyzes the data and finds that there are few customers between 2:00 PM and 4:00 PM on weekdays, and suggests to the user that a time sale be held during this time period.

[0055] The above is an embodiment of the present invention. This system allows retailers to obtain specific management strategies based on data in real time, thereby improving sales and efficiency.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The server periodically collects people flow data from mobile phone base stations, including information on people's movements by region and their stay by time of day.

[0059] Step 2:

[0060] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0061] Step 3:

[0062] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0063] Step 4:

[0064] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0065] Step 5:

[0066] The server analyzes payment data and analyzes sales patterns, which allows it to determine which products sell well at what times and which customer segments are most popular.

[0067] Step 6:

[0068] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0069] Step 7:

[0070] Based on the analysis results, the server generates business improvement proposals, such as proposing new store locations, revising product lineups for target demographics, and proposing limited-time sales during specific times.

[0071] Step 8:

[0072] The server provides the generated business improvement proposals to the user in real time via chatbots, email notifications, etc.

[0073] Step 9:

[0074] Users receive suggestions and then take specific measures based on them, such as introducing new products or holding limited-time sales during specific times.

[0075] Example 1

[0076] 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."

[0077] In conventional retail store management, effective use of foot traffic data and payment data has not been fully implemented, resulting in a lack of means to propose appropriate management improvement measures in real time. This has made it difficult to efficiently formulate strategies for store layout, marketing, and sales improvement. The present invention aims to solve this problem by providing a system that provides specific data-based management strategies for retail stores in real time.

[0078] 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.

[0079] In this invention, the server includes means for collecting people flow data from mobile phone base stations, means for acquiring payment data through a payment system API, and means for integrating and preprocessing the collected people flow data and the acquired payment data. This makes it possible to efficiently collect and integrate data, correct missing values, and remove outliers, and then provide appropriate management improvement proposals in real time.

[0080] A "mobile phone base station" is a basic infrastructure facility used to collect location information from mobile phone users.

[0081] "People flow data" is data that includes information on the movement of people in a specific area.

[0082] A "payment system API" is an application programming interface for providing payment data to other systems.

[0083] "Payment data" refers to data related to payments, including transaction date and time, transaction amount, transaction location, etc.

[0084] "Preprocessing" refers to the process of correcting missing data values, removing outliers, and preparing the data in a format suitable for analysis.

[0085] "Integrated data" is data collected from multiple data sources and converted into a single data set.

[0086] A "machine learning algorithm" is a mathematical technique used to analyze data and generate predictive models.

[0087] "Management improvement proposals" are specific management strategy proposals provided to retailers based on the results of data analysis.

[0088] "Real-time delivery means" refers to techniques and methods for instantly notifying users of generated suggestions.

[0089] This invention is a system that provides effective management improvement proposals to retail stores based on people flow data and payment data collected using mobile phone base stations and payment system APIs. The following hardware and software are used to implement this system.

[0090] Data collection

[0091] The server collects people flow data from mobile phone base stations. Specifically, it uses an API provided by the mobile phone company. Through this API, it obtains real-time information on the movement of mobile phone users in each region, streams the data using AWS Kinesis data streams, and stores it in S3 storage.

[0092] The server also uses the payment system API (e.g. Stripe API) to retrieve payment data, which includes transaction information such as transaction date, transaction amount, and transaction location, and stores it in an AWS RDS database.

[0093] Data Integration and Preprocessing

[0094] The server integrates the collected people flow data and payment data using Python's Pandas library. In the data integration process, the data is matched by time and location and converted into a single dataset. In the preprocessing stage, missing value correction and outlier removal are performed to improve data quality. Apache Spark is used to perform this preprocessing quickly on large amounts of data.

[0095] Data analysis

[0096] The AI ​​tools on the server perform analysis using the integrated data. A deep learning model is built using TensorFlow to analyze patterns of people flow and payments from time-series data. Clustering methods (e.g., K-means) are applied using Scikit-learn to extract customer segments with similar characteristics. Furthermore, an LSTM model is used to predict future demand.

[0097] Generate business improvement proposals

[0098] Based on the analysis results, the server generates specific proposals for business improvement, such as recommendations for new store opening areas, proposals for sales during specific time periods, marketing strategies for specific customer segments, etc. The analysis results are visualized using MPLib and presented to the user on a dashboard.

[0099] Real-time feedback

[0100] The generated suggestions are provided to the user in real time by the server, and the suggestions are notified in real time through a chatbot using the Slack API or Microsoft Teams API, allowing the user to respond immediately.

[0101] Specific examples

[0102] Example 1: Reviewing store locations

[0103] The server analyzes pedestrian flow data collected from mobile phone base stations to detect increases in the number of people in a specific area. Based on the analysis results, it proposes new store openings in that area. For example, it sends a notification saying, "Pedestrian flow around Akihabara Station has increased by 30% on weekends, so please consider opening a new store in the area."

[0104] Example 2: Distributing Target-exclusive coupons

[0105] The server analyzes payment data and determines that the customers who visit the store at certain times and on weekends are primarily students. Based on this information, it generates special coupons for students and makes marketing suggestions in real time. For example, it sends a suggestion such as, "Please issue a 10% off coupon to students on weekends."

[0106] Example 3: Running a Limited-Time Sale

[0107] The server will analyze the data and discover that there are few customers during certain times of the day. For example, it will suggest, "Since the number of customers is low between 2:00 PM and 4:00 PM on weekdays, we recommend that you hold a time sale during this time," and will also explain the specific steps to take.

[0108] Examples of specific prompts include, "Please suggest new store locations based on regional foot traffic data," "Please generate coupons for students who visit on weekends," and "Please suggest time sales to increase customer traffic between 2:00 and 4:00 PM."

[0109] As described above, by combining these methods, retailers can obtain specific management strategies based on data in real time, enabling them to improve efficiency and increase sales.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] The server collects people flow data from mobile phone base stations.

[0113] Specifically, the server sends a request to the API provided by the mobile phone company to obtain real-time movement information. This data includes information on people's movements in a specific area. The obtained data is streamed in real time using AWS Kinesis data streams and stored in an S3 bucket.

[0114] Input: Mobile operator API endpoint

[0115] Output: People flow data stored in an S3 bucket

[0116] Step 2:

[0117] The server obtains payment data through the payment system API.

[0118] Specifically, the server sends a request to the payment system's API (e.g., a general payment system's API) to obtain payment information such as transaction date and time, transaction amount, and transaction location. This data is then stored in an AWS RDS database.

[0119] Input: Payment system API endpoint

[0120] Output: Payment data stored in an RDS database

[0121] Step 3:

[0122] The server integrates and pre-processes the collected people flow data and payment data.

[0123] Specifically, the server uses Python's Pandas library to match data by time and location and convert it into a single dataset. Missing values ​​are corrected by estimation from past data and median interpolation, and outliers are also removed. The preprocessed data is then processed quickly as a large-scale dataset using Apache Spark.

[0124] Input: People flow data stored in an S3 bucket, payment data stored in an RDS database

[0125] Output: Integrated and preprocessed dataset

[0126] Step 4:

[0127] The AI ​​means in the server performs data analysis using the integrated data.

[0128] Specifically, the server uses TensorFlow to build a deep learning model and analyze patterns of people flow and payments from time-series data. It also uses Scikit-learn to apply clustering methods (e.g., K-means) to extract customer segments with similar characteristics. It also uses an LSTM model to predict future demand.

[0129] Input: Integrated and preprocessed dataset

[0130] Output: Analysis results (pattern analysis, clustering results, demand forecast)

[0131] Step 5:

[0132] The server generates a management improvement proposal based on the analysis results.

[0133] Specifically, the server uses the analysis results to recommend areas for new store openings, propose sales during specific time periods, and generate marketing strategies for specific customer segments.The analysis results are visualized using MPLib, and the proposals are displayed to the user on a dashboard.

[0134] Input: Analysis results

[0135] Output: Visualized business improvement proposals

[0136] Step 6:

[0137] The server provides the generated suggestions to the user in real time.

[0138] Specifically, the server uses the Slack API or Microsoft Teams API to notify the user of the suggestions through a chatbot, allowing the user to immediately receive and respond to the suggestions.

[0139] Input: Visualized business improvement proposals

[0140] Output: Real-time notifications via chatbots and notification tools

[0141] (Application example 1)

[0142] 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."

[0143] The current retail industry lacks the means to effectively analyze foot traffic and payment data and make immediate management improvement proposals. Store managers are also required to quickly visualize acquired data and take appropriate action in real time, but no system exists that can accurately meet this demand. This can lead to delayed management decisions and the risk of overlooking optimal strategies. Furthermore, specific action proposals are not immediately notified to users, making it difficult to respond in a timely manner.

[0144] 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.

[0145] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating management improvement proposals based on the analysis results, means for providing the generated proposals in real time, means for visually displaying the analysis results, and means for instantly notifying the proposals via push notifications. This enables store managers to obtain specific management strategies based on the data in real time, which can lead to increased sales and more efficient operations.

[0146] "People flow data" refers to information on the movement of people in a specific area.

[0147] "Payment Data" refers to data that includes information about a transaction, such as the date and time of the transaction, the transaction amount, and the location of the transaction.

[0148] "Aggregated People Flow Data" refers to people flow data obtained from cell phone towers and other data sources.

[0149] "Acquired Payment Data" refers to payment data acquired through payment system APIs, etc.

[0150] "Integrated data" refers to the integration of collected people flow data and captured payment data into a single dataset.

[0151] "AI means" refers to means of analyzing data using machine learning algorithms or other artificial intelligence techniques.

[0152] "Management improvement proposals" refer to specific management strategies and measures for retailers that are generated based on the results of data analysis.

[0153] "Means for providing in real time" refers to means for instantly providing generated management improvement proposals to users.

[0154] "Visual display means" refers to means for displaying analysis results and recommendations in a visual format, such as a graph or heat map.

[0155] "Means of immediately notifying users of suggestions via push notifications" refers to means of notifying users of important suggestions in real time via push notifications.

[0156] This invention is a system that provides effective management improvement proposals to retailers by collecting and analyzing foot traffic data and payment data. The system collects data using mobile phone base stations and payment system APIs, and generates and provides specific management strategies in real time through AI analysis. It also has a visual display and push notification function, helping users to take immediate action.

[0157] Hardware and software used

[0158] This system uses the following hardware and software:

[0159] Cell phone base stations: To collect people flow data

[0160] Server: Collects, consolidates, analyzes data, and generates recommendations

[0161] Payment System API: To obtain payment data

[0162] Machine learning algorithms (e.g., LinearRegression from scikit-learn) to perform data analysis

[0163] Visualization tools (e.g., Matplotlib, Pandas): to display the data visually

[0164] Push notification system: to notify you of proposals in real time

[0165] Data processing and calculation

[0166] The server performs the following process:

[0167] 1. Data collection: The server collects people flow data from mobile phone base stations and obtains payment data through the payment system API, including people movement information, transaction date and time, transaction amount, and transaction location.

[0168] 2. Data integration and preprocessing: The server integrates the collected people flow data and the acquired payment data into a single dataset, and performs preprocessing such as correcting missing values ​​and removing outliers.

[0169] 3. Data analysis: Using machine learning algorithms (e.g., Linear Regression in scikit-learn) in the server, the integrated data is analyzed. This includes analyzing foot traffic patterns, sales patterns, and forecasting future demand.

[0170] 4. Generation of business improvement proposals: Based on the analysis results using AI tools, specific business strategies are generated, such as optimal store layout, new store opening areas, optimizing product lineups, marketing strategies to target demographics, and holding limited-time sales.

[0171] 5. Real-time Delivery: Suggestions are provided to users in real-time by the server, including visual graphs, heatmaps, and instant suggestions via push notifications.

[0172] Specific examples

[0173] 1. Review of store locations:

[0174] The server detects an increase in foot traffic data in a specific area and suggests opening a new store in that area.

[0175] 2. Target Exclusive Coupon Distribution:

[0176] Payment data is analyzed, coupons are generated based on the customer demographic that visits the store in large numbers during specific times, and coupons are distributed in real time.

[0177] 3. Limited-Time Deals:

[0178] Analyze the decrease in the number of customers visiting a store during a specific time period and suggest holding a time sale during that time period.

[0179] Prompt Sentence Examples

[0180] "Use this dataset to analyze foot traffic and sales data by time of day and make specific proposals for improving business operations."

[0181] This system allows retailers to obtain specific management strategies based on data in real time, enabling them to improve sales and operate more efficiently.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] Data collection

[0185] The server collects data from mobile phone towers and payment system APIs. It obtains people flow data from mobile phone towers and payment data through the payment system API. The input people flow data includes information on people's movements in a specific area, and the payment data includes transaction date and time, transaction amount, transaction location, etc. The output is the raw data temporarily stored on the server.

[0186] Step 2:

[0187] Data Integration and Preprocessing

[0188] The server integrates the collected people flow data and payment data. First, it converts the collected data into a data frame (using the pandas library as an example). Next, it corrects missing values ​​(for example, forward interpolation) and removes outliers based on the data frame. Finally, it outputs an integrated dataset.

[0189] Step 3:

[0190] Data analysis

[0191] The server uses AI tools to analyze the integrated data. Specifically, it uses machine learning algorithms (e.g., Linear Regression in scikit-learn) to analyze foot traffic patterns and sales patterns. It uses the integrated data as input and obtains foot traffic trend data and sales trend data as the output after analysis.

[0192] Step 4:

[0193] Generate business improvement proposals

[0194] The server generates management improvement proposals based on the results of the data analysis. For example, it identifies differences between foot traffic trends and sales trends and determines management strategies. These include proposing areas for new store openings and holding time sales during specific time periods. This results in the output of specific management improvement proposals.

[0195] Step 5:

[0196] Visual Indication

[0197] The server visually displays the analysis results and management improvement proposals. For example, it uses Matplotlib to generate graphs and create heat maps. This allows the statistical data and proposals to be output in a user-friendly format.

[0198] Step 6:

[0199] Real-time notifications

[0200] The server uses push notifications to notify users of important suggestions in real time. Based on the analysis results, immediate business improvement suggestions are generated and sent to users' smartphones or other devices as push notifications. The input is the generated suggestion data, and the output is a notification sending log.

[0201] Through this series of steps, the system provides users with actionable business strategies in real time, maximizing their effectiveness.

[0202] 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.

[0203] This invention utilizes a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers, as well as an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0204] Program processing

[0205] Data collection

[0206] The server periodically obtains people flow data from mobile phone base stations. The people flow data includes information on people's movements in a specific area and their stay by time of day. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction.

[0207] Data Integration and Preprocessing

[0208] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0209] Data analysis

[0210] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to understand which products sell well at which times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0211] Generate business improvement proposals

[0212] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, an emotion engine analyzes the user's emotional state, and adjusts the content and timing of proposals based on the results.

[0213] Real-time feedback

[0214] The generated business improvement proposals are provided to the user in real time by the server via chatbots or email notifications. The proposals are optimized to suit the user's emotional state, as they also incorporate the analysis results of the emotion engine.

[0215] Specific examples

[0216] Example 1: Reviewing store locations

[0217] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0218] Example 2: Distributing Target-exclusive coupons

[0219] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0220] Example 3: Running a Limited-Time Sale

[0221] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0222] In this way, the system of the present invention provides specific management strategies based on data in real time, and by utilizing the emotion engine, it can make more personalized proposals, allowing retailers to improve their management in a data-driven and emotion-sensitive manner.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] The server periodically obtains people flow data from mobile phone base stations, which includes information on people's movements in specific areas and their stays by time of day.

[0226] Step 2:

[0227] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0228] Step 3:

[0229] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0230] Step 4:

[0231] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0232] Step 5:

[0233] The server analyzes payment data and sales patterns, which allows it to determine which products are selling at which times and which customer segments are most popular.

[0234] Step 6:

[0235] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0236] Step 7:

[0237] The server uses an emotion engine to analyze the user's emotional state, for example, by analyzing chat logs and behavioral data with customers to evaluate the user's current emotional state.

[0238] Step 8:

[0239] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, the server also takes into account the user's emotional state as determined by an emotion engine, adjusting the content and timing of the proposals.

[0240] Step 9:

[0241] The server provides the generated business improvement proposals to the user in real time via chatbots and email notifications, and since the analysis results of the emotion engine are also reflected, the proposals are optimized to suit the user's emotional state.

[0242] Step 10:

[0243] Users receive suggestions and can then take specific action based on them, such as introducing new products or holding limited-time sales during specific times, as well as improving customer service and following up based on the emotion engine's results.

[0244] Example 2

[0245] 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."

[0246] The current retail industry lacks the means to effectively utilize real-time foot traffic and payment data to improve management. Furthermore, there are no management strategies that take into account the emotional state of customers, making it difficult to improve customer satisfaction.

[0247] 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.

[0248] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results, means for providing the generated proposals in real time, emotion engine means for recognizing the emotional state of the user, and means for adjusting the content and timing of the proposals based on the emotional state of the user. This makes it possible to propose specific business strategies in real time using the people flow data and payment data, and further enables targeted proposals and follow-ups according to the emotional state of the customer, which is expected to improve customer satisfaction.

[0249] "People flow data" refers to data that includes information on people's movements in a specific area and information on their stay by time of day.

[0250] "Payment data" refers to data that includes the date, time, amount, and location of each transaction.

[0251] "Integrated data" refers to data that has been converted from collected people flow data and acquired payment data into a single dataset, with the format standardized and missing and outlier values ​​corrected.

[0252] "AI methods" are methods that use machine learning algorithms and data analysis tools to analyze integrated data and analyze traffic patterns and sales patterns.

[0253] "Management improvement proposals" are specific proposals generated based on the analysis results, such as selecting areas for new store openings, reviewing product lineups, and holding limited-time sales.

[0254] The "emotion engine means" is a means for recognizing the user's emotional state and adjusting the content and timing of suggestions based on the results.

[0255] "Means of providing in real time" refers to means of providing the generated business improvement proposals to users in real time via chatbots or email notifications.

[0256] This invention is a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers. It also uses an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0257] The server implements the system mainly using the following hardware and software:

[0258] 1. Cell phone base stations:

[0259] People flow data is periodically collected from mobile phone base stations, and includes information on people's movements in specific areas and their stays by time of day.

[0260] 2. Payment System API:

[0261] Use payment system APIs to retrieve payment data, including the date, time, amount, and location of each transaction.

[0262] 3. Data integration capabilities:

[0263] The server integrates the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0264] 4. AI analysis:

[0265] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns, determining which products sell well at which times and which customer demographics are most popular.

[0266] 5. Machine learning algorithms:

[0267] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0268] 6. Emotion Engine:

[0269] The emotional engine analyzes the user's emotional state and adjusts the content and timing of suggestions based on the results.

[0270] The generated business improvement proposals are provided to the user in real time by the server via chatbots and email notifications, and the analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state.

[0271] Specific examples

[0272] Example 1: Reviewing store locations

[0273] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0274] Example 2: Distributing Target-exclusive coupons

[0275] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0276] Example 3: Running a Limited-Time Sale

[0277] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0278] Prompt Sentence Examples

[0279] "Please explain the specific steps of a program that uses mobile phone tower and payment system APIs to collect data and provide real-time recommendations for selecting new store locations based on the analysis results. Also, please explain how an emotion engine is used to analyze the user's emotional state and adjust the recommendations based on the results."

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1:

[0282] Data collection

[0283] The server periodically obtains people flow data from mobile phone base stations. This people flow data includes information on people's movements in specific areas and their stay information by time period. The input is the people flow data from the mobile phone base stations, and the output is raw data stored in the server. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction. The input is payment data from the payment system API, and the output is raw data stored in the server.

[0284] The specific operation is as follows:

[0285] The server periodically accesses mobile phone base stations to obtain people flow data and stores it in an internal database.

[0286] The server calls the payment system API, retrieves the payment data, and stores it in an internal database.

[0287] Step 2:

[0288] Data Integration and Preprocessing

[0289] The server integrates the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is unified and missing and outliers are corrected. The input is the people flow data and payment data collected in step 1, and the output is the preprocessed integrated data.

[0290] The specific operation is as follows:

[0291] The server retrieves the collected people flow data and payment data from the database.

[0292] The server performs data preprocessing to format the data, impute missing values, and correct outliers.

[0293] The server integrates the curated data and generates a final integrated dataset.

[0294] Step 3:

[0295] Data analysis

[0296] The AI ​​tools within the server use the integrated data to analyze people flow patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to determine which products sell at what times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand. The input is the integrated data, and the output is pattern recognition and demand forecasts as the analysis results.

[0297] The specific operation is as follows:

[0298] The server performs AI analysis based on the integrated data.

[0299] The server uses the integrated data to detect patterns of people flow in specific areas and time periods.

[0300] The server analyzes the payment data and identifies sales patterns.

[0301] The server uses machine learning algorithms to predict future demand based on past data.

[0302] Step 4:

[0303] Generate business improvement proposals

[0304] The server generates business improvement proposals based on the analysis results. Proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. The server also analyzes the user's emotional state using an emotion engine, and adjusts the content and timing of proposals based on the results. The input is the analysis results, and the output is the generated proposals.

[0305] The specific operation is as follows:

[0306] The server automatically generates business improvement proposals based on the analysis results.

[0307] The proposals include selecting a store location, reviewing the product lineup, and holding limited-time sales.

[0308] The server optimizes the content and timing of proposals based on the results of the emotion engine.

[0309] Step 5:

[0310] Real-time feedback

[0311] The generated business improvement proposals are provided to the user in real time by the server. This is done via chatbots or email notifications. The analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state. The input is the generated proposal, and the output is the feedback the user receives.

[0312] The specific operation is as follows:

[0313] The server sends the generated suggestions to a chatbot or email notification system.

[0314] Provide users with real-time suggestions through chatbots and email notifications.

[0315] The suggestions are optimized based on the results of the emotion engine, providing feedback that matches the user's emotional state.

[0316] (Application example 2)

[0317] 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."

[0318] Conventional retail store management systems make management improvement proposals based solely on foot traffic data and payment data, but they are unable to consider the emotional state of customers and are only able to make proposals that lack individuality. Furthermore, there are only a limited number of systems available for making management improvement proposals in real time in brick-and-mortar stores, making it difficult to immediately improve store management. The present invention aims to solve these problems and provide a system that provides highly accurate management improvement proposals in real time.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0320] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data, the acquired payment data, and customer emotional data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results and the emotional state of the customer, and means for providing the generated proposals in real time. This makes it possible to make advanced business improvement proposals tailored to the emotional state of the customer in real time.

[0321] "People flow data" refers to data that indicates information on the movement and stay of people in a specific area.

[0322] "Payment Data" means data about payments, including the date, time, amount, and location of each transaction.

[0323] "Emotion data" refers to data that includes the results of analyzing a customer's emotional state based on their facial expressions and behavior.

[0324] "Integrated data" refers to data that has been collected, including people flow data, payment data, and emotion data, converted into a unified format and compiled into a single dataset.

[0325] "AI means" refers to artificial intelligence algorithms that analyze integrated data, visualize patterns, and forecast future demand.

[0326] "Management improvement proposals" are proposals based on the analysis results that propose specific management strategies such as selecting areas for new store openings, reviewing product lineups, and holding time sales during specific time periods.

[0327] "Means of providing in real time" refers to means of instantly providing generated business improvement proposals to users via chatbots or email notifications.

[0328] This invention is a system that collects and analyzes people flow data, payment data, and customer emotion data, and provides effective management improvement proposals to retail stores. This system includes the following elements.

[0329] System Configuration

[0330] 1. Data Collection

[0331] The server collects people flow data through mobile phone base stations, thereby obtaining information on people's movements in specific areas and their stays by time period. At the same time, it obtains payment data using payment system APIs (e.g., Stripe and PayPal). Furthermore, it uses facial recognition technology to collect customers' emotional states from in-store camera feeds. This is achieved using facial recognition services such as Amazon Rekognition and Microsoft Azure Cognitive Services.

[0332] 2. Data integration and preprocessing

[0333] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format, which also corrects for missing and outlier values.

[0334] 3. Data Analysis

[0335] The AI ​​tools within the server will use the integrated data to analyze foot traffic patterns. Predictive models using machine learning algorithms will be used to understand foot traffic trends in specific areas and time periods. Furthermore, payment data will be analyzed to analyze sales patterns and understand sales performance. Machine learning models from Azure or AWS may be used.

[0336] 4. Generate business improvement proposals

[0337] Based on the analysis results obtained by AI tools, specific business improvement proposals are generated, such as selecting new store locations, reviewing product lineups according to target demographics, and holding time sales during specific time periods. The content and timing of proposals are optimized based on the emotional state of customers.

[0338] 5. Real-time feedback

[0339] The generated suggestions are provided to users in real time, and information is subsequently sent via chatbots and email notifications, allowing employees and managers to take immediate action.

[0340] Specific examples

[0341] For example, if a certain area shows an increase in foot traffic but there are few competing stores in the area, the server will use this information to suggest new store openings to users.Also, if the majority of customers visiting on weekends are students and emotional data indicates low satisfaction, the server will suggest providing special coupons for students in real time.

[0342] Prompt Sentence Examples

[0343] You might prompt your generative AI model with the following:

[0344] Based on payment data and sentiment analysis data, please suggest a business strategy to improve customer experience. In particular, the majority of customers are young, and sentiment data indicates that they are not very satisfied. What improvement measures should be taken?

[0345] In this way, by implementing this invention, it becomes possible to make sophisticated business improvement proposals in real time that are tailored to the emotional state of the customer.

[0346] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0347] Step 1:

[0348] The server collects people flow data from mobile phone base stations. Specifically, it sends API requests to obtain information on people's movements by region and their stay by time of day. The input is the base station ID, and the output is people flow data.

[0349] Step 2:

[0350] The server collects payment data using the payment system API. Specifically, it accesses the payment system API to obtain the transaction date, time, amount, and location. The input is the API key, and the output is the payment data.

[0351] Step 3:

[0352] The server collects customer emotional states from in-store camera feeds using facial recognition technology. Specifically, it uses Amazon Rekognition and Microsoft Azure Cognitive Services to analyze emotional data from images. Image data is taken as input and emotional data is taken as output.

[0353] Step 4:

[0354] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format. This stage also corrects missing and outlier values. Multiple datasets are input, and the integrated data is output.

[0355] Step 5:

[0356] The AI ​​in the server uses the integrated data to analyze people flow patterns. It applies machine learning algorithms to understand people's movements in specific areas and time periods. The input is the integrated data and the output is the analysis results.

[0357] Step 6:

[0358] The server analyzes payment data and analyzes sales patterns. It uses AWS and Azure machine learning services for data analysis to understand production performance. The input is payment data and the output is sales patterns.

[0359] Step 7:

[0360] Based on the analysis results and emotional data obtained by AI, the server generates management improvement proposals such as selecting new store locations, reviewing product lineups, and holding time sales during specific time periods. The inputs are the analysis results and emotional data, and the output is management improvement proposals.

[0361] Step 8:

[0362] The server provides the generated proposals to the user in real time. Specifically, it sends the proposals via chatbot or email notification so that the store manager can respond immediately. The input is the business improvement proposal, and the output is a notification to the user.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] [Second embodiment]

[0367] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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).

[0373] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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."

[0379] This invention is a system that collects and analyzes people flow data and payment data, and provides effective management improvement proposals to retailers. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time.

[0380] Program processing

[0381] Data collection

[0382] The server collects people flow data from mobile phone towers, which includes information on people's movements in a specific area. The server also obtains payment data through the payment system API, which includes transaction date and time, transaction amount, transaction location, etc.

[0383] Data Integration and Preprocessing

[0384] The server integrates the collected people flow data and the acquired payment data into a single dataset, where it performs preprocessing such as correcting missing values ​​and removing outliers.

[0385] Data analysis

[0386] The AI ​​tools within the server analyze the integrated data, including analyzing foot traffic patterns using machine learning algorithms, analyzing sales patterns based on payment data, and forecasting future demand.

[0387] Generate business improvement proposals

[0388] Based on the analysis results, the server generates specific management improvement proposals for retailers, including optimal store layout, new store opening areas, product lineup optimization, marketing strategies for target demographics, and limited-time sales.

[0389] Real-time feedback

[0390] The generated business improvement proposals are provided to the user in real time by the server, via chatbots and other notification methods, allowing for immediate response.

[0391] Specific examples

[0392] Example 1: Reviewing store locations

[0393] This section explains the case where a proposal is made to consider opening a new store based on people flow data in a certain area. The server analyzes the people flow data to detect an increase in the number of people in a specific area. Based on this result, the server proposes to the user to open a new store in the same area.

[0394] Example 2: Distributing Target-exclusive coupons

[0395] Let's say you analyze payment data and determine that the customers who visit on weekends are students. The server uses this information to create special coupons for students and propose them to users in real time.

[0396] Example 3: Running a Limited-Time Sale

[0397] Let's consider a case where a decrease in the number of customers during a specific time period is analyzed and a time sale is held during that time period. The server analyzes the data and finds that there are few customers between 2:00 PM and 4:00 PM on weekdays, and suggests to the user that a time sale be held during this time period.

[0398] The above is an embodiment of the present invention. This system allows retailers to obtain specific management strategies based on data in real time, thereby improving sales and efficiency.

[0399] The processing flow will be explained below.

[0400] Step 1:

[0401] The server periodically collects people flow data from mobile phone base stations, including information on people's movements by region and their stay by time of day.

[0402] Step 2:

[0403] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0404] Step 3:

[0405] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0406] Step 4:

[0407] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0408] Step 5:

[0409] The server analyzes payment data and analyzes sales patterns, which allows it to determine which products sell well at what times and which customer segments are most popular.

[0410] Step 6:

[0411] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0412] Step 7:

[0413] Based on the analysis results, the server generates business improvement proposals, such as proposing new store locations, revising product lineups for target demographics, and proposing limited-time sales during specific times.

[0414] Step 8:

[0415] The server provides the generated business improvement proposals to the user in real time via chatbots, email notifications, etc.

[0416] Step 9:

[0417] Users receive suggestions and then take specific measures based on them, such as introducing new products or holding limited-time sales during specific times.

[0418] Example 1

[0419] 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."

[0420] In conventional retail store management, effective use of foot traffic data and payment data has not been fully implemented, resulting in a lack of means to propose appropriate management improvement measures in real time. This has made it difficult to efficiently formulate strategies for store layout, marketing, and sales improvement. The present invention aims to solve this problem by providing a system that provides specific data-based management strategies for retail stores in real time.

[0421] 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.

[0422] In this invention, the server includes means for collecting people flow data from mobile phone base stations, means for acquiring payment data through a payment system API, and means for integrating and preprocessing the collected people flow data and the acquired payment data. This makes it possible to efficiently collect and integrate data, correct missing values, and remove outliers, and then provide appropriate management improvement proposals in real time.

[0423] A "mobile phone base station" is a basic infrastructure facility used to collect location information from mobile phone users.

[0424] "People flow data" is data that includes information on the movement of people in a specific area.

[0425] A "payment system API" is an application programming interface for providing payment data to other systems.

[0426] "Payment data" refers to data related to payments, including transaction date and time, transaction amount, transaction location, etc.

[0427] "Preprocessing" refers to the process of correcting missing data values, removing outliers, and preparing the data in a format suitable for analysis.

[0428] "Integrated data" is data collected from multiple data sources and converted into a single data set.

[0429] A "machine learning algorithm" is a mathematical technique used to analyze data and generate predictive models.

[0430] "Management improvement proposals" are specific management strategy proposals provided to retailers based on the results of data analysis.

[0431] "Real-time delivery means" refers to techniques and methods for instantly notifying users of generated suggestions.

[0432] This invention is a system that provides effective management improvement proposals to retail stores based on people flow data and payment data collected using mobile phone base stations and payment system APIs. The following hardware and software are used to implement this system.

[0433] Data collection

[0434] The server collects people flow data from mobile phone base stations. Specifically, it uses an API provided by mobile phone companies. Through this API, it obtains real-time movement information of mobile phone users in each region, streams the data using AWS Kinesis data streams, and stores it in S3 storage.

[0435] The server also uses the payment system API (e.g. Stripe API) to retrieve payment data, which includes transaction information such as transaction date, transaction amount, and transaction location, and stores it in an AWS RDS database.

[0436] Data Integration and Preprocessing

[0437] The server integrates the collected people flow data and payment data using Python's Pandas library. In the data integration process, the data is matched by time and location and converted into a single dataset. In the preprocessing stage, missing value correction and outlier removal are performed to improve data quality. Apache Spark is used to perform this preprocessing quickly on large amounts of data.

[0438] Data analysis

[0439] The AI ​​tools on the server perform analysis using the integrated data. A deep learning model is built using TensorFlow to analyze patterns of people flow and payments from time-series data. Clustering methods (e.g., K-means) are applied using Scikit-learn to extract customer segments with similar characteristics. Furthermore, an LSTM model is used to predict future demand.

[0440] Generate business improvement proposals

[0441] Based on the analysis results, the server generates specific proposals for business improvement, such as recommendations for new store opening areas, proposals for sales during specific time periods, marketing strategies for specific customer segments, etc. The analysis results are visualized using MPLib and presented to the user on a dashboard.

[0442] Real-time feedback

[0443] The generated suggestions are provided to the user in real time by the server, and the suggestions are notified in real time through a chatbot using the Slack API or Microsoft Teams API, allowing the user to respond immediately.

[0444] Specific examples

[0445] Example 1: Reviewing store locations

[0446] The server analyzes pedestrian flow data collected from mobile phone base stations to detect increases in the number of people in a specific area. Based on the analysis results, it proposes new store openings in that area. For example, it sends a notification saying, "Pedestrian flow around Akihabara Station has increased by 30% on weekends, so please consider opening a new store in the area."

[0447] Example 2: Distributing Target-exclusive coupons

[0448] The server analyzes payment data and determines that the customers who visit the store at certain times and on weekends are primarily students. Based on this information, it generates special coupons for students and makes marketing suggestions in real time. For example, it sends a suggestion such as, "Please issue a 10% off coupon to students on weekends."

[0449] Example 3: Running a Limited-Time Sale

[0450] The server will analyze the data and discover that there are few customers during certain times of the day. For example, it will suggest, "Since the number of customers is low between 2:00 PM and 4:00 PM on weekdays, we recommend that you hold a time sale during this time," and will also explain the specific steps to take.

[0451] Examples of specific prompts include, "Please suggest new store locations based on regional foot traffic data," "Please generate coupons for students who visit on weekends," and "Please suggest time sales to increase customer traffic between 2:00 and 4:00 PM."

[0452] As described above, by combining these methods, retailers can obtain specific management strategies based on data in real time, enabling them to improve efficiency and increase sales.

[0453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0454] Step 1:

[0455] The server collects people flow data from mobile phone base stations.

[0456] Specifically, the server sends a request to the API provided by the mobile phone company to obtain real-time movement information. This data includes information on people's movements in a specific area. The obtained data is streamed in real time using AWS Kinesis data streams and stored in an S3 bucket.

[0457] Input: Mobile operator API endpoint

[0458] Output: People flow data stored in an S3 bucket

[0459] Step 2:

[0460] The server obtains payment data through the payment system API.

[0461] Specifically, the server sends a request to the payment system's API (e.g., a general payment system's API) to obtain payment information such as transaction date and time, transaction amount, and transaction location. This data is then stored in an AWS RDS database.

[0462] Input: Payment system API endpoint

[0463] Output: Payment data stored in an RDS database

[0464] Step 3:

[0465] The server integrates and pre-processes the collected people flow data and payment data.

[0466] Specifically, the server uses Python's Pandas library to match data by time and location and convert it into a single dataset. Missing values ​​are corrected by estimation from past data and median interpolation, and outliers are also removed. The preprocessed data is then processed quickly as a large-scale dataset using Apache Spark.

[0467] Input: People flow data stored in an S3 bucket, payment data stored in an RDS database

[0468] Output: Integrated and preprocessed dataset

[0469] Step 4:

[0470] The AI ​​means in the server performs data analysis using the integrated data.

[0471] Specifically, the server uses TensorFlow to build a deep learning model and analyze patterns of people flow and payments from time-series data. It also uses Scikit-learn to apply clustering methods (e.g., K-means) to extract customer segments with similar characteristics. It also uses an LSTM model to predict future demand.

[0472] Input: Integrated and preprocessed dataset

[0473] Output: Analysis results (pattern analysis, clustering results, demand forecast)

[0474] Step 5:

[0475] The server generates a management improvement proposal based on the analysis results.

[0476] Specifically, the server uses the analysis results to recommend areas for new store openings, propose sales during specific time periods, and generate marketing strategies for specific customer segments.The analysis results are visualized using MPLib, and the proposals are displayed to the user on a dashboard.

[0477] Input: Analysis results

[0478] Output: Visualized business improvement proposals

[0479] Step 6:

[0480] The server provides the generated suggestions to the user in real time.

[0481] Specifically, the server uses the Slack API or Microsoft Teams API to notify the user of the suggestions through a chatbot, allowing the user to immediately receive and respond to the suggestions.

[0482] Input: Visualized business improvement proposals

[0483] Output: Real-time notifications via chatbots and notification tools

[0484] (Application example 1)

[0485] 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."

[0486] The current retail industry lacks the means to effectively analyze foot traffic and payment data and make immediate management improvement proposals. Store managers are also required to quickly visualize acquired data and take appropriate action in real time, but no system exists that can accurately meet this demand. This can lead to delayed management decisions and the risk of overlooking optimal strategies. Furthermore, specific action proposals are not immediately notified to users, making it difficult to respond in a timely manner.

[0487] 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.

[0488] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating management improvement proposals based on the analysis results, means for providing the generated proposals in real time, means for visually displaying the analysis results, and means for instantly notifying the proposals via push notifications. This enables store managers to obtain specific management strategies based on the data in real time, which can lead to increased sales and more efficient operations.

[0489] "People flow data" refers to information on the movement of people in a specific area.

[0490] "Payment Data" refers to data that includes information about a transaction, such as the date and time of the transaction, the transaction amount, and the location of the transaction.

[0491] "Aggregated People Flow Data" refers to people flow data obtained from cell phone towers and other data sources.

[0492] "Acquired Payment Data" refers to payment data acquired through payment system APIs, etc.

[0493] "Integrated data" refers to the integration of collected people flow data and captured payment data into a single dataset.

[0494] "AI means" refers to means of analyzing data using machine learning algorithms or other artificial intelligence techniques.

[0495] "Management improvement proposals" refer to specific management strategies and measures for retailers that are generated based on the results of data analysis.

[0496] "Means for providing in real time" refers to means for instantly providing generated management improvement proposals to users.

[0497] "Visual display means" refers to means for displaying analysis results and recommendations in a visual format, such as a graph or heat map.

[0498] "Means of immediately notifying users of suggestions via push notifications" refers to means of notifying users of important suggestions in real time via push notifications.

[0499] This invention is a system that provides effective management improvement proposals to retailers by collecting and analyzing foot traffic data and payment data. The system collects data using mobile phone base stations and payment system APIs, and generates and provides specific management strategies in real time through AI analysis. It also has a visual display and push notification function, helping users to take immediate action.

[0500] Hardware and software used

[0501] This system uses the following hardware and software:

[0502] Cell phone base stations: To collect people flow data

[0503] Server: Collects, consolidates, analyzes data, and generates recommendations

[0504] Payment System API: To obtain payment data

[0505] Machine learning algorithms (e.g., LinearRegression from scikit-learn) to perform data analysis

[0506] Visualization tools (e.g., Matplotlib, Pandas): to display the data visually

[0507] Push notification system: to notify you of proposals in real time

[0508] Data processing and calculation

[0509] The server performs the following process:

[0510] 1. Data collection: The server collects people flow data from mobile phone base stations and obtains payment data through the payment system API, including people movement information, transaction date and time, transaction amount, and transaction location.

[0511] 2. Data integration and preprocessing: The server integrates the collected people flow data and the acquired payment data into a single dataset, and performs preprocessing such as correcting missing values ​​and removing outliers.

[0512] 3. Data analysis: Using machine learning algorithms (e.g., Linear Regression in scikit-learn) in the server, the integrated data is analyzed. This includes analyzing foot traffic patterns, sales patterns, and forecasting future demand.

[0513] 4. Generation of business improvement proposals: Based on the analysis results using AI tools, specific business strategies are generated, such as optimal store layout, new store opening areas, optimizing product lineups, marketing strategies to target demographics, and holding limited-time sales.

[0514] 5. Real-time Delivery: Suggestions are provided to users in real-time by the server, including visual graphs, heatmaps, and instant suggestions via push notifications.

[0515] Specific examples

[0516] 1. Review of store locations:

[0517] The server detects an increase in foot traffic data in a specific area and suggests opening a new store in that area.

[0518] 2. Target Exclusive Coupon Distribution:

[0519] Payment data is analyzed, coupons are generated based on the customer demographic that visits the store in large numbers during specific times, and coupons are distributed in real time.

[0520] 3. Limited-Time Deals:

[0521] Analyze the decrease in the number of customers visiting a store during a specific time period and suggest holding a time sale during that time period.

[0522] Prompt Sentence Examples

[0523] "Use this dataset to analyze foot traffic and sales data by time of day and make specific proposals for improving business operations."

[0524] This system allows retailers to obtain specific management strategies based on data in real time, enabling them to improve sales and operate more efficiently.

[0525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0526] Step 1:

[0527] Data collection

[0528] The server collects data from mobile phone towers and payment system APIs. It obtains people flow data from mobile phone towers and payment data through the payment system API. The input people flow data includes information on people's movements in a specific area, and the payment data includes transaction date and time, transaction amount, transaction location, etc. The output is the raw data temporarily stored on the server.

[0529] Step 2:

[0530] Data Integration and Preprocessing

[0531] The server integrates the collected people flow data and payment data. First, it converts the collected data into a data frame (using the pandas library as an example). Next, it corrects missing values ​​(for example, forward interpolation) and removes outliers based on the data frame. Finally, it outputs an integrated dataset.

[0532] Step 3:

[0533] Data analysis

[0534] The server uses AI tools to analyze the integrated data. Specifically, it uses machine learning algorithms (e.g., Linear Regression in scikit-learn) to analyze foot traffic patterns and sales patterns. It uses the integrated data as input and obtains foot traffic trend data and sales trend data as the output after analysis.

[0535] Step 4:

[0536] Generate business improvement proposals

[0537] The server generates management improvement proposals based on the results of the data analysis. For example, it identifies differences between foot traffic trends and sales trends and determines management strategies. These include proposing areas for new store openings and holding time sales during specific time periods. This results in the output of specific management improvement proposals.

[0538] Step 5:

[0539] Visual Indication

[0540] The server visually displays the analysis results and management improvement proposals. For example, it uses Matplotlib to generate graphs and create heat maps. This allows the statistical data and proposals to be output in a user-friendly format.

[0541] Step 6:

[0542] Real-time notifications

[0543] The server uses push notifications to notify users of important suggestions in real time. Based on the analysis results, immediate business improvement suggestions are generated and sent to users' smartphones or other devices as push notifications. The input is the generated suggestion data, and the output is a notification sending log.

[0544] Through this series of steps, the system provides users with actionable business strategies in real time, maximizing their effectiveness.

[0545] 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.

[0546] This invention utilizes a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers, as well as an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0547] Program processing

[0548] Data collection

[0549] The server periodically obtains people flow data from mobile phone base stations. The people flow data includes information on people's movements in a specific area and their stay by time of day. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction.

[0550] Data Integration and Preprocessing

[0551] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0552] Data analysis

[0553] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to understand which products sell well at which times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0554] Generate business improvement proposals

[0555] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, an emotion engine analyzes the user's emotional state, and adjusts the content and timing of proposals based on the results.

[0556] Real-time feedback

[0557] The generated business improvement proposals are provided to the user in real time by the server via chatbots or email notifications. The proposals are optimized to suit the user's emotional state, as they also incorporate the analysis results of the emotion engine.

[0558] Specific examples

[0559] Example 1: Reviewing store locations

[0560] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0561] Example 2: Distributing Target-exclusive coupons

[0562] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0563] Example 3: Running a Limited-Time Sale

[0564] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0565] In this way, the system of the present invention provides specific management strategies based on data in real time, and by utilizing the emotion engine, it can make more personalized proposals, allowing retailers to improve their management in a data-driven and emotion-sensitive manner.

[0566] The processing flow will be explained below.

[0567] Step 1:

[0568] The server periodically obtains people flow data from mobile phone base stations, which includes information on people's movements in specific areas and their stays by time of day.

[0569] Step 2:

[0570] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0571] Step 3:

[0572] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0573] Step 4:

[0574] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0575] Step 5:

[0576] The server analyzes payment data and sales patterns, which allows it to determine which products are selling at which times and which customer segments are most popular.

[0577] Step 6:

[0578] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0579] Step 7:

[0580] The server uses an emotion engine to analyze the user's emotional state, for example, by analyzing chat logs and behavioral data with customers to evaluate the user's current emotional state.

[0581] Step 8:

[0582] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, the server also takes into account the user's emotional state as determined by an emotion engine, adjusting the content and timing of the proposals.

[0583] Step 9:

[0584] The server provides the generated business improvement proposals to the user in real time via chatbots and email notifications, and since the analysis results of the emotion engine are also reflected, the proposals are optimized to suit the user's emotional state.

[0585] Step 10:

[0586] Users receive suggestions and can then take specific action based on them, such as introducing new products or holding limited-time sales during specific times, as well as improving customer service and following up based on the emotion engine's results.

[0587] Example 2

[0588] 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."

[0589] The current retail industry lacks the means to effectively utilize real-time foot traffic and payment data to improve management. Furthermore, there are no management strategies that take into account the emotional state of customers, making it difficult to improve customer satisfaction.

[0590] 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.

[0591] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results, means for providing the generated proposals in real time, emotion engine means for recognizing the emotional state of the user, and means for adjusting the content and timing of the proposals based on the emotional state of the user. This makes it possible to propose specific business strategies in real time using the people flow data and payment data, and further enables targeted proposals and follow-ups according to the emotional state of the customer, which is expected to improve customer satisfaction.

[0592] "People flow data" refers to data that includes information on people's movements in a specific area and information on their stay by time of day.

[0593] "Payment data" refers to data that includes the date, time, amount, and location of each transaction.

[0594] "Integrated data" refers to data that has been converted from collected people flow data and acquired payment data into a single dataset, with the format standardized and missing and outlier values ​​corrected.

[0595] "AI methods" are methods that use machine learning algorithms and data analysis tools to analyze integrated data and analyze traffic patterns and sales patterns.

[0596] "Management improvement proposals" are specific proposals generated based on the analysis results, such as selecting areas for new store openings, reviewing product lineups, and holding limited-time sales.

[0597] The "emotion engine means" is a means for recognizing the user's emotional state and adjusting the content and timing of suggestions based on the results.

[0598] "Means of providing in real time" refers to means of providing the generated business improvement proposals to users in real time via chatbots or email notifications.

[0599] This invention is a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers. It also uses an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0600] The server implements the system mainly using the following hardware and software:

[0601] 1. Cell phone base stations:

[0602] People flow data is periodically collected from mobile phone base stations, and includes information on people's movements in specific areas and their stays by time of day.

[0603] 2. Payment System API:

[0604] Use payment system APIs to retrieve payment data, including the date, time, amount, and location of each transaction.

[0605] 3. Data integration capabilities:

[0606] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0607] 4. AI analysis:

[0608] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns, determining which products sell well at which times and which customer demographics are most popular.

[0609] 5. Machine learning algorithms:

[0610] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0611] 6. Emotion Engine:

[0612] The emotional engine analyzes the user's emotional state and adjusts the content and timing of suggestions based on the results.

[0613] The generated business improvement proposals are provided to the user in real time by the server via chatbots and email notifications, and the analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state.

[0614] Specific examples

[0615] Example 1: Reviewing store locations

[0616] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0617] Example 2: Distributing Target-exclusive coupons

[0618] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0619] Example 3: Running a Limited-Time Sale

[0620] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0621] Prompt Sentence Examples

[0622] "Please explain the specific steps of a program that uses mobile phone tower and payment system APIs to collect data and provide real-time recommendations for selecting new store locations based on the analysis results. Also, please explain how an emotion engine is used to analyze the user's emotional state and adjust the recommendations based on the results."

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1:

[0625] Data collection

[0626] The server periodically obtains people flow data from mobile phone base stations. This people flow data includes information on people's movements in specific areas and their stay information by time period. The input is the people flow data from the mobile phone base stations, and the output is raw data stored in the server. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction. The input is payment data from the payment system API, and the output is raw data stored in the server.

[0627] The specific operation is as follows:

[0628] The server periodically accesses mobile phone base stations to obtain people flow data and stores it in an internal database.

[0629] The server calls the payment system API, retrieves the payment data, and stores it in an internal database.

[0630] Step 2:

[0631] Data Integration and Preprocessing

[0632] The server integrates the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is unified and missing and outliers are corrected. The input is the people flow data and payment data collected in step 1, and the output is the preprocessed integrated data.

[0633] The specific operation is as follows:

[0634] The server retrieves the collected people flow data and payment data from the database.

[0635] The server performs data preprocessing to format the data, impute missing values, and correct outliers.

[0636] The server integrates the curated data and generates a final integrated dataset.

[0637] Step 3:

[0638] Data analysis

[0639] The AI ​​tools within the server use the integrated data to analyze people flow patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to determine which products sell at what times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand. The input is the integrated data, and the output is pattern recognition and demand forecasts as the analysis results.

[0640] The specific operation is as follows:

[0641] The server performs AI analysis based on the integrated data.

[0642] The server uses the integrated data to detect patterns of people flow in specific areas and time periods.

[0643] The server analyzes the payment data and identifies sales patterns.

[0644] The server uses machine learning algorithms to predict future demand based on past data.

[0645] Step 4:

[0646] Generate business improvement proposals

[0647] The server generates business improvement proposals based on the analysis results. Proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. The server also analyzes the user's emotional state using an emotion engine, and adjusts the content and timing of proposals based on the results. The input is the analysis results, and the output is the generated proposals.

[0648] The specific operation is as follows:

[0649] The server automatically generates business improvement proposals based on the analysis results.

[0650] The proposals include selecting a store location, reviewing the product lineup, and holding limited-time sales.

[0651] The server optimizes the content and timing of proposals based on the results of the emotion engine.

[0652] Step 5:

[0653] Real-time feedback

[0654] The generated business improvement proposals are provided to the user in real time by the server. This is done via chatbots or email notifications. The analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state. The input is the generated proposal, and the output is the feedback the user receives.

[0655] The specific operation is as follows:

[0656] The server sends the generated suggestions to a chatbot or email notification system.

[0657] Provide users with real-time suggestions through chatbots and email notifications.

[0658] The suggestions are optimized based on the results of the emotion engine, providing feedback that matches the user's emotional state.

[0659] (Application example 2)

[0660] 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."

[0661] Conventional retail store management systems make management improvement proposals based solely on foot traffic data and payment data, but they are unable to consider the emotional state of customers and are only able to make proposals that lack individuality. Furthermore, there are only a limited number of systems available for making management improvement proposals in real time in brick-and-mortar stores, making it difficult to immediately improve store management. The present invention aims to solve these problems and provide a system that provides highly accurate management improvement proposals in real time.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0663] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data, the acquired payment data, and customer emotional data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results and the emotional state of the customer, and means for providing the generated proposals in real time. This makes it possible to make advanced business improvement proposals tailored to the emotional state of the customer in real time.

[0664] "People flow data" refers to data that indicates information on the movement and stay of people in a specific area.

[0665] "Payment Data" means data about payments, including the date, time, amount, and location of each transaction.

[0666] "Emotion data" refers to data that includes the results of analyzing a customer's emotional state based on their facial expressions and behavior.

[0667] "Integrated data" refers to data that has been collected, including people flow data, payment data, and emotion data, converted into a unified format and compiled into a single dataset.

[0668] "AI means" refers to artificial intelligence algorithms that analyze integrated data, visualize patterns, and forecast future demand.

[0669] "Management improvement proposals" are proposals based on the analysis results that propose specific management strategies such as selecting areas for new store openings, reviewing product lineups, and holding time sales during specific time periods.

[0670] "Means of providing in real time" refers to means of instantly providing generated business improvement proposals to users via chatbots or email notifications.

[0671] This invention is a system that collects and analyzes people flow data, payment data, and customer emotion data, and provides effective management improvement proposals to retail stores. This system includes the following elements.

[0672] System Configuration

[0673] 1. Data Collection

[0674] The server collects people flow data through mobile phone base stations, thereby obtaining information on people's movements in specific areas and their stays by time period. At the same time, it obtains payment data using payment system APIs (e.g., Stripe and PayPal). Furthermore, it uses facial recognition technology to collect customers' emotional states from in-store camera feeds. This is achieved using facial recognition services such as Amazon Rekognition and Microsoft Azure Cognitive Services.

[0675] 2. Data integration and preprocessing

[0676] The server then integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format, correcting for missing or outlier values ​​at this stage.

[0677] 3. Data Analysis

[0678] The AI ​​tools within the server will use the integrated data to analyze foot traffic patterns. Predictive models using machine learning algorithms will be used to understand foot traffic trends in specific areas and time periods. Furthermore, payment data will be analyzed to analyze sales patterns and understand sales performance. Machine learning models from Azure or AWS may be used.

[0679] 4. Generate business improvement proposals

[0680] Based on the analysis results obtained by AI tools, specific business improvement proposals are generated, such as selecting new store locations, reviewing product lineups according to target demographics, and holding time sales during specific time periods. The content and timing of proposals are optimized based on the emotional state of customers.

[0681] 5. Real-time feedback

[0682] The generated suggestions are provided to users in real time, and information is subsequently sent via chatbots and email notifications, allowing employees and managers to take immediate action.

[0683] Specific examples

[0684] For example, if a certain area shows an increase in foot traffic but there are few competing stores in the area, the server will use this information to suggest new store openings to users.Also, if the majority of customers visiting on weekends are students and emotional data indicates low satisfaction, the server will suggest providing special coupons for students in real time.

[0685] Prompt Sentence Examples

[0686] You might prompt your generative AI model with the following:

[0687] Based on payment data and sentiment analysis data, please suggest a business strategy to improve customer experience. In particular, the majority of customers are young, and sentiment data indicates that they are not very satisfied. What improvement measures should be taken?

[0688] In this way, by implementing this invention, it becomes possible to make sophisticated business improvement proposals in real time that are tailored to the emotional state of the customer.

[0689] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0690] Step 1:

[0691] The server collects people flow data from mobile phone base stations. Specifically, it sends API requests to obtain information on people's movements by region and their stay by time of day. The input is the base station ID, and the output is people flow data.

[0692] Step 2:

[0693] The server collects payment data using the payment system API. Specifically, it accesses the payment system API to obtain the transaction date, time, amount, and location. The input is the API key, and the output is the payment data.

[0694] Step 3:

[0695] The server collects customer emotional states from in-store camera feeds using facial recognition technology. Specifically, it uses Amazon Rekognition and Microsoft Azure Cognitive Services to analyze emotional data from images. Image data is taken as input and emotional data is taken as output.

[0696] Step 4:

[0697] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format. This stage also corrects missing and outlier values. Multiple datasets are input, and the integrated data is output.

[0698] Step 5:

[0699] The AI ​​in the server uses the integrated data to analyze people flow patterns. It applies machine learning algorithms to understand people's movements in specific areas and time periods. The input is the integrated data and the output is the analysis results.

[0700] Step 6:

[0701] The server analyzes payment data and analyzes sales patterns. It uses AWS and Azure machine learning services for data analysis to understand production performance. The input is payment data and the output is sales patterns.

[0702] Step 7:

[0703] Based on the analysis results and emotional data obtained by AI, the server generates management improvement proposals such as selecting new store locations, reviewing product lineups, and holding time sales during specific time periods. The inputs are the analysis results and emotional data, and the output is management improvement proposals.

[0704] Step 8:

[0705] The server provides the generated proposals to the user in real time. Specifically, it sends the proposals via chatbot or email notification so that the store manager can respond immediately. The input is the business improvement proposal, and the output is a notification to the user.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] [Third embodiment]

[0710] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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).

[0716] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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."

[0722] This invention is a system that collects and analyzes people flow data and payment data, and provides effective management improvement proposals to retailers. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time.

[0723] Program processing

[0724] Data collection

[0725] The server collects people flow data from mobile phone towers, which includes information on people's movements in a specific area. The server also obtains payment data through the payment system API, which includes transaction date and time, transaction amount, transaction location, etc.

[0726] Data Integration and Preprocessing

[0727] The server integrates the collected people flow data and the acquired payment data into a single dataset, where it performs preprocessing such as correcting missing values ​​and removing outliers.

[0728] Data analysis

[0729] The AI ​​tools within the server analyze the integrated data, including analyzing foot traffic patterns using machine learning algorithms, analyzing sales patterns based on payment data, and forecasting future demand.

[0730] Generate business improvement proposals

[0731] Based on the analysis results, the server generates specific management improvement proposals for retailers, including optimal store layout, new store opening areas, product lineup optimization, marketing strategies for target demographics, and limited-time sales.

[0732] Real-time feedback

[0733] The generated business improvement proposals are provided to the user in real time by the server, via chatbots and other notification methods, allowing for immediate response.

[0734] Specific examples

[0735] Example 1: Reviewing store locations

[0736] This section explains the case where a proposal is made to consider opening a new store based on people flow data in a certain area. The server analyzes the people flow data to detect an increase in the number of people in a specific area. Based on this result, the server proposes to the user to open a new store in the same area.

[0737] Example 2: Distributing Target-exclusive coupons

[0738] Let's say you analyze payment data and determine that the customers who visit your store on weekends are students. The server uses this information to create special coupons for students and suggests them to users in real time.

[0739] Example 3: Running a Limited-Time Sale

[0740] Let's consider a case where a decrease in the number of customers during a specific time period is analyzed and a time sale is held during that time period. The server analyzes the data and finds that there are few visitors between 2:00 PM and 4:00 PM on weekdays, and suggests to the user that a time sale be held during this time period.

[0741] The above is an embodiment of the present invention. This system allows retailers to obtain specific management strategies based on data in real time, thereby improving sales and efficiency.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] The server periodically collects people flow data from mobile phone base stations, including information on people's movements by region and their stay by time of day.

[0745] Step 2:

[0746] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0747] Step 3:

[0748] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0749] Step 4:

[0750] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0751] Step 5:

[0752] The server analyzes payment data and analyzes sales patterns, which allows it to determine which products sell well at what times and which customer segments are most popular.

[0753] Step 6:

[0754] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0755] Step 7:

[0756] Based on the analysis results, the server generates business improvement proposals, such as proposing new store locations, revising product lineups for target demographics, and proposing limited-time sales during specific times.

[0757] Step 8:

[0758] The server provides the generated business improvement proposals to the user in real time via chatbots, email notifications, etc.

[0759] Step 9:

[0760] Users receive suggestions and then take specific measures based on them, such as introducing new products or holding limited-time sales during specific times.

[0761] Example 1

[0762] 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."

[0763] In conventional retail store management, effective use of foot traffic data and payment data has not been fully implemented, resulting in a lack of means to propose appropriate management improvement measures in real time. This has made it difficult to efficiently formulate strategies for store layout, marketing, and sales improvement. The present invention aims to solve this problem by providing a system that provides specific data-based management strategies for retail stores in real time.

[0764] 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.

[0765] In this invention, the server includes means for collecting people flow data from mobile phone base stations, means for acquiring payment data through a payment system API, and means for integrating and preprocessing the collected people flow data and the acquired payment data. This makes it possible to efficiently collect and integrate data, correct missing values, and remove outliers, and then provide appropriate management improvement proposals in real time.

[0766] A "mobile phone base station" is a basic infrastructure facility used to collect location information from mobile phone users.

[0767] "People flow data" is data that includes information on the movement of people in a specific area.

[0768] A "payment system API" is an application programming interface for providing payment data to other systems.

[0769] "Payment data" refers to data related to payments, including transaction date and time, transaction amount, transaction location, etc.

[0770] "Preprocessing" refers to the process of correcting missing data values, removing outliers, and preparing the data in a format suitable for analysis.

[0771] "Integrated data" is data collected from multiple data sources and converted into a single data set.

[0772] A "machine learning algorithm" is a mathematical technique used to analyze data and generate predictive models.

[0773] "Management improvement proposals" are specific management strategy proposals provided to retailers based on the results of data analysis.

[0774] "Real-time delivery means" refers to techniques and methods for instantly notifying users of generated suggestions.

[0775] This invention is a system that provides effective management improvement proposals to retail stores based on people flow data and payment data collected using mobile phone base stations and payment system APIs. The following hardware and software are used to implement this system.

[0776] Data collection

[0777] The server collects people flow data from mobile phone base stations. Specifically, it uses an API provided by the mobile phone company. Through this API, it obtains real-time information on the movement of mobile phone users in each region, streams the data using AWS Kinesis data streams, and stores it in S3 storage.

[0778] The server also uses the payment system API (e.g. Stripe API) to retrieve payment data, which includes transaction information such as transaction date, transaction amount, and transaction location, and stores it in an AWS RDS database.

[0779] Data Integration and Preprocessing

[0780] The server integrates the collected people flow data and payment data using Python's Pandas library. In the data integration process, the data is matched by time and location and converted into a single dataset. In the preprocessing stage, missing value correction and outlier removal are performed to improve data quality. Apache Spark is used to perform this preprocessing quickly on large amounts of data.

[0781] Data analysis

[0782] The AI ​​tools on the server perform analysis using the integrated data. A deep learning model is built using TensorFlow to analyze patterns of people flow and payments from time-series data. Clustering methods (e.g., K-means) are applied using Scikit-learn to extract customer segments with similar characteristics. Furthermore, an LSTM model is used to predict future demand.

[0783] Generate business improvement proposals

[0784] Based on the analysis results, the server generates specific proposals for business improvement, such as recommendations for new store opening areas, proposals for sales during specific time periods, marketing strategies for specific customer segments, etc. The analysis results are visualized using MPLib and presented to the user on a dashboard.

[0785] Real-time feedback

[0786] The generated suggestions are provided to the user in real time by the server, and the suggestions are notified in real time through a chatbot using the Slack API or Microsoft Teams API, allowing the user to respond immediately.

[0787] Specific examples

[0788] Example 1: Reviewing store locations

[0789] The server analyzes pedestrian flow data collected from mobile phone base stations to detect increases in the number of people in a specific area. Based on the analysis results, it proposes new store openings in that area. For example, it sends a notification saying, "Pedestrian flow around Akihabara Station has increased by 30% on weekends, so please consider opening a new store in the area."

[0790] Example 2: Distributing Target-exclusive coupons

[0791] The server analyzes payment data and determines that the customers who visit the store at certain times and on weekends are primarily students. Based on this information, it generates special coupons for students and makes marketing suggestions in real time. For example, it sends a suggestion such as, "Please issue a 10% off coupon to students on weekends."

[0792] Example 3: Running a Limited-Time Sale

[0793] The server will analyze the data and discover that there are few customers during certain times of the day. For example, it will suggest, "Since the number of customers is low between 2:00 PM and 4:00 PM on weekdays, we recommend that you hold a time sale during this time," and will also explain the specific steps to take.

[0794] Examples of specific prompts include, "Please suggest new store locations based on regional foot traffic data," "Please generate coupons for students who visit on weekends," and "Please suggest time sales to increase customer traffic between 2:00 and 4:00 PM."

[0795] As described above, by combining these methods, retailers can obtain specific management strategies based on data in real time, enabling them to improve efficiency and increase sales.

[0796] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0797] Step 1:

[0798] The server collects people flow data from mobile phone base stations.

[0799] Specifically, the server sends a request to the API provided by the mobile phone company to obtain real-time movement information. This data includes information on people's movements in a specific area. The obtained data is streamed in real time using AWS Kinesis data streams and stored in an S3 bucket.

[0800] Input: Mobile operator API endpoint

[0801] Output: People flow data stored in an S3 bucket

[0802] Step 2:

[0803] The server obtains payment data through the payment system API.

[0804] Specifically, the server sends a request to the payment system's API (e.g., a general payment system's API) to obtain payment information such as transaction date and time, transaction amount, and transaction location. This data is then stored in an AWS RDS database.

[0805] Input: Payment system API endpoint

[0806] Output: Payment data stored in an RDS database

[0807] Step 3:

[0808] The server integrates and pre-processes the collected people flow data and payment data.

[0809] Specifically, the server uses Python's Pandas library to match data by time and location and convert it into a single dataset. Missing values ​​are corrected by estimation from past data and median interpolation, and outliers are also removed. The preprocessed data is then processed quickly as a large-scale dataset using Apache Spark.

[0810] Input: People flow data stored in an S3 bucket, payment data stored in an RDS database

[0811] Output: Integrated and preprocessed dataset

[0812] Step 4:

[0813] The AI ​​means in the server performs data analysis using the integrated data.

[0814] Specifically, the server uses TensorFlow to build a deep learning model and analyze patterns of people flow and payments from time-series data. It also uses Scikit-learn to apply clustering methods (e.g., K-means) to extract customer segments with similar characteristics. It also uses an LSTM model to predict future demand.

[0815] Input: Integrated and preprocessed dataset

[0816] Output: Analysis results (pattern analysis, clustering results, demand forecast)

[0817] Step 5:

[0818] The server generates a management improvement proposal based on the analysis results.

[0819] Specifically, the server uses the analysis results to recommend areas for new store openings, propose sales during specific time periods, and generate marketing strategies for specific customer segments.The analysis results are visualized using MPLib, and the proposals are displayed to the user on a dashboard.

[0820] Input: Analysis results

[0821] Output: Visualized business improvement proposals

[0822] Step 6:

[0823] The server provides the generated suggestions to the user in real time.

[0824] Specifically, the server uses the Slack API or Microsoft Teams API to notify the user of the suggestions through a chatbot, allowing the user to immediately receive and respond to the suggestions.

[0825] Input: Visualized business improvement proposals

[0826] Output: Real-time notifications via chatbots and notification tools

[0827] (Application example 1)

[0828] 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."

[0829] The current retail industry lacks the means to effectively analyze foot traffic and payment data and make immediate management improvement proposals. Store managers are also required to quickly visualize acquired data and take appropriate action in real time, but no system exists that can accurately meet this demand. This can lead to delayed management decisions and the risk of overlooking optimal strategies. Furthermore, specific action proposals are not immediately notified to users, making it difficult to respond in a timely manner.

[0830] 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.

[0831] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating management improvement proposals based on the analysis results, means for providing the generated proposals in real time, means for visually displaying the analysis results, and means for instantly notifying the proposals via push notifications. This enables store managers to obtain specific management strategies based on the data in real time, which can lead to increased sales and more efficient operations.

[0832] "People flow data" refers to information on the movement of people in a specific area.

[0833] "Payment Data" refers to data that includes information about a transaction, such as the date and time of the transaction, the transaction amount, and the location of the transaction.

[0834] "Aggregated People Flow Data" refers to people flow data obtained from cell phone towers and other data sources.

[0835] "Acquired Payment Data" refers to payment data acquired through payment system APIs, etc.

[0836] "Integrated data" refers to the integration of collected people flow data and captured payment data into a single dataset.

[0837] "AI means" refers to means of analyzing data using machine learning algorithms or other artificial intelligence techniques.

[0838] "Management improvement proposals" refer to specific management strategies and measures for retailers that are generated based on the results of data analysis.

[0839] "Means for providing in real time" refers to means for instantly providing generated management improvement proposals to users.

[0840] "Visual display means" refers to means for displaying analysis results and recommendations in a visual format, such as a graph or heat map.

[0841] "Means of immediately notifying users of suggestions via push notifications" refers to means of notifying users of important suggestions in real time via push notifications.

[0842] This invention is a system that provides effective management improvement proposals to retailers by collecting and analyzing foot traffic data and payment data. The system collects data using mobile phone base stations and payment system APIs, and generates and provides specific management strategies in real time through AI analysis. It also has a visual display and push notification function, helping users to take immediate action.

[0843] Hardware and software used

[0844] This system uses the following hardware and software:

[0845] Cell phone base stations: To collect people flow data

[0846] Server: Collects, consolidates, analyzes data, and generates recommendations

[0847] Payment System API: To obtain payment data

[0848] Machine learning algorithms (e.g., LinearRegression from scikit-learn) to perform data analysis

[0849] Visualization tools (e.g., Matplotlib, Pandas): to display the data visually

[0850] Push notification system: to notify you of proposals in real time

[0851] Data processing and calculation

[0852] The server performs the following process:

[0853] 1. Data collection: The server collects people flow data from mobile phone base stations and obtains payment data through the payment system API, including people movement information, transaction date and time, transaction amount, and transaction location.

[0854] 2. Data integration and preprocessing: The server integrates the collected people flow data and the acquired payment data into a single dataset, and performs preprocessing such as correcting missing values ​​and removing outliers.

[0855] 3. Data analysis: Using machine learning algorithms (e.g., Linear Regression in scikit-learn) in the server, the integrated data is analyzed. This includes analyzing foot traffic patterns, sales patterns, and forecasting future demand.

[0856] 4. Generation of business improvement proposals: Based on the analysis results using AI tools, specific business strategies are generated, such as optimal store layout, new store opening areas, optimizing product lineups, marketing strategies to target demographics, and holding limited-time sales.

[0857] 5. Real-time Delivery: Suggestions are provided to users in real-time by the server, including visual graphs, heatmaps, and instant suggestions via push notifications.

[0858] Specific examples

[0859] 1. Review of store locations:

[0860] The server detects an increase in foot traffic data in a specific area and suggests opening a new store in that area.

[0861] 2. Target Exclusive Coupon Distribution:

[0862] Payment data is analyzed, coupons are generated based on the customer demographic that visits the store in large numbers during specific times, and coupons are distributed in real time.

[0863] 3. Limited-Time Deals:

[0864] Analyze the decrease in the number of customers visiting a store during a specific time period and suggest holding a time sale during that time period.

[0865] Prompt Sentence Examples

[0866] "Use this dataset to analyze foot traffic and sales data by time of day and make specific proposals for improving business operations."

[0867] This system allows retailers to obtain specific management strategies based on data in real time, enabling them to improve sales and operate more efficiently.

[0868] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0869] Step 1:

[0870] Data collection

[0871] The server collects data from mobile phone towers and payment system APIs. It obtains people flow data from mobile phone towers and payment data through the payment system API. The input people flow data includes information on people's movements in a specific area, and the payment data includes transaction date and time, transaction amount, transaction location, etc. The output is the raw data temporarily stored on the server.

[0872] Step 2:

[0873] Data Integration and Preprocessing

[0874] The server integrates the collected people flow data and payment data. First, it converts the collected data into a data frame (using the pandas library as an example). Next, it corrects missing values ​​(for example, forward interpolation) and removes outliers based on the data frame. Finally, it outputs an integrated dataset.

[0875] Step 3:

[0876] Data analysis

[0877] The server uses AI tools to analyze the integrated data. Specifically, it uses machine learning algorithms (e.g., Linear Regression in scikit-learn) to analyze foot traffic patterns and sales patterns. It uses the integrated data as input and obtains foot traffic trend data and sales trend data as the output after analysis.

[0878] Step 4:

[0879] Generate business improvement proposals

[0880] The server generates management improvement proposals based on the results of the data analysis. For example, it identifies differences between foot traffic trends and sales trends and determines management strategies. These include proposing areas for new store openings and holding time sales during specific time periods. This results in the output of specific management improvement proposals.

[0881] Step 5:

[0882] Visual Indication

[0883] The server visually displays the analysis results and management improvement proposals. For example, it uses Matplotlib to generate graphs and create heat maps. This allows the statistical data and proposals to be output in a user-friendly format.

[0884] Step 6:

[0885] Real-time notifications

[0886] The server uses push notifications to notify users of important suggestions in real time. Based on the analysis results, immediate business improvement suggestions are generated and sent to users' smartphones or other devices as push notifications. The input is the generated suggestion data, and the output is a notification sending log.

[0887] Through this series of steps, the system provides users with actionable business strategies in real time, maximizing their effectiveness.

[0888] 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.

[0889] This invention utilizes a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers, as well as an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0890] Program processing

[0891] Data collection

[0892] The server periodically obtains people flow data from mobile phone base stations. The people flow data includes information on people's movements in a specific area and their stay by time of day. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction.

[0893] Data Integration and Preprocessing

[0894] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0895] Data analysis

[0896] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to understand which products sell well at which times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0897] Generate business improvement proposals

[0898] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, an emotion engine analyzes the user's emotional state, and adjusts the content and timing of proposals based on the results.

[0899] Real-time feedback

[0900] The generated business improvement proposals are provided to the user in real time by the server via chatbots or email notifications. The proposals are optimized to suit the user's emotional state, as they also incorporate the analysis results of the emotion engine.

[0901] Specific examples

[0902] Example 1: Reviewing store locations

[0903] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0904] Example 2: Distributing Target-exclusive coupons

[0905] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0906] Example 3: Running a Limited-Time Sale

[0907] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0908] In this way, the system of the present invention provides specific management strategies based on data in real time, and by utilizing the emotion engine, it can make more personalized proposals, allowing retailers to improve their management in a data-driven and emotion-sensitive manner.

[0909] The processing flow will be explained below.

[0910] Step 1:

[0911] The server periodically obtains people flow data from mobile phone base stations, which includes information on people's movements in specific areas and their stays by time of day.

[0912] Step 2:

[0913] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[0914] Step 3:

[0915] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[0916] Step 4:

[0917] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[0918] Step 5:

[0919] The server analyzes payment data and sales patterns, which allows it to determine which products are selling at which times and which customer segments are most popular.

[0920] Step 6:

[0921] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0922] Step 7:

[0923] The server uses an emotion engine to analyze the user's emotional state, for example, by analyzing chat logs and behavioral data with customers to evaluate the user's current emotional state.

[0924] Step 8:

[0925] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, the server also takes into account the user's emotional state as determined by an emotion engine, adjusting the content and timing of the proposals.

[0926] Step 9:

[0927] The server provides the generated business improvement proposals to the user in real time via chatbots and email notifications, and since the analysis results of the emotion engine are also reflected, the proposals are optimized to suit the user's emotional state.

[0928] Step 10:

[0929] Users receive suggestions and can then take specific action based on them, such as introducing new products or holding limited-time sales during specific times, as well as improving customer service and following up based on the emotion engine's results.

[0930] Example 2

[0931] 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."

[0932] The current retail industry lacks the means to effectively utilize real-time foot traffic and payment data to improve management. Furthermore, there are no management strategies that take into account the emotional state of customers, making it difficult to improve customer satisfaction.

[0933] 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.

[0934] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results, means for providing the generated proposals in real time, emotion engine means for recognizing the emotional state of the user, and means for adjusting the content and timing of the proposals based on the emotional state of the user. This makes it possible to propose specific business strategies in real time using the people flow data and payment data, and further enables targeted proposals and follow-ups according to the emotional state of the customer, which is expected to improve customer satisfaction.

[0935] "People flow data" refers to data that includes information on people's movements in a specific area and information on their stay by time of day.

[0936] "Payment data" refers to data that includes the date, time, amount, and location of each transaction.

[0937] "Integrated data" refers to data that has been converted from collected people flow data and acquired payment data into a single dataset, with the format standardized and missing and outlier values ​​corrected.

[0938] "AI methods" are methods that use machine learning algorithms and data analysis tools to analyze integrated data and analyze traffic patterns and sales patterns.

[0939] "Management improvement proposals" are specific proposals generated based on the analysis results, such as selecting areas for new store openings, reviewing product lineups, and holding limited-time sales.

[0940] The "emotion engine means" is a means for recognizing the user's emotional state and adjusting the content and timing of suggestions based on the results.

[0941] "Means of providing in real time" refers to means of providing the generated business improvement proposals to users in real time via chatbots or email notifications.

[0942] This invention is a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers. It also uses an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[0943] The server implements the system mainly using the following hardware and software:

[0944] 1. Cell phone base stations:

[0945] People flow data is periodically collected from mobile phone base stations, and includes information on people's movements in specific areas and their stays by time of day.

[0946] 2. Payment System API:

[0947] Use payment system APIs to retrieve payment data, including the date, time, amount, and location of each transaction.

[0948] 3. Data integration capabilities:

[0949] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[0950] 4. AI analysis:

[0951] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns, determining which products sell well at which times and which customer demographics are most popular.

[0952] 5. Machine learning algorithms:

[0953] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[0954] 6. Emotion Engine:

[0955] The emotional engine analyzes the user's emotional state and adjusts the content and timing of suggestions based on the results.

[0956] The generated business improvement proposals are provided to the user in real time by the server via chatbots and email notifications, and the analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state.

[0957] Specific examples

[0958] Example 1: Reviewing store locations

[0959] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[0960] Example 2: Distributing Target-exclusive coupons

[0961] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[0962] Example 3: Running a Limited-Time Sale

[0963] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[0964] Prompt Sentence Examples

[0965] "Please explain the specific steps of a program that uses mobile phone tower and payment system APIs to collect data and provide real-time recommendations for selecting new store locations based on the analysis results. Also, please explain how an emotion engine is used to analyze the user's emotional state and adjust the recommendations based on the results."

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] Step 1:

[0968] Data collection

[0969] The server periodically obtains people flow data from mobile phone base stations. This people flow data includes information on people's movements in specific areas and their stay information by time period. The input is the people flow data from the mobile phone base stations, and the output is raw data stored in the server. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction. The input is payment data from the payment system API, and the output is raw data stored in the server.

[0970] The specific operation is as follows:

[0971] The server periodically accesses mobile phone base stations to obtain people flow data and stores it in an internal database.

[0972] The server calls the payment system API, retrieves the payment data, and stores it in an internal database.

[0973] Step 2:

[0974] Data Integration and Preprocessing

[0975] The server integrates the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is unified and missing and outliers are corrected. The input is the people flow data and payment data collected in step 1, and the output is the preprocessed integrated data.

[0976] The specific operation is as follows:

[0977] The server retrieves the collected people flow data and payment data from the database.

[0978] The server performs data preprocessing to format the data, impute missing values, and correct outliers.

[0979] The server integrates the curated data and generates a final integrated dataset.

[0980] Step 3:

[0981] Data analysis

[0982] The AI ​​tools within the server use the integrated data to analyze people flow patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to determine which products sell at what times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand. The input is the integrated data, and the output is pattern recognition and demand forecasts as the analysis results.

[0983] The specific operation is as follows:

[0984] The server performs AI analysis based on the integrated data.

[0985] The server uses the integrated data to detect patterns of people flow in specific areas and time periods.

[0986] The server analyzes the payment data and identifies sales patterns.

[0987] The server uses machine learning algorithms to predict future demand based on past data.

[0988] Step 4:

[0989] Generate business improvement proposals

[0990] The server generates business improvement proposals based on the analysis results. Proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. The server also analyzes the user's emotional state using an emotion engine, and adjusts the content and timing of proposals based on the results. The input is the analysis results, and the output is the generated proposals.

[0991] The specific operation is as follows:

[0992] The server automatically generates business improvement proposals based on the analysis results.

[0993] The proposals include selecting a store location, reviewing the product lineup, and holding limited-time sales.

[0994] The server optimizes the content and timing of proposals based on the results of the emotion engine.

[0995] Step 5:

[0996] Real-time feedback

[0997] The generated business improvement proposals are provided to the user in real time by the server. This is done via chatbots or email notifications. The analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state. The input is the generated proposal, and the output is the feedback the user receives.

[0998] The specific operation is as follows:

[0999] The server sends the generated suggestions to a chatbot or email notification system.

[1000] Provide users with real-time suggestions through chatbots and email notifications.

[1001] The suggestions are optimized based on the results of the emotion engine, providing feedback that matches the user's emotional state.

[1002] (Application example 2)

[1003] 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."

[1004] Conventional retail store management systems make management improvement proposals based solely on foot traffic data and payment data, but they are unable to consider the emotional state of customers and are only able to make proposals that lack individuality. Furthermore, there are only a limited number of systems available for making management improvement proposals in real time in brick-and-mortar stores, making it difficult to immediately improve store management. The present invention aims to solve these problems and provide a system that provides highly accurate management improvement proposals in real time.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1006] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data, the acquired payment data, and customer emotional data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results and the emotional state of the customer, and means for providing the generated proposals in real time. This makes it possible to make advanced business improvement proposals tailored to the emotional state of the customer in real time.

[1007] "People flow data" refers to data that indicates information on the movement and stay of people in a specific area.

[1008] "Payment Data" means data about payments, including the date, time, amount, and location of each transaction.

[1009] "Emotion data" refers to data that includes the results of analyzing a customer's emotional state based on their facial expressions and behavior.

[1010] "Integrated data" refers to data that has been collected, including people flow data, payment data, and emotion data, converted into a unified format and compiled into a single dataset.

[1011] "AI means" refers to artificial intelligence algorithms that analyze integrated data, visualize patterns, and forecast future demand.

[1012] "Management improvement proposals" are proposals based on the analysis results that propose specific management strategies such as selecting areas for new store openings, reviewing product lineups, and holding time sales during specific time periods.

[1013] "Means of providing in real time" refers to means of instantly providing generated business improvement proposals to users via chatbots or email notifications.

[1014] This invention is a system that collects and analyzes people flow data, payment data, and customer emotion data, and provides effective management improvement proposals to retail stores. This system includes the following elements.

[1015] System Configuration

[1016] 1. Data Collection

[1017] The server collects people flow data through mobile phone base stations, thereby obtaining information on people's movements in specific areas and their stays by time period. At the same time, it obtains payment data using payment system APIs (e.g., Stripe and PayPal). Furthermore, it uses facial recognition technology to collect customers' emotional states from in-store camera feeds. This is achieved using facial recognition services such as Amazon Rekognition and Microsoft Azure Cognitive Services.

[1018] 2. Data integration and preprocessing

[1019] The server then integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format, correcting for missing or outlier values ​​at this stage.

[1020] 3. Data Analysis

[1021] The AI ​​tools within the server will use the integrated data to analyze foot traffic patterns. Predictive models using machine learning algorithms will be used to understand foot traffic trends in specific areas and time periods. Furthermore, payment data will be analyzed to analyze sales patterns and understand sales performance. Machine learning models from Azure or AWS may be used.

[1022] 4. Generate business improvement proposals

[1023] Based on the analysis results obtained by AI tools, specific business improvement proposals are generated, such as selecting new store locations, reviewing product lineups according to target demographics, and holding time sales during specific time periods. The content and timing of proposals are optimized based on the emotional state of customers.

[1024] 5. Real-time feedback

[1025] The generated suggestions are provided to users in real time, and information is subsequently sent via chatbots and email notifications, allowing employees and managers to take immediate action.

[1026] Specific examples

[1027] For example, if a certain area shows an increase in foot traffic but there are few competing stores in the area, the server will use this information to suggest new store openings to users.Also, if the majority of customers visiting on weekends are students and emotional data indicates low satisfaction, the server will suggest providing special coupons for students in real time.

[1028] Prompt Sentence Examples

[1029] You might prompt your generative AI model with the following:

[1030] Based on payment data and sentiment analysis data, please suggest a business strategy to improve customer experience. In particular, the majority of customers are young, and sentiment data indicates that they are not very satisfied. What improvement measures should be taken?

[1031] In this way, by implementing this invention, it becomes possible to make sophisticated business improvement proposals in real time that are tailored to the emotional state of the customer.

[1032] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1033] Step 1:

[1034] The server collects people flow data from mobile phone base stations. Specifically, it sends API requests to obtain information on people's movements by region and their stay by time of day. The input is the base station ID, and the output is people flow data.

[1035] Step 2:

[1036] The server collects payment data using the payment system API. Specifically, it accesses the payment system API to obtain the transaction date, time, amount, and location. The input is the API key, and the output is the payment data.

[1037] Step 3:

[1038] The server collects customer emotional states from in-store camera feeds using facial recognition technology. Specifically, it uses Amazon Rekognition and Microsoft Azure Cognitive Services to analyze emotional data from images. Image data is taken as input and emotional data is taken as output.

[1039] Step 4:

[1040] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format. This stage also corrects missing and outlier values. Multiple datasets are input, and the integrated data is output.

[1041] Step 5:

[1042] The AI ​​in the server uses the integrated data to analyze people flow patterns. It applies machine learning algorithms to understand people's movements in specific areas and time periods. The input is the integrated data and the output is the analysis results.

[1043] Step 6:

[1044] The server analyzes payment data and analyzes sales patterns. It uses AWS and Azure machine learning services for data analysis to understand production performance. The input is payment data and the output is sales patterns.

[1045] Step 7:

[1046] Based on the analysis results and emotional data obtained by AI, the server generates management improvement proposals such as selecting new store locations, reviewing product lineups, and holding time sales during specific time periods. The inputs are the analysis results and emotional data, and the output is management improvement proposals.

[1047] Step 8:

[1048] The server provides the generated proposals to the user in real time. Specifically, it sends the proposals via chatbot or email notification so that the store manager can respond immediately. The input is the business improvement proposal, and the output is a notification to the user.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] [Fourth embodiment]

[1053] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1054] 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.

[1055] 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).

[1056] 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.

[1057] 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.

[1058] 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).

[1059] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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."

[1066] This invention is a system that collects and analyzes people flow data and payment data, and provides effective management improvement proposals to retailers. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time.

[1067] Program processing

[1068] Data collection

[1069] The server collects people flow data from mobile phone towers, which includes information on people's movements in a specific area. The server also obtains payment data through the payment system API, which includes transaction date and time, transaction amount, transaction location, etc.

[1070] Data Integration and Preprocessing

[1071] The server integrates the collected people flow data and the acquired payment data into a single dataset, where it performs preprocessing such as correcting missing values ​​and removing outliers.

[1072] Data analysis

[1073] The AI ​​tools within the server analyze the integrated data, including analyzing foot traffic patterns using machine learning algorithms, analyzing sales patterns based on payment data, and forecasting future demand.

[1074] Generate business improvement proposals

[1075] Based on the analysis results, the server generates specific management improvement proposals for retailers, including optimal store layout, new store opening areas, product lineup optimization, marketing strategies for target demographics, and limited-time sales.

[1076] Real-time feedback

[1077] The generated business improvement proposals are provided to the user in real time by the server, via chatbots and other notification methods, allowing for immediate response.

[1078] Specific examples

[1079] Example 1: Reviewing store locations

[1080] This section explains the case where a proposal is made to consider opening a new store based on people flow data in a certain area. The server analyzes the people flow data to detect an increase in the number of people in a specific area. Based on this result, the server proposes to the user to open a new store in the same area.

[1081] Example 2: Distributing Target-exclusive coupons

[1082] Let's say you analyze payment data and determine that the customers who visit on weekends are students. The server uses this information to create special coupons for students and propose them to users in real time.

[1083] Example 3: Running a Limited-Time Sale

[1084] Let's consider a case where a decrease in the number of customers during a specific time period is analyzed and a time sale is held during that time period. The server analyzes the data and finds that there are few customers between 2:00 PM and 4:00 PM on weekdays, and suggests to the user that a time sale be held during this time period.

[1085] The above is an embodiment of the present invention. This system allows retailers to obtain specific management strategies based on data in real time, thereby improving sales and efficiency.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] The server periodically collects people flow data from mobile phone base stations, including information on people's movements by region and their stay by time of day.

[1089] Step 2:

[1090] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[1091] Step 3:

[1092] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[1093] Step 4:

[1094] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[1095] Step 5:

[1096] The server analyzes payment data and analyzes sales patterns, which allows it to determine which products sell well at what times and which customer segments are most popular.

[1097] Step 6:

[1098] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[1099] Step 7:

[1100] Based on the analysis results, the server generates business improvement proposals, such as proposing new store locations, revising product lineups for target demographics, and proposing limited-time sales during specific times.

[1101] Step 8:

[1102] The server provides the generated business improvement proposals to the user in real time via chatbots, email notifications, etc.

[1103] Step 9:

[1104] Users receive suggestions and then take specific measures based on them, such as introducing new products or holding limited-time sales during specific times.

[1105] Example 1

[1106] 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."

[1107] In conventional retail store management, effective use of foot traffic data and payment data has not been fully implemented, resulting in a lack of means to propose appropriate management improvement measures in real time. This has made it difficult to efficiently formulate strategies for store layout, marketing, and sales improvement. The present invention aims to solve this problem by providing a system that provides specific data-based management strategies for retail stores in real time.

[1108] 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.

[1109] In this invention, the server includes means for collecting people flow data from mobile phone base stations, means for acquiring payment data through a payment system API, and means for integrating and preprocessing the collected people flow data and the acquired payment data. This makes it possible to efficiently collect and integrate data, correct missing values, and remove outliers, and then provide appropriate management improvement proposals in real time.

[1110] A "mobile phone base station" is a basic infrastructure facility used to collect location information from mobile phone users.

[1111] "People flow data" is data that includes information on the movement of people in a specific area.

[1112] A "payment system API" is an application programming interface for providing payment data to other systems.

[1113] "Payment data" refers to data related to payments, including transaction date and time, transaction amount, transaction location, etc.

[1114] "Preprocessing" refers to the process of correcting missing data values, removing outliers, and preparing the data in a format suitable for analysis.

[1115] "Integrated data" is data collected from multiple data sources and converted into a single data set.

[1116] A "machine learning algorithm" is a mathematical technique used to analyze data and generate predictive models.

[1117] "Management improvement proposals" are specific management strategy proposals provided to retailers based on the results of data analysis.

[1118] "Real-time delivery means" refers to techniques and methods for instantly notifying users of generated suggestions.

[1119] This invention is a system that provides effective management improvement proposals to retail stores based on people flow data and payment data collected using mobile phone base stations and payment system APIs. The following hardware and software are used to implement this system.

[1120] Data collection

[1121] The server collects people flow data from mobile phone base stations. Specifically, it uses an API provided by the mobile phone company. Through this API, it obtains real-time information on the movement of mobile phone users in each region, streams the data using AWS Kinesis data streams, and stores it in S3 storage.

[1122] The server also uses the payment system API (e.g. Stripe API) to retrieve payment data, which includes transaction information such as transaction date, transaction amount, and transaction location, and stores it in an AWS RDS database.

[1123] Data Integration and Preprocessing

[1124] The server integrates the collected people flow data and payment data using Python's Pandas library. In the data integration process, the data is matched by time and location and converted into a single dataset. In the preprocessing stage, missing value correction and outlier removal are performed to improve data quality. Apache Spark is used to perform this preprocessing quickly on large amounts of data.

[1125] Data analysis

[1126] The AI ​​tools on the server perform analysis using the integrated data. A deep learning model is built using TensorFlow to analyze patterns of people flow and payments from time-series data. Clustering methods (e.g., K-means) are applied using Scikit-learn to extract customer segments with similar characteristics. Furthermore, an LSTM model is used to predict future demand.

[1127] Generate business improvement proposals

[1128] Based on the analysis results, the server generates specific proposals for business improvement, such as recommendations for new store opening areas, proposals for sales during specific time periods, marketing strategies for specific customer segments, etc. The analysis results are visualized using MPLib and presented to the user on a dashboard.

[1129] Real-time feedback

[1130] The generated suggestions are provided to the user in real time by the server, and the suggestions are notified in real time through a chatbot using the Slack API or Microsoft Teams API, allowing the user to respond immediately.

[1131] Specific examples

[1132] Example 1: Reviewing store locations

[1133] The server analyzes pedestrian flow data collected from mobile phone base stations to detect increases in the number of people in a specific area. Based on the analysis results, it proposes new store openings in that area. For example, it sends a notification saying, "Pedestrian flow around Akihabara Station has increased by 30% on weekends, so please consider opening a new store in the area."

[1134] Example 2: Distributing Target-exclusive coupons

[1135] The server analyzes payment data and determines that the customers who visit the store at certain times and on weekends are primarily students. Based on this information, it generates special coupons for students and makes marketing suggestions in real time. For example, it sends a suggestion such as, "Please issue a 10% off coupon to students on weekends."

[1136] Example 3: Running a Limited-Time Sale

[1137] The server will analyze the data and discover that there are few customers during certain times of the day. For example, it will suggest, "Since the number of customers is low between 2:00 PM and 4:00 PM on weekdays, we recommend that you hold a time sale during this time," and will also explain the specific steps to take.

[1138] Examples of specific prompts include, "Please suggest new store locations based on regional foot traffic data," "Please generate coupons for students who visit on weekends," and "Please suggest time sales to increase customer traffic between 2:00 and 4:00 PM."

[1139] As described above, by combining these methods, retailers can obtain specific management strategies based on data in real time, enabling them to improve efficiency and increase sales.

[1140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1141] Step 1:

[1142] The server collects people flow data from mobile phone base stations.

[1143] Specifically, the server sends a request to the API provided by the mobile phone company to obtain real-time movement information. This data includes information on people's movements in a specific area. The obtained data is streamed in real time using AWS Kinesis data streams and stored in an S3 bucket.

[1144] Input: Mobile operator API endpoint

[1145] Output: People flow data stored in an S3 bucket

[1146] Step 2:

[1147] The server obtains payment data through the payment system API.

[1148] Specifically, the server sends a request to the payment system's API (e.g., a general payment system's API) to obtain payment information such as transaction date and time, transaction amount, and transaction location. This data is then stored in an AWS RDS database.

[1149] Input: Payment system API endpoint

[1150] Output: Payment data stored in an RDS database

[1151] Step 3:

[1152] The server integrates and pre-processes the collected people flow data and payment data.

[1153] Specifically, the server uses Python's Pandas library to match data by time and location and convert it into a single dataset. Missing values ​​are corrected by estimation from past data and median interpolation, and outliers are also removed. The preprocessed data is then processed quickly as a large-scale dataset using Apache Spark.

[1154] Input: People flow data stored in an S3 bucket, payment data stored in an RDS database

[1155] Output: Integrated and preprocessed dataset

[1156] Step 4:

[1157] The AI ​​means in the server performs data analysis using the integrated data.

[1158] Specifically, the server uses TensorFlow to build a deep learning model and analyze patterns of people flow and payments from time-series data. It also uses Scikit-learn to apply clustering methods (e.g., K-means) to extract customer segments with similar characteristics. It also uses an LSTM model to predict future demand.

[1159] Input: Integrated and preprocessed dataset

[1160] Output: Analysis results (pattern analysis, clustering results, demand forecast)

[1161] Step 5:

[1162] The server generates a management improvement proposal based on the analysis results.

[1163] Specifically, the server uses the analysis results to recommend areas for new store openings, propose sales during specific time periods, and generate marketing strategies for specific customer segments.The analysis results are visualized using MPLib, and the proposals are displayed to the user on a dashboard.

[1164] Input: Analysis results

[1165] Output: Visualized business improvement proposals

[1166] Step 6:

[1167] The server provides the generated suggestions to the user in real time.

[1168] Specifically, the server uses the Slack API or Microsoft Teams API to notify the user of the suggestions through a chatbot, allowing the user to immediately receive and respond to the suggestions.

[1169] Input: Visualized business improvement proposals

[1170] Output: Real-time notifications via chatbots and notification tools

[1171] (Application example 1)

[1172] 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."

[1173] The current retail industry lacks the means to effectively analyze foot traffic and payment data and make immediate management improvement proposals. Store managers are also required to quickly visualize acquired data and take appropriate action in real time, but no system exists that can accurately meet this demand. This can lead to delayed management decisions and the risk of overlooking optimal strategies. Furthermore, specific action proposals are not immediately notified to users, making it difficult to respond in a timely manner.

[1174] 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.

[1175] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating management improvement proposals based on the analysis results, means for providing the generated proposals in real time, means for visually displaying the analysis results, and means for instantly notifying the proposals via push notifications. This enables store managers to obtain specific management strategies based on the data in real time, which can lead to increased sales and more efficient operations.

[1176] "People flow data" refers to information on the movement of people in a specific area.

[1177] "Payment Data" refers to data that includes information about a transaction, such as the date and time of the transaction, the transaction amount, and the location of the transaction.

[1178] "Aggregated People Flow Data" refers to people flow data obtained from cell phone towers and other data sources.

[1179] "Acquired Payment Data" refers to payment data acquired through payment system APIs, etc.

[1180] "Integrated data" refers to the integration of collected people flow data and captured payment data into a single dataset.

[1181] "AI means" refers to means of analyzing data using machine learning algorithms or other artificial intelligence techniques.

[1182] "Management improvement proposals" refer to specific management strategies and measures for retailers that are generated based on the results of data analysis.

[1183] "Means for providing in real time" refers to means for instantly providing generated management improvement proposals to users.

[1184] "Visual display means" refers to means for displaying analysis results and recommendations in a visual format, such as a graph or heat map.

[1185] "Means of immediately notifying users of suggestions via push notifications" refers to means of notifying users of important suggestions in real time via push notifications.

[1186] This invention is a system that provides effective management improvement proposals to retailers by collecting and analyzing foot traffic data and payment data. The system collects data using mobile phone base stations and payment system APIs, and generates and provides specific management strategies in real time through AI analysis. It also has a visual display and push notification function, helping users to take immediate action.

[1187] Hardware and software used

[1188] This system uses the following hardware and software:

[1189] Cell phone base stations: To collect people flow data

[1190] Server: Collects, consolidates, analyzes data, and generates recommendations

[1191] Payment System API: To obtain payment data

[1192] Machine learning algorithms (e.g., LinearRegression from scikit-learn) to perform data analysis

[1193] Visualization tools (e.g., Matplotlib, Pandas): to display the data visually

[1194] Push notification system: to notify you of proposals in real time

[1195] Data processing and calculation

[1196] The server performs the following process:

[1197] 1. Data collection: The server collects people flow data from mobile phone base stations and obtains payment data through the payment system API, including people movement information, transaction date and time, transaction amount, and transaction location.

[1198] 2. Data integration and preprocessing: The server integrates the collected people flow data and the acquired payment data into a single dataset, and performs preprocessing such as correcting missing values ​​and removing outliers.

[1199] 3. Data analysis: Using machine learning algorithms (e.g., Linear Regression in scikit-learn) in the server, the integrated data is analyzed. This includes analyzing foot traffic patterns, sales patterns, and forecasting future demand.

[1200] 4. Generation of business improvement proposals: Based on the analysis results using AI tools, specific business strategies are generated, such as optimal store layout, new store opening areas, optimizing product lineups, marketing strategies to target demographics, and holding limited-time sales.

[1201] 5. Real-time Delivery: Suggestions are provided to users in real-time by the server, including visual graphs, heatmaps, and instant suggestions via push notifications.

[1202] Specific examples

[1203] 1. Review of store locations:

[1204] The server detects an increase in foot traffic data in a specific area and suggests opening a new store in that area.

[1205] 2. Target Exclusive Coupon Distribution:

[1206] Payment data is analyzed, coupons are generated based on the customer demographic that visits the store in large numbers during specific times, and coupons are distributed in real time.

[1207] 3. Limited-Time Deals:

[1208] Analyze the decrease in the number of customers visiting a store during a specific time period and suggest holding a time sale during that time period.

[1209] Prompt Sentence Examples

[1210] "Use this dataset to analyze foot traffic and sales data by time of day and make specific proposals for improving business operations."

[1211] This system allows retailers to obtain specific management strategies based on data in real time, enabling them to improve sales and operate more efficiently.

[1212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1213] Step 1:

[1214] Data collection

[1215] The server collects data from mobile phone towers and payment system APIs. It obtains people flow data from mobile phone towers and payment data through the payment system API. The input people flow data includes information on people's movements in a specific area, and the payment data includes transaction date and time, transaction amount, transaction location, etc. The output is the raw data temporarily stored on the server.

[1216] Step 2:

[1217] Data Integration and Preprocessing

[1218] The server integrates the collected people flow data and payment data. First, it converts the collected data into a data frame (using the pandas library as an example). Next, it corrects missing values ​​(for example, forward interpolation) and removes outliers based on the data frame. Finally, it outputs an integrated dataset.

[1219] Step 3:

[1220] Data analysis

[1221] The server uses AI tools to analyze the integrated data. Specifically, it uses machine learning algorithms (e.g., Linear Regression in scikit-learn) to analyze foot traffic patterns and sales patterns. It uses the integrated data as input and obtains foot traffic trend data and sales trend data as the output after analysis.

[1222] Step 4:

[1223] Generate business improvement proposals

[1224] The server generates management improvement proposals based on the results of the data analysis. For example, it identifies differences between foot traffic trends and sales trends and determines management strategies. These include proposing areas for new store openings and holding time sales during specific time periods. This results in the output of specific management improvement proposals.

[1225] Step 5:

[1226] Visual Indication

[1227] The server visually displays the analysis results and management improvement proposals. For example, it uses Matplotlib to generate graphs and create heat maps. This allows the statistical data and proposals to be output in a user-friendly format.

[1228] Step 6:

[1229] Real-time notifications

[1230] The server uses push notifications to notify users of important suggestions in real time. Based on the analysis results, immediate business improvement suggestions are generated and sent to users' smartphones or other devices as push notifications. The input is the generated suggestion data, and the output is a notification sending log.

[1231] Through this series of steps, the system provides users with actionable business strategies in real time, maximizing their effectiveness.

[1232] 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.

[1233] This invention utilizes a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers, as well as an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[1234] Program processing

[1235] Data collection

[1236] The server periodically obtains people flow data from mobile phone base stations. The people flow data includes information on people's movements in a specific area and their stay by time of day. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction.

[1237] Data Integration and Preprocessing

[1238] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[1239] Data analysis

[1240] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to understand which products sell well at which times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[1241] Generate business improvement proposals

[1242] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, an emotion engine analyzes the user's emotional state, and adjusts the content and timing of proposals based on the results.

[1243] Real-time feedback

[1244] The generated business improvement proposals are provided to the user in real time by the server via chatbots or email notifications. The proposals are optimized to suit the user's emotional state, as they also incorporate the analysis results of the emotion engine.

[1245] Specific examples

[1246] Example 1: Reviewing store locations

[1247] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[1248] Example 2: Distributing Target-exclusive coupons

[1249] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[1250] Example 3: Running a Limited-Time Sale

[1251] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[1252] In this way, the system of the present invention provides specific management strategies based on data in real time, and by utilizing the emotion engine, it can make more personalized proposals, allowing retailers to improve their management in a data-driven and emotion-sensitive manner.

[1253] The processing flow will be explained below.

[1254] Step 1:

[1255] The server periodically obtains people flow data from mobile phone base stations, which includes information on people's movements in specific areas and their stays by time of day.

[1256] Step 2:

[1257] The server uses the payment system API to retrieve payment data, which includes the date, time, amount, and location of each transaction.

[1258] Step 3:

[1259] The server integrates the collected people flow data and payment data. At this stage, the data format is standardized and missing and outliers are corrected.

[1260] Step 4:

[1261] The AI ​​in the server uses the integrated data to analyze people flow patterns and understand people's movements in specific areas and time periods.

[1262] Step 5:

[1263] The server analyzes payment data and sales patterns, which allows it to determine which products are selling at which times and which customer segments are most popular.

[1264] Step 6:

[1265] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[1266] Step 7:

[1267] The server uses an emotion engine to analyze the user's emotional state, for example, by analyzing chat logs and behavioral data with customers to evaluate the user's current emotional state.

[1268] Step 8:

[1269] The server generates business improvement proposals based on the analysis results. These proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. Furthermore, the server also takes into account the user's emotional state as determined by an emotion engine, adjusting the content and timing of the proposals.

[1270] Step 9:

[1271] The server provides the generated business improvement proposals to the user in real time via chatbots and email notifications, and since the analysis results of the emotion engine are also reflected, the proposals are optimized to suit the user's emotional state.

[1272] Step 10:

[1273] Users receive suggestions and can then take specific action based on them, such as introducing new products or holding limited-time sales during specific times, as well as improving customer service and following up based on the emotion engine's results.

[1274] Example 2

[1275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] The current retail industry lacks the means to effectively utilize real-time foot traffic and payment data to improve management. Furthermore, there are no management strategies that take into account the emotional state of customers, making it difficult to improve customer satisfaction.

[1277] 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.

[1278] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data and the acquired payment data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results, means for providing the generated proposals in real time, emotion engine means for recognizing the emotional state of the user, and means for adjusting the content and timing of the proposals based on the emotional state of the user. This makes it possible to propose specific business strategies in real time using the people flow data and payment data, and further enables targeted proposals and follow-ups according to the emotional state of the customer, which is expected to improve customer satisfaction.

[1279] "People flow data" refers to data that includes information on people's movements in a specific area and information on their stay by time of day.

[1280] "Payment data" refers to data that includes the date, time, amount, and location of each transaction.

[1281] "Integrated data" refers to data that has been converted from collected people flow data and acquired payment data into a single dataset, with the format standardized and missing and outlier values ​​corrected.

[1282] "AI methods" are methods that use machine learning algorithms and data analysis tools to analyze integrated data and analyze traffic patterns and sales patterns.

[1283] "Management improvement proposals" are specific proposals generated based on the analysis results, such as selecting areas for new store openings, reviewing product lineups, and holding limited-time sales.

[1284] The "emotion engine means" is a means for recognizing the user's emotional state and adjusting the content and timing of suggestions based on the results.

[1285] "Means of providing in real time" refers to means of providing the generated business improvement proposals to users in real time via chatbots or email notifications.

[1286] This invention is a system that collects and analyzes people flow data and payment data to provide effective management improvement proposals to retailers. It also uses an emotion engine that recognizes user emotions. This system collects data using mobile phone base stations and payment system APIs, analyzes the data using AI, and provides specific management strategies in real time. The emotion engine also analyzes the user's emotional state and makes proposals based on that.

[1287] The server implements the system mainly using the following hardware and software:

[1288] 1. Cell phone base stations:

[1289] People flow data is periodically collected from mobile phone base stations, and includes information on people's movements in specific areas and their stays by time of day.

[1290] 2. Payment System API:

[1291] Use payment system APIs to retrieve payment data, including the date, time, amount, and location of each transaction.

[1292] 3. Data integration capabilities:

[1293] The server combines the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is standardized and missing and outliers are corrected.

[1294] 4. AI analysis:

[1295] The AI ​​in the server uses the integrated data to analyze foot traffic patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns, determining which products sell well at which times and which customer demographics are most popular.

[1296] 5. Machine learning algorithms:

[1297] The server uses machine learning algorithms to predict future demand, enabling optimal resource allocation and inventory management.

[1298] 6. Emotion Engine:

[1299] The emotional engine analyzes the user's emotional state and adjusts the content and timing of suggestions based on the results.

[1300] The generated business improvement proposals are provided to the user in real time by the server via chatbots and email notifications, and the analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state.

[1301] Specific examples

[1302] Example 1: Reviewing store locations

[1303] If the flow of people in a certain area is increasing but there are few competing stores in the area, the server will use this information to suggest new store openings to the user.

[1304] Example 2: Distributing Target-exclusive coupons

[1305] If payment data reveals that many of the weekend customers are students, the server will create special coupons for students and provide them to users in real time. If the emotion engine also recognizes that customers are not satisfied, it will suggest follow-up measures accordingly.

[1306] Example 3: Running a Limited-Time Sale

[1307] If the analysis reveals that there are few customers during a particular time period, the server will suggest to the user that a limited-time sale be held during that time period.Furthermore, if the emotion engine determines that the user is in a bad mood, it will suggest adding an incentive to the limited-time sale during that time period.

[1308] Prompt Sentence Examples

[1309] "Please explain the specific steps of a program that uses mobile phone tower and payment system APIs to collect data and provide real-time recommendations for selecting new store locations based on the analysis results. Also, please explain how an emotion engine is used to analyze the user's emotional state and adjust the recommendations based on the results."

[1310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] Data collection

[1313] The server periodically obtains people flow data from mobile phone base stations. This people flow data includes information on people's movements in specific areas and their stay information by time period. The input is the people flow data from the mobile phone base stations, and the output is raw data stored in the server. The server also obtains payment data using a payment system API. The payment data includes the date, time, amount, and location of each transaction. The input is payment data from the payment system API, and the output is raw data stored in the server.

[1314] The specific operation is as follows:

[1315] The server periodically accesses mobile phone base stations to obtain people flow data and stores it in an internal database.

[1316] The server calls the payment system API, retrieves the payment data, and stores it in an internal database.

[1317] Step 2:

[1318] Data Integration and Preprocessing

[1319] The server integrates the collected people flow data and payment data and converts them into a single dataset. At this stage, the data format is unified and missing and outliers are corrected. The input is the people flow data and payment data collected in step 1, and the output is the preprocessed integrated data.

[1320] The specific operation is as follows:

[1321] The server retrieves the collected people flow data and payment data from the database.

[1322] The server performs data preprocessing to format the data, impute missing values, and correct outliers.

[1323] The server integrates the curated data and generates a final integrated dataset.

[1324] Step 3:

[1325] Data analysis

[1326] The AI ​​tools within the server use the integrated data to analyze people flow patterns, understanding people's movements in specific areas and time periods. The server also analyzes payment data and analyzes sales patterns. This allows it to determine which products sell at what times and which customer demographics are most popular. It also uses machine learning algorithms to predict future demand. The input is the integrated data, and the output is pattern recognition and demand forecasts as the analysis results.

[1327] The specific operation is as follows:

[1328] The server performs AI analysis based on the integrated data.

[1329] The server uses the integrated data to detect patterns of people flow in specific areas and time periods.

[1330] The server analyzes the payment data and identifies sales patterns.

[1331] The server uses machine learning algorithms to predict future demand based on past data.

[1332] Step 4:

[1333] Generate business improvement proposals

[1334] The server generates business improvement proposals based on the analysis results. Proposals include selecting areas for new store openings, revising product lineups to suit target demographics, and holding time sales during specific time periods. The server also analyzes the user's emotional state using an emotion engine, and adjusts the content and timing of proposals based on the results. The input is the analysis results, and the output is the generated proposals.

[1335] The specific operation is as follows:

[1336] The server automatically generates business improvement proposals based on the analysis results.

[1337] The proposals include selecting a store location, reviewing the product lineup, and holding limited-time sales.

[1338] The server optimizes the content and timing of proposals based on the results of the emotion engine.

[1339] Step 5:

[1340] Real-time feedback

[1341] The generated business improvement proposals are provided to the user in real time by the server. This is done via chatbots or email notifications. The analysis results of the emotion engine are also reflected, so the proposals are optimized to suit the user's emotional state. The input is the generated proposal, and the output is the feedback the user receives.

[1342] The specific operation is as follows:

[1343] The server sends the generated suggestions to a chatbot or email notification system.

[1344] Provide users with real-time suggestions through chatbots and email notifications.

[1345] The suggestions are optimized based on the results of the emotion engine, providing feedback that matches the user's emotional state.

[1346] (Application example 2)

[1347] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1348] Conventional retail store management systems make management improvement proposals based solely on foot traffic data and payment data, but they are unable to consider the emotional state of customers and are only able to make proposals that lack individuality. Furthermore, there are only a limited number of systems available for making management improvement proposals in real time in brick-and-mortar stores, making it difficult to immediately improve store management. The present invention aims to solve these problems and provide a system that provides highly accurate management improvement proposals in real time.

[1349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1350] In this invention, the server includes means for collecting people flow data, means for acquiring payment data, means for integrating the collected people flow data, the acquired payment data, and customer emotional data, AI means for analyzing the integrated data, means for generating business improvement proposals based on the analysis results and the emotional state of the customer, and means for providing the generated proposals in real time. This makes it possible to make advanced business improvement proposals tailored to the emotional state of the customer in real time.

[1351] "People flow data" refers to data that indicates information on the movement and stay of people in a specific area.

[1352] "Payment Data" means data about payments, including the date, time, amount, and location of each transaction.

[1353] "Emotion data" refers to data that includes the results of analyzing a customer's emotional state based on their facial expressions and behavior.

[1354] "Integrated data" refers to data that has been collected, including people flow data, payment data, and emotion data, converted into a unified format and compiled into a single dataset.

[1355] "AI means" refers to artificial intelligence algorithms that analyze integrated data, visualize patterns, and forecast future demand.

[1356] "Management improvement proposals" are proposals based on the analysis results that propose specific management strategies such as selecting areas for new store openings, reviewing product lineups, and holding time sales during specific time periods.

[1357] "Means of providing in real time" refers to means of instantly providing generated business improvement proposals to users via chatbots or email notifications.

[1358] This invention is a system that collects and analyzes people flow data, payment data, and customer emotion data, and provides effective management improvement proposals to retail stores. This system includes the following elements.

[1359] System Configuration

[1360] 1. Data Collection

[1361] The server collects people flow data through mobile phone base stations, thereby obtaining information on people's movements in specific areas and their stays by time period. At the same time, it obtains payment data using payment system APIs (e.g., Stripe and PayPal). Furthermore, it uses facial recognition technology to collect customers' emotional states from in-store camera feeds. This is achieved using facial recognition services such as Amazon Rekognition and Microsoft Azure Cognitive Services.

[1362] 2. Data integration and preprocessing

[1363] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format, which also corrects for missing and outlier values.

[1364] 3. Data Analysis

[1365] The AI ​​tools within the server will use the integrated data to analyze foot traffic patterns. Predictive models using machine learning algorithms will be used to understand foot traffic trends in specific areas and time periods. Furthermore, payment data will be analyzed to analyze sales patterns and understand sales performance. Machine learning models from Azure or AWS may be used.

[1366] 4. Generate business improvement proposals

[1367] Based on the analysis results obtained by AI tools, specific business improvement proposals are generated, such as selecting new store locations, reviewing product lineups according to target demographics, and holding time sales during specific time periods. The content and timing of proposals are optimized based on the emotional state of customers.

[1368] 5. Real-time feedback

[1369] The generated suggestions are provided to users in real time, and information is subsequently sent via chatbots and email notifications, allowing employees and managers to take immediate action.

[1370] Specific examples

[1371] For example, if a certain area shows an increase in foot traffic but there are few competing stores in the area, the server will use this information to suggest new store openings to users.Also, if the majority of customers visiting on weekends are students and emotional data indicates low satisfaction, the server will suggest providing special coupons for students in real time.

[1372] Prompt Sentence Examples

[1373] You might prompt your generative AI model with the following:

[1374] Based on payment data and sentiment analysis data, please suggest a business strategy to improve customer experience. In particular, the majority of customers are young, and sentiment data indicates that they are not very satisfied. What improvement measures should be taken?

[1375] In this way, by implementing this invention, it becomes possible to make sophisticated business improvement proposals in real time that are tailored to the emotional state of the customer.

[1376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1377] Step 1:

[1378] The server collects people flow data from mobile phone base stations. Specifically, it sends API requests to obtain information on people's movements by region and their stay by time of day. The input is the base station ID, and the output is people flow data.

[1379] Step 2:

[1380] The server collects payment data using the payment system API. Specifically, it accesses the payment system API to obtain the transaction date, time, amount, and location. The input is the API key, and the output is the payment data.

[1381] Step 3:

[1382] The server collects customer emotional states from in-store camera feeds using facial recognition technology. Specifically, it uses Amazon Rekognition and Microsoft Azure Cognitive Services to analyze emotional data from images. Image data is taken as input and emotional data is taken as output.

[1383] Step 4:

[1384] The server integrates the collected people flow data, payment data, and sentiment data and converts them into a consistent format. This stage also corrects missing and outlier values. Multiple datasets are input, and the integrated data is output.

[1385] Step 5:

[1386] The AI ​​in the server uses the integrated data to analyze people flow patterns. It applies machine learning algorithms to understand people's movements in specific areas and time periods. The input is the integrated data and the output is the analysis results.

[1387] Step 6:

[1388] The server analyzes payment data and analyzes sales patterns. It uses AWS and Azure machine learning services for data analysis to understand production performance. The input is payment data and the output is sales patterns.

[1389] Step 7:

[1390] Based on the analysis results and emotional data obtained by AI, the server generates management improvement proposals such as selecting new store locations, reviewing product lineups, and holding time sales during specific time periods. The inputs are the analysis results and emotional data, and the output is management improvement proposals.

[1391] Step 8:

[1392] The server provides the generated proposals to the user in real time. Specifically, it sends the proposals via chatbot or email notification so that the store manager can respond immediately. The input is the business improvement proposal, and the output is a notification to the user.

[1393] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1394] 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.

[1395] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1396] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1397] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1398] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1399] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1400] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1401] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1402] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1403] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1404] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1405] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1406] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1407] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1408] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1409] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1410] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1411] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1412] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1413] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1414] The following is further disclosed regarding the above embodiment.

[1415] (Claim 1)

[1416] a means of collecting people flow data;

[1417] a means for obtaining payment data;

[1418] A means for integrating the collected people flow data with the acquired payment data;

[1419] AI means to analyze the integrated data;

[1420] A means for generating management improvement proposals based on the analysis results;

[1421] A means to deliver generated suggestions in real time

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, wherein the means for collecting people flow data obtains the people flow data from mobile phone base stations.

[1425] (Claim 3)

[1426] 10. The system of claim 1, wherein the means for obtaining payment data obtains the payment data through a payment system API.

[1427] "Example 1"

[1428] (Claim 1)

[1429] a means for collecting people flow data from mobile phone base stations;

[1430] A means of obtaining payment data through the payment system API;

[1431] A means for integrating and preprocessing the collected people flow data and the acquired payment data;

[1432] a means for analyzing the integrated data using a machine learning algorithm;

[1433] A means for generating business improvement proposals based on the analysis results;

[1434] A means of providing and communicating generated suggestions in real time

[1435] A system including:

[1436] (Claim 2)

[1437] The system according to claim 1, wherein the system performs missing value correction and outlier removal on the collected people flow data and the acquired payment data.

[1438] (Claim 3)

[1439] 10. The system of claim 1, wherein the machine learning algorithm performs various analyses, including future demand forecasts.

[1440] "Application Example 1"

[1441] (Claim 1)

[1442] a means of collecting people flow data;

[1443] a means for obtaining payment data;

[1444] A means for integrating the collected people flow data with the acquired payment data;

[1445] AI means to analyze the integrated data;

[1446] A means for generating management improvement proposals based on the analysis results;

[1447] a means for providing generated suggestions in real time;

[1448] a means for visually displaying the analysis results;

[1449] Push notifications to instantly notify you of offers

[1450] A system including:

[1451] (Claim 2)

[1452] 10. The system of claim 1, wherein the means for collecting people flow data obtains the people flow data from mobile phone base stations.

[1453] (Claim 3)

[1454] 10. The system of claim 1, wherein the means for obtaining payment data obtains the payment data through a payment system API.

[1455] "Example 2: Combining Emotion Engines"

[1456] (Claim 1)

[1457] a means of collecting people flow data;

[1458] a means for obtaining payment data;

[1459] A means for integrating the collected people flow data with the acquired payment data;

[1460] AI means to analyze the integrated data;

[1461] A means for generating management improvement proposals based on the analysis results;

[1462] a means for providing generated suggestions in real time;

[1463] emotion engine means for recognizing an emotional state of a user;

[1464] A means to adjust the content and timing of suggestions based on the user's emotional state

[1465] A system including:

[1466] (Claim 2)

[1467] 10. The system of claim 1, wherein the means for collecting people flow data obtains the people flow data from mobile phone base stations.

[1468] (Claim 3)

[1469] 10. The system of claim 1, wherein the means for obtaining payment data obtains the payment data through a payment system API.

[1470] "Application example 2 when combining emotion engines"

[1471] (Claim 1)

[1472] a means of collecting people flow data;

[1473] a means for obtaining payment data;

[1474] A means for integrating the collected people flow data, the acquired payment data, and the customer sentiment data;

[1475] AI means to analyze the integrated data;

[1476] A means for generating business improvement proposals based on the analysis results and the emotional state of the customer;

[1477] A means to deliver generated suggestions in real time

[1478] A system including:

[1479] (Claim 2)

[1480] 10. The system of claim 1, wherein the means for collecting people flow data obtains the people flow data from mobile phone base stations.

[1481] (Claim 3)

[1482] 10. The system of claim 1, wherein the means for obtaining payment data obtains the payment data through a payment system API. [Explanation of symbols]

[1483] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting people flow data; a means for obtaining payment data; A means for integrating the collected people flow data with the acquired payment data; AI means to analyze the integrated data; A means for generating management improvement proposals based on the analysis results; A means to deliver generated suggestions in real time A system including:

2. 2. The system of claim 1, wherein the means for collecting people flow data obtains the people flow data from a mobile phone base station.

3. 10. The system of claim 1, wherein the means for obtaining payment data obtains the payment data through a payment system API.

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