system

Abstract

Provide a system. 【Solution means】 Means for obtaining sales data, Means for obtaining settlement data, Means for integrating the obtained data and preprocessing it into an analyzable form, Means for analyzing the generated data to identify target customers, Means for visualizing and displaying the analysis results, Means for designing and proposing marketing measures for target customers, Means for proposing related merchandise and services to franchise stores, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional digital payment system, the information obtained by a franchise is limited to payment-related information, and it has taken a lot of labor and time to effectively utilize this information to formulate a marketing strategy. Also, it has been difficult to identify target customers, and it has been difficult to formulate effective sales promotion measures. Furthermore, although comprehensive support for store operations is expected through proposals for related merchandise and services, there has been no system to achieve this.

Means for Solving the Problems

[0005] This invention provides a system that acquires sales data and payment data, integrates and preprocesses them, analyzes them, and identifies target customers. Furthermore, it visualizes and displays the analysis results in an intuitively understandable way, enabling merchants to easily utilize the data. In addition, it provides a system that comprehensively supports store operations by including means for designing and proposing specific marketing measures for target customers and means for proposing related products and services to merchants.

[0006] "Sales data" refers to information about the sale of goods and services at a store, including details such as date and time, product name, price, and quantity.

[0007] "Payment data" refers to information that includes details of payments made by consumers during transactions, such as payment method, transaction amount, and date and time.

[0008] "Integration" is the process of centralizing information obtained from different data sources and organizing it into an analyzable format.

[0009] "Preprocessing" refers to processes performed to convert raw data into a format that is easier to analyze, such as removing unnecessary data, imputing missing values, and standardizing data formats.

[0010] "Analysis" is the process of identifying useful information and patterns from collected, integrated, and pre-processed data.

[0011] A "target customer" is a group of customers identified as the target of specific sales promotion activities or marketing initiatives.

[0012] "Visualization" is a technique for displaying analysis results in a format that is easy for humans to understand, and this is done through formats such as graphs, charts, and dashboards.

[0013] "Marketing measures" refer to sales promotion strategies and specific activities conducted for target customers, including methods such as distributing coupons and running advertisements.

[0014] "Merchandise" is a general term for the goods and services sold, and in particular refers to goods and services related to sales promotion activities and store operations. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This system integrates and analyzes sales data and digital payment data from member stores to identify target customers and propose marketing strategies. The system primarily consists of a cloud-based server and member store terminals, and operates as follows:

[0037] The server first connects to the merchant's POS system via API to acquire sales data for goods and services in real time. Furthermore, it is integrated with the payment platform to acquire payment data in parallel. This information is centrally managed on the server and pre-processed into a format suitable for analysis.

[0038] The terminal receives pre-processed data sent from the server and displays it on a dedicated dashboard. The dashboard provides visualized information such as sales trends and customer segments, helping users make data-driven decisions. Furthermore, it incorporates interactive filtering functions to improve the user experience (UX).

[0039] Store operators, as users, can implement specific sales promotion activities based on marketing strategies proposed using the analysis results. For example, they can issue limited-time coupons to specific customer segments or introduce a points system to encourage repeat purchases. Furthermore, by introducing the suggested related products and services, they can improve operational efficiency and increase profitability.

[0040] As a concrete example, suppose a store discovers through its server's analysis process that a particular product category sells well on weekends. Based on this information, the server suggests automatically sending promotional emails to customer groups who show interest in that product category on weekends. In this way, the store can conduct efficient and effective marketing activities.

[0041] This system aims to maximize the use of merchants' sales and customer data, improving the quality of decision-making while simultaneously reducing operating costs. This will allow merchants to experience the advantages of digital payment systems, leading to further adoption and usage.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server connects to the merchant's POS system via API to acquire sales data in real time. It also acquires payment data from the payment platform and integrates this data.

[0045] Step 2:

[0046] The server performs preprocessing on the acquired data, such as removing duplicates and noise and filling in missing data. This process standardizes the data format and prepares it for analysis.

[0047] Step 3:

[0048] The server inputs pre-processed data into an AI algorithm to analyze sales patterns and customer purchasing trends. The analysis process includes techniques such as clustering and regression analysis to identify target customers.

[0049] Step 4:

[0050] The server visualizes the analysis results as graphs and charts and sends them to the merchant's terminal. The terminal receives this data and displays it on a dashboard, allowing users to intuitively understand the data.

[0051] Step 5:

[0052] Users review the analysis results through a dashboard on their device and consider the suggested marketing strategies. For example, they might discuss issuing coupons to specific customer groups or implementing promotional campaigns.

[0053] Step 6:

[0054] The server also suggests other related products and services to the user, supporting the overall efficiency of store operations. Based on these suggestions, the user can consider introducing new products and services.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In commercial transactions, the technology for efficiently collecting and integrating sales and payment information to provide customers with optimal market strategies is not yet sufficiently developed. As a result, sellers find it difficult to make data-driven decisions quickly, making it challenging to achieve appropriate customer targeting and effective sales promotion activities. Furthermore, existing systems often require manual data preprocessing and analysis, posing a significant challenge in terms of time and effort.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for acquiring commercial transaction information, means for acquiring payment information, and means for integrating the acquired information and preprocessing the data into a format suitable for analysis. This enables accurate and rapid data collection and processing. Furthermore, it can identify designated customer groups, provide visualized analysis results, and automatically generate market strategy proposals using a generative AI model, thereby realizing more efficient and strategic sales promotion activities.

[0060] "Commercial transaction information" refers to various information related to the sale of goods generated in commercial activities, including product identification codes, sales times, quantities, and prices.

[0061] "Payment information" refers to information about the payment methods, purchase amounts, settlement methods, and dates of purchases used by customers in relation to commercial transactions.

[0062] A "generative AI model" refers to a program developed based on machine learning algorithms that automatically analyzes sales data and customer data to identify targeted customer groups and propose market strategies.

[0063] "Data preprocessing" refers to a series of operations to transform acquired raw data into a format suitable for analysis, and includes data normalization, imputation of missing values, and correction of outliers.

[0064] "Visualized analysis results" refers to data representations that display the information obtained through analysis in the form of graphs and charts, providing data in a format that is easy for users to understand.

[0065] "Market strategy proposals" refer to strategies and plans for sales promotion activities and product recommendations targeted at specific customer groups, based on analyzed data.

[0066] "Machine learning techniques" refer to technologies that enable computers to learn patterns in data and automatically perform tasks such as prediction and classification, and involve training models using algorithms.

[0067] This invention provides a system for efficiently collecting, analyzing, visualizing, and proposing market strategies in commercial transaction activities. This system primarily includes a server utilizing cloud technology and terminals suitable for user access.

[0068] The server first collects transaction information from the store's POS system. To do this, the server connects to the POS system via an API and collects data such as product identification codes, sales time, quantity, and price. A cloud computing platform can be used for this process. If a specific cloud platform is to be specified, Amazon Web Services (AWS®) is available.

[0069] The server simultaneously collects payment information from digital payment platforms. APIs are used for this data collection, capturing information such as customer ID, purchase amount, payment method, and purchase date and time. For example, Stripe and PayPal are considered as common electronic payment systems.

[0070] After data collection, the server integrates and preprocesses the data. The Python pandas library is recommended for this purpose. Data normalization, missing values ​​are filled in, outliers are corrected, and the data is converted into a format suitable for optimal analysis.

[0071] The analysis utilizes generative AI models, employing machine learning algorithms to analyze customer purchasing patterns and preferences. Specifically, the AI ​​platform on Google Cloud is used to identify customer needs using TENSORFLOW®, enabling more accurate customer targeting.

[0072] Based on the analysis, the generated AI model proposes market strategies, which are then displayed on the user's device. This display utilizes data visualization tools such as Tableau and Power BI, providing a visual and interactive dashboard. This makes it easier for users to make decisions based on the visualized analysis results.

[0073] Store operators, as users, can implement specific sales promotion activities based on the information obtained from the dashboard. For example, automated distribution of promotional emails to specific customer groups may be suggested. An example of a prompt to instruct the generating AI model to perform this operation could be: "Analyze recent sales data and suggest the best-selling products and recommended market strategies for them."

[0074] This system provides users with a powerful tool to effectively implement market strategies using digital technology and improve their competitiveness.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives transaction information as input from the store's POS system. Specifically, it uses an API to retrieve data such as product identification codes, sales time, quantity, and price. Based on this input data, the server temporarily stores the data and organizes and stores each transaction information in a database.

[0078] Step 2:

[0079] The server receives payment information as input from the digital payment platform. The server retrieves information such as customer ID, purchase amount, payment method, and purchase date and time via an API. Based on this information, the server integrates commercial transaction information and payment information and generates a dataset for centralized management.

[0080] Step 3:

[0081] The server receives the dataset as input for preprocessing. Using Python libraries, the server normalizes the data, imputes missing values, corrects outliers, and processes it into a format suitable for analysis. This results in clean, consistent output data, ready to proceed to the next analysis step.

[0082] Step 4:

[0083] The server receives pre-processed data as input using a generative AI model. Here, based on machine learning algorithms, the data is analyzed to identify customer purchasing patterns and preferences. This step provides insights necessary for identifying target customer groups and proposing market strategies. The server saves these analysis results as output.

[0084] Step 5:

[0085] The terminal receives analysis results provided by the server as input. The terminal uses data visualization tools to display the information on a dashboard. Users can visually review sales trends and customer segmentation through interactive options. This output data provides users with a basis for planning market strategies.

[0086] Step 6:

[0087] Users plan and execute specific market strategies using insights from the dashboard. For example, they can use prompts provided by a generative AI model to automatically generate marketing strategies such as, "Analyze recent sales data to suggest the best-selling products and recommended market strategies for them." By executing this activity, users can efficiently and effectively increase sales.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In recent years, with the spread of online shopping, consumer purchasing behavior has diversified. It has become extremely important to propose effective marketing strategies for each consumer and improve customer satisfaction. However, relying solely on regular sales data and payment data makes it difficult to fully understand customer intentions and preferences, posing a challenge in conducting accurate promotions and product recommendations.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, and means for integrating the acquired data and preprocessing it into an analyzable format. This enables effective notification of product suggestions and promotions optimized for each customer.

[0093] "Sales data" refers to data that records information about the purchase of goods or the use of services.

[0094] "Settlement data" refers to data that includes information about the payment method and amount in a transaction.

[0095] "Data integration" is the process of combining different types of data, such as sales data and payment data, and converting them into a consistent format.

[0096] "Preprocessing methods" refer to methods for preparing and organizing data in order to perform analysis.

[0097] "Identifying target customers" means identifying a specific group of consumers that will be the focus of marketing activities.

[0098] "Visualization of analysis results" is a technique that makes data analysis results easier to understand by visually representing them using diagrams, tables, and other visual aids.

[0099] "Marketing measures" refer to strategies and activities implemented with the aim of promoting the sale of a product.

[0100] "Product suggestion notification" means informing customers of suitable products based on the analysis results.

[0101] This system operates on a cloud-based server and integrates sales and payment data to propose effective marketing strategies. The system's program is built using Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It also performs computations on the cloud using AWS Lambda.

[0102] The server first acquires corporate sales and payment data in real time via APIs. This data undergoes initial data cleansing and preprocessing to a format suitable for analysis. Next, customer segmentation is analyzed using machine learning models to identify target customers. At this stage, TensorFlow and PyTorch are utilized to identify consumer purchasing patterns.

[0103] Product recommendations are sent to identified target customers. The recommendations are automatically generated on the server and sent to customers via a smartphone application. This notification provides promotional information about products optimized for the target customers.

[0104] On the terminal, users can view the analysis results sent from the server on a dedicated dashboard. This dashboard visualizes sales trends and customer segment information to support users in making decisions. Furthermore, interactive filtering functions allow users to select data based on specific criteria and proceed with detailed analysis.

[0105] For example, if a consumer frequently purchases fitness equipment, the system automatically generates promotions offering discount coupons for fitness-related products to that consumer. Such personalized marketing strategies improve customer satisfaction.

[0106] The generative AI model operates with prompts like the following:

[0107] "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server retrieves sales and payment data from companies via an API. The inputs it receives are sales history and payment records. The server integrates these different data sources and converts them into a consistent format. The output is the integrated data.

[0111] Step 2:

[0112] The server cleanses the acquired integrated data. The input is the integrated data. The server removes duplicates and errors from the data, generating a clean dataset suitable for analysis. The output is the pre-processed, clean data.

[0113] Step 3:

[0114] The server analyzes clean data using machine learning algorithms. It uses pre-processed data as input. The server analyzes consumer purchasing patterns and identifies target customers using TensorFlow or PyTorch. The output is a list of identified target customers.

[0115] Step 4:

[0116] The server generates customized product suggestions based on identified target customers. A generative AI model is used in this process. The input is a list of target customers, and the server creates suggestions using a specific prompt: "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase." The output is optimized product suggestions for each customer.

[0117] Step 5:

[0118] The terminal displays product suggestions sent from the server on a dashboard. The input is product suggestion data. The terminal provides visualized information to the user and assists the user in performing detailed analysis through interactive filtering functions. The output is a display of product suggestions that the user can visually confirm.

[0119] Step 6:

[0120] Users implement marketing strategies and promotional activities based on suggestions displayed on the dashboard. The input is the information from the dashboard. Based on the provided information, users can issue coupons to specific customer segments or revise product pricing. The output is the results of the implemented marketing strategies.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This system acquires and integrates sales and payment data from member stores, identifies target customers based on this data, and proposes marketing strategies. Furthermore, by combining this with an emotion engine, the present invention recognizes user emotions and improves the accuracy of marketing strategies.

[0123] The server first acquires sales data from the merchant's POS system and payment data from the payment platform in real time. Next, it organizes the acquired data, performs preprocessing such as noise removal, and converts it into a format suitable for analysis.

[0124] Furthermore, this system analyzes customer purchase history and feedback information via an emotion engine to predict their current emotional state and purchase intent. The results of the emotion engine are integrated with sales and payment data, enabling a deeper understanding of customers.

[0125] The terminal displays analysis results sent from the server and the output of the emotion engine in a dashboard format. Through this, users can easily review the data and develop marketing strategies for specific customer groups. Suggested strategies include segmentation based on emotion data and personalized promotions.

[0126] As a concrete example, suppose a store's emotional intelligence engine predicts that customers visiting on weekends will have a high purchasing intent. Based on this information and sales / payment data, the server automatically generates weekend-only promotional strategies and recommends them to users. As a result, the store can efficiently execute promotional activities and expect increased sales.

[0127] In this way, by utilizing integrated data across the entire system and taking user emotions into consideration, it becomes possible to design and execute more precise targeting and effective marketing strategies. As a result, member stores can improve customer satisfaction and increase revenue.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server connects to the merchant's POS system and retrieves sales data instantly. It also retrieves payment data from the digital payment platform and integrates the data from both sources.

[0131] Step 2:

[0132] The server performs preprocessing on the acquired raw data. Specifically, it removes duplicate data, standardizes the format, and imputes missing values ​​to prepare the data for analysis.

[0133] Step 3:

[0134] The server activates the emotion engine and analyzes the customer's emotional state and purchase intent based on past purchase history and feedback data. The analysis results are then used for subsequent analysis.

[0135] Step 4:

[0136] The server combines pre-processed sales and payment data with sentiment analysis results to perform a comprehensive customer analysis. This analysis uses machine learning algorithms to segment target customers and predict purchasing trends.

[0137] Step 5:

[0138] The server visualizes the analysis results and sends them to the terminal. The terminal displays the received data on a dashboard, allowing the user to examine the data in detail using various filtering and visualization options.

[0139] Step 6:

[0140] Based on the information in the dashboard, users consider and select specific marketing strategies for their target customers. These strategies include personalized promotions based on sentiment data and the execution of timely campaigns.

[0141] Step 7:

[0142] The server generates suggestions for related products and new services, and provides feedback to users. This allows member stores to develop concrete strategies to further improve operational efficiency.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Many commercial facilities and retail stores are required to develop effective marketing strategies by utilizing customer purchase history and payment information. However, traditional systems have made it difficult to efficiently integrate this data and gain useful insights. Furthermore, they have been unable to design strategies that take into account consumer emotions and purchasing intent, resulting in a failure to improve revenue and customer satisfaction.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for acquiring sales information, means for acquiring payment information, and means for integrating the acquired information and preprocessing it into an analyzable format. This enables centralized data management and the design and execution of sophisticated marketing strategies that even take into account the emotional state of customers.

[0148] "Sales information" refers to data related to the sales of each product or service in commercial activities. Specifically, it includes sales quantity, sales amount, and sales date and time.

[0149] "Payment information" refers to data related to payments made by the buyer. This includes credit card information, payment amount, and payment date and time.

[0150] "Customer emotional state" refers to the psychological reactions and emotions consumers have towards products and services. Data analyzed using an emotion engine falls into this category.

[0151] "Target consumers" refer to customers identified as highly likely to purchase a particular product or service. They are the primary target of marketing strategies.

[0152] "Sales promotion measures" refer to strategic activities aimed at encouraging specific consumer segments to purchase products or services. This includes segmentation and promotion.

[0153] "Products and services" refer to all items with commercial value provided by a company or business. This includes goods, software, and various services.

[0154] An integrated system is required to implement this invention. This system mainly consists of three elements: a server, a terminal, and a user.

[0155] The server first retrieves sales and payment information from external systems. Specifically, it accesses POS systems and payment platforms using APIs to collect sales and payment information in real time. The collected information is stored in a database, where it is then organized and noise-reduced. General database software can be used for database management.

[0156] Next, the server utilizes an emotion analysis engine to analyze the customer's emotional state from their past purchase history and feedback data. This emotion analysis engine uses natural language processing and machine learning algorithms to quantify the customer's emotional state. The results of the emotion analysis are integrated with sales and payment information to obtain deeper insights.

[0157] Furthermore, the server uses a generative AI model to analyze data and identify target consumers. Based on the generated data, it creates prompts and generates appropriate marketing strategies.

[0158] The device displays the data processing results and sentiment analysis findings transmitted from the server on a dashboard. Users can use this dashboard to intuitively review the data and devise marketing strategies. The device can be run on a standard computer or tablet device.

[0159] For example, if sentiment analysis predicts that a store's customers' purchasing intent will increase on a particular weekend, the server will automatically generate weekend-only promotional strategies based on this information. For instance, it might generate a prompt message such as, "Identify target customers and propose optimal marketing strategies based on the following data. This data includes sales data, payment information, and customer sentiment analysis results."

[0160] Thus, this invention provides a foundation for formulating effective marketing strategies by handling digital information in an integrated and analytical manner. Through this system, users can accurately execute strategies to improve customer satisfaction and increase sales.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server collects sales and payment information. It connects to POS systems and payment platforms and retrieves data in real time via APIs. The inputs are sales and payment information, and the output is the collected raw data. The server stores this data in a database.

[0164] Step 2:

[0165] The server preprocesses the acquired data. The input is raw data, and it performs missing value imputation, noise removal, and conversion to the required format. The output is organized data in an analyzable format. Here, missing values ​​are imputed with the mean and outliers are removed.

[0166] Step 3:

[0167] The server uses an emotion analysis engine to analyze the customer's emotional state. Inputs include purchase history and feedback information, and text mining techniques are used to score emotions. The output is numerical emotion data. The server integrates this emotion data with sales information.

[0168] Step 4:

[0169] The server analyzes integrated data using machine learning algorithms to identify target consumers. The input consists of organized data and sentiment data, and the output is a list of target consumers. The server generates customer segments using clustering techniques.

[0170] Step 5:

[0171] The server automatically generates marketing strategies using a generative AI model. The input is a list of target consumers, and prompts are created to instruct the AI ​​model. The output is the proposed marketing strategies. Prompts include phrases such as, "Identify target customers based on the following data and propose the most suitable marketing strategies."

[0172] Step 6:

[0173] The terminal displays analysis results and strategies from the server on a dashboard. Input is data sent from the server, and output is a visual dashboard display. The terminal visualizes the data in graph and table formats.

[0174] Step 7:

[0175] Users review the dashboard, select identified strategies, and execute them. The input is the data on the dashboard, and the output is the strategies to be implemented. Users apply the promotional methods they deem effective based on their own judgment.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Traditional marketing strategies struggled to accurately grasp customer emotions and individual purchasing intentions, making efficient targeting difficult. Furthermore, while real-time sentiment analysis-based promotional displays were essential, the technology to achieve this was limited. Therefore, developing strategies that truly capture the interest of in-store customers was a challenge.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, means for integrating the acquired data and preprocessing it into an analyzable format, means for detecting facial expressions in real time and identifying emotional states, and means for displaying personalized promotions based on the identified emotional states. This enables personalized and dynamic marketing strategies that take into account the real-time emotional states of customers visiting the store.

[0181] "Sales data" refers to transaction information generated when goods or services are sold, and includes details such as quantity, date of sale, and price.

[0182] "Payment data" refers to data related to payment methods and payment information at the time of purchase, including card information used and payment amounts.

[0183] "Emotional state" refers to an individual's current emotional state, a psychological state inferred from facial expressions, tone of voice, and other factors.

[0184] "Promotion" refers to specific sales promotion activities carried out to encourage the sale of products or services, and includes measures such as discounts and special offers.

[0185] A "target customer" is a customer who is the target of a specific marketing initiative, and is a customer segment identified based on their purchase history and interests.

[0186] "Facial expression detection" is a process that uses cameras and sensors to read the features of an individual's face and analyze their emotions in real time.

[0187] "Integrating data" is the process of combining multiple datasets obtained from different sources into a single, accessible format.

[0188] A system implementing this invention is constructed using multiple hardware and software components. The main components include a sensor device for acquiring sales data, a data network for acquiring payment data, a server for integrating and pre-processing the data, and a camera and a real-time sentiment analysis engine for identifying emotional states.

[0189] The server first acquires and integrates sales and payment data from merchants in real time. This involves using data feeds via the information network and local databases. This integrated data is preprocessed to remove noise and format it into an analyzable format. The information is then analyzed by machine learning algorithms to identify target customers.

[0190] The smart display, acting as a terminal, is equipped with a camera to detect the faces of customers. This terminal captures the facial expressions of individual customers and uses an emotion analysis engine to identify their emotional state in real time. Examples of available software include facial recognition APIs and emotion analysis models. Based on this, specific promotional information is displayed on the screen in real time to stimulate customer purchasing intent.

[0191] Users can view the analysis results sent from the server and the output of the sentiment analysis engine in a dashboard format. Through this dashboard, they can quickly decide on and implement marketing strategies for specific customer groups.

[0192] For example, on a hot summer day, a possible strategy could be to instantly display a discount campaign for cold drinks on a screen based on the tired expression of a customer who visits the store. Furthermore, by inputting a prompt to the generating AI model such as, "If the customer's expression indicates 'fatigue,' please devise a promotional strategy that proposes a discount campaign for cold drinks," the system can automatically generate flexible sales strategies.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server retrieves sales data from the merchant's POS system and simultaneously collects payment data from the payment platform. Input data includes purchased items, sales amount, and payment method. The server aggregates and temporarily stores this data.

[0196] Step 2:

[0197] The server preprocesses the acquired sales and payment data. This preprocessing removes noise from the data and formats it for analysis. The input is raw sales and payment data, and the output is a unified, formatted dataset. This process is performed using a data cleaning algorithm.

[0198] Step 3:

[0199] The server passes pre-processed data to a machine learning algorithm to identify target customers. Here, the input data includes customer purchase history and attributes, and the output is a list of identified target customers. A clustering algorithm is used in this process.

[0200] Step 4:

[0201] The terminal's smart display acquires images of customers' faces via a camera. The input is real-time video of the customers, and the output is detected facial feature data. This data is then sent to the next processing step using facial recognition software.

[0202] Step 5:

[0203] The terminal utilizes an emotion analysis engine to analyze the emotional state of customers based on detected facial feature data. The input is facial feature data, and the output is data indicating the current emotional state. A deep learning model is used for this process.

[0204] Step 6:

[0205] The terminal combines target customer data received from the server with analyzed sentiment data to construct appropriate promotional information. The input is target customer data and sentiment state data, and the output is a personalized promotional message. This process is performed using a generative AI model.

[0206] Step 7:

[0207] The user reviews the promotional information displayed on the device and selects an effective marketing strategy for their customers. The output includes specific examples as prompts, such as, "If a customer's facial expression indicates 'fatigue,' devise a promotional strategy that proposes a discount campaign on cold drinks." The user decides which strategy to implement and puts that information into action from the device.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This system integrates and analyzes sales data and digital payment data from member stores to identify target customers and propose marketing strategies. The system primarily consists of a cloud-based server and member store terminals, and operates as follows:

[0225] The server first connects to the merchant's POS system via API to acquire sales data for goods and services in real time. Furthermore, it is integrated with the payment platform to acquire payment data in parallel. This information is centrally managed on the server and pre-processed into a format suitable for analysis.

[0226] The terminal receives pre-processed data sent from the server and displays it on a dedicated dashboard. The dashboard provides visualized information such as sales trends and customer segments, helping users make data-driven decisions. Furthermore, it incorporates interactive filtering functions to improve the user experience (UX).

[0227] Store operators, as users, can implement specific sales promotion activities based on marketing strategies proposed using the analysis results. For example, they can issue limited-time coupons to specific customer segments or introduce a points system to encourage repeat purchases. Furthermore, by introducing the suggested related products and services, they can improve operational efficiency and increase profitability.

[0228] As a concrete example, suppose a store discovers through its server's analysis process that a particular product category sells well on weekends. Based on this information, the server suggests automatically sending promotional emails to customer groups who show interest in that product category on weekends. In this way, the store can conduct efficient and effective marketing activities.

[0229] This system aims to maximize the use of merchants' sales and customer data, improving the quality of decision-making while simultaneously reducing operating costs. This will allow merchants to experience the advantages of digital payment systems, leading to further adoption and usage.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server connects to the merchant's POS system via API to acquire sales data in real time. It also acquires payment data from the payment platform and integrates this data.

[0233] Step 2:

[0234] The server performs preprocessing on the acquired data, such as removing duplicates and noise and filling in missing data. This process standardizes the data format and prepares it for analysis.

[0235] Step 3:

[0236] The server inputs pre-processed data into an AI algorithm to analyze sales patterns and customer purchasing trends. The analysis process includes techniques such as clustering and regression analysis to identify target customers.

[0237] Step 4:

[0238] The server visualizes the analysis results as graphs and charts and sends them to the merchant's terminal. The terminal receives this data and displays it on a dashboard, allowing users to intuitively understand the data.

[0239] Step 5:

[0240] Users review the analysis results through a dashboard on their device and consider the suggested marketing strategies. For example, they might discuss issuing coupons to specific customer groups or implementing promotional campaigns.

[0241] Step 6:

[0242] The server also suggests other related products and services to the user, supporting the overall efficiency of store operations. Based on these suggestions, the user can consider introducing new products and services.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In commercial transactions, the technology for efficiently collecting and integrating sales and payment information to provide customers with optimal market strategies is not yet sufficiently developed. As a result, sellers find it difficult to make data-driven decisions quickly, making it challenging to achieve appropriate customer targeting and effective sales promotion activities. Furthermore, existing systems often require manual data preprocessing and analysis, posing a significant challenge in terms of time and effort.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for acquiring commercial transaction information, means for acquiring payment information, and means for integrating the acquired information and preprocessing the data into a format suitable for analysis. This enables accurate and rapid data collection and processing. Furthermore, it can identify designated customer groups, provide visualized analysis results, and automatically generate market strategy proposals using a generative AI model, thereby realizing more efficient and strategic sales promotion activities.

[0248] "Commercial transaction information" refers to various information related to the sale of goods generated in commercial activities, including product identification codes, sales times, quantities, and prices.

[0249] "Payment information" refers to information about the payment methods, purchase amounts, settlement methods, and dates of purchases used by customers in relation to commercial transactions.

[0250] A "generative AI model" refers to a program developed based on machine learning algorithms that automatically analyzes sales data and customer data to identify targeted customer groups and propose market strategies.

[0251] "Data preprocessing" refers to a series of operations to transform acquired raw data into a format suitable for analysis, and includes data normalization, imputation of missing values, and correction of outliers.

[0252] "Visualized analysis results" refers to data representations that display the information obtained through analysis in the form of graphs and charts, providing data in a format that is easy for users to understand.

[0253] "Market strategy proposals" refer to strategies and plans for sales promotion activities and product recommendations targeted at specific customer groups, based on analyzed data.

[0254] "Machine learning techniques" refer to technologies that enable computers to learn patterns in data and automatically perform tasks such as prediction and classification, and involve training models using algorithms.

[0255] This invention provides a system for efficiently collecting, analyzing, visualizing, and proposing market strategies in commercial transaction activities. This system primarily includes a server utilizing cloud technology and terminals suitable for user access.

[0256] The server first collects transaction information from the store's POS system. To do this, the server connects to the POS system via an API and collects data such as product identification codes, sales times, quantities, and prices. This process can utilize a cloud computing platform. If a specific cloud platform is required, Amazon Web Services (AWS) is an option.

[0257] The server simultaneously collects payment information from digital payment platforms. APIs are used for this data collection, capturing information such as customer ID, purchase amount, payment method, and purchase date and time. For example, Stripe and PayPal are considered as common electronic payment systems.

[0258] After data collection, the server integrates and preprocesses the data. The Python pandas library is recommended for this purpose. Data normalization, missing values ​​are filled in, outliers are corrected, and the data is converted into a format suitable for optimal analysis.

[0259] The analysis utilizes generative AI models, employing machine learning algorithms to analyze customer purchasing patterns and preferences. Specifically, the Google Cloud AI platform leverages TensorFlow to identify customer needs, enabling more accurate customer targeting.

[0260] Based on the analysis, the generated AI model proposes market strategies, which are then displayed on the user's device. This display utilizes data visualization tools such as Tableau and Power BI, providing a visual and interactive dashboard. This makes it easier for users to make decisions based on the visualized analysis results.

[0261] Store operators, as users, can implement specific sales promotion activities based on the information obtained from the dashboard. For example, automated distribution of promotional emails to specific customer groups may be suggested. An example of a prompt to instruct the generating AI model to perform this operation could be: "Analyze recent sales data and suggest the best-selling products and recommended market strategies for them."

[0262] This system provides users with a powerful tool to effectively implement market strategies using digital technology and improve their competitiveness.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server receives transaction information as input from the store's POS system. Specifically, it uses an API to retrieve data such as product identification codes, sales time, quantity, and price. Based on this input data, the server temporarily stores the data and organizes and stores each transaction information in a database.

[0266] Step 2:

[0267] The server receives payment information as input from the digital payment platform. The server retrieves information such as customer ID, purchase amount, payment method, and purchase date and time via an API. Based on this information, the server integrates commercial transaction information and payment information and generates a dataset for centralized management.

[0268] Step 3:

[0269] The server receives the dataset as input for preprocessing. Using Python libraries, the server normalizes the data, imputes missing values, corrects outliers, and processes it into a format suitable for analysis. This results in clean, consistent output data, ready to proceed to the next analysis step.

[0270] Step 4:

[0271] The server receives pre-processed data as input using a generative AI model. Here, based on machine learning algorithms, the data is analyzed to identify customer purchasing patterns and preferences. This step provides insights necessary for identifying target customer groups and proposing market strategies. The server saves these analysis results as output.

[0272] Step 5:

[0273] The terminal receives analysis results provided by the server as input. The terminal uses data visualization tools to display the information on a dashboard. Users can visually review sales trends and customer segmentation through interactive options. This output data provides users with a basis for planning market strategies.

[0274] Step 6:

[0275] Users plan and execute specific market strategies using insights from the dashboard. For example, they can use prompts provided by a generative AI model to automatically generate marketing strategies such as, "Analyze recent sales data to suggest the best-selling products and recommended market strategies for them." By executing this activity, users can efficiently and effectively increase sales.

[0276] (Application Example 1)

[0277] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] In recent years, with the spread of online shopping, consumer purchasing behavior has diversified. It has become extremely important to propose effective marketing strategies for each consumer and improve customer satisfaction. However, relying solely on regular sales data and payment data makes it difficult to fully understand customer intentions and preferences, posing a challenge in conducting accurate promotions and product recommendations.

[0279] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0280] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, and means for integrating the acquired data and preprocessing it into an analyzable format. This enables effective notification of product suggestions and promotions optimized for each customer.

[0281] "Sales data" refers to data that records information about the purchase of goods or the use of services.

[0282] "Settlement data" refers to data that includes information about the payment method and amount in a transaction.

[0283] "Data integration" refers to the act of consolidating different types of data, such as sales data and payment data, and converting them into a consistent format.

[0284] "Means for preprocessing" refers to the method of preparing and organizing data for analysis.

[0285] "Identification of target customers" refers to identifying a specific group of consumers who are the target of marketing activities.

[0286] "Visualization of analysis results" is a technology that visually represents the analysis results of data in the form of graphs, tables, etc., to make it easier to understand.

[0287] "Marketing measures" refer to the strategies and activities implemented to promote the sales of products.

[0288] "Notification of product recommendations" refers to informing customers of products suitable for them based on the analysis results.

[0289] This system operates based on a server in the cloud and proposes effective marketing measures by integrating sales data and payment data. The system's program is built using Python and utilizes machine learning libraries such as TensorFlow and PyTorch. Additionally, AWS Lambda is used for computing processing on the cloud.

[0290] The server first obtains the enterprise's sales data and payment data in real time via an API. This data is subjected to data cleansing at the initial stage and preprocessed into a format suitable for analysis. Next, customer segments are analyzed using a machine learning model to identify target customers. At this stage, TensorFlow and PyTorch are utilized to identify consumers' purchasing patterns.

[0291] Product recommendations are sent to identified target customers. The recommendations are automatically generated on the server and sent to customers via a smartphone application. This notification provides promotional information about products optimized for the target customers.

[0292] On the terminal, users can view the analysis results sent from the server on a dedicated dashboard. This dashboard visualizes sales trends and customer segment information to support users in making decisions. Furthermore, interactive filtering functions allow users to select data based on specific criteria and proceed with detailed analysis.

[0293] For example, if a consumer frequently purchases fitness equipment, the system automatically generates promotions offering discount coupons for fitness-related products to that consumer. Such personalized marketing strategies improve customer satisfaction.

[0294] The generative AI model operates with prompts like the following:

[0295] "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server retrieves sales and payment data from companies via an API. The inputs it receives are sales history and payment records. The server integrates these different data sources and converts them into a consistent format. The output is the integrated data.

[0299] Step 2:

[0300] The server performs cleansing of the acquired integrated data. The input is the integrated data. The server removes duplicates and errors in the data and generates a clean dataset suitable for analysis. The output is the preprocessed clean data.

[0301] Step 3:

[0302] The server analyzes the clean data using a machine learning algorithm. The preprocessed data is used as the input. The server uses TensorFlow or PyTorch to analyze consumers' purchase patterns and identify target customers. The output is a list of the identified target customers.

[0303] Step 4:

[0304] The server generates customized product recommendations based on the identified target customers. A generative AI model is used in this process. The input is the target customer list, and the server creates recommendations using a specific prompt sentence "Please generate product recommendations to promote the next purchase based on the purchase history analysis of User A." The output is optimized product recommendations for each customer.

[0305] Step 5:

[0306] The terminal displays the product recommendations sent from the server on the dashboard. The input is the product recommendation data. The terminal provides the visualized information to the user and supports the user to perform detailed analysis through an interactive filtering function. The output is a display of the product recommendations that the user can visually confirm.

[0307] Step 6:

[0308] Users implement marketing strategies and promotional activities based on suggestions displayed on the dashboard. The input is the information from the dashboard. Based on the provided information, users can issue coupons to specific customer segments or revise product pricing. The output is the results of the implemented marketing strategies.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This system acquires and integrates sales and payment data from member stores, identifies target customers based on this data, and proposes marketing strategies. Furthermore, by combining this with an emotion engine, the present invention recognizes user emotions and improves the accuracy of marketing strategies.

[0311] The server first acquires sales data from the merchant's POS system and payment data from the payment platform in real time. Next, it organizes the acquired data, performs preprocessing such as noise removal, and converts it into a format suitable for analysis.

[0312] Furthermore, this system analyzes customer purchase history and feedback information via an emotion engine to predict their current emotional state and purchase intent. The results of the emotion engine are integrated with sales and payment data, enabling a deeper understanding of customers.

[0313] The terminal displays analysis results sent from the server and the output of the emotion engine in a dashboard format. Through this, users can easily review the data and develop marketing strategies for specific customer groups. Suggested strategies include segmentation based on emotion data and personalized promotions.

[0314] As a concrete example, suppose a store's emotional intelligence engine predicts that customers visiting on weekends will have a high purchasing intent. Based on this information and sales / payment data, the server automatically generates weekend-only promotional strategies and recommends them to users. As a result, the store can efficiently execute promotional activities and expect increased sales.

[0315] In this way, by utilizing integrated data across the entire system and taking user emotions into consideration, it becomes possible to design and execute more precise targeting and effective marketing strategies. As a result, member stores can improve customer satisfaction and increase revenue.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server connects to the merchant's POS system and retrieves sales data instantly. It also retrieves payment data from the digital payment platform and integrates the data from both sources.

[0319] Step 2:

[0320] The server performs preprocessing on the acquired raw data. Specifically, it removes duplicate data, standardizes the format, and imputes missing values ​​to prepare the data for analysis.

[0321] Step 3:

[0322] The server activates the emotion engine and analyzes the customer's emotional state and purchase intent based on past purchase history and feedback data. The analysis results are then used for subsequent analysis.

[0323] Step 4:

[0324] The server combines pre-processed sales and payment data with sentiment analysis results to perform a comprehensive customer analysis. This analysis uses machine learning algorithms to segment target customers and predict purchasing trends.

[0325] Step 5:

[0326] The server visualizes the analysis results and sends them to the terminal. The terminal displays the received data on a dashboard, allowing the user to examine the data in detail using various filtering and visualization options.

[0327] Step 6:

[0328] Based on the information in the dashboard, users consider and select specific marketing strategies for their target customers. These strategies include personalized promotions based on sentiment data and the execution of timely campaigns.

[0329] Step 7:

[0330] The server generates suggestions for related products and new services, and provides feedback to users. This allows member stores to develop concrete strategies to further improve operational efficiency.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Many commercial facilities and retail stores are required to develop effective marketing strategies by utilizing customer purchase history and payment information. However, traditional systems have made it difficult to efficiently integrate this data and gain useful insights. Furthermore, they have been unable to design strategies that take into account consumer emotions and purchasing intent, resulting in a failure to improve revenue and customer satisfaction.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for acquiring sales information, means for acquiring payment information, and means for integrating the acquired information and preprocessing it into an analyzable format. This enables centralized data management and the design and execution of sophisticated marketing strategies that even take into account the emotional state of customers.

[0336] "Sales information" refers to data related to the sales of each product or service in commercial activities. Specifically, it includes sales quantity, sales amount, and sales date and time.

[0337] "Payment information" refers to data related to payments made by the buyer. This includes credit card information, payment amount, and payment date and time.

[0338] "Customer emotional state" refers to the psychological reactions and emotions consumers have towards products and services. Data analyzed using an emotion engine falls into this category.

[0339] "Target consumers" refer to customers identified as highly likely to purchase a particular product or service. They are the primary target of marketing strategies.

[0340] "Sales promotion measures" refer to strategic activities aimed at encouraging specific consumer segments to purchase products or services. This includes segmentation and promotion.

[0341] "Products and services" refer to all items with commercial value provided by a company or business. This includes goods, software, and various services.

[0342] An integrated system is required to implement this invention. This system mainly consists of three elements: a server, a terminal, and a user.

[0343] The server first retrieves sales and payment information from external systems. Specifically, it accesses POS systems and payment platforms using APIs to collect sales and payment information in real time. The collected information is stored in a database, where it is then organized and noise-reduced. General database software can be used for database management.

[0344] Next, the server utilizes an emotion analysis engine to analyze the customer's emotional state from their past purchase history and feedback data. This emotion analysis engine uses natural language processing and machine learning algorithms to quantify the customer's emotional state. The results of the emotion analysis are integrated with sales and payment information to obtain deeper insights.

[0345] Furthermore, the server uses a generative AI model to analyze data and identify target consumers. Based on the generated data, it creates prompts and generates appropriate marketing strategies.

[0346] The device displays the data processing results and sentiment analysis findings transmitted from the server on a dashboard. Users can use this dashboard to intuitively review the data and devise marketing strategies. The device can be run on a standard computer or tablet device.

[0347] For example, if sentiment analysis predicts that a store's customers' purchasing intent will increase on a particular weekend, the server will automatically generate weekend-only promotional strategies based on this information. For instance, it might generate a prompt message such as, "Identify target customers and propose optimal marketing strategies based on the following data. This data includes sales data, payment information, and customer sentiment analysis results."

[0348] Thus, this invention provides a foundation for formulating effective marketing strategies by handling digital information in an integrated and analytical manner. Through this system, users can accurately execute strategies to improve customer satisfaction and increase sales.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The server collects sales and payment information. It connects to POS systems and payment platforms and retrieves data in real time via APIs. The inputs are sales and payment information, and the output is the collected raw data. The server stores this data in a database.

[0352] Step 2:

[0353] The server preprocesses the acquired data. The input is raw data, and it performs missing value imputation, noise removal, and conversion to the required format. The output is organized data in an analyzable format. Here, missing values ​​are imputed with the mean and outliers are removed.

[0354] Step 3:

[0355] The server uses an emotion analysis engine to analyze the customer's emotional state. Inputs include purchase history and feedback information, and text mining techniques are used to score emotions. The output is numerical emotion data. The server integrates this emotion data with sales information.

[0356] Step 4:

[0357] The server analyzes integrated data using machine learning algorithms to identify target consumers. The input consists of organized data and sentiment data, and the output is a list of target consumers. The server generates customer segments using clustering techniques.

[0358] Step 5:

[0359] The server automatically generates marketing strategies using a generative AI model. The input is a list of target consumers, and prompts are created to instruct the AI ​​model. The output is the proposed marketing strategies. Prompts include phrases such as, "Identify target customers based on the following data and propose the most suitable marketing strategies."

[0360] Step 6:

[0361] The terminal displays analysis results and strategies from the server on a dashboard. Input is data sent from the server, and output is a visual dashboard display. The terminal visualizes the data in graph and table formats.

[0362] Step 7:

[0363] Users review the dashboard, select identified strategies, and execute them. The input is the data on the dashboard, and the output is the strategies to be implemented. Users apply the promotional methods they deem effective based on their own judgment.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] Traditional marketing strategies struggled to accurately grasp customer emotions and individual purchasing intentions, making efficient targeting difficult. Furthermore, while real-time sentiment analysis-based promotional displays were essential, the technology to achieve this was limited. Therefore, developing strategies that truly capture the interest of in-store customers was a challenge.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, means for integrating the acquired data and preprocessing it into an analyzable format, means for detecting facial expressions in real time and identifying emotional states, and means for displaying personalized promotions based on the identified emotional states. This enables personalized and dynamic marketing strategies that take into account the real-time emotional states of customers visiting the store.

[0369] "Sales data" refers to transaction information generated when goods or services are sold, and includes details such as quantity, date of sale, and price.

[0370] "Payment data" refers to data related to payment methods and payment information at the time of purchase, including card information used and payment amounts.

[0371] "Emotional state" refers to an individual's current emotional state, a psychological state inferred from facial expressions, tone of voice, and other factors.

[0372] "Promotion" refers to specific sales promotion activities carried out to encourage the sale of products or services, and includes measures such as discounts and special offers.

[0373] A "target customer" is a customer who is the target of a specific marketing initiative, and is a customer segment identified based on their purchase history and interests.

[0374] "Facial expression detection" is a process that uses cameras and sensors to read the features of an individual's face and analyze their emotions in real time.

[0375] "Integrating data" is the process of combining multiple datasets obtained from different sources into a single, accessible format.

[0376] A system implementing this invention is constructed using multiple hardware and software components. The main components include a sensor device for acquiring sales data, a data network for acquiring payment data, a server for integrating and pre-processing the data, and a camera and a real-time sentiment analysis engine for identifying emotional states.

[0377] The server first acquires and integrates sales and payment data from merchants in real time. This involves using data feeds via the information network and local databases. This integrated data is preprocessed to remove noise and format it into an analyzable format. The information is then analyzed by machine learning algorithms to identify target customers.

[0378] The smart display, acting as a terminal, is equipped with a camera to detect the faces of customers. This terminal captures the facial expressions of individual customers and uses an emotion analysis engine to identify their emotional state in real time. Examples of available software include facial recognition APIs and emotion analysis models. Based on this, specific promotional information is displayed on the screen in real time to stimulate customer purchasing intent.

[0379] Users can view the analysis results sent from the server and the output of the sentiment analysis engine in a dashboard format. Through this dashboard, they can quickly decide on and implement marketing strategies for specific customer groups.

[0380] For example, on a hot summer day, a possible strategy could be to instantly display a discount campaign for cold drinks on a screen based on the tired expression of a customer who visits the store. Furthermore, by inputting a prompt to the generating AI model such as, "If the customer's expression indicates 'fatigue,' please devise a promotional strategy that proposes a discount campaign for cold drinks," the system can automatically generate flexible sales strategies.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The server retrieves sales data from the merchant's POS system and simultaneously collects payment data from the payment platform. Input data includes purchased items, sales amount, and payment method. The server aggregates and temporarily stores this data.

[0384] Step 2:

[0385] The server preprocesses the acquired sales and payment data. This preprocessing removes noise from the data and formats it for analysis. The input is raw sales and payment data, and the output is a unified, formatted dataset. This process is performed using a data cleaning algorithm.

[0386] Step 3:

[0387] The server passes pre-processed data to a machine learning algorithm to identify target customers. Here, the input data includes customer purchase history and attributes, and the output is a list of identified target customers. A clustering algorithm is used in this process.

[0388] Step 4:

[0389] The terminal's smart display acquires images of customers' faces via a camera. The input is real-time video of the customers, and the output is detected facial feature data. This data is then sent to the next processing step using facial recognition software.

[0390] Step 5:

[0391] The terminal utilizes an emotion analysis engine to analyze the emotional state of customers based on detected facial feature data. The input is facial feature data, and the output is data indicating the current emotional state. A deep learning model is used for this process.

[0392] Step 6:

[0393] The terminal combines target customer data received from the server with analyzed sentiment data to construct appropriate promotional information. The input is target customer data and sentiment state data, and the output is a personalized promotional message. This process is performed using a generative AI model.

[0394] Step 7:

[0395] The user reviews the promotional information displayed on the device and selects an effective marketing strategy for their customers. The output includes specific examples as prompts, such as, "If a customer's facial expression indicates 'fatigue,' devise a promotional strategy that proposes a discount campaign on cold drinks." The user decides which strategy to implement and puts that information into action from the device.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This system integrates and analyzes sales data and digital payment data from member stores to identify target customers and propose marketing strategies. The system primarily consists of a cloud-based server and member store terminals, and operates as follows:

[0413] The server first connects to the merchant's POS system via API to acquire sales data for goods and services in real time. Furthermore, it is integrated with the payment platform to acquire payment data in parallel. This information is centrally managed on the server and pre-processed into a format suitable for analysis.

[0414] The terminal receives pre-processed data sent from the server and displays it on a dedicated dashboard. The dashboard provides visualized information such as sales trends and customer segments, helping users make data-driven decisions. Furthermore, it incorporates interactive filtering functions to improve the user experience (UX).

[0415] Store operators, as users, can implement specific sales promotion activities based on marketing strategies proposed using the analysis results. For example, they can issue limited-time coupons to specific customer segments or introduce a points system to encourage repeat purchases. Furthermore, by introducing the suggested related products and services, they can improve operational efficiency and increase profitability.

[0416] As a concrete example, suppose a store discovers through its server's analysis process that a particular product category sells well on weekends. Based on this information, the server suggests automatically sending promotional emails to customer groups who show interest in that product category on weekends. In this way, the store can conduct efficient and effective marketing activities.

[0417] This system aims to maximize the use of merchants' sales and customer data, improving the quality of decision-making while simultaneously reducing operating costs. This will allow merchants to experience the advantages of digital payment systems, leading to further adoption and usage.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server connects to the merchant's POS system via API to acquire sales data in real time. It also acquires payment data from the payment platform and integrates this data.

[0421] Step 2:

[0422] The server performs preprocessing on the acquired data, such as removing duplicates and noise and filling in missing data. This process standardizes the data format and prepares it for analysis.

[0423] Step 3:

[0424] The server inputs pre-processed data into an AI algorithm to analyze sales patterns and customer purchasing trends. The analysis process includes techniques such as clustering and regression analysis to identify target customers.

[0425] Step 4:

[0426] The server visualizes the analysis results as graphs and charts and sends them to the merchant's terminal. The terminal receives this data and displays it on a dashboard, allowing users to intuitively understand the data.

[0427] Step 5:

[0428] Users review the analysis results through a dashboard on their device and consider the suggested marketing strategies. For example, they might discuss issuing coupons to specific customer groups or implementing promotional campaigns.

[0429] Step 6:

[0430] The server also suggests other related products and services to the user, supporting the overall efficiency of store operations. Based on these suggestions, the user can consider introducing new products and services.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] In commercial transactions, the technology for efficiently collecting and integrating sales and payment information to provide customers with optimal market strategies is not yet sufficiently developed. As a result, sellers find it difficult to make data-driven decisions quickly, making it challenging to achieve appropriate customer targeting and effective sales promotion activities. Furthermore, existing systems often require manual data preprocessing and analysis, posing a significant challenge in terms of time and effort.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes means for acquiring commercial transaction information, means for acquiring payment information, and means for integrating the acquired information and preprocessing the data into a format suitable for analysis. This enables accurate and rapid data collection and processing. Furthermore, it can identify designated customer groups, provide visualized analysis results, and automatically generate market strategy proposals using a generative AI model, thereby realizing more efficient and strategic sales promotion activities.

[0436] "Commercial transaction information" refers to various information related to the sale of goods generated in commercial activities, including product identification codes, sales times, quantities, and prices.

[0437] "Payment information" refers to information about the payment methods, purchase amounts, settlement methods, and dates of purchases used by customers in relation to commercial transactions.

[0438] A "generative AI model" refers to a program developed based on machine learning algorithms that automatically analyzes sales data and customer data to identify targeted customer groups and propose market strategies.

[0439] "Data preprocessing" refers to a series of operations to transform acquired raw data into a format suitable for analysis, and includes data normalization, imputation of missing values, and correction of outliers.

[0440] "Visualized analysis results" refers to data representations that display the information obtained through analysis in the form of graphs and charts, providing data in a format that is easy for users to understand.

[0441] "Market strategy proposals" refer to strategies and plans for sales promotion activities and product recommendations targeted at specific customer groups, based on analyzed data.

[0442] "Machine learning techniques" refer to technologies that enable computers to learn patterns in data and automatically perform tasks such as prediction and classification, and involve training models using algorithms.

[0443] This invention provides a system for efficiently collecting, analyzing, visualizing, and proposing market strategies in commercial transaction activities. This system primarily includes a server utilizing cloud technology and terminals suitable for user access.

[0444] The server first collects transaction information from the store's POS system. To do this, the server connects to the POS system via an API and collects data such as product identification codes, sales times, quantities, and prices. This process can utilize a cloud computing platform. If a specific cloud platform is required, Amazon Web Services (AWS) is an option.

[0445] The server simultaneously collects payment information from digital payment platforms. APIs are used for this data collection, capturing information such as customer ID, purchase amount, payment method, and purchase date and time. For example, Stripe and PayPal are considered as common electronic payment systems.

[0446] After data collection, the server integrates and preprocesses the data. The Python pandas library is recommended for this purpose. Data normalization, missing values ​​are filled in, outliers are corrected, and the data is converted into a format suitable for optimal analysis.

[0447] The analysis utilizes generative AI models, employing machine learning algorithms to analyze customer purchasing patterns and preferences. Specifically, the Google Cloud AI platform leverages TensorFlow to identify customer needs, enabling more accurate customer targeting.

[0448] Based on the analysis, the generated AI model proposes market strategies, which are then displayed on the user's device. This display utilizes data visualization tools such as Tableau and Power BI, providing a visual and interactive dashboard. This makes it easier for users to make decisions based on the visualized analysis results.

[0449] Store operators, as users, can implement specific sales promotion activities based on the information obtained from the dashboard. For example, automated distribution of promotional emails to specific customer groups may be suggested. An example of a prompt to instruct the generating AI model to perform this operation could be: "Analyze recent sales data and suggest the best-selling products and recommended market strategies for them."

[0450] This system provides users with a powerful tool to effectively implement market strategies using digital technology and improve their competitiveness.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The server receives transaction information as input from the store's POS system. Specifically, it uses an API to retrieve data such as product identification codes, sales time, quantity, and price. Based on this input data, the server temporarily stores the data and organizes and stores each transaction information in a database.

[0454] Step 2:

[0455] The server receives payment information as input from the digital payment platform. The server retrieves information such as customer ID, purchase amount, payment method, and purchase date and time via an API. Based on this information, the server integrates commercial transaction information and payment information and generates a dataset for centralized management.

[0456] Step 3:

[0457] The server receives the dataset as input for preprocessing. Using Python libraries, the server normalizes the data, imputes missing values, corrects outliers, and processes it into a format suitable for analysis. This results in clean, consistent output data, ready to proceed to the next analysis step.

[0458] Step 4:

[0459] The server receives pre-processed data as input using a generative AI model. Here, based on machine learning algorithms, the data is analyzed to identify customer purchasing patterns and preferences. This step provides insights necessary for identifying target customer groups and proposing market strategies. The server saves these analysis results as output.

[0460] Step 5:

[0461] The terminal receives analysis results provided by the server as input. The terminal uses data visualization tools to display the information on a dashboard. Users can visually review sales trends and customer segmentation through interactive options. This output data provides users with a basis for planning market strategies.

[0462] Step 6:

[0463] Users plan and execute specific market strategies using insights from the dashboard. For example, they can use prompts provided by a generative AI model to automatically generate marketing strategies such as, "Analyze recent sales data to suggest the best-selling products and recommended market strategies for them." By executing this activity, users can efficiently and effectively increase sales.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] In recent years, with the spread of online shopping, consumer purchasing behavior has diversified. It has become extremely important to propose effective marketing strategies for each consumer and improve customer satisfaction. However, relying solely on regular sales data and payment data makes it difficult to fully understand customer intentions and preferences, posing a challenge in conducting accurate promotions and product recommendations.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, and means for integrating the acquired data and preprocessing it into an analyzable format. This enables effective notification of product suggestions and promotions optimized for each customer.

[0469] "Sales data" refers to data that records information about the purchase of goods or the use of services.

[0470] "Settlement data" refers to data that includes information about the payment method and amount in a transaction.

[0471] "Data integration" is the process of combining different types of data, such as sales data and payment data, and converting them into a consistent format.

[0472] "Preprocessing methods" refer to methods for preparing and organizing data in order to perform analysis.

[0473] "Identifying target customers" means identifying a specific group of consumers that will be the focus of marketing activities.

[0474] "Visualization of analysis results" is a technique that makes data analysis results easier to understand by visually representing them using diagrams, tables, and other visual aids.

[0475] "Marketing measures" refer to strategies and activities implemented with the aim of promoting the sale of a product.

[0476] "Product suggestion notification" means informing customers of suitable products based on the analysis results.

[0477] This system operates on a cloud-based server and integrates sales and payment data to propose effective marketing strategies. The system's program is built using Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It also performs computations on the cloud using AWS Lambda.

[0478] The server first acquires corporate sales and payment data in real time via APIs. This data undergoes initial data cleansing and preprocessing to a format suitable for analysis. Next, customer segmentation is analyzed using machine learning models to identify target customers. At this stage, TensorFlow and PyTorch are utilized to identify consumer purchasing patterns.

[0479] Product recommendations are sent to identified target customers. The recommendations are automatically generated on the server and sent to customers via a smartphone application. This notification provides promotional information about products optimized for the target customers.

[0480] On the terminal, users can view the analysis results sent from the server on a dedicated dashboard. This dashboard visualizes sales trends and customer segment information to support users in making decisions. Furthermore, interactive filtering functions allow users to select data based on specific criteria and proceed with detailed analysis.

[0481] For example, if a consumer frequently purchases fitness equipment, the system automatically generates promotions offering discount coupons for fitness-related products to that consumer. Such personalized marketing strategies improve customer satisfaction.

[0482] The generative AI model operates with prompts like the following:

[0483] "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase."

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The server retrieves sales and payment data from companies via an API. The inputs it receives are sales history and payment records. The server integrates these different data sources and converts them into a consistent format. The output is the integrated data.

[0487] Step 2:

[0488] The server cleanses the acquired integrated data. The input is the integrated data. The server removes duplicates and errors from the data, generating a clean dataset suitable for analysis. The output is the pre-processed, clean data.

[0489] Step 3:

[0490] The server analyzes clean data using machine learning algorithms. It uses pre-processed data as input. The server analyzes consumer purchasing patterns and identifies target customers using TensorFlow or PyTorch. The output is a list of identified target customers.

[0491] Step 4:

[0492] The server generates customized product suggestions based on identified target customers. A generative AI model is used in this process. The input is a list of target customers, and the server creates suggestions using a specific prompt: "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase." The output is optimized product suggestions for each customer.

[0493] Step 5:

[0494] The terminal displays product suggestions sent from the server on a dashboard. The input is product suggestion data. The terminal provides visualized information to the user and assists the user in performing detailed analysis through interactive filtering functions. The output is a display of product suggestions that the user can visually confirm.

[0495] Step 6:

[0496] Users implement marketing strategies and promotional activities based on suggestions displayed on the dashboard. The input is the information from the dashboard. Based on the provided information, users can issue coupons to specific customer segments or revise product pricing. The output is the results of the implemented marketing strategies.

[0497] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0498] This system acquires and integrates sales and payment data from member stores, identifies target customers based on this data, and proposes marketing strategies. Furthermore, by combining this with an emotion engine, the present invention recognizes user emotions and improves the accuracy of marketing strategies.

[0499] The server first acquires sales data from the merchant's POS system and payment data from the payment platform in real time. Next, it organizes the acquired data, performs preprocessing such as noise removal, and converts it into a format suitable for analysis.

[0500] Furthermore, this system analyzes customer purchase history and feedback information via an emotion engine to predict their current emotional state and purchase intent. The results of the emotion engine are integrated with sales and payment data, enabling a deeper understanding of customers.

[0501] The terminal displays analysis results sent from the server and the output of the emotion engine in a dashboard format. Through this, users can easily review the data and develop marketing strategies for specific customer groups. Suggested strategies include segmentation based on emotion data and personalized promotions.

[0502] As a concrete example, suppose a store's emotional intelligence engine predicts that customers visiting on weekends will have a high purchasing intent. Based on this information and sales / payment data, the server automatically generates weekend-only promotional strategies and recommends them to users. As a result, the store can efficiently execute promotional activities and expect increased sales.

[0503] In this way, by utilizing integrated data across the entire system and taking user emotions into consideration, it becomes possible to design and execute more precise targeting and effective marketing strategies. As a result, member stores can improve customer satisfaction and increase revenue.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The server connects to the merchant's POS system and retrieves sales data instantly. It also retrieves payment data from the digital payment platform and integrates the data from both sources.

[0507] Step 2:

[0508] The server performs preprocessing on the acquired raw data. Specifically, it removes duplicate data, standardizes the format, and imputes missing values ​​to prepare the data for analysis.

[0509] Step 3:

[0510] The server activates the emotion engine and analyzes the customer's emotional state and purchase intent based on past purchase history and feedback data. The analysis results are then used for subsequent analysis.

[0511] Step 4:

[0512] The server combines pre-processed sales and payment data with sentiment analysis results to perform a comprehensive customer analysis. This analysis uses machine learning algorithms to segment target customers and predict purchasing trends.

[0513] Step 5:

[0514] The server visualizes the analysis results and sends them to the terminal. The terminal displays the received data on a dashboard, allowing the user to examine the data in detail using various filtering and visualization options.

[0515] Step 6:

[0516] Based on the information in the dashboard, users consider and select specific marketing strategies for their target customers. These strategies include personalized promotions based on sentiment data and the execution of timely campaigns.

[0517] Step 7:

[0518] The server generates suggestions for related products and new services, and provides feedback to users. This allows member stores to develop concrete strategies to further improve operational efficiency.

[0519] (Example 2)

[0520] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] Many commercial facilities and retail stores are required to develop effective marketing strategies by utilizing customer purchase history and payment information. However, traditional systems have made it difficult to efficiently integrate this data and gain useful insights. Furthermore, they have been unable to design strategies that take into account consumer emotions and purchasing intent, resulting in a failure to improve revenue and customer satisfaction.

[0522] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0523] In this invention, the server includes means for acquiring sales information, means for acquiring payment information, and means for integrating the acquired information and preprocessing it into an analyzable format. This enables centralized data management and the design and execution of sophisticated marketing strategies that even take into account the emotional state of customers.

[0524] "Sales information" refers to data related to the sales of each product or service in commercial activities. Specifically, it includes sales quantity, sales amount, and sales date and time.

[0525] "Payment information" refers to data related to payments made by the buyer. This includes credit card information, payment amount, and payment date and time.

[0526] "Customer emotional state" refers to the psychological reactions and emotions consumers have towards products and services. Data analyzed using an emotion engine falls into this category.

[0527] "Target consumers" refer to customers identified as highly likely to purchase a particular product or service. They are the primary target of marketing strategies.

[0528] "Sales promotion measures" refer to strategic activities aimed at encouraging specific consumer segments to purchase products or services. This includes segmentation and promotion.

[0529] "Products and services" refer to all items with commercial value provided by a company or business. This includes goods, software, and various services.

[0530] An integrated system is required to implement this invention. This system mainly consists of three elements: a server, a terminal, and a user.

[0531] The server first retrieves sales and payment information from external systems. Specifically, it accesses POS systems and payment platforms using APIs to collect sales and payment information in real time. The collected information is stored in a database, where it is then organized and noise-reduced. General database software can be used for database management.

[0532] Next, the server utilizes an emotion analysis engine to analyze the customer's emotional state from their past purchase history and feedback data. This emotion analysis engine uses natural language processing and machine learning algorithms to quantify the customer's emotional state. The results of the emotion analysis are integrated with sales and payment information to obtain deeper insights.

[0533] Furthermore, the server uses a generative AI model to analyze data and identify target consumers. Based on the generated data, it creates prompts and generates appropriate marketing strategies.

[0534] The device displays the data processing results and sentiment analysis findings transmitted from the server on a dashboard. Users can use this dashboard to intuitively review the data and devise marketing strategies. The device can be run on a standard computer or tablet device.

[0535] For example, if sentiment analysis predicts that a store's customers' purchasing intent will increase on a particular weekend, the server will automatically generate weekend-only promotional strategies based on this information. For instance, it might generate a prompt message such as, "Identify target customers and propose optimal marketing strategies based on the following data. This data includes sales data, payment information, and customer sentiment analysis results."

[0536] Thus, this invention provides a foundation for formulating effective marketing strategies by handling digital information in an integrated and analytical manner. Through this system, users can accurately execute strategies to improve customer satisfaction and increase sales.

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The server collects sales and payment information. It connects to POS systems and payment platforms and retrieves data in real time via APIs. The inputs are sales and payment information, and the output is the collected raw data. The server stores this data in a database.

[0540] Step 2:

[0541] The server preprocesses the acquired data. The input is raw data, and it performs missing value imputation, noise removal, and conversion to the required format. The output is organized data in an analyzable format. Here, missing values ​​are imputed with the mean and outliers are removed.

[0542] Step 3:

[0543] The server uses an emotion analysis engine to analyze the customer's emotional state. Inputs include purchase history and feedback information, and text mining techniques are used to score emotions. The output is numerical emotion data. The server integrates this emotion data with sales information.

[0544] Step 4:

[0545] The server analyzes integrated data using machine learning algorithms to identify target consumers. The input consists of organized data and sentiment data, and the output is a list of target consumers. The server generates customer segments using clustering techniques.

[0546] Step 5:

[0547] The server automatically generates marketing strategies using a generative AI model. The input is a list of target consumers, and prompts are created to instruct the AI ​​model. The output is the proposed marketing strategies. Prompts include phrases such as, "Identify target customers based on the following data and propose the most suitable marketing strategies."

[0548] Step 6:

[0549] The terminal displays analysis results and strategies from the server on a dashboard. Input is data sent from the server, and output is a visual dashboard display. The terminal visualizes the data in graph and table formats.

[0550] Step 7:

[0551] Users review the dashboard, select identified strategies, and execute them. The input is the data on the dashboard, and the output is the strategies to be implemented. Users apply the promotional methods they deem effective based on their own judgment.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] Traditional marketing strategies struggled to accurately grasp customer emotions and individual purchasing intentions, making efficient targeting difficult. Furthermore, while real-time sentiment analysis-based promotional displays were essential, the technology to achieve this was limited. Therefore, developing strategies that truly capture the interest of in-store customers was a challenge.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, means for integrating the acquired data and preprocessing it into an analyzable format, means for detecting facial expressions in real time and identifying emotional states, and means for displaying personalized promotions based on the identified emotional states. This enables personalized and dynamic marketing strategies that take into account the real-time emotional states of customers visiting the store.

[0557] "Sales data" refers to transaction information generated when goods or services are sold, and includes details such as quantity, date of sale, and price.

[0558] "Payment data" refers to data related to payment methods and payment information at the time of purchase, including card information used and payment amounts.

[0559] "Emotional state" refers to an individual's current emotional state, a psychological state inferred from facial expressions, tone of voice, and other factors.

[0560] "Promotion" refers to specific sales promotion activities carried out to encourage the sale of products or services, and includes measures such as discounts and special offers.

[0561] A "target customer" is a customer who is the target of a specific marketing initiative, and is a customer segment identified based on their purchase history and interests.

[0562] "Facial expression detection" is a process that uses cameras and sensors to read the features of an individual's face and analyze their emotions in real time.

[0563] "Integrating data" is the process of combining multiple datasets obtained from different sources into a single, accessible format.

[0564] A system implementing this invention is constructed using multiple hardware and software components. The main components include a sensor device for acquiring sales data, a data network for acquiring payment data, a server for integrating and pre-processing the data, and a camera and a real-time sentiment analysis engine for identifying emotional states.

[0565] The server first acquires and integrates sales and payment data from merchants in real time. This involves using data feeds via the information network and local databases. This integrated data is preprocessed to remove noise and format it into an analyzable format. The information is then analyzed by machine learning algorithms to identify target customers.

[0566] The smart display, acting as a terminal, is equipped with a camera to detect the faces of customers. This terminal captures the facial expressions of individual customers and uses an emotion analysis engine to identify their emotional state in real time. Examples of available software include facial recognition APIs and emotion analysis models. Based on this, specific promotional information is displayed on the screen in real time to stimulate customer purchasing intent.

[0567] Users can view the analysis results sent from the server and the output of the sentiment analysis engine in a dashboard format. Through this dashboard, they can quickly decide on and implement marketing strategies for specific customer groups.

[0568] For example, on a hot summer day, a possible strategy could be to instantly display a discount campaign for cold drinks on a screen based on the tired expression of a customer who visits the store. Furthermore, by inputting a prompt to the generating AI model such as, "If the customer's expression indicates 'fatigue,' please devise a promotional strategy that proposes a discount campaign for cold drinks," the system can automatically generate flexible sales strategies.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server retrieves sales data from the merchant's POS system and simultaneously collects payment data from the payment platform. Input data includes purchased items, sales amount, and payment method. The server aggregates and temporarily stores this data.

[0572] Step 2:

[0573] The server preprocesses the acquired sales and payment data. This preprocessing removes noise from the data and formats it for analysis. The input is raw sales and payment data, and the output is a unified, formatted dataset. This process is performed using a data cleaning algorithm.

[0574] Step 3:

[0575] The server passes pre-processed data to a machine learning algorithm to identify target customers. Here, the input data includes customer purchase history and attributes, and the output is a list of identified target customers. A clustering algorithm is used in this process.

[0576] Step 4:

[0577] The terminal's smart display acquires images of customers' faces via a camera. The input is real-time video of the customers, and the output is detected facial feature data. This data is then sent to the next processing step using facial recognition software.

[0578] Step 5:

[0579] The terminal utilizes an emotion analysis engine to analyze the emotional state of customers based on detected facial feature data. The input is facial feature data, and the output is data indicating the current emotional state. A deep learning model is used for this process.

[0580] Step 6:

[0581] The terminal combines target customer data received from the server with analyzed sentiment data to construct appropriate promotional information. The input is target customer data and sentiment state data, and the output is a personalized promotional message. This process is performed using a generative AI model.

[0582] Step 7:

[0583] The user reviews the promotional information displayed on the device and selects an effective marketing strategy for their customers. The output includes specific examples as prompts, such as, "If a customer's facial expression indicates 'fatigue,' devise a promotional strategy that proposes a discount campaign on cold drinks." The user decides which strategy to implement and puts that information into action from the device.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This system integrates and analyzes sales data and digital payment data from member stores to identify target customers and propose marketing strategies. The system primarily consists of a cloud-based server and member store terminals, and operates as follows:

[0602] The server first connects to the merchant's POS system via API to acquire sales data for goods and services in real time. Furthermore, it is integrated with the payment platform to acquire payment data in parallel. This information is centrally managed on the server and pre-processed into a format suitable for analysis.

[0603] The terminal receives pre-processed data sent from the server and displays it on a dedicated dashboard. The dashboard provides visualized information such as sales trends and customer segments, helping users make data-driven decisions. Furthermore, it incorporates interactive filtering functions to improve the user experience (UX).

[0604] Store operators, as users, can implement specific sales promotion activities based on marketing strategies proposed using the analysis results. For example, they can issue limited-time coupons to specific customer segments or introduce a points system to encourage repeat purchases. Furthermore, by introducing the suggested related products and services, they can improve operational efficiency and increase profitability.

[0605] As a concrete example, suppose a store discovers through its server's analysis process that a particular product category sells well on weekends. Based on this information, the server suggests automatically sending promotional emails to customer groups who show interest in that product category on weekends. In this way, the store can conduct efficient and effective marketing activities.

[0606] This system aims to maximize the use of merchants' sales and customer data, improving the quality of decision-making while simultaneously reducing operating costs. This will allow merchants to experience the advantages of digital payment systems, leading to further adoption and usage.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The server connects to the merchant's POS system via API to acquire sales data in real time. It also acquires payment data from the payment platform and integrates this data.

[0610] Step 2:

[0611] The server performs preprocessing on the acquired data, such as removing duplicates and noise and filling in missing data. This process standardizes the data format and prepares it for analysis.

[0612] Step 3:

[0613] The server inputs pre-processed data into an AI algorithm to analyze sales patterns and customer purchasing trends. The analysis process includes techniques such as clustering and regression analysis to identify target customers.

[0614] Step 4:

[0615] The server visualizes the analysis results as graphs and charts and sends them to the merchant's terminal. The terminal receives this data and displays it on a dashboard, allowing users to intuitively understand the data.

[0616] Step 5:

[0617] Users review the analysis results through a dashboard on their device and consider the suggested marketing strategies. For example, they might discuss issuing coupons to specific customer groups or implementing promotional campaigns.

[0618] Step 6:

[0619] The server also suggests other related products and services to the user, supporting the overall efficiency of store operations. Based on these suggestions, the user can consider introducing new products and services.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In commercial transactions, the technology for efficiently collecting and integrating sales and payment information to provide customers with optimal market strategies is not yet sufficiently developed. As a result, sellers find it difficult to make data-driven decisions quickly, making it challenging to achieve appropriate customer targeting and effective sales promotion activities. Furthermore, existing systems often require manual data preprocessing and analysis, posing a significant challenge in terms of time and effort.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes means for acquiring commercial transaction information, means for acquiring payment information, and means for integrating the acquired information and preprocessing the data into a format suitable for analysis. This enables accurate and rapid data collection and processing. Furthermore, it can identify designated customer groups, provide visualized analysis results, and automatically generate market strategy proposals using a generative AI model, thereby realizing more efficient and strategic sales promotion activities.

[0625] "Commercial transaction information" refers to various information related to the sale of goods generated in commercial activities, including product identification codes, sales times, quantities, and prices.

[0626] "Payment information" refers to information about the payment methods, purchase amounts, settlement methods, and dates of purchases used by customers in relation to commercial transactions.

[0627] A "generative AI model" refers to a program developed based on machine learning algorithms that automatically analyzes sales data and customer data to identify targeted customer groups and propose market strategies.

[0628] "Data preprocessing" refers to a series of operations to transform acquired raw data into a format suitable for analysis, and includes data normalization, imputation of missing values, and correction of outliers.

[0629] "Visualized analysis results" refers to data representations that display the information obtained through analysis in the form of graphs and charts, providing data in a format that is easy for users to understand.

[0630] "Market strategy proposals" refer to strategies and plans for sales promotion activities and product recommendations targeted at specific customer groups, based on analyzed data.

[0631] "Machine learning techniques" refer to technologies that enable computers to learn patterns in data and automatically perform tasks such as prediction and classification, and involve training models using algorithms.

[0632] This invention provides a system for efficiently collecting, analyzing, visualizing, and proposing market strategies in commercial transaction activities. This system primarily includes a server utilizing cloud technology and terminals suitable for user access.

[0633] The server first collects transaction information from the store's POS system. To do this, the server connects to the POS system via an API and collects data such as product identification codes, sales times, quantities, and prices. This process can utilize a cloud computing platform. If a specific cloud platform is required, Amazon Web Services (AWS) is an option.

[0634] The server simultaneously collects payment information from digital payment platforms. APIs are used for this data collection, capturing information such as customer ID, purchase amount, payment method, and purchase date and time. For example, Stripe and PayPal are considered as common electronic payment systems.

[0635] After data collection, the server integrates and preprocesses the data. The Python pandas library is recommended for this purpose. Data normalization, missing values ​​are filled in, outliers are corrected, and the data is converted into a format suitable for optimal analysis.

[0636] The analysis utilizes generative AI models, employing machine learning algorithms to analyze customer purchasing patterns and preferences. Specifically, the Google Cloud AI platform leverages TensorFlow to identify customer needs, enabling more accurate customer targeting.

[0637] Based on the analysis, the generated AI model proposes market strategies, which are then displayed on the user's device. This display utilizes data visualization tools such as Tableau and Power BI, providing a visual and interactive dashboard. This makes it easier for users to make decisions based on the visualized analysis results.

[0638] Store operators, as users, can implement specific sales promotion activities based on the information obtained from the dashboard. For example, automated distribution of promotional emails to specific customer groups may be suggested. An example of a prompt to instruct the generating AI model to perform this operation could be: "Analyze recent sales data and suggest the best-selling products and recommended market strategies for them."

[0639] This system provides users with a powerful tool to effectively implement market strategies using digital technology and improve their competitiveness.

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The server receives transaction information as input from the store's POS system. Specifically, it uses an API to retrieve data such as product identification codes, sales time, quantity, and price. Based on this input data, the server temporarily stores the data and organizes and stores each transaction information in a database.

[0643] Step 2:

[0644] The server receives payment information as input from the digital payment platform. The server retrieves information such as customer ID, purchase amount, payment method, and purchase date and time via an API. Based on this information, the server integrates commercial transaction information and payment information and generates a dataset for centralized management.

[0645] Step 3:

[0646] The server receives the dataset as input for preprocessing. Using Python libraries, the server normalizes the data, imputes missing values, corrects outliers, and processes it into a format suitable for analysis. This results in clean, consistent output data, ready to proceed to the next analysis step.

[0647] Step 4:

[0648] The server receives pre-processed data as input using a generative AI model. Here, based on machine learning algorithms, the data is analyzed to identify customer purchasing patterns and preferences. This step provides insights necessary for identifying target customer groups and proposing market strategies. The server saves these analysis results as output.

[0649] Step 5:

[0650] The terminal receives analysis results provided by the server as input. The terminal uses data visualization tools to display the information on a dashboard. Users can visually review sales trends and customer segmentation through interactive options. This output data provides users with a basis for planning market strategies.

[0651] Step 6:

[0652] Users plan and execute specific market strategies using insights from the dashboard. For example, they can use prompts provided by a generative AI model to automatically generate marketing strategies such as, "Analyze recent sales data to suggest the best-selling products and recommended market strategies for them." By executing this activity, users can efficiently and effectively increase sales.

[0653] (Application Example 1)

[0654] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] In recent years, with the spread of online shopping, consumer purchasing behavior has diversified. It has become extremely important to propose effective marketing strategies for each consumer and improve customer satisfaction. However, relying solely on regular sales data and payment data makes it difficult to fully understand customer intentions and preferences, posing a challenge in conducting accurate promotions and product recommendations.

[0656] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0657] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, and means for integrating the acquired data and preprocessing it into an analyzable format. This enables effective notification of product suggestions and promotions optimized for each customer.

[0658] "Sales data" refers to data that records information about the purchase of goods or the use of services.

[0659] "Settlement data" refers to data that includes information about the payment method and amount in a transaction.

[0660] "Data integration" is the process of combining different types of data, such as sales data and payment data, and converting them into a consistent format.

[0661] "Preprocessing methods" refer to methods for preparing and organizing data in order to perform analysis.

[0662] "Identifying target customers" means identifying a specific group of consumers that will be the focus of marketing activities.

[0663] "Visualization of analysis results" is a technique that makes data analysis results easier to understand by visually representing them using diagrams, tables, and other visual aids.

[0664] "Marketing measures" refer to strategies and activities implemented with the aim of promoting the sale of a product.

[0665] "Product suggestion notification" means informing customers of suitable products based on the analysis results.

[0666] This system operates on a cloud-based server and integrates sales and payment data to propose effective marketing strategies. The system's program is built using Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It also performs computations on the cloud using AWS Lambda.

[0667] The server first acquires corporate sales and payment data in real time via APIs. This data undergoes initial data cleansing and preprocessing to a format suitable for analysis. Next, customer segmentation is analyzed using machine learning models to identify target customers. At this stage, TensorFlow and PyTorch are utilized to identify consumer purchasing patterns.

[0668] Product recommendations are sent to identified target customers. The recommendations are automatically generated on the server and sent to customers via a smartphone application. This notification provides promotional information about products optimized for the target customers.

[0669] On the terminal, users can view the analysis results sent from the server on a dedicated dashboard. This dashboard visualizes sales trends and customer segment information to support users in making decisions. Furthermore, interactive filtering functions allow users to select data based on specific criteria and proceed with detailed analysis.

[0670] For example, if a consumer frequently purchases fitness equipment, the system automatically generates promotions offering discount coupons for fitness-related products to that consumer. Such personalized marketing strategies improve customer satisfaction.

[0671] The generative AI model operates with prompts like the following:

[0672] "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase."

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server retrieves sales and payment data from companies via an API. The inputs it receives are sales history and payment records. The server integrates these different data sources and converts them into a consistent format. The output is the integrated data.

[0676] Step 2:

[0677] The server cleanses the acquired integrated data. The input is the integrated data. The server removes duplicates and errors from the data, generating a clean dataset suitable for analysis. The output is the pre-processed, clean data.

[0678] Step 3:

[0679] The server analyzes clean data using machine learning algorithms. It uses pre-processed data as input. The server analyzes consumer purchasing patterns and identifies target customers using TensorFlow or PyTorch. The output is a list of identified target customers.

[0680] Step 4:

[0681] The server generates customized product suggestions based on identified target customers. A generative AI model is used in this process. The input is a list of target customers, and the server creates suggestions using a specific prompt: "Based on user A's purchase history analysis, generate product suggestions to encourage their next purchase." The output is optimized product suggestions for each customer.

[0682] Step 5:

[0683] The terminal displays product suggestions sent from the server on a dashboard. The input is product suggestion data. The terminal provides visualized information to the user and assists the user in performing detailed analysis through interactive filtering functions. The output is a display of product suggestions that the user can visually confirm.

[0684] Step 6:

[0685] Users implement marketing strategies and promotional activities based on suggestions displayed on the dashboard. The input is the information from the dashboard. Based on the provided information, users can issue coupons to specific customer segments or revise product pricing. The output is the results of the implemented marketing strategies.

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This system acquires and integrates sales and payment data from member stores, identifies target customers based on this data, and proposes marketing strategies. Furthermore, by combining this with an emotion engine, the present invention recognizes user emotions and improves the accuracy of marketing strategies.

[0688] The server first acquires sales data from the merchant's POS system and payment data from the payment platform in real time. Next, it organizes the acquired data, performs preprocessing such as noise removal, and converts it into a format suitable for analysis.

[0689] Furthermore, this system analyzes customer purchase history and feedback information via an emotion engine to predict their current emotional state and purchase intent. The results of the emotion engine are integrated with sales and payment data, enabling a deeper understanding of customers.

[0690] The terminal displays analysis results sent from the server and the output of the emotion engine in a dashboard format. Through this, users can easily review the data and develop marketing strategies for specific customer groups. Suggested strategies include segmentation based on emotion data and personalized promotions.

[0691] As a concrete example, suppose a store's emotional intelligence engine predicts that customers visiting on weekends will have a high purchasing intent. Based on this information and sales / payment data, the server automatically generates weekend-only promotional strategies and recommends them to users. As a result, the store can efficiently execute promotional activities and expect increased sales.

[0692] In this way, by utilizing integrated data across the entire system and taking user emotions into consideration, it becomes possible to design and execute more precise targeting and effective marketing strategies. As a result, member stores can improve customer satisfaction and increase revenue.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The server connects to the merchant's POS system and retrieves sales data instantly. It also retrieves payment data from the digital payment platform and integrates the data from both sources.

[0696] Step 2:

[0697] The server performs preprocessing on the acquired raw data. Specifically, it removes duplicate data, standardizes the format, and imputes missing values ​​to prepare the data for analysis.

[0698] Step 3:

[0699] The server activates the emotion engine and analyzes the customer's emotional state and purchase intent based on past purchase history and feedback data. The analysis results are then used for subsequent analysis.

[0700] Step 4:

[0701] The server combines pre-processed sales and payment data with sentiment analysis results to perform a comprehensive customer analysis. This analysis uses machine learning algorithms to segment target customers and predict purchasing trends.

[0702] Step 5:

[0703] The server visualizes the analysis results and sends them to the terminal. The terminal displays the received data on a dashboard, allowing the user to examine the data in detail using various filtering and visualization options.

[0704] Step 6:

[0705] Based on the information in the dashboard, users consider and select specific marketing strategies for their target customers. These strategies include personalized promotions based on sentiment data and the execution of timely campaigns.

[0706] Step 7:

[0707] The server generates suggestions for related products and new services, and provides feedback to users. This allows member stores to develop concrete strategies to further improve operational efficiency.

[0708] (Example 2)

[0709] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] Many commercial facilities and retail stores are required to develop effective marketing strategies by utilizing customer purchase history and payment information. However, traditional systems have made it difficult to efficiently integrate this data and gain useful insights. Furthermore, they have been unable to design strategies that take into account consumer emotions and purchasing intent, resulting in a failure to improve revenue and customer satisfaction.

[0711] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0712] In this invention, the server includes means for acquiring sales information, means for acquiring payment information, and means for integrating the acquired information and preprocessing it into an analyzable format. This enables centralized data management and the design and execution of sophisticated marketing strategies that even take into account the emotional state of customers.

[0713] "Sales information" refers to data related to the sales of each product or service in commercial activities. Specifically, it includes sales quantity, sales amount, and sales date and time.

[0714] "Payment information" refers to data related to payments made by the buyer. This includes credit card information, payment amount, and payment date and time.

[0715] "Customer emotional state" refers to the psychological reactions and emotions consumers have towards products and services. Data analyzed using an emotion engine falls into this category.

[0716] "Target consumers" refer to customers identified as highly likely to purchase a particular product or service. They are the primary target of marketing strategies.

[0717] "Sales promotion measures" refer to strategic activities aimed at encouraging specific consumer segments to purchase products or services. This includes segmentation and promotion.

[0718] "Products and services" refer to all items with commercial value provided by a company or business. This includes goods, software, and various services.

[0719] An integrated system is required to implement this invention. This system mainly consists of three elements: a server, a terminal, and a user.

[0720] The server first retrieves sales and payment information from external systems. Specifically, it accesses POS systems and payment platforms using APIs to collect sales and payment information in real time. The collected information is stored in a database, where it is then organized and noise-reduced. General database software can be used for database management.

[0721] Next, the server utilizes an emotion analysis engine to analyze the customer's emotional state from their past purchase history and feedback data. This emotion analysis engine uses natural language processing and machine learning algorithms to quantify the customer's emotional state. The results of the emotion analysis are integrated with sales and payment information to obtain deeper insights.

[0722] Furthermore, the server uses a generative AI model to analyze data and identify target consumers. Based on the generated data, it creates prompts and generates appropriate marketing strategies.

[0723] The device displays the data processing results and sentiment analysis findings transmitted from the server on a dashboard. Users can use this dashboard to intuitively review the data and devise marketing strategies. The device can be run on a standard computer or tablet device.

[0724] For example, if sentiment analysis predicts that a store's customers' purchasing intent will increase on a particular weekend, the server will automatically generate weekend-only promotional strategies based on this information. For instance, it might generate a prompt message such as, "Identify target customers and propose optimal marketing strategies based on the following data. This data includes sales data, payment information, and customer sentiment analysis results."

[0725] Thus, this invention provides a foundation for formulating effective marketing strategies by handling digital information in an integrated and analytical manner. Through this system, users can accurately execute strategies to improve customer satisfaction and increase sales.

[0726] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0727] Step 1:

[0728] The server collects sales and payment information. It connects to POS systems and payment platforms and retrieves data in real time via APIs. The inputs are sales and payment information, and the output is the collected raw data. The server stores this data in a database.

[0729] Step 2:

[0730] The server preprocesses the acquired data. The input is raw data, and it performs missing value imputation, noise removal, and conversion to the required format. The output is organized data in an analyzable format. Here, missing values ​​are imputed with the mean and outliers are removed.

[0731] Step 3:

[0732] The server uses an emotion analysis engine to analyze the customer's emotional state. Inputs include purchase history and feedback information, and text mining techniques are used to score emotions. The output is numerical emotion data. The server integrates this emotion data with sales information.

[0733] Step 4:

[0734] The server analyzes integrated data using machine learning algorithms to identify target consumers. The input consists of organized data and sentiment data, and the output is a list of target consumers. The server generates customer segments using clustering techniques.

[0735] Step 5:

[0736] The server automatically generates marketing strategies using a generative AI model. The input is a list of target consumers, and prompts are created to instruct the AI ​​model. The output is the proposed marketing strategies. Prompts include phrases such as, "Identify target customers based on the following data and propose the most suitable marketing strategies."

[0737] Step 6:

[0738] The terminal displays analysis results and strategies from the server on a dashboard. Input is data sent from the server, and output is a visual dashboard display. The terminal visualizes the data in graph and table formats.

[0739] Step 7:

[0740] Users review the dashboard, select identified strategies, and execute them. The input is the data on the dashboard, and the output is the strategies to be implemented. Users apply the promotional methods they deem effective based on their own judgment.

[0741] (Application Example 2)

[0742] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] Traditional marketing strategies struggled to accurately grasp customer emotions and individual purchasing intentions, making efficient targeting difficult. Furthermore, while real-time sentiment analysis-based promotional displays were essential, the technology to achieve this was limited. Therefore, developing strategies that truly capture the interest of in-store customers was a challenge.

[0744] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0745] In this invention, the server includes means for acquiring sales data, means for acquiring payment data, means for integrating the acquired data and preprocessing it into an analyzable format, means for detecting facial expressions in real time and identifying emotional states, and means for displaying personalized promotions based on the identified emotional states. This enables personalized and dynamic marketing strategies that take into account the real-time emotional states of customers visiting the store.

[0746] "Sales data" refers to transaction information generated when goods or services are sold, and includes details such as quantity, date of sale, and price.

[0747] "Payment data" refers to data related to payment methods and payment information at the time of purchase, including card information used and payment amounts.

[0748] "Emotional state" refers to an individual's current emotional state, a psychological state inferred from facial expressions, tone of voice, and other factors.

[0749] "Promotion" refers to specific sales promotion activities carried out to encourage the sale of products or services, and includes measures such as discounts and special offers.

[0750] A "target customer" is a customer who is the target of a specific marketing initiative, and is a customer segment identified based on their purchase history and interests.

[0751] "Facial expression detection" is a process that uses cameras and sensors to read the features of an individual's face and analyze their emotions in real time.

[0752] "Integrating data" is the process of combining multiple datasets obtained from different sources into a single, accessible format.

[0753] A system implementing this invention is constructed using multiple hardware and software components. The main components include a sensor device for acquiring sales data, a data network for acquiring payment data, a server for integrating and pre-processing the data, and a camera and a real-time sentiment analysis engine for identifying emotional states.

[0754] The server first acquires and integrates sales and payment data from merchants in real time. This involves using data feeds via the information network and local databases. This integrated data is preprocessed to remove noise and format it into an analyzable format. The information is then analyzed by machine learning algorithms to identify target customers.

[0755] The smart display, acting as a terminal, is equipped with a camera to detect the faces of customers. This terminal captures the facial expressions of individual customers and uses an emotion analysis engine to identify their emotional state in real time. Examples of available software include facial recognition APIs and emotion analysis models. Based on this, specific promotional information is displayed on the screen in real time to stimulate customer purchasing intent.

[0756] Users can view the analysis results sent from the server and the output of the sentiment analysis engine in a dashboard format. Through this dashboard, they can quickly decide on and implement marketing strategies for specific customer groups.

[0757] For example, on a hot summer day, a possible strategy could be to instantly display a discount campaign for cold drinks on a screen based on the tired expression of a customer who visits the store. Furthermore, by inputting a prompt to the generating AI model such as, "If the customer's expression indicates 'fatigue,' please devise a promotional strategy that proposes a discount campaign for cold drinks," the system can automatically generate flexible sales strategies.

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The server retrieves sales data from the merchant's POS system and simultaneously collects payment data from the payment platform. Input data includes purchased items, sales amount, and payment method. The server aggregates and temporarily stores this data.

[0761] Step 2:

[0762] The server preprocesses the acquired sales and payment data. This preprocessing removes noise from the data and formats it for analysis. The input is raw sales and payment data, and the output is a unified, formatted dataset. This process is performed using a data cleaning algorithm.

[0763] Step 3:

[0764] The server passes pre-processed data to a machine learning algorithm to identify target customers. Here, the input data includes customer purchase history and attributes, and the output is a list of identified target customers. A clustering algorithm is used in this process.

[0765] Step 4:

[0766] The terminal's smart display acquires images of customers' faces via a camera. The input is real-time video of the customers, and the output is detected facial feature data. This data is then sent to the next processing step using facial recognition software.

[0767] Step 5:

[0768] The terminal utilizes an emotion analysis engine to analyze the emotional state of customers based on detected facial feature data. The input is facial feature data, and the output is data indicating the current emotional state. A deep learning model is used for this process.

[0769] Step 6:

[0770] The terminal combines target customer data received from the server with analyzed sentiment data to construct appropriate promotional information. The input is target customer data and sentiment state data, and the output is a personalized promotional message. This process is performed using a generative AI model.

[0771] Step 7:

[0772] The user reviews the promotional information displayed on the device and selects an effective marketing strategy for their customers. The output includes specific examples as prompts, such as, "If a customer's facial expression indicates 'fatigue,' devise a promotional strategy that proposes a discount campaign on cold drinks." The user decides which strategy to implement and puts that information into action from the device.

[0773] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0774] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0775] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0776] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0777] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0778] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0779] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0780] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0781] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0782] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0783] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0784] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0785] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0786] 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.

[0787] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0788] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0789] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0790] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0791] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0792] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0793] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] Means of obtaining sales data,

[0797] Means of obtaining payment data,

[0798] A means for integrating the acquired data and preprocessing it into an analyzable format,

[0799] A means to analyze the generated data and identify target customers,

[0800] Means for visualizing and displaying analysis results,

[0801] A means of designing and proposing marketing strategies for target customers,

[0802] A means of proposing related products and services to member stores,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein sales data is acquired by a sensor device or a data network.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that it uses a machine learning algorithm to identify target customers.

[0808] "Example 1"

[0809] (Claim 1)

[0810] Means of obtaining commercial transaction information,

[0811] Means of obtaining payment information,

[0812] A means of integrating the acquired information and preprocessing the data into a format suitable for analysis,

[0813] A means for identifying a designated customer group using pre-processed information,

[0814] A means of visualizing the analysis results and displaying them on a screen,

[0815] A means of designing and proposing market strategies suitable for a designated customer group,

[0816] A means of proposing related products and support to providers,

[0817] A means of having interactive operation functions for data visualization,

[0818] A means for creating input text to automatically generate marketing proposals using a generative AI model,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein commercial transaction information is acquired by a detection device or an information and communication network.

[0822] (Claim 3)

[0823] The system according to claim 1, characterized in that it utilizes machine learning techniques to identify the designated customer group.

[0824] "Application Example 1"

[0825] (Claim 1)

[0826] Means of obtaining sales data,

[0827] Means of obtaining payment data,

[0828] A means for integrating the acquired data and preprocessing it into an analyzable format,

[0829] A means of analyzing the generated data to identify target customers,

[0830] A means of visualizing and displaying the analysis results,

[0831] A means of designing and proposing marketing strategies for target customers,

[0832] A means of proposing related products and services to companies,

[0833] A means of notifying customers of product recommendations optimized for each customer based on customer segment analysis,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein sales data is acquired by a detection device or an information and communication network.

[0837] (Claim 3)

[0838] The system according to claim 1, which uses a machine learning algorithm to identify target customers.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] Means of obtaining sales information,

[0842] Means of obtaining payment information,

[0843] A means for integrating the acquired information and preprocessing it into an analyzable format,

[0844] A means of analyzing the emotional state of customers,

[0845] A means for analyzing the generated data and identifying target consumers,

[0846] Means for visualizing and displaying analysis results,

[0847] A means of designing and proposing sales promotion measures for target consumers,

[0848] A means of proposing related products and services to the customer,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein sales information is acquired by a sensor device or an information network.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that it uses machine learning algorithms and generative AI models in identifying target consumers.

[0854] "Application example 2 when combining with an emotional engine"

[0855] (Claim 1)

[0856] Means of obtaining sales data,

[0857] Means of obtaining payment data,

[0858] A means for integrating the acquired data and preprocessing it into an analyzable format,

[0859] A means to analyze the generated data and identify target customers,

[0860] Means for visualizing and displaying analysis results,

[0861] A means of designing and proposing marketing strategies for target customers,

[0862] A means of proposing related products and services to the main entity,

[0863] A means for detecting facial expressions in real time and identifying emotional states,

[0864] A means of displaying personalized promotions based on identified emotional states,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein sales data is acquired by a detection device or an information network.

[0868] (Claim 3)

[0869] The system according to claim 1, characterized in that it uses a machine learning algorithm to identify target customers and their emotional states. [Explanation of symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining sales data, Means of obtaining payment data, A means for integrating the acquired data and preprocessing it into an analyzable format, A means to analyze the generated data and identify target customers, Means for visualizing and displaying analysis results, A means of designing and proposing marketing strategies for target customers, A means of proposing related products and services to member stores, A system that includes this.

2. The system according to claim 1, wherein sales data is acquired by a sensor device or a data network.

3. The system according to claim 1, characterized in that it uses a machine learning algorithm to identify target customers.

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