System

Abstract

To provide a system which recommends an individualized content or service.SOLUTION: A system includes: means for acquiring client information relating to a user; means for acquiring location information of the user; means for acquiring crowd flow information relating to movements or stays of people in a location of the user; means for acquiring purchase information relating to purchase of the user; and means for recommending a content or service, that can be provided to the user, using a prompt sentence instructing recommendation of the content or service, which can be provided to the user, and a generative AI model on the basis of the client information, the location information, the crowd flow information and the purchase information which are acquired.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional mobile carrier services have not adequately provided personalized services that utilize customer information, location information, people flow information, and purchasing information, making it difficult to increase ARPU. [Means for solving the problem]

[0005] The present invention provides a system that uses natural language processing technology, particularly generative AI, to combine customer information, location information, people flow information, and purchasing information to provide personalized content and services, thereby improving ARPU. [Brief explanation of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that uses generative AI as natural language processing technology. This system has a means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. This acquired information is processed by generative AI and converted into information for providing personalized content and services. For example, when a customer is staying in a specific area, the system can acquire information on the people flow information of that area.

[0029] Based on the customer's purchase information and other relevant information, specific services can be provided to the customer, which will increase ARPU.

[0030] "Example 2"

[0031] Another embodiment of the present invention is a system that uses generative AI. This system has means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, based on information that a customer has purchased a specific product, it is possible to provide the customer with related products and services. This results in an increase in ARPU.

[0032] "Example 3"

[0033] Yet another embodiment of the present invention is a system that uses generative AI. This system has means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, based on a customer's location information, services related to that location can be provided. This results in an increase in ARPU.

[0034] The processing flow of each embodiment will be described below.

[0035] "Example 1"

[0036] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0037] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0038] Step 3: For example, when a customer is staying in a specific area, specific services are provided to the customer based on information on the flow of people and purchasing information in that area.

[0039] Step 4: This will result in increased ARPU.

[0040] "Example 2"

[0041] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0042] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0043] Step 3: For example, based on information about a customer's purchase of a specific product, offer the customer related products or services.

[0044] Step 4: This will result in increased ARPU.

[0045] "Example 3"

[0046] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0047] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0048] Step 3: For example, provide location-related services based on the customer's location information.

[0049] Step 4: This will result in increased ARPU.

[0050] Example 1

[0051] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0052] Conventional systems did not adequately process information to provide personalized content and services to customers, making it difficult to improve customer satisfaction and revenue. Furthermore, pre-processing and generative AI models to effectively utilize acquired information were insufficient, making it difficult to provide services in real time.

[0053] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0054] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for the generative AI model to generate personalized content and services, means for transmitting the generated content and services to a terminal, and means for the terminal to display the content and services to a user. This makes it possible to provide personalized content and services to customers in real time.

[0055] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0056] "Customer information" refers to data such as personal information, behavioral history, and purchase history about customers.

[0057] "Location information" is data indicating the customer's current location and past movement history.

[0058] "People flow information" refers to data such as people's movement patterns and length of stay in a particular area.

[0059] "Purchase information" is data about products and services purchased by customers.

[0060] "Preprocessing" refers to the process of converting the acquired raw data into a format suitable for analysis and generative AI models.

[0061] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[0062] "Personalized content and services" are information and services that are customized to a customer's specific needs and circumstances.

[0063] A "terminal" is a device used by a user (for example, a smartphone or tablet).

[0064] "Real-time" refers to the time frame in which data is acquired, processed, and results provided almost immediately.

[0065] This invention is a system that uses natural language processing technology to acquire customer information, location information, people flow information, and purchasing information, and provides personalized content and services based on this information. Specific embodiments of this system are described below.

[0066] System configuration

[0067] Hardware and Software

[0068] The server uses the following hardware and software:

[0069] Hardware: High-performance server machine

[0070] Software: Database management system, API interface, generative AI model (e.g., OpenAI's GPT-4)

[0071] A terminal is a device used by a user that uses the following hardware and software:

[0072] Hardware: Smartphones, tablets

[0073] Software: Mobile applications, notification systems

[0074] Data acquisition and preprocessing

[0075] The server retrieves customer information, location information, foot traffic information, and purchase information from the mobile carrier's database. This information is obtained in real time through API. For example, a location information API is used to confirm that a customer's current location is Shibuya, Tokyo.

[0076] The acquired information is preprocessed. Specifically, the customer's current location is identified based on the location information, and information on people flow and purchasing in that area is organized. For example, an SQL query is executed to extract the latest people flow data and purchasing trends in Shibuya.

[0077] Using generative AI models

[0078] The server inputs the preprocessed information into the generative AI model. The prompt text is, "Generate appropriate services and content for customers currently in Shibuya based on the latest Shibuya pedestrian flow and purchasing information."

[0079] The generative AI model generates personalized content and services based on input information, such as information about events taking place in Shibuya or discount coupons for specific stores.

[0080] Providing content and services

[0081] The server sends the generated content and services to the user's device. For example, it uses Firebase Cloud Messaging to send generated coupon information and event information to the user's smartphone as a push notification.

[0082] The device displays the received content and services to the user. For example, a message such as "Shibuya Festival is now on! 20% discount coupons are available at certain stores" will be displayed in the smartphone's notification bar.

[0083] Specific examples

[0084] For example, if a customer is staying in Shibuya, Tokyo, the server will analyze Shibuya's pedestrian flow information and purchasing information and use the generative AI model to provide the customer with appropriate services. An example of a prompt sentence would be, "For customers currently staying in Shibuya, please generate appropriate services and content based on the latest pedestrian flow information and purchasing information for Shibuya."

[0085] In this way, it is possible to provide customers with personalized content and services in real time.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] The server acquires customer information, location information, people flow information, and purchasing information.

[0089] Input: API request from the carrier database

[0090] Output: Customer's current location, past movement history, people flow data, purchase history

[0091] Specific operation: The server sends an API request and receives the customer's location information and purchase history as a response. For example, it verifies that the customer's current location is Shibuya, Tokyo.

[0092] Step 2:

[0093] The server preprocesses the retrieved information.

[0094] Input: Acquired customer information, location information, people flow information, purchase information

[0095] Output: Preprocessed data (location information, people flow data, purchasing trends)

[0096] Specific operation: The server identifies the customer's current location based on the location information and organizes information on people flow and purchasing in the area. For example, it executes SQL queries to extract the latest people flow data and purchasing trends in Shibuya.

[0097] Step 3:

[0098] The server inputs the preprocessed information into the generative AI model.

[0099] Input: preprocessed data, prompt statement

[0100] Output: Input data to a generative AI model

[0101] Specific operation: The server converts the preprocessed data into JSON format and sends it to the generative AI model along with a prompt. For example, it sends the data to the API of the generative AI model using an HTTP request.

[0102] Step 4:

[0103] Generative AI models generate personalized content and services.

[0104] Input: Input data to the generative AI model

[0105] Output: personalized content and services (e.g., event information, discount coupons)

[0106] How it works: The generative AI model analyzes the data it receives and generates appropriate content and services, such as "Events taking place in Shibuya: Shibuya Festival, 20% off coupons at specific stores."

[0107] Step 5:

[0108] The server transmits the generated content and services to the terminal.

[0109] Input: Generated content and services

[0110] Output: Notification data to the device

[0111] Specific operation: The server sends the generated content or service to the user's device as a push notification, for example, using Firebase Cloud Messaging.

[0112] Step 6:

[0113] The terminal displays content and services to the user.

[0114] Input: Notification data to the device

[0115] Output: The content or service that is displayed to the user

[0116] Specific operation: The device displays the received notification to the user. For example, the notification bar on the smartphone displays "Shibuya Festival is underway! 20% discount coupons are available at certain stores."

[0117] (Application example 1)

[0118] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0119] Traditional customer service in brick-and-mortar stores relied on general promotions and product recommendations, making it difficult to provide services based on individual customer needs and purchasing history. This meant that improvements in customer satisfaction and maximization of sales were not fully achieved. Furthermore, it was not possible to utilize customer location information and people flow information in real time, making it difficult to provide timely services.

[0120] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0121] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing personalized product recommendations and promotion information in a physical store based on the customer's location information and purchase history. This makes it possible to provide personalized services according to customer needs, thereby improving customer satisfaction and maximizing sales.

[0122] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0123] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0124] "Location information" is data indicating the customer's current location and past movement history.

[0125] "People flow information" is data that shows the movement patterns and length of stay of people in a particular area or store.

[0126] "Purchase information" is data about products and services purchased by customers.

[0127] "Personalized content and services" are information and services that are tailored to a customer's individual needs and preferences.

[0128] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0129] A "brick and mortar store" is a retail outlet that exists in a physical location.

[0130] "Product recommendation" refers to suggesting specific products based on a customer's preferences and purchasing history.

[0131] "Promotional information" is information intended to promote the sale of specific products or services.

[0132] "ARPU" is an indicator that shows average revenue per user.

[0133] A system for implementing this invention is configured as follows. First, the server uses generative AI as a means using natural language processing technology. Smartphones and other mobile devices are used as a means for acquiring customer information, location information, people flow information, and purchasing information. These devices acquire location information using technologies such as GPS, Wi-Fi, and Bluetooth, and collect data on customer purchasing history and preferences.

[0134] The server combines the acquired information to provide personalized content and services. Specifically, when a customer is in a physical store, it generates personalized product recommendations and promotional information based on the customer's location and purchase history. This allows customers to receive information tailored to their needs in real time.

[0135] Hardware and software used

[0136] Hardware: Smartphones, servers

[0137] Software: Python, Geopy, Requests library

[0138] Data processing and calculation

[0139] 1. Acquisition of customer information: Smartphones acquire information such as customer location, purchase history, and visit times. This data is collected from mobile carrier databases and store POS systems.

[0140] 2. Content generation using generative AI: The server sends prompts to a generative AI model based on acquired customer information to generate personalized product recommendations and promotional information.

[0141] 3. Displaying the results: The generated content is displayed on the smartphone screen.

[0142] Specific examples

[0143] For example, if a customer is staying at a particular shopping mall in Tokyo, the generative AI will determine, based on the customer's past purchasing history and current location, that "this customer might be interested in a new fashion item," and provide them with promotional information for that particular store.

[0144] Prompt Sentence Examples

[0145] Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], current location: (35.6895, 139.6917), visit time: 2023-10-01 14:30.

[0146] In this way, the shopping experience in physical stores can be improved.

[0147] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0148] Step 1:

[0149] The terminal acquires the customer's location information.

[0150] Input: Location data obtained using technologies such as GPS, Wi-Fi, and Bluetooth.

[0151] Data processing: Convert location information into latitude and longitude format.

[0152] Output: Customer's current location (e.g., latitude 35.6895, longitude 139.6917).

[0153] Step 2:

[0154] The terminal acquires the customer's purchase history.

[0155] Input: Purchase history data obtained from store POS systems and mobile carrier databases.

[0156] Data processing: Convert purchase history into list format.

[0157] Output: Customer's purchase history (e.g. ["item1", "item2"]).

[0158] Step 3:

[0159] The terminal acquires the customer's visiting time.

[0160] Input: The current date and time obtained from the device's internal clock.

[0161] Data processing: Convert date and time data into a standard format.

[0162] Output: Customer visiting time (e.g. 2023-10-01 14:30).

[0163] Step 4:

[0164] The server sends prompts to the generative AI model based on the acquired customer information.

[0165] Input: Customer ID, purchase history, location information, visit time.

[0166] Data processing: Incorporate this information into the prompt sentence.

[0167] Output: Prompt statement (e.g., Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], Current location: (35.6895, 139.6917), Visit time: 2023-10-01 14:30).

[0168] Step 5:

[0169] The server receives the response from the generative AI model.

[0170] Input: Prompt statement.

[0171] Data processing: A generative AI model generates personalized product recommendations and promotional information based on prompts.

[0172] Output: Generated content (e.g., recommended product lists and promotional information).

[0173] Step 6:

[0174] The terminal displays the generated content to the customer.

[0175] Input: Generated content.

[0176] Data processing: Converting content into a format suitable for the device screen.

[0177] Output: Personalized product recommendations and promotional information displayed on the customer's smartphone screen.

[0178] In this way, customers receive information tailored to their needs in real time.

[0179] Example 2

[0180] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0181] With conventional systems, it was possible to obtain customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. Furthermore, the content and services provided did not adequately address customer needs, making it difficult to improve average revenue per user (ARPU). Furthermore, data preprocessing and the use of generative AI models were insufficient, preventing efficient information processing.

[0182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0183] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating content and services personalized by the generative AI model, and means for providing the generated content and services. This makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per unit (ARPU).

[0184] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0185] "Customer information" is data about a customer, including, for example, name, age, gender, purchase history, and the like.

[0186] "Location information" is data that indicates the geographical location of a specific object, and includes coordinate information such as latitude and longitude.

[0187] "People flow information" is data on the movements and stays of people in specific areas or locations.

[0188] "Purchase information" refers to data about products or services purchased by a customer, including the date and time of purchase, the place of purchase, and the purchase amount.

[0189] "Preprocessing" refers to a series of operations to convert acquired data into a format suitable for analysis and processing, including data cleaning and format standardization.

[0190] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[0191] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular customer.

[0192] "Providing" refers to the act of presenting the generated content or service to a customer.

[0193] "Average Revenue Per Unit (ARPU)" is a metric that indicates the average revenue per customer over a specific period of time.

[0194] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[0195] The server first obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the mobile carrier's database via API and obtains the necessary data, such as the customer's name, age, gender, purchase history, current location, and place of stay.

[0196] Next, the server preprocesses the acquired data. This includes cleaning the data (filling in missing values ​​and removing outliers) and standardizing the format. For example, the Python Pandas library is used to convert the latitude and longitude data of the location information into a standard format.

[0197] The preprocessed data is input to the generative AI model. The server converts the preprocessed data into JSON format and inputs it to the generative AI model. An example of a specific prompt sentence is as follows:

[0198] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[0199] Location: {Latitude: 35.6895, Longitude: 139.6917}

[0200] People flow information: {Current location: Shinjuku Station}

[0201] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[0202] The generative AI model generates personalized content and services based on input data. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to that customer. Specifically, it might generate a suggestion such as, "If you purchase a smartphone case near Shinjuku Station, you can receive a discount at a specific store."

[0203] Finally, the server sends the generated content and services to the device. The device displays the received content to the user. For example, it displays suggestions to the user using the smartphone's push notification function. This allows the user to receive information and services that are optimized for them.

[0204] This system makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per user (ARPU).

[0205] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0206] Step 1: Get the data

[0207] The server obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the database through an API to obtain information such as the customer's name, age, gender, purchase history, current location, and where they are staying. The input is the API request, and the output is the various pieces of information obtained.

[0208] Step 2: Preprocessing the data

[0209] The server preprocesses the acquired data. Specifically, it uses Python's Pandas library to clean the data (fill in missing values ​​and remove outliers) and standardize the format. For example, it converts latitude and longitude location data into a standard format. The input is the acquired raw data, and the output is the preprocessed data.

[0210] Step 3: Input to the generative AI model

[0211] The server inputs the preprocessed data into the generative AI model. Specifically, it converts the preprocessed data into JSON format and inputs it into the generative AI model. The input is the preprocessed data, and the output is a prompt sentence for the generative AI model. An example of a prompt sentence is as follows:

[0212] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[0213] Location: {Latitude: 35.6895, Longitude: 139.6917}

[0214] People flow information: {Current location: Shinjuku Station}

[0215] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[0216] Step 4: Generate personalized content

[0217] A generative AI model generates personalized content and services based on the input prompt. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to the customer. The input is the prompt, and the output is the generated personalized content.

[0218] Step 5: Providing content

[0219] The server sends the generated content to the device. The device displays the received content to the user. Specifically, the device uses the smartphone's push notification function to display suggestions to the user. The input is the generated content, and the output is a notification displayed to the user. This allows the user to receive information and services that are optimized for them.

[0220] (Application example 2)

[0221] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0222] Traditional product proposals and promotions in brick-and-mortar stores are unable to adequately meet the needs of each individual customer, making it difficult to improve customer satisfaction and maximize sales. Furthermore, providing personalized information in real time is difficult, making it difficult to effectively stimulate customers' purchasing desire.

[0223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0224] In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing product suggestions and promotion information in real time based on the customer's location information and purchase history. This makes it possible to provide optimal product suggestions and promotion information to each individual customer in real time.

[0225] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0226] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0227] "Location information" is data that indicates a customer's current geographic location.

[0228] "People flow information" refers to data such as people's movement patterns and length of stay in a specific area.

[0229] "Purchase information" is data about products and services purchased by customers.

[0230] "Individualized content and services" refers to information and services provided tailored to the needs and preferences of each individual customer.

[0231] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0232] "Means for providing product suggestions and promotional information in real time" refers to technology for instantly providing product and promotional information according to the customer's current situation.

[0233] "ARPU" stands for average revenue per user, and is an indicator of average revenue per customer.

[0234] As an embodiment of the present invention, a smart shopping assistant system for a brick-and-mortar store will be described as an example. This system provides product suggestions and promotion information in real time based on customer location information and purchase history.

[0235] System configuration

[0236] The system consists of the following main components:

[0237] 1. Server: Executes generative AI models using natural language processing technology.

[0238] 2. Terminal: A device carried by the customer, such as a smartphone or smart glasses.

[0239] 3. Database: Stores customer information, location information, people flow information, and purchasing information.

[0240] Program processing

[0241] The server implements the system using the following means:

[0242] 1. Means using natural language processing technology: Generative AI models are used to generate optimal product suggestions and promotional information for customers.

[0243] 2. How to obtain customer information: Obtain customer personal information and purchasing history from the database.

[0244] 3. Means of obtaining location information: The customer's current location is obtained using technologies such as GPS and beacons.

[0245] 4. Means of acquiring people flow information: Sensors and cameras are used to acquire people's movement patterns and stay times within the store.

[0246] 5. Means of obtaining purchasing information: Information on the products and services purchased by customers is obtained from the POS system.

[0247] 6. Means of providing personalized content and services: By combining the various pieces of information obtained, we can provide each customer with optimal product suggestions and promotional information in real time.

[0248] Data processing and calculation

[0249] The server uses a programming language such as Python to perform the following tasks:

[0250] 1. Data acquisition: Customer information, location information, people flow information, and purchasing information are acquired from databases and sensors.

[0251] 2. Sending prompts to the generative AI model: Based on the acquired data, prompts are sent to the generative AI model to generate product suggestions and promotional information.

[0252] 3. Real-time provision: The generated information is sent to the terminal and displayed to the customer in real time.

[0253] Specific examples

[0254] For example, if a customer is in "Aisle 5" and has previously purchased "item1" and "item2," we can use this information to send the following prompt to our generative AI model:

[0255] Prompt Sentence Examples

[0256] Customer ID: 12345, Current Location: Aisle 5, Purchase History: ['item1', 'item2']

[0257] Based on this prompt, the generative AI model will return recommended products related to "Aisle 5" or based on past purchase history. For example, "item 3" or "item 4" might be suggested. This way, the model can provide personalized product suggestions to improve the shopping experience.

[0258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0259] Step 1:

[0260] The server retrieves customer information from a database. The input is the customer ID, and the output is the customer's personal information and purchase history. Specifically, it sends a query to the database using the customer ID as a key to retrieve the corresponding customer information.

[0261] Step 2:

[0262] The terminal acquires the customer's current location information. The input is a signal from a GPS or beacon, and the output is the customer's current location. Specifically, the terminal periodically sends the location information to the server.

[0263] Step 3:

[0264] The server acquires people flow information from sensors and cameras. The input is data from the sensors and cameras, and the output is people's movement patterns and stay times within the store. Specifically, the server analyzes the data from the sensors and cameras to generate people flow information.

[0265] Step 4:

[0266] The server obtains purchase information from the POS system. The input is data from the POS system, and the output is information about the products and services purchased by the customer. Specifically, it analyzes the transaction data from the POS system and obtains the purchase information.

[0267] Step 5:

[0268] The server combines the acquired information to generate a prompt. The input is customer information, location information, people flow information, and purchasing information, and the output is a prompt to be sent to the generative AI model. Specifically, each piece of information is compiled in text format to generate the prompt.

[0269] Step 6:

[0270] The server sends prompt sentences to the generative AI model to generate product suggestions and promotion information. The input is the prompt sentence, and the output is the generated product suggestions and promotion information. Specifically, the server sends prompt sentences to the generative AI model and receives the returned information.

[0271] Step 7:

[0272] The server sends the generated product suggestions and promotion information to the terminal. The input is the generated information, and the output is the information displayed on the terminal. Specifically, the generated information is sent to the terminal, and the terminal displays it to the customer.

[0273] Step 8:

[0274] The user checks the product suggestions and promotion information displayed on the device and considers purchasing if necessary. The input is the information displayed on the device, and the output is the user's purchasing behavior. Specifically, the user operates the device to check the product information and decides to purchase.

[0275] Example 3

[0276] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0277] Conventional systems can collect customer information, location information, people flow information, and purchasing information individually, but it is difficult to integrate this information to provide personalized content and services in real time. Furthermore, the content and services provided often do not match customer needs, making it difficult to improve ARPU (average revenue per user).

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0279] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating personalized content and services using the generative AI model, and means for delivering the generated content and services to user terminals. This makes it possible to provide personalized content and services that meet customer needs in real time, thereby improving ARPU.

[0280] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0281] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0282] "Location information" refers to data related to a customer's current location and past movement history.

[0283] "People flow information" is data on people's movement patterns and length of stay within a particular area or facility.

[0284] "Purchase information" is data about products and services that customers have purchased in the past.

[0285] "Preprocessing" is the process of converting acquired data into a format suitable for generative AI models by cleaning, normalizing, and filling in missing values.

[0286] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data.

[0287] "Personalized content and services" are information and services that are customized to meet a customer's specific needs and preferences.

[0288] A "user terminal" is a device that is directly operated by a user, such as a smartphone, tablet, or PC.

[0289] "Delivery" is the process of transmitting generated content or services to user terminals.

[0290] "ARPU" stands for Average Revenue Per User and refers to the average revenue per user.

[0291] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[0292] First, the server acquires customer information, location information, foot traffic information, and purchase information. This information is collected in real time from the mobile carrier's database using API. For example, the moment a user enters a shopping mall, their location information is sent to the server.

[0293] The server then preprocesses the acquired data, which includes cleaning, normalizing, and imputing missing values. For example, if location information is missing, it imputes the closest past location information.

[0294] The preprocessed data is input into a generative AI model. For example, OpenAI's GPT-4 is used as a generative AI model. The server inputs the preprocessed data into GPT-4 to generate personalized content and services. An example of a specific prompt is, "If the user is in a shopping mall, please generate content that provides the most suitable store and sale information based on their location information."

[0295] The generative AI model generates personalized content based on input data. For example, if a user is in a shopping mall, it generates content based on the user's location, such as, "There is currently a sale at ABC store in the shopping mall. We will notify you of sales information related to products you have previously purchased at this store."

[0296] Finally, the server delivers the generated content to the user's device. Specifically, it sends a push notification to the user's smartphone. The user can tap the notification to view more information.

[0297] This system allows users to receive information that is most suitable for them in real time, improving their shopping experience. It also allows stores to effectively approach target customers, which is expected to increase sales. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[0298] Step 1:

[0299] The server uses APIs to obtain customer information, location information, people flow information, and purchase information from the mobile carrier's database in real time. API requests are sent as input, and data such as the customer's current location, past movement history, and purchase history is received as output. Specifically, the server accesses the API endpoint and specifies the required parameters to obtain data.

[0300] Step 2:

[0301] The server preprocesses the acquired data. It uses the raw data acquired in step 1 as input and obtains cleaned, normalized data as output. Specific operations include data cleaning (removal of noise and outliers), normalization (standardization of data format), and missing value completion (completion using past data).

[0302] Step 3:

[0303] The server inputs the preprocessed data into the generative AI model. The preprocessed data is used as input, and personalized content generated by the generative AI model is obtained as output. Specifically, the server creates a prompt for the generative AI model (e.g., GPT-4) and inputs the preprocessed data. An example of a prompt is, "If the user is in a shopping mall, please generate content to provide the most suitable store and sale information based on their location information."

[0304] Step 4:

[0305] The generative AI model generates personalized content based on the input data. It uses the data entered in step 3 and the prompt sentence as input, and obtains personalized content as output. Specifically, the generative AI model analyzes the input data and generates information that best suits the user's current situation. For example, it generates content such as, "There is currently a sale at ABC store in the shopping mall. We will notify you of sale information related to products you have previously purchased at this store."

[0306] Step 5:

[0307] The server delivers the generated content to the user's device. The generated content is used as input, and a notification is sent to the user's device as output. Specifically, the server uses a push notification service to send the generated content to the user's smartphone. The user can tap the notification to view more information.

[0308] In this way, the system delivers personalized content and services that meet customer needs in real time, resulting in increased ARPU.

[0309] (Application example 3)

[0310] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0311] With conventional systems, it was difficult to provide personalized content and services by effectively utilizing customer location information and purchase history. This also made it difficult to improve ARPU (Average Revenue Per User). Furthermore, there was a lack of means to provide personalized content in real time through smartphone applications.

[0312] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, means for sending prompt sentences to a generative AI model to generate personalized content, and means for providing the generated content through a smartphone application. This makes it possible to effectively utilize customer location information and purchase history to provide personalized content in real time.

[0313] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0314] "Customer information" refers to data such as personal information, purchase history, and behavioral history about customers.

[0315] "Location information" is information about a specific place or location, and includes geographical data such as latitude and longitude.

[0316] "People flow information" is data on the movements and stays of people in specific areas or locations.

[0317] "Purchase information" is data about products and services purchased by customers.

[0318] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data or prompts.

[0319] A "prompt" is an instruction given to a generative AI model to generate a specific output.

[0320] A "smartphone application" is a software program that runs on a smartphone.

[0321] "Personalized content" is information or services that are customized to meet the needs and preferences of a particular customer.

[0322] "ARPU" stands for Average Revenue Per User and refers to the average revenue per user.

[0323] A system for carrying out this invention is configured as follows: The server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchasing information, means for combining the acquired information to provide personalized content and services, means for sending prompt sentences to a generative AI model to generate personalized content, and means for providing the generated content through a smartphone application.

[0324] Explanation of program processing

[0325] 1. Obtaining customer information:

[0326] The server accesses a database to acquire customer information, which includes personal information, purchase history, and behavioral history of the customer.

[0327] 2. Location information acquisition:

[0328] The server uses the GPS function of the smartphone to obtain the customer's current location information, which allows the server to determine where the customer is currently located.

[0329] 3. Obtaining people flow information:

[0330] The server retrieves data about people's movements and stays in specific areas and locations, which can come from mobile carrier databases or public data sources.

[0331] 4. Acquisition of purchasing information:

[0332] The server retrieves data about the products and services that the customer has previously purchased, which is stored in a database as the customer's purchase history.

[0333] 5. Sending prompts to the generative AI model:

[0334] The server sends a prompt to the generative AI model based on the acquired customer information, location information, people flow information, and purchase information. An example of a prompt is, "Generate recommended products for customer ID: 12345 based on their current location (35.6895,139.6917)."

[0335] 6. Personalized content generation:

[0336] The generative AI model generates personalized content based on the prompt, taking into account the customer's current location and past purchasing history.

[0337] 7. Provision through smartphone applications:

[0338] The server provides the generated personalized content to customers through a smartphone application, allowing customers to check recommended products and services in real time through the smartphone application.

[0339] Specific examples

[0340] For example, if the customer ID is "12345," assume that the current location is Tokyo (latitude 35.6895, longitude 139.6917). This customer has previously purchased "item1" and "item2" and visited "store1" and "store2." Based on this information, the following prompt is sent to the generative AI model:

[0341] For customer ID: 12345, generate product recommendations based on current location (35.6895,139.6917).

[0342] Based on this prompt, the generative AI model suggests products on sale at nearby stores, new products related to past purchases, etc. This allows customers to see personalized recommendations in real time through a smartphone application.

[0343] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0344] Step 1:

[0345] The server obtains customer information. A customer ID is given as input. The server accesses the database and obtains data such as the customer's personal information, purchase history, and behavioral history. The server obtains customer information as output.

[0346] Step 2:

[0347] The server obtains the customer's current location information. As input, the latitude and longitude obtained from the smartphone's GPS function are given. The server processes this and determines the customer's current location. As output, the location information is obtained.

[0348] Step 3:

[0349] The server acquires people flow information. As input, it is given data about people's movements and stays in a particular area or location. The server retrieves this information from mobile carrier databases and public data sources. As output, it obtains people flow information.

[0350] Step 4:

[0351] The server obtains purchasing information. As input, it receives the customer's purchasing history. The server accesses a database to obtain data about the products and services the customer has purchased in the past. As output, it obtains purchasing information.

[0352] Step 5:

[0353] The server sends prompts to the generative AI model based on the acquired customer information, location information, people flow information, and purchasing information. This information is given as input. The server combines this information to generate prompts and sends them to the generative AI model. The output from the generative AI model is personalized content.

[0354] Step 6:

[0355] The server provides personalized content obtained from the generative AI model through a smartphone application. The generated content is given as input. The server sends this content to the smartphone application and displays it to the user. The output is the personalized content that the user can view.

[0356] Specifically, for example, if the customer ID is "12345," the server retrieves the customer's information from the database and obtains their current location from the smartphone's GPS. Next, it obtains people flow information from the mobile carrier's database and the customer's purchase history from the database. Based on this information, the server sends a prompt to the generative AI model: "Generate recommended products for customer ID: 12345 based on their current location (35.6895, 139.6917)." The personalized content obtained from the generative AI model is sent to the smartphone application and displayed to the user.

[0357] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0358] "Example 1"

[0359] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes user emotions to adjust content and services according to the user's emotional state. Specifically, if a user expresses joy, content and services that amplify the user's joy are provided to the user. On the other hand, if a user expresses sadness, content and services that comfort the user are provided to the user. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[0360] "Example 2"

[0361] Another embodiment of the present invention provides a system that combines an emotion engine. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes the user's emotions to adjust content and services according to the user's emotional state. Specifically, if a user is expressing excitement, content and services that maintain the user's excitement are provided. On the other hand, if a user is expressing calm, content and services that promote relaxation are provided. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[0362] "Example 3"

[0363] As yet another embodiment of the present invention, a system incorporating an emotion engine is provided. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes the user's emotions to adjust content and services according to the user's emotional state. Specifically, if a user expresses surprise, content and services that amplify the user's surprise are provided. Also, if a user expresses relief, content and services that increase the user's sense of relief are provided. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[0364] The processing flow of each embodiment will be described below.

[0365] "Example 1"

[0366] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[0367] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[0368] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[0369] "Example 2"

[0370] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[0371] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[0372] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[0373] "Example 3"

[0374] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[0375] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[0376] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[0377] Example 1

[0378] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0379] Conventional systems could collect customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. In addition, it was not possible to adjust content and services according to the user's emotional state, which meant that improvements to the user experience and ARPU were not fully realized.

[0380] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0381] In this invention, the server includes means for acquiring customer information about a user, means for acquiring location information of the user, means for acquiring people flow information about people's movements or stays at the user's location, means for acquiring purchase information about the user's purchases, and means for recommending content or services available to the user based on the acquired customer information, location information, people flow information, and purchase information using a prompt sentence instructing the system to recommend content or services available to the user and a generative AI model. The server further includes means for recognizing the user's emotional state using an emotion engine, and the recommending means recommends content or services available to the user based on the acquired customer information, location information, people flow information, purchase information, and the user's emotional state using a prompt sentence instructing the system to recommend content or services available to the user and the generative AI model. This makes it possible to integrate various information to provide personalized content and services and further adjust them according to the user's emotional state. Furthermore, when the user is located within a commercial facility, the recommending means recommends discount information or product information of stores within the commercial facility as content or services that can be provided to the user.

[0382] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0383] "Customer information" is data about a customer, including name, age, gender, purchase history, etc.

[0384] "Location information" is data that indicates the geographic location of a particular device or user.

[0385] "People flow information" is data on the movements and stays of people in specific areas or locations.

[0386] "Purchase information" is data related to purchases made by a customer, including the date and time of purchase, the purchased product, and the purchase amount.

[0387] "Cleaning" is the process of dealing with missing or outliers in data and improving data quality.

[0388] "Preprocessing" refers to the initial processing of data to convert it into a format suitable for analysis or model input.

[0389] A "feature" is an attribute of data that contains important information in data analysis and machine learning models.

[0390] A "generative AI model" is an artificial intelligence model that generates new data or content based on input data.

[0391] A "prompt" is an instruction to be input into a generative AI model, and serves as the basis for the content generated by the model.

[0392] An "emotion engine" is a technology for recognizing and analyzing a user's emotional state.

[0393] "Content" is a general term for information and services provided to users.

[0394] "Service" refers to the functions and conveniences provided to users.

[0395] "Adjustment" is the process of changing content or settings to suit specific conditions or circumstances.

[0396] "ARPU" is an indicator that shows the average revenue per user.

[0397] This invention is a system that uses natural language processing technology to acquire customer information, location information, people flow information, and purchasing information, and integrates this information to provide personalized content and services. Furthermore, it can adjust content and services according to the user's emotional state.

[0398] Hardware and software used

[0399] The server is equipped with hardware and software for accessing the database, specifically using a SQL database, Python, Pandas, Scikit-learn, a generative AI model (e.g., OpenAI's GPT-4), and an emotion engine (e.g., Affectiva's emotion recognition technology).

[0400] Data acquisition and preprocessing

[0401] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. The retrieved data is cleaned and preprocessed using Python's Pandas library, which fills in missing and outlier values ​​and improves data quality.

[0402] Data analysis and feature extraction

[0403] The server analyzes the preprocessed data and performs clustering and classification using the Scikit-learn library, which extracts important features.

[0404] Prompt generation for generative AI models

[0405] The server generates a prompt sentence to be input to the generative AI model based on the extracted features. Specifically, the prompt sentence is constructed using Python string manipulation.

[0406] Content Generation and Emotion Recognition

[0407] The server inputs the generated prompt sentences into a generative AI model to generate data representing content and services to recommend to the user. An emotion engine is used to recognize the user's emotional state, and the recommended content is adjusted according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state.

[0408] Content provision

[0409] The terminal provides the user with data representing the personalized content and services sent from the server, and makes the data accessible to the user through a user interface.

[0410] Specific examples

[0411] For example, if the user is in a shopping mall and is expressing a happy emotion, the following process may occur:

[0412] 1. The server obtains the user's customer information, location information, and purchase information from the mobile carrier's database, and also obtains people flow information at the user's location from another server.

[0413] 2. The server cleans the acquired data and imputes missing values.

[0414] 3. The server analyzes the data and determines that the user is at a shopping mall.

[0415] 4. The server generates a prompt sentence saying, "The user is staying at a shopping mall. Please generate data representing the optimal content and services to recommend to the user."

[0416] 5. The server inputs the prompt sentence into the generative AI model and generates data representing discount information and recommended product information for a specific store.

[0417] 6. The server uses an emotion engine to recognize the user's emotion of joy.

[0418] 7. The server generates data representing content and services to recommend to the user, taking into account the emotion of joy. Specifically, the server generates a prompt such as, "The user is at a shopping mall and is expressing an emotion of joy. Please generate data representing optimal content and services to recommend to the user." The server inputs the prompt into the generative AI model and generates data representing discount information and recommended product information for a specific store.

[0419] 8. The terminal provides the user with data representing the generated content, allowing the user to access it.

[0420] In this way, the user can receive the most appropriate service according to his / her emotional state.

[0421] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0422] Step 1:

[0423] The server accesses the mobile carrier's database to obtain customer information, location information, people flow information, and purchase information. The input is a database query, and the output is the obtained raw data. Specifically, the server executes an SQL query to extract the required data.

[0424] Step 2:

[0425] The server cleans and preprocesses the acquired data. The input is the raw data acquired in step 1, and the output is the cleaned and preprocessed data. Specifically, the server uses the Python Pandas library to manipulate the data frame, impute missing values, and process outliers.

[0426] Step 3:

[0427] The server analyzes the preprocessed data and extracts features. The input is the data preprocessed in step 2, and the output is the extracted features. Specifically, the server uses the Scikit-learn library to perform clustering and classification and extract important features.

[0428] Step 4:

[0429] The server generates a prompt sentence to be input to the generative AI model based on the extracted features. The input is the features extracted in step 3, and the output is the generated prompt sentence. Specifically, the server constructs the prompt sentence using Python string operations.

[0430] Step 5:

[0431] The server inputs the generated prompt sentence into the generative AI model to generate personalized content or services. The input is the prompt sentence generated in step 4, and the output is data representing the generated content or service. Specifically, the server sends an API request and receives data representing the generated content from the generative AI model.

[0432] Step 6:

[0433] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial expression and voice data, and the output is the recognized emotional state. Specifically, the server inputs the user's facial expression and voice data into the emotion engine and analyzes the emotional state.

[0434] Step 7:

[0435] The server generates data representing content and services to recommend to the user, taking into account the user's emotional state recognized by the emotion engine. The server generates data representing content and services to recommend to the user, using a prompt sentence that instructs the server to recommend content or services that can be provided to the user based on the acquired customer information, location information, people flow information, purchase information, and the user's emotional state, and a generative AI model. For example, if the user is located within a commercial facility, data is generated that recommends discount information or product information for stores within the commercial facility as content or services recommended to the user.

[0436] Step 8:

[0437] The terminal provides the user with data representing the personalized content or service sent from the server. The input is the data representing the content or service generated in step 7, and the output is the data representing the content or service presented to the user. Specifically, the terminal displays the data representing the content through a user interface, allowing the user to access it.

[0438] (Application example 1)

[0439] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0440] Traditional customer service in brick-and-mortar stores lacked individualization and was difficult to provide services tailored to the customer's emotional state. Furthermore, there was a lack of appropriate content and services to stimulate customer purchasing motivation, making it difficult to increase ARPU (average revenue per customer).

[0441] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for recognizing emotional state, means for combining the acquired information to provide personalized content and services, and means for adjusting content and services according to the emotional state. This makes it possible to provide personalized content and services according to the emotional state of the customer, thereby improving ARPU.

[0442] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0443] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0444] "Location information" refers to data related to a customer's current location and past movement history.

[0445] "People flow information" is data on people's movement patterns and congestion conditions in specific areas or within stores.

[0446] "Purchase information" is data about products and services that customers have purchased in the past.

[0447] "Emotional state" is data that indicates the customer's current emotional or psychological state.

[0448] "Personalized content and services" means customized information and services tailored to a customer's specific needs and preferences.

[0449] The "means for adjusting content and services according to emotional state" is a technology for recognizing a customer's emotional state and changing the content and services provided based on that state.

[0450] "Generative AI" is an artificial intelligence technology that generates new text and content based on input data.

[0451] "ARPU" is an indicator that shows average revenue per customer.

[0452] A system for implementing the present invention is configured as follows.

[0453] First, the server is equipped with a generative AI that uses natural language processing technology. This generative AI acquires customer information, location information, people flow information, and purchasing information, and generates personalized content and services based on this data. It also uses an emotion engine to recognize the customer's emotional state and adjust the content and services provided according to that emotional state.

[0454] Hardware and software used

[0455] Hardware: Smartphones, servers

[0456] Software: EmotionRecognizer (emotion recognition engine), NLPModel (generative AI model)

[0457] Data processing and calculation

[0458] 1. Obtaining customer information, location information, people flow information, and purchasing information:

[0459] The server obtains location information from the customer's smartphone, as well as the customer's past purchase history and traffic flow information from a database.

[0460] 2. Emotion recognition:

[0461] When a customer takes a picture of their face using their smartphone camera, the image is sent to the server, which uses EmotionRecognizer to recognize the customer's emotional state from the image.

[0462] 3. Personalized content generation:

[0463] The server uses an NLP model to generate personalized content and services based on the acquired customer information, location information, people flow information, purchasing information, and recognized emotional state.

[0464] Specific examples

[0465] For example, when a customer uses a smartphone in a physical store, the server obtains the customer's location information and recommends specific products based on the customer's foot traffic and past purchase history. If the customer is smiling, the emotion engine recognizes "joy" and provides special promotional information. Conversely, if the customer looks tired, the emotion engine recognizes "fatigue" and recommends products or services that will help them relax.

[0466] Prompt Sentence Examples

[0467] "Customer information: {"location": "store_123", "flow": "high", "purchase_history": ["item_1", ​​"item_2"]}, Emotion: "Joy""

[0468] By inputting this prompt into a generative AI model, personalized content is generated.

[0469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0470] Step 1:

[0471] The server obtains location information from the customer's smartphone. Specifically, it obtains the current location using the smartphone's GPS function and sends that data to the server. The input is the smartphone's GPS data, and the output is the location information sent to the server.

[0472] Step 2:

[0473] The server retrieves the customer's past purchase history and people flow information from the database. Specifically, it searches the database based on the customer ID and retrieves the related purchase history and people flow information. The input is the customer ID, and the output is the purchase history and people flow information.

[0474] Step 3:

[0475] A user takes a facial image using the smartphone camera and sends the image to the server. The input is the facial image taken by the smartphone camera, and the output is the facial image sent to the server.

[0476] Step 4:

[0477] The server uses EmotionRecognizer to recognize the customer's emotional state from the facial image sent. Specifically, it analyzes the facial image and identifies emotions such as joy, sadness, and anger. The input is the facial image, and the output is the recognized emotional state.

[0478] Step 5:

[0479] The server uses an NLP model to generate personalized content and services based on the acquired customer information, location information, people flow information, purchasing information, and recognized emotional state. Specifically, this data is input into a generative AI model as prompt sentences to generate personalized content. The inputs are customer information, location information, people flow information, purchasing information, and emotional state, and the output is personalized content and services.

[0480] Step 6:

[0481] The server sends the generated personalized content or service to the customer's smartphone. Specifically, the generated content is delivered to the smartphone as a message or notification. The input is the personalized content or service, and the output is the content or service displayed on the customer's smartphone.

[0482] Example 2

[0483] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0484] Conventional systems could collect customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. Furthermore, content and services were not adjusted according to the user's emotional state, so improvements in user experience and ARPU (average revenue per user) were not fully realized.

[0485] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0486] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed data into a generative AI model, means for generating personalized content and services using the generative AI model, and means for providing the generated content and services. This makes it possible to provide personalized content and services by integrating the various pieces of acquired information, and further to adjust content and services according to the user's emotional state, thereby improving the user experience and increasing ARPU.

[0487] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0488] "Customer information" is data about a customer, including name, age, gender, purchase history, and the like.

[0489] "Location information" is data that indicates the geographic location of a particular device or individual.

[0490] "People flow information" is data on the movements and stays of people in specific areas or locations.

[0491] "Purchase information" is data about products and services purchased by customers.

[0492] "Preprocessing" refers to the process of converting acquired data into a format suitable for analysis or model input.

[0493] A "generative AI model" is an artificial intelligence model that generates new data or content based on input data.

[0494] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular user.

[0495] "Emotional state" is data that indicates the user's current emotions and moods.

[0496] "ARPU" stands for average revenue per user, and is an indicator of the average revenue per user over a specific period of time.

[0497] MODE FOR CARRYING OUT THE INVENTION

[0498] This invention is a system that combines natural language processing technology and generative AI models to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Furthermore, by adjusting content and services according to the user's emotional state, it improves the user experience and increases ARPU.

[0499] Hardware and software used

[0500] server:

[0501] Database access API

[0502] Natural Language Processing Engine

[0503] Emotion Recognition Engine

[0504] Generative AI Models

[0505] Data Preprocessing Module

[0506] Content Delivery Module

[0507] Device:

[0508] Smartphone

[0509] tablet

[0510] PC

[0511] software:

[0512] Natural language processing libraries (e.g. NLTK, spaCy)

[0513] Emotion recognition library (e.g., IBM Watson(R), Microsoft(R) Azure(R) Emotion API)

[0514] Generative AI models (e.g., GPT-3, BERT)

[0515] Database management system (e.g., MySQL (registered trademark), PostgreSQL)

[0516] Data processing and calculation

[0517] The server retrieves customer information, location information, foot traffic information, and purchase information from the mobile carrier's database. The retrieved data is cleaned and normalized by a data preprocessing module. The preprocessed data is then input into a generative AI model to generate personalized content and services.

[0518] The generated content and services are sent to the terminal through the content delivery module. Depending on the user's emotional state, the emotion recognition engine analyzes the user's emotions, and the content and services are adjusted based on the results.

[0519] Specific examples

[0520] Example 1:

[0521] If a customer recently purchased a new camera on their smartphone, they will be shown ads for camera accessories and photo editing software.

[0522] Example prompt sentence:

[0523] "Generate a list of recommendations for related camera accessories and photo editing software for customers who recently purchased a camera."

[0524] Example 2:

[0525] If a user posts on social media that they are "very tired today," the app will display recommendations for relaxation music and meditation apps.

[0526] Example prompt sentence:

[0527] "If a user feels tired, generate a list of recommendations for relaxation music and meditation apps for that user."

[0528] In this way, the server integrates various data and uses generative AI models to provide personalized content and services. Furthermore, an emotion recognition engine can be used to tailor content and services according to the user's emotional state, resulting in an improved user experience and increased ARPU.

[0529] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0530] Program processing flow

[0531] Systems using generative AI

[0532] Step 1:

[0533] Data Acquisition

[0534] The server obtains customer information, location information, people flow information, and purchasing information from the mobile carrier's database.

[0535] Input: API request for database access

[0536] Output: Raw data of customer information, location information, people flow information, and purchasing information

[0537] Specific operation: The server accesses the database through the API and sends a request to "get the purchase history of customer ID 12345."

[0538] Step 2:

[0539] Data Preprocessing

[0540] The server preprocesses the acquired data, specifically cleaning and normalizing the data.

[0541] Input: Raw data of customer information, location information, people flow information, and purchasing information

[0542] Output: Preprocessed data

[0543] Specific operation: The server normalizes the data by imputing missing values ​​and removing outliers. For example, if there are missing values ​​in the purchase history, it imputes them with the average value.

[0544] Step 3:

[0545] Input to generative AI models

[0546] The server inputs the preprocessed data into a generative AI model.

[0547] Input: Preprocessed data

[0548] Output: Input data to the generative AI model

[0549] Specific operation: The server converts the preprocessed data into JSON format and feeds it to the generative AI model. For example, "Convert the purchase history of customer ID 12345 into JSON format and input it into the model."

[0550] Step 4:

[0551] Content Generation

[0552] Generative AI models generate personalized content and services based on input data.

[0553] Input: The input data to the generative AI model

[0554] Output: personalized content and services

[0555] What it does: The generative AI model generates a list of related product recommendations based on a customer's recent purchases. For example, "generate a list of camera accessory recommendations for a customer who recently purchased a camera."

[0556] Step 5:

[0557] Content provider

[0558] The server transmits the generated content and services to the terminal and provides them to the user.

[0559] Input: Personalized content and services

[0560] Output: Content delivery to user devices

[0561] Specific operation: The server sends a push notification to the user's smartphone to display advertisements for related products. For example, "Send a push notification with a list of camera accessory recommendations to customer ID 12345."

[0562] A system that combines emotion engines

[0563] Step 1:

[0564] Data Acquisition

[0565] The server uses natural language processing technology to obtain customer information, location information, people flow information, and purchasing information.

[0566] Input: SNS API request

[0567] Output: Raw data of customer information, location information, people flow information, and purchasing information

[0568] Specific operation: The server retrieves user posts through the SNS API. For example, it sends a request to "Get the latest SNS posts from user ID 67890."

[0569] Step 2:

[0570] emotion recognition

[0571] The server uses an emotion engine to recognize the user's emotional state.

[0572] Input: Social media post data

[0573] Output: User emotion data

[0574] How it works: The server uses natural language processing technology to analyze emotions from text data. For example, it recognizes that a user is excited when they post something like, "Today is so much fun!"

[0575] Step 3:

[0576] Data Preprocessing

[0577] The server preprocesses the acquired data and emotion information, specifically by cleaning and normalizing the data.

[0578] Input: customer information, location information, people flow information, purchasing information, emotional data

[0579] Output: Preprocessed data

[0580] Specific operation: The server converts the emotion data into a numerical value and integrates it with other data. For example, the emotion of "excitement" is expressed as a numerical value of "5" and integrated with purchase history data.

[0581] Step 4:

[0582] Input to generative AI models

[0583] The server inputs the preprocessed data into a generative AI model.

[0584] Input: Preprocessed data

[0585] Output: Input data to the generative AI model

[0586] Specific operation: The server converts the preprocessed data into JSON format and feeds it to the generative AI model. For example, "Convert the emotion data and purchase history of user ID 67890 into JSON format and input it into the model."

[0587] Step 5:

[0588] Content Generation

[0589] The generative AI model generates content and services based on the user's emotional state based on input data.

[0590] Input: The input data to the generative AI model

[0591] Output: Content and services based on the user's emotional state

[0592] Specific behavior: The generative AI model generates entertainment content for excited users. For example, "Generate entertainment content for excited users."

[0593] Step 6:

[0594] Content provider

[0595] The server transmits the generated content and services to the terminal and provides them to the user.

[0596] Input: Content and services tailored to the user's emotional state

[0597] Output: Content delivery to user devices

[0598] Specific operation: The server sends a push notification to the user's smartphone, displaying recommendations for relaxation music and meditation apps. For example, "Send a push notification with a list of recommended relaxation music and meditation apps to user ID 67890."

[0599] (Application example 2)

[0600] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0601] Traditional customer service in brick-and-mortar stores was insufficiently personalized, making it difficult to maximize customer motivation. Furthermore, because services could not be tailored to the customer's emotional state, it was difficult to improve customer satisfaction or ARPU (average revenue per unit).

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0603] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for recognizing emotional states, means for combining the acquired information to provide personalized content and services, and means for displaying personalized content and services on a smart device. This makes it possible to provide personalized content and services according to the emotional state of the customer, thereby improving customer satisfaction and ARPU.

[0604] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0605] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0606] "Location information" refers to data related to a customer's current location and travel route.

[0607] "People flow information" is data on people's movement patterns and length of stay in a particular area.

[0608] "Purchase information" is data about products and services purchased by customers.

[0609] "Emotional state" is data that indicates the customer's current emotional or psychological state.

[0610] "Personalized content and services" are specific content and services provided based on a customer's individual information and status.

[0611] A "smart device" is an electronic device that is connected to the Internet and can display and operate information.

[0612] "Generative AI" is an artificial intelligence technology that generates new content and information based on data.

[0613] "ARPU" stands for average revenue per user, and is an indicator of average revenue per customer.

[0614] The system for implementing the present invention uses the following hardware and software.

[0615] Hardware

[0616] Smart glasses (e.g., Google® Glass®)

[0617] Camera (built into smart glasses)

[0618] software

[0619] OpenCV (camera image processing)

[0620] EmotionRecognizer (emotion recognition engine)

[0621] ContentGenerator (generative AI model)

[0622] LocationTracker (location information acquisition)

[0623] PurchaseHistory (purchase history management)

[0624] CustomerData (Customer Information Management)

[0625] System configuration

[0626] The server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchasing information, means for recognizing emotional states, means for combining the acquired information to provide personalized content and services, and means for displaying personalized content and services on a smart device.

[0627] Processing flow

[0628] 1. Acquisition of customer information

[0629] The server uses customer information management software to acquire personal information and purchase history of customers, which allows the server to understand customer preferences and past purchasing behavior.

[0630] 2. Obtaining location information

[0631] The server uses location information acquisition software to acquire the customer's current location and route in real time, allowing the server to determine the area in which the customer is located.

[0632] 3. Obtaining people flow information

[0633] The server uses people flow information acquisition software to acquire people's movement patterns and stay times in specific areas, which allows the system to grasp the congestion situation within the store and popular areas.

[0634] 4. Acquisition of purchasing information

[0635] The server uses purchase history management software to obtain information about the products and services purchased by customers, allowing the server to understand the purchasing trends of customers.

[0636] 5. Recognizing emotional states

[0637] The smart glasses use a built-in camera to capture a customer's face in real time, and an emotion recognition engine to recognize the customer's emotional state, allowing the system to understand the customer's current emotional state.

[0638] 6. Generating personalized content and services

[0639] The server uses a generative AI model to combine acquired customer information, location information, people flow information, purchasing information, and emotional state to generate personalized content and services that are optimal for each customer.

[0640] 7. Display on smart devices

[0641] The smart glasses display generated content and services in real time, allowing customers to instantly access information that is most relevant to them.

[0642] Specific examples

[0643] For example, if a customer stops in front of a particular product shelf in a store and shows an excited expression, promotions and discount information related to that product will be displayed on the smart glasses.

[0644] Prompt Sentence Examples

[0645] If a customer stops in front of a particular shelf in your store and shows excitement, show them promotions and discounts related to that product.

[0646] In this way, smart shopping assistants can improve customers' shopping experience and contribute to increased sales for stores.

[0647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0648] Step 1:

[0649] The server uses customer information management software to obtain the customer's personal information and purchase history. The customer's ID is required as input, and data on the customer's preferences and past purchasing behavior is obtained as output. This data is the basis for providing personalized services to the customer.

[0650] Step 2:

[0651] The server uses location information acquisition software to acquire the customer's current location and movement route in real time. Location information from the customer's smart device is required as input, and data on the customer's current location and movement pattern is obtained as output. This data is used to determine which area the customer is in.

[0652] Step 3:

[0653] The server uses people flow information acquisition software to acquire people's movement patterns and stay times in specific areas. Data from sensors and cameras inside the store is required as input, and data on the congestion status and popular areas in each area is obtained as output. This data is used to understand people flow within the store and predict customer behavior.

[0654] Step 4:

[0655] The server uses purchase history management software to obtain information about the products and services purchased by customers. The inputs are the customer's ID and a purchase history database, and the output is data about the customer's purchasing trends. This data is used to analyze customer purchasing behavior and provide personalized services.

[0656] Step 5:

[0657] The smart glasses have a built-in camera that captures the customer's face in real time, and an emotion recognition engine is used to recognize the customer's emotional state. The camera footage is required as input, and data about the customer's current emotional state is obtained as output. This data is used to provide services that correspond to the customer's emotions.

[0658] Step 6:

[0659] The server uses a generative AI model to combine acquired customer information, location information, people flow information, purchasing information, and emotional state to generate personalized content and services that are optimal for each customer. The various data mentioned above is required as input, and data related to personalized content and services is obtained as output. This data becomes the specific service content provided to the customer.

[0660] Step 7:

[0661] The smart glasses display generated content and services in real time. Data about the generated content and services is required as input, and the output is information displayed in the customer's field of view. This information is used by customers to instantly obtain the information that is most suitable for them.

[0662] In this way, smart shopping assistants can improve customers' shopping experience and contribute to increased sales for stores.

[0663] Example 3

[0664] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0665] While conventional systems can acquire customer information, location information, people flow information, and purchasing information individually, it is difficult to integrate this information to provide personalized content and services. Furthermore, content is not adjusted according to the user's emotional state, resulting in a decline in the quality of the user experience and no improvement in ARPU (average revenue per user). To solve these issues, a system is needed that can integrate acquired information and further adjust content according to the user's emotional state.

[0666] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0667] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for pre-processing each acquired information, means for generating personalized content and services using a generative AI model, means for analyzing a user's emotional state using an emotion engine, means for adjusting the generated content and services according to the user's emotional state, and means for providing the adjusted content and services. This makes it possible to integrate the acquired information and further adjust content according to the user's emotional state.

[0668] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0669] "Customer information" refers to data about customers, such as personal information, purchasing history, and preferences.

[0670] "Location information" is data that indicates the geographical location of a user or an object.

[0671] "People flow information" is data that shows the movement patterns and length of stay of people within a specific area or facility.

[0672] "Purchase information" is data about products and services purchased by customers.

[0673] "Preprocessing" refers to the process of converting acquired data into a format suitable for analysis or generation.

[0674] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data.

[0675] An "emotion engine" is a technology for analyzing a user's emotional state and detecting specific emotions.

[0676] "Content adjustment" refers to the process of modifying generated content in response to the user's emotional state and other conditions.

[0677] "ARPU" means average revenue per user and is an indicator of the average revenue per user over a specific period of time.

[0678] This invention is a system that combines a generative AI model and an emotion engine, which acquires customer information, location information, people flow information, and purchasing information, and provides personalized content and services based on this information. Furthermore, it has the function of adjusting content and services according to the user's emotional state.

[0679] Hardware and software used

[0680] Hardware: Servers, devices (smartphones, tablets, etc.)

[0681] Software: Generative AI models (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL), natural language processing technologies (e.g., spaCy)

[0682] Explanation of program processing

[0683] 1. Data Acquisition

[0684] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, it accesses the database via API and retrieves the necessary information through queries.

[0685] The device uses the GPS function to obtain the user's real-time location information, for example, by using the location information service on a smartphone.

[0686] 2. Data Preprocessing

[0687] The server preprocesses the acquired data, specifically cleaning it (filling in missing values ​​and removing outliers) and normalizing it, for example converting location coordinate data into a standard format.

[0688] 3. Content Generation

[0689] The server inputs the preprocessed data into a generative AI model. Specifically, it generates a prompt sentence and inputs it into the generative AI model (e.g., GPT-4). For example, the prompt sentence is, "If the user is in a shopping mall, please suggest the optimal shopping route."

[0690] The generative AI model generates personalized content based on prompts, such as suggesting the best shopping route based on store and sale information within a shopping mall.

[0691] 4. Emotion analysis

[0692] The device analyzes the user's emotional state using an emotion engine. Specifically, it uses a camera and microphone to analyze the user's facial expressions and tone of voice to determine the emotional state. For example, it can detect whether the user is showing signs of surprise.

[0693] 5. Content Adjustment

[0694] The server receives the analysis results from the emotion engine and adjusts the generated content. Specifically, it changes the content and tone of the content according to the user's emotional state. For example, if the user expresses surprise, it adds entertainment content that amplifies that emotion.

[0695] 6. Content Provision

[0696] The device then provides the user with tailored content, specifically displayed on the screen of a smartphone or tablet, such as optimal shopping routes or entertainment content.

[0697] Specific examples

[0698] Example 1: Providing services in a shopping mall

[0699] The server obtains the customer's location information within the shopping mall and inputs the prompt statement "When the user is in the shopping mall, please suggest the optimal shopping route" into the generative AI model.

[0700] The generative AI model generates a shopping route and returns it to the server.

[0701] The terminal displays the generated shopping route to the user.

[0702] Example 2: Content adjustment based on emotions

[0703] The device uses an emotion engine to detect when the user is expressing surprise.

[0704] The server inputs a prompt to the generative AI model: "If the user expresses surprise, generate entertainment content that amplifies that emotion."

[0705] The generative AI model generates entertainment content and returns it to the server.

[0706] The terminal provides the generated entertainment content to the user.

[0707] This system provides personalized services based on the user's location information and emotional state, and is expected to improve ARPU. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0708] Step 1: Get the data

[0709] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, it accesses the database via API and retrieves the required information via queries. The input is an API request, and the output is a dataset of customer information, location information, people flow information, and purchase information.

[0710] Step 2: Get real-time location information

[0711] The device obtains the user's real-time location information using the GPS function. Specifically, it uses the smartphone's location information service. The input is data from the GPS sensor, and the output is the coordinate information of the user's current location.

[0712] Step 3: Preprocessing the data

[0713] The server preprocesses the acquired data. Specifically, it cleans the data (fills in missing values ​​and removes outliers) and normalizes it. The input is the data acquired in Step 1 and Step 2, and the output is a preprocessed, clean dataset.

[0714] Step 4: Content Generation

[0715] The server inputs the preprocessed data into a generative AI model. Specifically, it generates a prompt sentence and inputs it into the generative AI model (e.g., GPT-4). For example, the prompt sentence is, "If the user is in a shopping mall, please suggest the optimal shopping route." The input is the preprocessed data and the prompt sentence, and the output is the generated personalized content.

[0716] Step 5: Sentiment Analysis

[0717] The device analyzes the user's emotional state using an emotion engine. Specifically, it uses a camera and microphone to analyze the user's facial expressions and tone of voice to determine the emotional state. The input is data from the camera and microphone, and the output is the analysis result of the user's emotional state.

[0718] Step 6: Adjust content

[0719] The server receives the analysis results from the emotion engine and adjusts the generated content. Specifically, it changes the content and tone of the content according to the user's emotional state. For example, if the user expresses surprise, it adds entertainment content that amplifies that emotion. The inputs are the generated content and the emotion analysis results, and the output is the adjusted content.

[0720] Step 7: Provide content

[0721] The terminal provides the adapted content to the user, specifically, by displaying it on the screen of a smartphone or tablet. The input is the adapted content, and the output is the content provided to the user.

[0722] In this way, the system aims to increase ARPU by providing personalized services based on the user's location and emotional state.

[0723] (Application example 3)

[0724] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0725] Traditional customer service in brick-and-mortar stores was not personalized enough, making it difficult to provide services that reflected the emotional state of the customer. It was also difficult to provide personalized product information and promotions in real time, making it difficult to improve the customer experience. This meant that improvements in customer satisfaction and average revenue per customer (ARPU) were not sufficiently achieved.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0727] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, means for recognizing emotional states, means for adjusting content and services according to the emotional states, and means for providing personalized product information and promotions in real time using smart devices. This makes it possible to provide personalized product information and promotions in real time according to the emotional states of customers.

[0728] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0729] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0730] "Location information" is data that indicates a specific location or coordinates using GPS or other technologies.

[0731] "People flow information" is data on the movement patterns and density of people within a specific area.

[0732] "Purchase information" is data about products and services purchased by customers.

[0733] "Emotional state" refers to the psychological state of a customer that can be inferred from their facial expressions and behavior.

[0734] "Generative AI" is an artificial intelligence technology that generates new content and information based on input data.

[0735] A "smart device" is an electronic device that has Internet connectivity and can run a variety of applications.

[0736] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular customer.

[0737] "Promotion" refers to activities to advertise specific products or services to customers and encourage them to purchase them.

[0738] "Real-time" means that data acquisition and processing occurs immediately.

[0739] A system for implementing this invention includes a server, a smart device (such as a smartphone or smart glasses), and various sensors. The server is equipped with a generative AI system using natural language processing technology to acquire customer information, location information, pedestrian flow information, and purchase information, and generates personalized content and services based on this information. Furthermore, it uses an emotion engine to recognize the customer's emotional state and adjusts the content and services according to that emotional state.

[0740] Hardware and software used

[0741] Hardware: Smartphone, smart glasses, GPS sensor, camera

[0742] Software: Python, EmotionRecognizer library, AIContentGenerator library, REST API

[0743] Data processing and calculation

[0744] 1. Obtaining customer information: The server uses a REST API to obtain data such as the customer's personal information, purchase history, and preferences.

[0745] 2. Obtaining location information: Use the GPS function of the smart device to obtain the customer's current location.

[0746] 3. Obtaining people flow information: Analyzing people movement patterns and density within a specific area based on sensor data.

[0747] 4. Obtaining purchasing information: Obtaining customer purchase history and current purchasing behavior from the database.

[0748] 5. Emotional state recognition: Using the smart device camera, analyze the customer's facial expressions and recognize their emotional state using the EmotionRecognizer library.

[0749] 6. Content generation: Generative AI is used to generate personalized product information and promotions based on acquired information.

[0750] 7. Real-time notifications: Providing personalized product information and promotions to smart devices in real time based on the customer's emotional state.

[0751] Specific examples

[0752] For example, when a customer is in a specific area of ​​a physical store, the smart glasses can analyze the customer's facial expression and, if they show signs of surprise, display a promotion for a new product to that customer, which is generated by generative AI based on the customer's purchasing history and preferences.

[0753] Prompt Sentence Examples

[0754] Customer ID: 12345

[0755] Location: Latitude 35.6895, Longitude 139.6917

[0756] Emotional state: Surprise

[0757] Content to be generated: Promotion of new products

[0758] In this way, personalized product information and promotions can be provided in real time according to the customer's emotional state.

[0759] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0760] Step 1:

[0761] The server retrieves customer information using a REST API. Specifically, it receives a customer ID as input and retrieves data such as the customer's personal information, purchase history, and preferences from the database. This results in a detailed customer profile being output.

[0762] Step 2:

[0763] The terminal (smart device) acquires the customer's current location using its GPS function. Specifically, it receives location information from the terminal's GPS sensor as input and outputs latitude and longitude data. This identifies the customer's current location.

[0764] Step 3:

[0765] The server acquires people flow information based on sensor data. Specifically, it receives data on people's movement patterns and density from sensors within a specific area as input and outputs the analysis results. This provides people flow information within the area.

[0766] Step 4:

[0767] The server retrieves purchasing information from the database. Specifically, it receives data on the customer's purchase history and current purchasing behavior as input and outputs purchasing information. This allows the customer's purchasing trends to be understood.

[0768] Step 5:

[0769] The terminal (smart device) uses a camera to capture the customer's facial expression and recognize their emotional state. Specifically, it receives image data acquired from the camera as input, analyzes the emotional state using the EmotionRecognizer library, and outputs emotional data. This identifies the customer's emotional state.

[0770] Step 6:

[0771] The server uses generative AI to generate personalized content. Specifically, it receives customer information, location information, people flow information, purchasing information, and emotional state as input, generates personalized product information and promotions using the AIContentGenerator library, and outputs the content data. This generates the most suitable content for each customer.

[0772] Step 7:

[0773] The terminal (smart device) notifies the customer of the generated content in real time. Specifically, it receives content data from the server as input and displays it on the smart device's display. This allows customers to receive personalized product information and promotions in real time.

[0774] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0775] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[0776] , obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0778] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0779] [Second embodiment]

[0780] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0781] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0782] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0783] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0784] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0785] The camera 42 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0786] It is a small digital camera equipped with an imaging element such as a range sensor, and captures an image of the surroundings of the user 20 (for example, an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0787] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0788] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0789] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0790] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0791] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0792] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0793] "Example 1"

[0794] One embodiment of the present invention is a system that uses generative AI as natural language processing technology. This system has means for acquiring customer information, location information, people flow information, and purchasing information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, when a customer is staying in a specific area, specific services can be provided to the customer based on the people flow information and purchasing information for that area. This can result in an increase in ARPU.

[0795] "Example 2"

[0796] Another embodiment of the present invention is a system that uses generative AI. This system has means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, based on information that a customer has purchased a specific product, it is possible to provide the customer with related products and services. This results in an increase in ARPU.

[0797] "Example 3"

[0798] Yet another embodiment of the present invention is a system that uses generative AI. This system has a means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier database. The acquired information is then used by generative AI.

[0799] The data is then processed by the mobile app and converted into information that can be used to provide personalized content and services. For example, based on a customer's location, services related to that location can be provided, which can increase ARPU.

[0800] The processing flow of each embodiment will be described below.

[0801] "Example 1"

[0802] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0803] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0804] Step 3: For example, when a customer is staying in a specific area, specific services are provided to the customer based on information on the flow of people and purchasing information in that area.

[0805] Step 4: This will result in increased ARPU.

[0806] "Example 2"

[0807] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0808] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0809] Step 3: For example, based on information about a customer's purchase of a specific product, offer the customer related products or services.

[0810] Step 4: This will result in increased ARPU.

[0811] "Example 3"

[0812] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[0813] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[0814] Step 3: For example, provide location-related services based on the customer's location information.

[0815] Step 4: This will result in increased ARPU.

[0816] Example 1

[0817] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] Conventional systems did not adequately process information to provide personalized content and services to customers, making it difficult to improve customer satisfaction and revenue. Furthermore, pre-processing and generative AI models to effectively utilize acquired information were insufficient, making it difficult to provide services in real time.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0820] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for the generative AI model to generate personalized content and services, means for transmitting the generated content and services to a terminal, and means for the terminal to display the content and services to a user. This makes it possible to provide personalized content and services to customers in real time.

[0821] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0822] "Customer information" refers to data such as personal information, behavioral history, and purchase history about customers.

[0823] "Location information" is data indicating the customer's current location and past movement history.

[0824] "People flow information" refers to data such as people's movement patterns and length of stay in a particular area.

[0825] "Purchase information" is data about products and services purchased by customers.

[0826] "Preprocessing" refers to the process of converting the acquired raw data into a format suitable for analysis and generative AI models.

[0827] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[0828] "Personalized content and services" are information and services that are customized to a customer's specific needs and circumstances.

[0829] A "terminal" is a device used by a user (for example, a smartphone or tablet).

[0830] "Real-time" refers to the time frame in which data is acquired, processed, and results provided almost immediately.

[0831] This invention is a system that uses natural language processing technology to acquire customer information, location information, people flow information, and purchasing information, and provides personalized content and services based on this information. Specific embodiments of this system are described below.

[0832] System configuration

[0833] Hardware and Software

[0834] The server uses the following hardware and software:

[0835] Hardware: High-performance server machine

[0836] Software: Database management system, API interface, generative AI model (e.g., OpenAI's GPT-4)

[0837] A terminal is a device used by a user that uses the following hardware and software:

[0838] Hardware: Smartphones, tablets

[0839] Software: Mobile applications, notification systems

[0840] Data acquisition and preprocessing

[0841] The server retrieves customer information, location information, foot traffic information, and purchase information from the mobile carrier's database. This information is obtained in real time through API. For example, a location information API is used to confirm that a customer's current location is Shibuya, Tokyo.

[0842] The acquired information is preprocessed. Specifically, the customer's current location is identified based on the location information, and information on people flow and purchasing in that area is organized. For example, an SQL query is executed to extract the latest people flow data and purchasing trends in Shibuya.

[0843] Using generative AI models

[0844] The server inputs the preprocessed information into the generative AI model. The prompt text is, "Generate appropriate services and content for customers currently in Shibuya based on the latest Shibuya pedestrian flow and purchasing information."

[0845] The generative AI model generates personalized content and services based on input information, such as information about events taking place in Shibuya or discount coupons for specific stores.

[0846] Providing content and services

[0847] The server sends the generated content and services to the user's device. For example, it uses Firebase Cloud Messaging to send generated coupon information and event information to the user's smartphone as a push notification.

[0848] The device displays the received content and services to the user. For example, a message such as "Shibuya Festival is now on! 20% discount coupons are available at certain stores" will be displayed in the smartphone's notification bar.

[0849] Specific examples

[0850] For example, if a customer is staying in Shibuya, Tokyo, the server will analyze Shibuya's pedestrian flow information and purchasing information and use the generative AI model to provide the customer with appropriate services. An example of a prompt sentence would be, "For customers currently staying in Shibuya, please generate appropriate services and content based on the latest pedestrian flow information and purchasing information for Shibuya."

[0851] In this way, it is possible to provide customers with personalized content and services in real time.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The server acquires customer information, location information, people flow information, and purchasing information.

[0855] Input: API request from the carrier database

[0856] Output: Customer's current location, past movement history, people flow data, purchase history

[0857] Specific operation: The server sends an API request and receives the customer's location information and purchase history as a response. For example, it verifies that the customer's current location is Shibuya, Tokyo.

[0858] Step 2:

[0859] The server preprocesses the retrieved information.

[0860] Input: Acquired customer information, location information, people flow information, purchase information

[0861] Output: Preprocessed data (location information, people flow data, purchasing trends)

[0862] Specific operation: The server identifies the customer's current location based on the location information and organizes information on people flow and purchasing in the area. For example, it executes SQL queries to extract the latest people flow data and purchasing trends in Shibuya.

[0863] Step 3:

[0864] The server inputs the preprocessed information into the generative AI model.

[0865] Input: preprocessed data, prompt statement

[0866] Output: Input data to a generative AI model

[0867] Specific operation: The server converts the preprocessed data into JSON format and sends it to the generative AI model along with a prompt. For example, it sends the data to the API of the generative AI model using an HTTP request.

[0868] Step 4:

[0869] Generative AI models generate personalized content and services.

[0870] Input: Input data to the generative AI model

[0871] Output: personalized content and services (e.g., event information, discount coupons)

[0872] How it works: The generative AI model analyzes the data it receives and generates appropriate content and services, such as "Events taking place in Shibuya: Shibuya Festival, 20% off coupons at specific stores."

[0873] Step 5:

[0874] The server transmits the generated content and services to the terminal.

[0875] Input: Generated content and services

[0876] Output: Notification data to the device

[0877] Specific operation: The server sends the generated content or service to the user's device as a push notification, for example, using Firebase Cloud Messaging.

[0878] Step 6:

[0879] The terminal displays content and services to the user.

[0880] Input: Notification data to the device

[0881] Output: The content or service that is displayed to the user

[0882] Specific operation: The device displays the received notification to the user. For example, the notification bar on the smartphone displays "Shibuya Festival is underway! 20% discount coupons are available at certain stores."

[0883] (Application example 1)

[0884] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0885] Traditional customer service in brick-and-mortar stores relied on general promotions and product recommendations, making it difficult to provide services based on individual customer needs and purchasing history. This meant that improvements in customer satisfaction and maximization of sales were not fully achieved. Furthermore, it was not possible to utilize customer location information and people flow information in real time, making it difficult to provide timely services.

[0886] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0887] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing personalized product recommendations and promotion information in a physical store based on the customer's location information and purchase history. This makes it possible to provide personalized services according to customer needs, thereby improving customer satisfaction and maximizing sales.

[0888] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[0889] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0890] "Location information" is data indicating the customer's current location and past movement history.

[0891] "People flow information" is data that shows the movement patterns and length of stay of people in a particular area or store.

[0892] "Purchase information" is data about products and services purchased by customers.

[0893] "Personalized content and services" are information and services that are tailored to a customer's individual needs and preferences.

[0894] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0895] A "brick and mortar store" is a retail outlet that exists in a physical location.

[0896] "Product recommendation" refers to suggesting specific products based on a customer's preferences and purchasing history.

[0897] "Promotional information" is information intended to promote the sale of specific products or services.

[0898] "ARPU" is an indicator that shows average revenue per user.

[0899] A system for implementing this invention is configured as follows. First, the server uses generative AI as a means using natural language processing technology. Smartphones and other mobile devices are used as a means for acquiring customer information, location information, people flow information, and purchasing information. These devices acquire location information using technologies such as GPS, Wi-Fi, and Bluetooth, and collect data on customer purchasing history and preferences.

[0900] The server combines the acquired information to provide personalized content and services. Specifically, when a customer is in a physical store, it generates personalized product recommendations and promotional information based on the customer's location and purchase history. This allows customers to receive information tailored to their needs in real time.

[0901] Hardware and software used

[0902] Hardware: Smartphones, servers

[0903] Software: Python, Geopy, Requests library

[0904] Data processing and calculation

[0905] 1. Acquisition of customer information: Smartphones acquire information such as customer location, purchase history, and visit times. This data is collected from mobile carrier databases and store POS systems.

[0906] 2. Content generation using generative AI: The server sends prompts to a generative AI model based on acquired customer information to generate personalized product recommendations and promotional information.

[0907] 3. Displaying the results: The generated content is displayed on the smartphone screen.

[0908] Specific examples

[0909] For example, if a customer is staying at a particular shopping mall in Tokyo, the generative AI will determine, based on the customer's past purchasing history and current location, that "this customer might be interested in a new fashion item," and provide them with promotional information for that particular store.

[0910] Prompt Sentence Examples

[0911] Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], current location: (35.6895, 139.6917), visit time: 2023-10-01 14:30.

[0912] In this way, the shopping experience in physical stores can be improved.

[0913] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0914] Step 1:

[0915] The terminal acquires the customer's location information.

[0916] Input: Location data obtained using technologies such as GPS, Wi-Fi, and Bluetooth.

[0917] Data processing: Convert location information into latitude and longitude format.

[0918] Output: Customer's current location (e.g., latitude 35.6895, longitude 139.6917).

[0919] Step 2:

[0920] The terminal acquires the customer's purchase history.

[0921] Input: Purchase history data obtained from store POS systems and mobile carrier databases.

[0922] Data processing: Convert purchase history into list format.

[0923] Output: Customer's purchase history (e.g. ["item1", "item2"]).

[0924] Step 3:

[0925] The terminal acquires the customer's visiting time.

[0926] Input: The current date and time obtained from the device's internal clock.

[0927] Data processing: Convert date and time data into a standard format.

[0928] Output: Customer visiting time (e.g. 2023-10-01 14:30).

[0929] Step 4:

[0930] The server sends prompts to the generative AI model based on the acquired customer information.

[0931] Input: Customer ID, purchase history, location information, visit time.

[0932] Data processing: Incorporate this information into the prompt sentence.

[0933] Output: Prompt statement (e.g., Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], Current location: (35.6895, 139.6917), Visit time: 2023-10-01 14:30).

[0934] Step 5:

[0935] The server receives the response from the generative AI model.

[0936] Input: Prompt statement.

[0937] Data processing: A generative AI model generates personalized product recommendations and promotional information based on prompts.

[0938] Output: Generated content (e.g., recommended product lists and promotional information).

[0939] Step 6:

[0940] The terminal displays the generated content to the customer.

[0941] Input: Generated content.

[0942] Data processing: Converting content into a format suitable for the device screen.

[0943] Output: Personalized product recommendations and promotional information displayed on the customer's smartphone screen.

[0944] In this way, customers receive information tailored to their needs in real time.

[0945] Example 2

[0946] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0947] With conventional systems, it was possible to obtain customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. Furthermore, the content and services provided did not adequately address customer needs, making it difficult to improve average revenue per user (ARPU). Furthermore, data preprocessing and the use of generative AI models were insufficient, preventing efficient information processing.

[0948] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0949] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating content and services personalized by the generative AI model, and means for providing the generated content and services. This makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per unit (ARPU).

[0950] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0951] "Customer information" is data about a customer, including, for example, name, age, gender, purchase history, and the like.

[0952] "Location information" is data that indicates the geographical location of a specific object, and includes coordinate information such as latitude and longitude.

[0953] "People flow information" is data on the movements and stays of people in specific areas or locations.

[0954] "Purchase information" refers to data about products or services purchased by a customer, including the date and time of purchase, the place of purchase, and the purchase amount.

[0955] "Preprocessing" refers to a series of operations to convert acquired data into a format suitable for analysis and processing, including data cleaning and format standardization.

[0956] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[0957] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular customer.

[0958] "Providing" refers to the act of presenting the generated content or service to a customer.

[0959] "Average Revenue Per Unit (ARPU)" is a metric that indicates the average revenue per customer over a specific period of time.

[0960] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[0961] The server first obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the mobile carrier's database via API and obtains the necessary data, such as the customer's name, age, gender, purchase history, current location, and place of stay.

[0962] Next, the server preprocesses the acquired data. This includes cleaning the data (filling in missing values ​​and removing outliers) and standardizing the format. For example, the Python Pandas library is used to convert the latitude and longitude data of the location information into a standard format.

[0963] The preprocessed data is input to the generative AI model. The server converts the preprocessed data into JSON format and inputs it to the generative AI model. An example of a specific prompt sentence is as follows:

[0964] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[0965] Location: {Latitude: 35.6895, Longitude: 139.6917}

[0966] People flow information: {Current location: Shinjuku Station}

[0967] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[0968] The generative AI model generates personalized content and services based on input data. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to that customer. Specifically, it might generate a suggestion such as, "If you purchase a smartphone case near Shinjuku Station, you can receive a discount at a specific store."

[0969] Finally, the server sends the generated content and services to the device. The device displays the received content to the user. For example, it displays suggestions to the user using the smartphone's push notification function. This allows the user to receive information and services that are optimized for them.

[0970] This system makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per user (ARPU).

[0971] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0972] Step 1: Get the data

[0973] The server obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the database through an API to obtain information such as the customer's name, age, gender, purchase history, current location, and where they are staying. The input is the API request, and the output is the various pieces of information obtained.

[0974] Step 2: Preprocessing the data

[0975] The server preprocesses the acquired data. Specifically, it uses Python's Pandas library to clean the data (fill in missing values ​​and remove outliers) and standardize the format. For example, it converts latitude and longitude location data into a standard format. The input is the acquired raw data, and the output is the preprocessed data.

[0976] Step 3: Input to the generative AI model

[0977] The server inputs the preprocessed data into the generative AI model. Specifically, it converts the preprocessed data into JSON format and inputs it into the generative AI model. The input is the preprocessed data, and the output is a prompt sentence for the generative AI model. An example of a prompt sentence is as follows:

[0978] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[0979] Location: {Latitude: 35.6895, Longitude: 139.6917}

[0980] People flow information: {Current location: Shinjuku Station}

[0981] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[0982] Step 4: Generate personalized content

[0983] A generative AI model generates personalized content and services based on the input prompt. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to the customer. The input is the prompt, and the output is the generated personalized content.

[0984] Step 5: Providing content

[0985] The server sends the generated content to the device. The device displays the received content to the user. Specifically, the device uses the smartphone's push notification function to display suggestions to the user. The input is the generated content, and the output is a notification displayed to the user. This allows the user to receive information and services that are optimized for them.

[0986] (Application example 2)

[0987] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0988] Traditional product proposals and promotions in brick-and-mortar stores are unable to adequately meet the needs of each individual customer, making it difficult to improve customer satisfaction and maximize sales. Furthermore, providing personalized information in real time is difficult, making it difficult to effectively stimulate customers' purchasing desire.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0990] In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing product suggestions and promotion information in real time based on the customer's location information and purchase history. This makes it possible to provide optimal product suggestions and promotion information to each individual customer in real time.

[0991] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[0992] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[0993] "Location information" is data that indicates a customer's current geographic location.

[0994] "People flow information" refers to data such as people's movement patterns and length of stay in a specific area.

[0995] "Purchase information" is data about products and services purchased by customers.

[0996] "Individualized content and services" refers to information and services provided tailored to the needs and preferences of each individual customer.

[0997] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[0998] "Means for providing product suggestions and promotional information in real time" refers to technology for instantly providing product and promotional information according to the customer's current situation.

[0999] "ARPU" stands for average revenue per user, and is an indicator of average revenue per customer.

[1000] As an embodiment of the present invention, a smart shopping assistant system for a brick-and-mortar store will be described as an example. This system provides product suggestions and promotion information in real time based on customer location information and purchase history.

[1001] System configuration

[1002] The system consists of the following main components:

[1003] 1. Server: Executes generative AI models using natural language processing technology.

[1004] 2. Terminal: A device carried by the customer, such as a smartphone or smart glasses.

[1005] 3. Database: Stores customer information, location information, people flow information, and purchasing information.

[1006] Program processing

[1007] The server implements the system using the following means:

[1008] 1. Means using natural language processing technology: Generative AI models are used to generate optimal product suggestions and promotional information for customers.

[1009] 2. How to obtain customer information: Obtain customer personal information and purchasing history from the database.

[1010] 3. Means of obtaining location information: The customer's current location is obtained using technologies such as GPS and beacons.

[1011] 4. Means of acquiring people flow information: Sensors and cameras are used to acquire people's movement patterns and stay times within the store.

[1012] 5. Means of obtaining purchasing information: Information on the products and services purchased by customers is obtained from the POS system.

[1013] 6. Means of providing personalized content and services: By combining the various pieces of information obtained, we can provide each customer with optimal product suggestions and promotional information in real time.

[1014] Data processing and calculation

[1015] The server uses a programming language such as Python to perform the following tasks:

[1016] 1. Data acquisition: Customer information, location information, people flow information, and purchasing information are acquired from databases and sensors.

[1017] 2. Sending prompts to the generative AI model: Based on the acquired data, prompts are sent to the generative AI model to generate product suggestions and promotional information.

[1018] 3. Real-time provision: The generated information is sent to the terminal and displayed to the customer in real time.

[1019] Specific examples

[1020] For example, if a customer is in "Aisle 5" and has previously purchased "item1" and "item2," we can use this information to send the following prompt to our generative AI model:

[1021] Prompt Sentence Examples

[1022] Customer ID: 12345, Current Location: Aisle 5, Purchase History: ['item1', 'item2']

[1023] Based on this prompt, the generative AI model will return recommended products related to "Aisle 5" or based on past purchase history. For example, "item 3" or "item 4" might be suggested. This way, the model can provide personalized product suggestions to improve the shopping experience.

[1024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1025] Step 1:

[1026] The server retrieves customer information from a database. The input is the customer ID, and the output is the customer's personal information and purchase history. Specifically, it sends a query to the database using the customer ID as a key to retrieve the corresponding customer information.

[1027] Step 2:

[1028] The terminal acquires the customer's current location information. The input is a signal from a GPS or beacon, and the output is the customer's current location. Specifically, the terminal periodically sends the location information to the server.

[1029] Step 3:

[1030] The server acquires people flow information from sensors and cameras. The input is data from the sensors and cameras, and the output is people's movement patterns and stay times within the store. Specifically, the server analyzes the data from the sensors and cameras to generate people flow information.

[1031] Step 4:

[1032] The server obtains purchase information from the POS system. The input is data from the POS system, and the output is information about the products and services purchased by the customer. Specifically, it analyzes the transaction data from the POS system and obtains the purchase information.

[1033] Step 5:

[1034] The server combines the acquired information to generate a prompt. The input is customer information, location information, people flow information, and purchasing information, and the output is a prompt to be sent to the generative AI model. Specifically, each piece of information is compiled in text format to generate the prompt.

[1035] Step 6:

[1036] The server sends prompt sentences to the generative AI model to generate product suggestions and promotion information. The input is the prompt sentence, and the output is the generated product suggestions and promotion information. Specifically, the server sends prompt sentences to the generative AI model and receives the returned information.

[1037] Step 7:

[1038] The server sends the generated product suggestions and promotion information to the terminal. The input is the generated information, and the output is the information displayed on the terminal. Specifically, the generated information is sent to the terminal, and the terminal displays it to the customer.

[1039] Step 8:

[1040] The user checks the product suggestions and promotion information displayed on the device and considers purchasing if necessary. The input is the information displayed on the device, and the output is the user's purchasing behavior. Specifically, the user operates the device to check the product information and decides to purchase.

[1041] Example 3

[1042] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1043] Conventional systems can collect customer information, location information, people flow information, and purchasing information individually, but it is difficult to integrate this information to provide personalized content and services in real time. Furthermore, the content and services provided often do not match customer needs, making it difficult to improve ARPU (average revenue per user).

[1044] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1045] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating personalized content and services using the generative AI model, and means for delivering the generated content and services to user terminals. This makes it possible to provide personalized content and services that meet customer needs in real time, thereby improving ARPU.

[1046] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1047] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1048] "Location information" refers to data related to a customer's current location and past movement history.

[1049] "People flow information" is data on people's movement patterns and length of stay within a particular area or facility.

[1050] "Purchase information" is data about products and services that customers have purchased in the past.

[1051] "Preprocessing" is the process of converting acquired data into a format suitable for generative AI models by cleaning, normalizing, and filling in missing values.

[1052] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data.

[1053] "Personalized content and services" are information and services that are customized to meet a customer's specific needs and preferences.

[1054] A "user terminal" is a device that is directly operated by a user, such as a smartphone, tablet, or PC.

[1055] "Delivery" is the process of transmitting generated content or services to user terminals.

[1056] "ARPU" stands for Average Revenue Per User and refers to the average revenue per user.

[1057] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[1058] First, the server acquires customer information, location information, foot traffic information, and purchase information. This information is collected in real time from the mobile carrier's database using API. For example, the moment a user enters a shopping mall, their location information is sent to the server.

[1059] The server then preprocesses the acquired data, which includes cleaning, normalizing, and imputing missing values. For example, if location information is missing, it imputes the closest past location information.

[1060] The preprocessed data is input into a generative AI model. For example, OpenAI's GPT-4 is used as a generative AI model. The server inputs the preprocessed data into GPT-4 to generate personalized content and services. An example of a specific prompt is, "If the user is in a shopping mall, please generate content that provides the most suitable store and sale information based on their location information."

[1061] The generative AI model generates personalized content based on input data. For example, if a user is in a shopping mall, it generates content based on the user's location, such as, "There is currently a sale at ABC store in the shopping mall. We will notify you of sales information related to products you have previously purchased at this store."

[1062] Finally, the server delivers the generated content to the user's device. Specifically, it sends a push notification to the user's smartphone. The user can tap the notification to view more information.

[1063] This system allows users to receive information that is most suitable for them in real time, improving their shopping experience. It also allows stores to effectively approach target customers, which is expected to increase sales. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1064] Step 1:

[1065] The server uses APIs to obtain customer information, location information, people flow information, and purchase information from the mobile carrier's database in real time. API requests are sent as input, and data such as the customer's current location, past movement history, and purchase history is received as output. Specifically, the server accesses the API endpoint and specifies the required parameters to obtain data.

[1066] Step 2:

[1067] The server preprocesses the acquired data. It uses the raw data acquired in step 1 as input and obtains cleaned, normalized data as output. Specific operations include data cleaning (removal of noise and outliers), normalization (standardization of data format), and missing value completion (completion using past data).

[1068] Step 3:

[1069] The server inputs the preprocessed data into the generative AI model. The preprocessed data is used as input, and personalized content generated by the generative AI model is obtained as output. Specifically, the server creates a prompt for the generative AI model (e.g., GPT-4) and inputs the preprocessed data. An example of a prompt is, "If the user is in a shopping mall, please generate content to provide the most suitable store and sale information based on their location information."

[1070] Step 4:

[1071] The generative AI model generates personalized content based on the input data. It uses the data entered in step 3 and the prompt sentence as input, and obtains personalized content as output. Specifically, the generative AI model analyzes the input data and generates information that best suits the user's current situation. For example, it generates content such as, "There is currently a sale at ABC store in the shopping mall. We will notify you of sale information related to products you have previously purchased at this store."

[1072] Step 5:

[1073] The server delivers the generated content to the user's device. The generated content is used as input, and a notification is sent to the user's device as output. Specifically, the server uses a push notification service to send the generated content to the user's smartphone. The user can tap the notification to view more information.

[1074] In this way, the system delivers personalized content and services that meet customer needs in real time, resulting in increased ARPU.

[1075] (Application example 3)

[1076] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1077] With conventional systems, it was difficult to provide personalized content and services by effectively utilizing customer location information and purchase history. This also made it difficult to improve ARPU (Average Revenue Per User). Furthermore, there was a lack of means to provide personalized content in real time through smartphone applications.

[1078] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, means for sending prompt sentences to a generative AI model to generate personalized content, and means for providing the generated content through a smartphone application. This makes it possible to effectively utilize customer location information and purchase history to provide personalized content in real time.

[1079] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1080] "Customer information" refers to data such as personal information, purchase history, and behavioral history about customers.

[1081] "Location information" is information about a specific place or location, and includes geographical data such as latitude and longitude.

[1082] "People flow information" is data on the movements and stays of people in specific areas or locations.

[1083] "Purchase information" is data about products and services purchased by customers.

[1084] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data or prompts.

[1085] A "prompt" is an instruction given to a generative AI model to generate a specific output.

[1086] A "smartphone application" is a software program that runs on a smartphone.

[1087] "Personalized content" is information or services that are customized to meet the needs and preferences of a particular customer.

[1088] "ARPU" stands for Average Revenue Per User and refers to the average revenue per user.

[1089] A system for carrying out this invention is configured as follows: The server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchasing information, means for combining the acquired information to provide personalized content and services, means for sending prompt sentences to a generative AI model to generate personalized content, and means for providing the generated content through a smartphone application.

[1090] Explanation of program processing

[1091] 1. Obtaining customer information:

[1092] The server accesses a database to acquire customer information, which includes personal information, purchase history, and behavioral history of the customer.

[1093] 2. Location information acquisition:

[1094] The server uses the GPS function of the smartphone to obtain the customer's current location information, which allows the server to determine where the customer is currently located.

[1095] 3. Obtaining people flow information:

[1096] The server retrieves data about people's movements and stays in specific areas and locations, which can come from mobile carrier databases or public data sources.

[1097] 4. Acquisition of purchasing information:

[1098] The server retrieves data about the products and services that the customer has previously purchased, which is stored in a database as the customer's purchase history.

[1099] 5. Sending prompts to the generative AI model:

[1100] The server sends a prompt to the generative AI model based on the acquired customer information, location information, people flow information, and purchase information. An example of a prompt is, "Generate recommended products for customer ID: 12345 based on their current location (35.6895,139.6917)."

[1101] 6. Personalized content generation:

[1102] The generative AI model generates personalized content based on the prompt, taking into account the customer's current location and past purchasing history.

[1103] 7. Provision through smartphone applications:

[1104] The server provides the generated personalized content to customers through a smartphone application, allowing customers to check recommended products and services in real time through the smartphone application.

[1105] Specific examples

[1106] For example, if the customer ID is "12345," assume that the current location is Tokyo (latitude 35.6895, longitude 139.6917). This customer has previously purchased "item1" and "item2" and visited "store1" and "store2." Based on this information, the following prompt is sent to the generative AI model:

[1107] For customer ID: 12345, generate product recommendations based on current location (35.6895,139.6917).

[1108] Based on this prompt, the generative AI model suggests products on sale at nearby stores, new products related to past purchases, etc. This allows customers to see personalized recommendations in real time through a smartphone application.

[1109] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1110] Step 1:

[1111] The server obtains customer information. A customer ID is given as input. The server accesses the database and obtains data such as the customer's personal information, purchase history, and behavioral history. The server obtains customer information as output.

[1112] Step 2:

[1113] The server obtains the customer's current location information. As input, the latitude and longitude obtained from the smartphone's GPS function are given. The server processes this and determines the customer's current location. As output, the location information is obtained.

[1114] Step 3:

[1115] The server acquires people flow information. As input, it is given data about people's movements and stays in a particular area or location. The server retrieves this information from mobile carrier databases and public data sources. As output, it obtains people flow information.

[1116] Step 4:

[1117] The server obtains purchasing information. As input, it receives the customer's purchasing history. The server accesses a database to obtain data about the products and services the customer has purchased in the past. As output, it obtains purchasing information.

[1118] Step 5:

[1119] The server sends prompts to the generative AI model based on the acquired customer information, location information, people flow information, and purchasing information. This information is given as input. The server combines this information to generate prompts and sends them to the generative AI model. The output from the generative AI model is personalized content.

[1120] Step 6:

[1121] The server provides personalized content obtained from the generative AI model through a smartphone application. The generated content is given as input. The server sends this content to the smartphone application and displays it to the user. The output is the personalized content that the user can view.

[1122] Specifically, for example, if the customer ID is "12345," the server retrieves the customer's information from the database and obtains their current location from the smartphone's GPS. Next, it obtains people flow information from the mobile carrier's database and the customer's purchase history from the database. Based on this information, the server sends a prompt to the generative AI model: "Generate recommended products for customer ID: 12345 based on their current location (35.6895, 139.6917)." The personalized content obtained from the generative AI model is sent to the smartphone application and displayed to the user.

[1123] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1124] "Example 1"

[1125] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes user emotions to adjust content and services according to the user's emotional state. Specifically, if a user expresses joy, content and services that amplify the user's joy are provided to the user. On the other hand, if a user expresses sadness, content and services that comfort the user are provided to the user. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[1126] "Example 2"

[1127] Another embodiment of the present invention provides a system that combines an emotion engine. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes the user's emotions to adjust content and services according to the user's emotional state. Specifically, if a user is expressing excitement, content and services that maintain the user's excitement are provided. On the other hand, if a user is expressing calm, content and services that promote relaxation are provided. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[1128] "Example 3"

[1129] As yet another embodiment of the present invention, a system incorporating an emotion engine is provided. This system acquires natural language processing technology, customer information, location information, people flow information, and purchasing information, and combines this information to provide personalized content and services. Furthermore, this system uses an emotion engine that recognizes the user's emotions to adjust content and services according to the user's emotional state. Specifically, if a user expresses surprise, content and services that amplify the user's surprise are provided. Also, if a user expresses relief, content and services that increase the user's sense of relief are provided. This provides more personalized services according to the user's emotional state, and is expected to increase ARPU.

[1130] The processing flow of each embodiment will be described below.

[1131] "Example 1"

[1132] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[1133] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[1134] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[1135] "Example 2"

[1136] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[1137] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[1138] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[1139] "Example 3"

[1140] Step 1: The system uses natural language processing techniques to analyze input from the user and recognize the user's emotional state.

[1141] Step 2: The system acquires customer information, location information, people flow information, and purchasing information.

[1142] Step 3: The system combines the acquired information with the user's emotional state to provide personalized content and services.

[1143] Example 1

[1144] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1145] Conventional systems could collect customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. In addition, it was not possible to adjust content and services according to the user's emotional state, which meant that improvements to the user experience and ARPU were not fully realized.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1147] In this invention, the server includes means for acquiring customer information about a user, means for acquiring location information of the user, means for acquiring people flow information about people's movements or stays at the user's location, means for acquiring purchase information about the user's purchases, and means for recommending content or services available to the user based on the acquired customer information, location information, people flow information, and purchase information using a prompt sentence instructing the system to recommend content or services available to the user and a generative AI model. The server further includes means for recognizing the user's emotional state using an emotion engine, and the recommending means recommends content or services available to the user based on the acquired customer information, location information, people flow information, purchase information, and the user's emotional state using a prompt sentence instructing the system to recommend content or services available to the user and the generative AI model. This makes it possible to integrate various information to provide personalized content and services and further adjust them according to the user's emotional state. Furthermore, when the user is located within a commercial facility, the recommending means recommends discount information or product information of stores within the commercial facility as content or services that can be provided to the user.

[1148] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[1149] "Customer information" is data about a customer, including name, age, gender, purchase history, etc.

[1150] "Location information" is data that indicates the geographic location of a particular device or user.

[1151] "People flow information" is data on the movements and stays of people in specific areas or locations.

[1152] "Purchase information" is data related to purchases made by a customer, including the date and time of purchase, the purchased product, and the purchase amount.

[1153] "Cleaning" is the process of dealing with missing or outliers in data and improving data quality.

[1154] "Preprocessing" refers to the initial processing of data to convert it into a format suitable for analysis or model input.

[1155] A "feature" is an attribute of data that contains important information in data analysis and machine learning models.

[1156] A "generative AI model" is an artificial intelligence model that generates new data or content based on input data.

[1157] A "prompt" is an instruction to be input into a generative AI model, and serves as the basis for the content generated by the model.

[1158] An "emotion engine" is a technology for recognizing and analyzing a user's emotional state.

[1159] "Content" is a general term for information and services provided to users.

[1160] "Service" refers to the functions and conveniences provided to users.

[1161] "Adjustment" is the process of changing content or settings to suit specific conditions or circumstances.

[1162] "ARPU" is an indicator that shows the average revenue per user.

[1163] This invention is a system that uses natural language processing technology to acquire customer information, location information, people flow information, and purchasing information, and integrates this information to provide personalized content and services. Furthermore, it can adjust content and services according to the user's emotional state.

[1164] Hardware and software used

[1165] The server is equipped with hardware and software for accessing the database, specifically using a SQL database, Python, Pandas, Scikit-learn, a generative AI model (e.g., OpenAI's GPT-4), and an emotion engine (e.g., Affectiva's emotion recognition technology).

[1166] Data acquisition and preprocessing

[1167] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. The retrieved data is cleaned and preprocessed using Python's Pandas library, which fills in missing and outlier values ​​and improves data quality.

[1168] Data analysis and feature extraction

[1169] The server analyzes the preprocessed data and performs clustering and classification using the Scikit-learn library, which extracts important features.

[1170] Prompt generation for generative AI models

[1171] The server generates a prompt sentence to be input to the generative AI model based on the extracted features. Specifically, the prompt sentence is constructed using Python string manipulation.

[1172] Content Generation and Emotion Recognition

[1173] The server inputs the generated prompt sentences into a generative AI model to generate data representing content and services to recommend to the user. An emotion engine is used to recognize the user's emotional state, and the recommended content is adjusted according to the user's emotional state. The emotion engine analyzes the user's facial expressions and voice data to identify the user's emotional state.

[1174] Content provision

[1175] The terminal provides the user with data representing the personalized content and services sent from the server, and makes the data accessible to the user through a user interface.

[1176] Specific examples

[1177] For example, if the user is in a shopping mall and is expressing a happy emotion, the following process may occur:

[1178] 1. The server obtains the user's customer information, location information, and purchase information from the mobile carrier's database, and also obtains people flow information at the user's location from another server.

[1179] 2. The server cleans the acquired data and imputes missing values.

[1180] 3. The server analyzes the data and determines that the user is at a shopping mall.

[1181] 4. The server generates a prompt sentence saying, "The user is staying at a shopping mall. Please generate data representing the optimal content and services to recommend to the user."

[1182] 5. The server inputs the prompt sentence into the generative AI model and generates data representing discount information and recommended product information for a specific store.

[1183] 6. The server uses an emotion engine to recognize the user's emotion of joy.

[1184] 7. The server generates data representing content and services to recommend to the user, taking into account the emotion of joy. Specifically, the server generates a prompt such as, "The user is at a shopping mall and is expressing an emotion of joy. Please generate data representing optimal content and services to recommend to the user." The server inputs the prompt into the generative AI model and generates data representing discount information and recommended product information for a specific store.

[1185] 8. The terminal provides the user with data representing the generated content, allowing the user to access it.

[1186] In this way, the user can receive the most appropriate service according to his / her emotional state.

[1187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1188] Step 1:

[1189] The server accesses the mobile carrier's database to obtain customer information, location information, people flow information, and purchase information. The input is a database query, and the output is the obtained raw data. Specifically, the server executes an SQL query to extract the required data.

[1190] Step 2:

[1191] The server cleans and preprocesses the acquired data. The input is the raw data acquired in step 1, and the output is the cleaned and preprocessed data. Specifically, the server uses the Python Pandas library to manipulate the data frame, impute missing values, and process outliers.

[1192] Step 3:

[1193] The server analyzes the preprocessed data and extracts features. The input is the data preprocessed in step 2, and the output is the extracted features. Specifically, the server uses the Scikit-learn library to perform clustering and classification and extract important features.

[1194] Step 4:

[1195] The server generates a prompt sentence to be input to the generative AI model based on the extracted features. The input is the features extracted in step 3, and the output is the generated prompt sentence. Specifically, the server constructs the prompt sentence using Python string operations.

[1196] Step 5:

[1197] The server inputs the generated prompt sentence into the generative AI model to generate personalized content or services. The input is the prompt sentence generated in step 4, and the output is data representing the generated content or service. Specifically, the server sends an API request and receives data representing the generated content from the generative AI model.

[1198] Step 6:

[1199] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial expression and voice data, and the output is the recognized emotional state. Specifically, the server inputs the user's facial expression and voice data into the emotion engine and analyzes the emotional state.

[1200] Step 7:

[1201] The server generates data representing content and services to recommend to the user, taking into account the user's emotional state recognized by the emotion engine. The server generates data representing content and services to recommend to the user, using a prompt sentence that instructs the server to recommend content or services that can be provided to the user based on the acquired customer information, location information, people flow information, purchase information, and the user's emotional state, and a generative AI model. For example, if the user is located within a commercial facility, data is generated that recommends discount information or product information for stores within the commercial facility as content or services recommended to the user.

[1202] Step 8:

[1203] The terminal provides the user with data representing the personalized content or service sent from the server. The input is the data representing the content or service generated in step 7, and the output is the data representing the content or service presented to the user. Specifically, the terminal displays the data representing the content through a user interface, allowing the user to access it.

[1204] (Application example 1)

[1205] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1206] Traditional customer service in brick-and-mortar stores lacked individualization and was difficult to provide services tailored to the customer's emotional state. Furthermore, there was a lack of appropriate content and services to stimulate customer purchasing motivation, making it difficult to increase ARPU (average revenue per customer).

[1207] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for recognizing emotional state, means for combining the acquired information to provide personalized content and services, and means for adjusting content and services according to the emotional state. This makes it possible to provide personalized content and services according to the emotional state of the customer, thereby improving ARPU.

[1208] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[1209] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1210] "Location information" refers to data related to a customer's current location and past movement history.

[1211] "People flow information" is data on people's movement patterns and congestion conditions in specific areas or within stores.

[1212] "Purchase information" is data about products and services that customers have purchased in the past.

[1213] "Emotional state" is data that indicates the customer's current emotional or psychological state.

[1214] "Personalized content and services" means customized information and services tailored to a customer's specific needs and preferences.

[1215] The "means for adjusting content and services according to emotional state" is a technology for recognizing a customer's emotional state and changing the content and services provided based on that state.

[1216] "Generative AI" is an artificial intelligence technology that generates new text and content based on input data.

[1217] "ARPU" is an indicator that shows average revenue per customer.

[1218] A system for implementing the present invention is configured as follows.

[1219] First, the server is equipped with a generative AI that uses natural language processing technology. This generative AI acquires customer information, location information, people flow information, and purchasing information, and generates personalized content and services based on this data. It also uses an emotion engine to recognize the customer's emotional state and adjust the content and services provided according to that emotional state.

[1220] Hardware and software used

[1221] Hardware: Smartphones, servers

[1222] Software: EmotionRecognizer (emotion recognition engine), NLPModel (generative AI model)

[1223] Data processing and calculation

[1224] 1. Obtaining customer information, location information, people flow information, and purchasing information:

[1225] The server obtains location information from the customer's smartphone, as well as the customer's past purchase history and traffic flow information from a database.

[1226] 2. Emotion recognition:

[1227] When a customer takes a picture of their face using their smartphone camera, the image is sent to the server, which uses EmotionRecognizer to recognize the customer's emotional state from the image.

[1228] 3. Personalized content generation:

[1229] The server uses an NLP model to generate personalized content and services based on the acquired customer information, location information, people flow information, purchasing information, and recognized emotional state.

[1230] Specific examples

[1231] For example, when a customer uses a smartphone in a physical store, the server obtains the customer's location information and recommends specific products based on the customer's foot traffic and past purchase history. If the customer is smiling, the emotion engine recognizes "joy" and provides special promotional information. Conversely, if the customer looks tired, the emotion engine recognizes "fatigue" and recommends products or services that will help them relax.

[1232] Prompt Sentence Examples

[1233] "Customer information: {"location": "store_123", "flow": "high", "purchase_history": ["item_1", ​​"item_2"]}, Emotion: "Joy""

[1234] By inputting this prompt into a generative AI model, personalized content is generated.

[1235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1236] Step 1:

[1237] The server obtains location information from the customer's smartphone. Specifically, it obtains the current location using the smartphone's GPS function and sends that data to the server. The input is the smartphone's GPS data, and the output is the location information sent to the server.

[1238] Step 2:

[1239] The server retrieves the customer's past purchase history and people flow information from the database. Specifically, it searches the database based on the customer ID and retrieves the related purchase history and people flow information. The input is the customer ID, and the output is the purchase history and people flow information.

[1240] Step 3:

[1241] A user takes a facial image using the smartphone camera and sends the image to the server. The input is the facial image taken by the smartphone camera, and the output is the facial image sent to the server.

[1242] Step 4:

[1243] The server uses EmotionRecognizer to recognize the customer's emotional state from the facial image sent. Specifically, it analyzes the facial image and identifies emotions such as joy, sadness, and anger. The input is the facial image, and the output is the recognized emotional state.

[1244] Step 5:

[1245] The server uses an NLP model to generate personalized content and services based on the acquired customer information, location information, people flow information, purchasing information, and recognized emotional state. Specifically, this data is input into a generative AI model as prompt sentences to generate personalized content. The inputs are customer information, location information, people flow information, purchasing information, and emotional state, and the output is personalized content and services.

[1246] Step 6:

[1247] The server sends the generated personalized content or service to the customer's smartphone. Specifically, the generated content is delivered to the smartphone as a message or notification. The input is the personalized content or service, and the output is the content or service displayed on the customer's smartphone.

[1248] Example 2

[1249] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1250] Conventional systems could collect customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. Furthermore, content and services were not adjusted according to the user's emotional state, so improvements in user experience and ARPU (average revenue per user) were not fully realized.

[1251] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1252] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed data into a generative AI model, means for generating personalized content and services using the generative AI model, and means for providing the generated content and services. This makes it possible to provide personalized content and services by integrating the various pieces of acquired information, and further to adjust content and services according to the user's emotional state, thereby improving the user experience and increasing ARPU.

[1253] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[1254] "Customer information" is data about a customer, including name, age, gender, purchase history, and the like.

[1255] "Location information" is data that indicates the geographic location of a particular device or individual.

[1256] "People flow information" is data on the movements and stays of people in specific areas or locations.

[1257] "Purchase information" is data about products and services purchased by customers.

[1258] "Preprocessing" refers to the process of converting acquired data into a format suitable for analysis or model input.

[1259] A "generative AI model" is an artificial intelligence model that generates new data or content based on input data.

[1260] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular user.

[1261] "Emotional state" is data that indicates the user's current emotions and moods.

[1262] "ARPU" stands for average revenue per user, and is an indicator of the average revenue per user over a specific period of time.

[1263] MODE FOR CARRYING OUT THE INVENTION

[1264] This invention is a system that combines natural language processing technology and generative AI models to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Furthermore, by adjusting content and services according to the user's emotional state, it improves the user experience and increases ARPU.

[1265] Hardware and software used

[1266] server:

[1267] Database access API

[1268] Natural Language Processing Engine

[1269] Emotion Recognition Engine

[1270] Generative AI Models

[1271] Data Preprocessing Module

[1272] Content Delivery Module

[1273] Device:

[1274] Smartphone

[1275] tablet

[1276] PC

[1277] software:

[1278] Natural language processing libraries (e.g. NLTK, spaCy)

[1279] Emotion recognition libraries (e.g. IBM Watson, Microsoft Azure Emotion API)

[1280] Generative AI models (e.g., GPT-3, BERT)

[1281] Database management systems (e.g. MySQL, PostgreSQL)

[1282] Data processing and calculation

[1283] The server retrieves customer information, location information, foot traffic information, and purchase information from the mobile carrier's database. The retrieved data is cleaned and normalized by a data preprocessing module. The preprocessed data is then input into a generative AI model to generate personalized content and services.

[1284] The generated content and services are sent to the terminal through the content delivery module. Depending on the user's emotional state, the emotion recognition engine analyzes the user's emotions, and the content and services are adjusted based on the results.

[1285] Specific examples

[1286] Example 1:

[1287] If a customer recently purchased a new camera on their smartphone, they will be shown ads for camera accessories and photo editing software.

[1288] Example prompt sentence:

[1289] "Generate a list of recommendations for related camera accessories and photo editing software for customers who recently purchased a camera."

[1290] Example 2:

[1291] If a user posts on social media that they are "very tired today," the app will display recommendations for relaxation music and meditation apps.

[1292] Example prompt sentence:

[1293] "If a user feels tired, generate a list of recommendations for relaxation music and meditation apps for that user."

[1294] In this way, the server integrates various data and uses generative AI models to provide personalized content and services. Furthermore, an emotion recognition engine can be used to tailor content and services according to the user's emotional state, resulting in an improved user experience and increased ARPU.

[1295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1296] Program processing flow

[1297] Systems using generative AI

[1298] Step 1:

[1299] Data Acquisition

[1300] The server obtains customer information, location information, people flow information, and purchasing information from the mobile carrier's database.

[1301] Input: API request for database access

[1302] Output: Raw data of customer information, location information, people flow information, and purchasing information

[1303] Specific operation: The server accesses the database through the API and sends a request to "get the purchase history of customer ID 12345."

[1304] Step 2:

[1305] Data Preprocessing

[1306] The server preprocesses the acquired data, specifically cleaning and normalizing the data.

[1307] Input: Raw data of customer information, location information, people flow information, and purchasing information

[1308] Output: Preprocessed data

[1309] Specific operation: The server normalizes the data by imputing missing values ​​and removing outliers. For example, if there are missing values ​​in the purchase history, it imputes them with the average value.

[1310] Step 3:

[1311] Input to generative AI models

[1312] The server inputs the preprocessed data into a generative AI model.

[1313] Input: Preprocessed data

[1314] Output: Input data to the generative AI model

[1315] Specific operation: The server converts the preprocessed data into JSON format and feeds it to the generative AI model. For example, "Convert the purchase history of customer ID 12345 into JSON format and input it into the model."

[1316] Step 4:

[1317] Content Generation

[1318] Generative AI models generate personalized content and services based on input data.

[1319] Input: The input data to the generative AI model

[1320] Output: personalized content and services

[1321] What it does: The generative AI model generates a list of related product recommendations based on a customer's recent purchases. For example, "generate a list of camera accessory recommendations for a customer who recently purchased a camera."

[1322] Step 5:

[1323] Content provider

[1324] The server transmits the generated content and services to the terminal and provides them to the user.

[1325] Input: Personalized content and services

[1326] Output: Content delivery to user devices

[1327] Specific operation: The server sends a push notification to the user's smartphone to display advertisements for related products. For example, "Send a push notification with a list of camera accessory recommendations to customer ID 12345."

[1328] A system that combines emotion engines

[1329] Step 1:

[1330] Data Acquisition

[1331] The server uses natural language processing technology to obtain customer information, location information, people flow information, and purchasing information.

[1332] Input: SNS API request

[1333] Output: Raw data of customer information, location information, people flow information, and purchasing information

[1334] Specific operation: The server retrieves user posts through the SNS API. For example, it sends a request to "Get the latest SNS posts from user ID 67890."

[1335] Step 2:

[1336] emotion recognition

[1337] The server uses an emotion engine to recognize the user's emotional state.

[1338] Input: Social media post data

[1339] Output: User emotion data

[1340] How it works: The server uses natural language processing technology to analyze emotions from text data. For example, it recognizes that a user is excited when they post something like, "Today is so much fun!"

[1341] Step 3:

[1342] Data Preprocessing

[1343] The server preprocesses the acquired data and emotion information, specifically by cleaning and normalizing the data.

[1344] Input: customer information, location information, people flow information, purchasing information, emotional data

[1345] Output: Preprocessed data

[1346] Specific operation: The server converts the emotion data into a numerical value and integrates it with other data. For example, the emotion of "excitement" is expressed as a numerical value of "5" and integrated with purchase history data.

[1347] Step 4:

[1348] Input to generative AI models

[1349] The server inputs the preprocessed data into a generative AI model.

[1350] Input: Preprocessed data

[1351] Output: Input data to the generative AI model

[1352] Specific operation: The server converts the preprocessed data into JSON format and feeds it to the generative AI model. For example, "Convert the emotion data and purchase history of user ID 67890 into JSON format and input it into the model."

[1353] Step 5:

[1354] Content Generation

[1355] The generative AI model generates content and services based on the user's emotional state based on input data.

[1356] Input: The input data to the generative AI model

[1357] Output: Content and services based on the user's emotional state

[1358] Specific behavior: The generative AI model generates entertainment content for excited users. For example, "Generate entertainment content for excited users."

[1359] Step 6:

[1360] Content provider

[1361] The server transmits the generated content and services to the terminal and provides them to the user.

[1362] Input: Content and services tailored to the user's emotional state

[1363] Output: Content delivery to user devices

[1364] Specific operation: The server sends a push notification to the user's smartphone, displaying recommendations for relaxation music and meditation apps. For example, "Send a push notification with a list of recommended relaxation music and meditation apps to user ID 67890."

[1365] (Application example 2)

[1366] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1367] Traditional customer service in brick-and-mortar stores was insufficiently personalized, making it difficult to maximize customer motivation. Furthermore, because services could not be tailored to the customer's emotional state, it was difficult to improve customer satisfaction or ARPU (average revenue per unit).

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1369] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for recognizing emotional states, means for combining the acquired information to provide personalized content and services, and means for displaying personalized content and services on a smart device. This makes it possible to provide personalized content and services according to the emotional state of the customer, thereby improving customer satisfaction and ARPU.

[1370] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1371] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1372] "Location information" refers to data related to a customer's current location and travel route.

[1373] "People flow information" is data on people's movement patterns and length of stay in a particular area.

[1374] "Purchase information" is data about products and services purchased by customers.

[1375] "Emotional state" is data that indicates the customer's current emotional or psychological state.

[1376] "Personalized content and services" are specific content and services provided based on a customer's individual information and status.

[1377] A "smart device" is an electronic device that is connected to the Internet and can display and operate information.

[1378] "Generative AI" is an artificial intelligence technology that generates new content and information based on data.

[1379] "ARPU" stands for average revenue per user, and is an indicator of average revenue per customer.

[1380] The system for implementing the present invention uses the following hardware and software.

[1381] Hardware

[1382] Smart glasses (e.g. Google Glass)

[1383] Camera (built into smart glasses)

[1384] software

[1385] OpenCV (camera image processing)

[1386] EmotionRecognizer (emotion recognition engine)

[1387] ContentGenerator (generative AI model)

[1388] LocationTracker (location information acquisition)

[1389] PurchaseHistory (purchase history management)

[1390] CustomerData (Customer Information Management)

[1391] System configuration

[1392] The server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchasing information, means for recognizing emotional states, means for combining the acquired information to provide personalized content and services, and means for displaying personalized content and services on a smart device.

[1393] Processing flow

[1394] 1. Acquisition of customer information

[1395] The server uses customer information management software to acquire personal information and purchase history of customers, which allows the server to understand customer preferences and past purchasing behavior.

[1396] 2. Obtaining location information

[1397] The server uses location information acquisition software to acquire the customer's current location and route in real time, allowing the server to determine the area in which the customer is located.

[1398] 3. Obtaining people flow information

[1399] The server uses people flow information acquisition software to acquire people's movement patterns and stay times in specific areas, which allows the system to grasp the congestion situation within the store and popular areas.

[1400] 4. Acquisition of purchasing information

[1401] The server uses purchase history management software to obtain information about the products and services purchased by customers, allowing the server to understand the purchasing trends of customers.

[1402] 5. Recognizing emotional states

[1403] The smart glasses use a built-in camera to capture a customer's face in real time, and an emotion recognition engine to recognize the customer's emotional state, allowing the system to understand the customer's current emotional state.

[1404] 6. Generating personalized content and services

[1405] The server uses a generative AI model to combine acquired customer information, location information, people flow information, purchasing information, and emotional state to generate personalized content and services that are optimal for each customer.

[1406] 7. Display on smart devices

[1407] The smart glasses display generated content and services in real time, allowing customers to instantly access information that is most relevant to them.

[1408] Specific examples

[1409] For example, if a customer stops in front of a particular product shelf in a store and shows an excited expression, promotions and discount information related to that product will be displayed on the smart glasses.

[1410] Prompt Sentence Examples

[1411] If a customer stops in front of a particular shelf in your store and shows excitement, show them promotions and discounts related to that product.

[1412] In this way, smart shopping assistants can improve customers' shopping experience and contribute to increased sales for stores.

[1413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1414] Step 1:

[1415] The server uses customer information management software to obtain the customer's personal information and purchase history. The customer's ID is required as input, and data on the customer's preferences and past purchasing behavior is obtained as output. This data is the basis for providing personalized services to the customer.

[1416] Step 2:

[1417] The server uses location information acquisition software to acquire the customer's current location and movement route in real time. Location information from the customer's smart device is required as input, and data on the customer's current location and movement pattern is obtained as output. This data is used to determine which area the customer is in.

[1418] Step 3:

[1419] The server uses people flow information acquisition software to acquire people's movement patterns and stay times in specific areas. Data from sensors and cameras inside the store is required as input, and data on the congestion status and popular areas in each area is obtained as output. This data is used to understand people flow within the store and predict customer behavior.

[1420] Step 4:

[1421] The server uses purchase history management software to obtain information about the products and services purchased by customers. The inputs are the customer's ID and a purchase history database, and the output is data about the customer's purchasing trends. This data is used to analyze customer purchasing behavior and provide personalized services.

[1422] Step 5:

[1423] The smart glasses have a built-in camera that captures the customer's face in real time, and an emotion recognition engine is used to recognize the customer's emotional state. The camera footage is required as input, and data about the customer's current emotional state is obtained as output. This data is used to provide services that correspond to the customer's emotions.

[1424] Step 6:

[1425] The server uses a generative AI model to combine acquired customer information, location information, people flow information, purchasing information, and emotional state to generate personalized content and services that are optimal for each customer. The various data mentioned above is required as input, and data related to personalized content and services is obtained as output. This data becomes the specific service content provided to the customer.

[1426] Step 7:

[1427] The smart glasses display generated content and services in real time. Data about the generated content and services is required as input, and the output is information displayed in the customer's field of view. This information is used by customers to instantly obtain the information that is most suitable for them.

[1428] In this way, smart shopping assistants can improve customers' shopping experience and contribute to increased sales for stores.

[1429] Example 3

[1430] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1431] While conventional systems can acquire customer information, location information, people flow information, and purchasing information individually, it is difficult to integrate this information to provide personalized content and services. Furthermore, content is not adjusted according to the user's emotional state, resulting in a decline in the quality of the user experience and no improvement in ARPU (average revenue per user). To solve these issues, a system is needed that can integrate acquired information and further adjust content according to the user's emotional state.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1433] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for pre-processing each acquired information, means for generating personalized content and services using a generative AI model, means for analyzing a user's emotional state using an emotion engine, means for adjusting the generated content and services according to the user's emotional state, and means for providing the adjusted content and services. This makes it possible to integrate the acquired information and further adjust content according to the user's emotional state.

[1434] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1435] "Customer information" refers to data about customers, such as personal information, purchasing history, and preferences.

[1436] "Location information" is data that indicates the geographical location of a user or an object.

[1437] "People flow information" is data that shows the movement patterns and length of stay of people within a specific area or facility.

[1438] "Purchase information" is data about products and services purchased by customers.

[1439] "Preprocessing" refers to the process of converting acquired data into a format suitable for analysis or generation.

[1440] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data.

[1441] An "emotion engine" is a technology for analyzing a user's emotional state and detecting specific emotions.

[1442] "Content adjustment" refers to the process of modifying generated content in response to the user's emotional state and other conditions.

[1443] "ARPU" means average revenue per user and is an indicator of the average revenue per user over a specific period of time.

[1444] This invention is a system that combines a generative AI model and an emotion engine, which acquires customer information, location information, people flow information, and purchasing information, and provides personalized content and services based on this information. Furthermore, it has the function of adjusting content and services according to the user's emotional state.

[1445] Hardware and software used

[1446] Hardware: Servers, devices (smartphones, tablets, etc.)

[1447] Software: Generative AI models (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL), natural language processing technologies (e.g., spaCy)

[1448] Explanation of program processing

[1449] 1. Data Acquisition

[1450] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, it accesses the database via API and retrieves the necessary information through queries.

[1451] The device uses the GPS function to obtain the user's real-time location information, for example, by using the location information service on a smartphone.

[1452] 2. Data Preprocessing

[1453] The server preprocesses the acquired data, specifically cleaning it (filling in missing values ​​and removing outliers) and normalizing it, for example converting location coordinate data into a standard format.

[1454] 3. Content Generation

[1455] The server inputs the preprocessed data into a generative AI model. Specifically, it generates a prompt sentence and inputs it into the generative AI model (e.g., GPT-4). For example, the prompt sentence is, "If the user is in a shopping mall, please suggest the optimal shopping route."

[1456] The generative AI model generates personalized content based on prompts, such as suggesting the best shopping route based on store and sale information within a shopping mall.

[1457] 4. Emotion analysis

[1458] The device analyzes the user's emotional state using an emotion engine. Specifically, it uses a camera and microphone to analyze the user's facial expressions and tone of voice to determine the emotional state. For example, it can detect whether the user is showing signs of surprise.

[1459] 5. Content Adjustment

[1460] The server receives the analysis results from the emotion engine and adjusts the generated content. Specifically, it changes the content and tone of the content according to the user's emotional state. For example, if the user expresses surprise, it adds entertainment content that amplifies that emotion.

[1461] 6. Content Provision

[1462] The device then provides the user with tailored content, specifically displayed on the screen of a smartphone or tablet, such as optimal shopping routes or entertainment content.

[1463] Specific examples

[1464] Example 1: Providing services in a shopping mall

[1465] The server obtains the customer's location information within the shopping mall and inputs the prompt statement "When the user is in the shopping mall, please suggest the optimal shopping route" into the generative AI model.

[1466] The generative AI model generates a shopping route and returns it to the server.

[1467] The terminal displays the generated shopping route to the user.

[1468] Example 2: Content adjustment based on emotions

[1469] The device uses an emotion engine to detect when the user is expressing surprise.

[1470] The server inputs a prompt to the generative AI model: "If the user expresses surprise, generate entertainment content that amplifies that emotion."

[1471] The generative AI model generates entertainment content and returns it to the server.

[1472] The terminal provides the generated entertainment content to the user.

[1473] This system provides personalized services based on the user's location information and emotional state, and is expected to improve ARPU. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1474] Step 1: Get the data

[1475] The server retrieves customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, it accesses the database via API and retrieves the required information via queries. The input is an API request, and the output is a dataset of customer information, location information, people flow information, and purchase information.

[1476] Step 2: Get real-time location information

[1477] The device obtains the user's real-time location information using the GPS function. Specifically, it uses the smartphone's location information service. The input is data from the GPS sensor, and the output is the coordinate information of the user's current location.

[1478] Step 3: Preprocessing the data

[1479] The server preprocesses the acquired data. Specifically, it cleans the data (fills in missing values ​​and removes outliers) and normalizes it. The input is the data acquired in Step 1 and Step 2, and the output is a preprocessed, clean dataset.

[1480] Step 4: Content Generation

[1481] The server inputs the preprocessed data into a generative AI model. Specifically, it generates a prompt sentence and inputs it into the generative AI model (e.g., GPT-4). For example, the prompt sentence is, "If the user is in a shopping mall, please suggest the optimal shopping route." The input is the preprocessed data and the prompt sentence, and the output is the generated personalized content.

[1482] Step 5: Sentiment Analysis

[1483] The device analyzes the user's emotional state using an emotion engine. Specifically, it uses a camera and microphone to analyze the user's facial expressions and tone of voice to determine the emotional state. The input is data from the camera and microphone, and the output is the analysis result of the user's emotional state.

[1484] Step 6: Adjust content

[1485] The server receives the analysis results from the emotion engine and adjusts the generated content. Specifically, it changes the content and tone of the content according to the user's emotional state. For example, if the user expresses surprise, it adds entertainment content that amplifies that emotion. The inputs are the generated content and the emotion analysis results, and the output is the adjusted content.

[1486] Step 7: Provide content

[1487] The terminal provides the adapted content to the user, specifically, by displaying it on the screen of a smartphone or tablet. The input is the adapted content, and the output is the content provided to the user.

[1488] In this way, the system aims to increase ARPU by providing personalized services based on the user's location and emotional state.

[1489] (Application example 3)

[1490] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1491] Traditional customer service in brick-and-mortar stores was not personalized enough, making it difficult to provide services that reflected the emotional state of the customer. It was also difficult to provide personalized product information and promotions in real time, making it difficult to improve the customer experience. This meant that improvements in customer satisfaction and average revenue per customer (ARPU) were not sufficiently achieved.

[1492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1493] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, means for recognizing emotional states, means for adjusting content and services according to the emotional states, and means for providing personalized product information and promotions in real time using smart devices. This makes it possible to provide personalized product information and promotions in real time according to the emotional states of customers.

[1494] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1495] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1496] "Location information" is data that indicates a specific location or coordinates using GPS or other technologies.

[1497] "People flow information" is data on the movement patterns and density of people within a specific area.

[1498] "Purchase information" is data about products and services purchased by customers.

[1499] "Emotional state" refers to the psychological state of a customer that can be inferred from their facial expressions and behavior.

[1500] "Generative AI" is an artificial intelligence technology that generates new content and information based on input data.

[1501] A "smart device" is an electronic device that has Internet connectivity and can run a variety of applications.

[1502] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular customer.

[1503] "Promotion" refers to activities to advertise specific products or services to customers and encourage them to purchase them.

[1504] "Real-time" means that data acquisition and processing occurs immediately.

[1505] A system for implementing this invention includes a server, a smart device (such as a smartphone or smart glasses), and various sensors. The server is equipped with a generative AI system using natural language processing technology to acquire customer information, location information, pedestrian flow information, and purchase information, and generates personalized content and services based on this information. Furthermore, it uses an emotion engine to recognize the customer's emotional state and adjusts the content and services according to that emotional state.

[1506] Hardware and software used

[1507] Hardware: Smartphone, smart glasses, GPS sensor, camera

[1508] Software: Python, EmotionRecognizer library, AIContentGenerator library, REST API

[1509] Data processing and calculation

[1510] 1. Obtaining customer information: The server uses a REST API to obtain data such as the customer's personal information, purchase history, and preferences.

[1511] 2. Obtaining location information: Use the GPS function of the smart device to obtain the customer's current location.

[1512] 3. Obtaining people flow information: Analyzing people movement patterns and density within a specific area based on sensor data.

[1513] 4. Obtaining purchasing information: Obtaining customer purchase history and current purchasing behavior from the database.

[1514] 5. Emotional state recognition: Using the smart device camera, analyze the customer's facial expressions and recognize their emotional state using the EmotionRecognizer library.

[1515] 6. Content generation: Generative AI is used to generate personalized product information and promotions based on acquired information.

[1516] 7. Real-time notifications: Providing personalized product information and promotions to smart devices in real time based on the customer's emotional state.

[1517] Specific examples

[1518] For example, when a customer is in a specific area of ​​a physical store, the smart glasses can analyze the customer's facial expression and, if they show signs of surprise, display a promotion for a new product to that customer, which is generated by generative AI based on the customer's purchasing history and preferences.

[1519] Prompt Sentence Examples

[1520] Customer ID: 12345

[1521] Location: Latitude 35.6895, Longitude 139.6917

[1522] Emotional state: Surprise

[1523] Content to be generated: Promotion of new products

[1524] In this way, personalized product information and promotions can be provided in real time according to the customer's emotional state.

[1525] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1526] Step 1:

[1527] The server retrieves customer information using a REST API. Specifically, it receives a customer ID as input and retrieves data such as the customer's personal information, purchase history, and preferences from the database. This results in a detailed customer profile being output.

[1528] Step 2:

[1529] The terminal (smart device) acquires the customer's current location using its GPS function. Specifically, it receives location information from the terminal's GPS sensor as input and outputs latitude and longitude data. This identifies the customer's current location.

[1530] Step 3:

[1531] The server acquires people flow information based on sensor data. Specifically, it receives data on people's movement patterns and density from sensors within a specific area as input and outputs the analysis results. This provides people flow information within the area.

[1532] Step 4:

[1533] The server retrieves purchasing information from the database. Specifically, it receives data on the customer's purchase history and current purchasing behavior as input and outputs purchasing information. This allows the customer's purchasing trends to be understood.

[1534] Step 5:

[1535] The terminal (smart device) uses a camera to capture the customer's facial expression and recognize their emotional state. Specifically, it receives image data acquired from the camera as input, analyzes the emotional state using the EmotionRecognizer library, and outputs emotional data. This identifies the customer's emotional state.

[1536] Step 6:

[1537] The server uses generative AI to generate personalized content. Specifically, it receives customer information, location information, people flow information, purchasing information, and emotional state as input, generates personalized product information and promotions using the AIContentGenerator library, and outputs the content data. This generates the most suitable content for each customer.

[1538] Step 7:

[1539] The terminal (smart device) notifies the customer of the generated content in real time. Specifically, it receives content data from the server as input and displays it on the smart device's display. This allows customers to receive personalized product information and promotions in real time.

[1540] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1541] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image are also input.

[1542] The data generation model 58 performs inference on the input inference data according to the instructions given by the prompts, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1543] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1544] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1545] [Third embodiment]

[1546] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1547] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1548] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1549] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1550] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1551] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1552] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1553] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1554] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1555] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1556] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1557] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1558] "Example 1"

[1559] One embodiment of the present invention is a system that uses generative AI as natural language processing technology. This system has means for acquiring customer information, location information, people flow information, and purchasing information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, when a customer is staying in a specific area, specific services can be provided to the customer based on the people flow information and purchasing information for that area. This can result in an increase in ARPU.

[1560] "Example 2"

[1561] Another embodiment of the present invention is a system that uses generative AI. This system has means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, based on information that a customer has purchased a specific product, it is possible to provide the customer with related products and services. This results in an increase in ARPU.

[1562] "Example 3"

[1563] Yet another embodiment of the present invention is a system that uses generative AI. This system has means for acquiring customer information, location information, people flow information, and purchase information. This information is acquired, for example, from a mobile carrier's database. The acquired information is processed by the generative AI and converted into information for providing personalized content and services. For example, based on a customer's location information, services related to that location can be provided. This results in an increase in ARPU.

[1564] The processing flow of each embodiment will be described below.

[1565] "Example 1"

[1566] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[1567] Step 2: The acquired information is processed by the generative AI.

[1568] The information is transformed into information to provide personalized content and services.

[1569] Step 3: For example, when a customer is staying in a specific area, specific services are provided to the customer based on information on the flow of people and purchasing information in that area.

[1570] Step 4: This will result in increased ARPU.

[1571] "Example 2"

[1572] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[1573] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[1574] Step 3: For example, based on information about a customer's purchase of a specific product, offer the customer related products or services.

[1575] Step 4: This will result in increased ARPU.

[1576] "Example 3"

[1577] Step 1: The system obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database.

[1578] Step 2: This information is then processed by generative AI, which transforms it into information that can be used to provide personalized content and services.

[1579] Step 3: For example, provide location-related services based on the customer's location information.

[1580] Step 4: This will result in increased ARPU.

[1581] Example 1

[1582] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1583] Conventional systems did not adequately process information to provide personalized content and services to customers, making it difficult to improve customer satisfaction and revenue. Furthermore, pre-processing and generative AI models to effectively utilize acquired information were insufficient, making it difficult to provide services in real time.

[1584] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1585] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for the generative AI model to generate personalized content and services, means for transmitting the generated content and services to a terminal, and means for the terminal to display the content and services to a user. This makes it possible to provide personalized content and services to customers in real time.

[1586] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[1587] "Customer information" refers to data such as personal information, behavioral history, and purchase history about customers.

[1588] "Location information" is data indicating the customer's current location and past movement history.

[1589] "People flow information" refers to data such as people's movement patterns and length of stay in a particular area.

[1590] "Purchase information" is data about products and services purchased by customers.

[1591] "Preprocessing" refers to the process of converting the acquired raw data into a format suitable for analysis and generative AI models.

[1592] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[1593] "Personalized content and services" are information and services that are customized to a customer's specific needs and circumstances.

[1594] A "terminal" is a device used by a user (for example, a smartphone or tablet).

[1595] "Real-time" refers to the time frame in which data is acquired, processed, and results provided almost immediately.

[1596] This invention is a system that uses natural language processing technology to acquire customer information, location information, people flow information, and purchasing information, and provides personalized content and services based on this information. Specific embodiments of this system are described below.

[1597] System configuration

[1598] Hardware and Software

[1599] The server uses the following hardware and software:

[1600] Hardware: High-performance server machine

[1601] Software: Database management system, API interface, generative AI model (e.g., OpenAI's GPT-4)

[1602] A terminal is a device used by a user that uses the following hardware and software:

[1603] Hardware: Smartphones, tablets

[1604] Software: Mobile applications, notification systems

[1605] Data acquisition and preprocessing

[1606] The server retrieves customer information, location information, foot traffic information, and purchase information from the mobile carrier's database. This information is obtained in real time through API. For example, a location information API is used to confirm that a customer's current location is Shibuya, Tokyo.

[1607] The acquired information is preprocessed. Specifically, the customer's current location is identified based on the location information, and information on people flow and purchasing in that area is organized. For example, an SQL query is executed to extract the latest people flow data and purchasing trends in Shibuya.

[1608] Using generative AI models

[1609] The server inputs the preprocessed information into the generative AI model. The prompt text is, "Generate appropriate services and content for customers currently in Shibuya based on the latest Shibuya pedestrian flow and purchasing information."

[1610] The generative AI model generates personalized content and services based on input information, such as information about events taking place in Shibuya or discount coupons for specific stores.

[1611] Providing content and services

[1612] The server sends the generated content and services to the user's device. For example, it uses Firebase Cloud Messaging to send generated coupon information and event information to the user's smartphone as a push notification.

[1613] The device displays the received content and services to the user. For example, a message such as "Shibuya Festival is now on! 20% discount coupons are available at certain stores" will be displayed in the smartphone's notification bar.

[1614] Specific examples

[1615] For example, if a customer is staying in Shibuya, Tokyo, the server will analyze Shibuya's pedestrian flow information and purchasing information and use the generative AI model to provide the customer with appropriate services. An example of a prompt sentence would be, "For customers currently staying in Shibuya, please generate appropriate services and content based on the latest pedestrian flow information and purchasing information for Shibuya."

[1616] In this way, it is possible to provide customers with personalized content and services in real time.

[1617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1618] Step 1:

[1619] The server acquires customer information, location information, people flow information, and purchasing information.

[1620] Input: API request from the carrier database

[1621] Output: Customer's current location, past movement history, people flow data, purchase history

[1622] Specific operation: The server sends an API request and receives the customer's location information and purchase history as a response. For example, it verifies that the customer's current location is Shibuya, Tokyo.

[1623] Step 2:

[1624] The server preprocesses the retrieved information.

[1625] Input: Acquired customer information, location information, people flow information, purchase information

[1626] Output: Preprocessed data (location information, people flow data, purchasing trends)

[1627] Specific operation: The server identifies the customer's current location based on the location information and organizes information on people flow and purchasing in the area. For example, it executes SQL queries to extract the latest people flow data and purchasing trends in Shibuya.

[1628] Step 3:

[1629] The server inputs the preprocessed information into the generative AI model.

[1630] Input: preprocessed data, prompt statement

[1631] Output: Input data to a generative AI model

[1632] Specific operation: The server converts the preprocessed data into JSON format and sends it to the generative AI model along with a prompt. For example, it sends the data to the API of the generative AI model using an HTTP request.

[1633] Step 4:

[1634] Generative AI models generate personalized content and services.

[1635] Input: Input data to the generative AI model

[1636] Output: personalized content and services (e.g., event information, discount coupons)

[1637] How it works: The generative AI model analyzes the data it receives and generates appropriate content and services, such as "Events taking place in Shibuya: Shibuya Festival, 20% off coupons at specific stores."

[1638] Step 5:

[1639] The server transmits the generated content and services to the terminal.

[1640] Input: Generated content and services

[1641] Output: Notification data to the device

[1642] Specific operation: The server sends the generated content or service to the user's device as a push notification, for example, using Firebase Cloud Messaging.

[1643] Step 6:

[1644] The terminal displays content and services to the user.

[1645] Input: Notification data to the device

[1646] Output: The content or service that is displayed to the user

[1647] Specific operation: The device displays the received notification to the user. For example, the notification bar on the smartphone displays "Shibuya Festival is underway! 20% discount coupons are available at certain stores."

[1648] (Application example 1)

[1649] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1650] Traditional customer service in brick-and-mortar stores relied on general promotions and product recommendations, making it difficult to provide services based on individual customer needs and purchasing history. This meant that improvements in customer satisfaction and maximization of sales were not fully achieved. Furthermore, it was not possible to utilize customer location information and people flow information in real time, making it difficult to provide timely services.

[1651] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1652] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing personalized product recommendations and promotion information in a physical store based on the customer's location information and purchase history. This makes it possible to provide personalized services according to customer needs, thereby improving customer satisfaction and maximizing sales.

[1653] "Natural language processing technology" is a technology that enables computers to understand, generate, and respond to human language.

[1654] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1655] "Location information" is data indicating the customer's current location and past movement history.

[1656] "People flow information" is data that shows the movement patterns and length of stay of people in a particular area or store.

[1657] "Purchase information" is data about products and services purchased by customers.

[1658] "Personalized content and services" are information and services that are tailored to a customer's individual needs and preferences.

[1659] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[1660] A "brick and mortar store" is a retail outlet that exists in a physical location.

[1661] "Product recommendation" refers to suggesting specific products based on a customer's preferences and purchasing history.

[1662] "Promotional information" is information intended to promote the sale of specific products or services.

[1663] "ARPU" is an indicator that shows average revenue per user.

[1664] A system for implementing this invention is configured as follows. First, the server uses generative AI as a means using natural language processing technology. Smartphones and other mobile devices are used as a means for acquiring customer information, location information, people flow information, and purchasing information. These devices acquire location information using technologies such as GPS, Wi-Fi, and Bluetooth, and collect data on customer purchasing history and preferences.

[1665] The server combines the acquired information to provide personalized content and services. Specifically, when a customer is in a physical store, it generates personalized product recommendations and promotional information based on the customer's location and purchase history. This allows customers to receive information tailored to their needs in real time.

[1666] Hardware and software used

[1667] Hardware: Smartphones, servers

[1668] Software: Python, Geopy, Requests library

[1669] Data processing and calculation

[1670] 1. Acquisition of customer information: Smartphones acquire information such as customer location, purchase history, and visit times. This data is collected from mobile carrier databases and store POS systems.

[1671] 2. Content generation using generative AI: The server sends prompts to a generative AI model based on acquired customer information to generate personalized product recommendations and promotional information.

[1672] 3. Displaying the results: The generated content is displayed on the smartphone screen.

[1673] Specific examples

[1674] For example, if a customer is staying at a particular shopping mall in Tokyo, the generative AI will determine, based on the customer's past purchasing history and current location, that "this customer might be interested in a new fashion item," and provide them with promotional information for that particular store.

[1675] Prompt Sentence Examples

[1676] Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], current location: (35.6895, 139.6917), visit time: 2023-10-01 14:30.

[1677] In this way, the shopping experience in physical stores can be improved.

[1678] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1679] Step 1:

[1680] The terminal acquires the customer's location information.

[1681] Input: Location data obtained using technologies such as GPS, Wi-Fi, and Bluetooth.

[1682] Data processing: Convert location information into latitude and longitude format.

[1683] Output: Customer's current location (e.g., latitude 35.6895, longitude 139.6917).

[1684] Step 2:

[1685] The terminal acquires the customer's purchase history.

[1686] Input: Purchase history data obtained from store POS systems and mobile carrier databases.

[1687] Data processing: Convert purchase history into list format.

[1688] Output: Customer's purchase history (e.g. ["item1", "item2"]).

[1689] Step 3:

[1690] The terminal acquires the customer's visiting time.

[1691] Input: The current date and time obtained from the device's internal clock.

[1692] Data processing: Convert date and time data into a standard format.

[1693] Output: Customer visiting time (e.g. 2023-10-01 14:30).

[1694] Step 4:

[1695] The server sends prompts to the generative AI model based on the acquired customer information.

[1696] Input: Customer ID, purchase history, location information, visit time.

[1697] Data processing: Incorporate this information into the prompt sentence.

[1698] Output: Prompt statement (e.g., Generate product recommendations for customer ID: 12345. Past purchase history: ["item1", "item2"], Current location: (35.6895, 139.6917), Visit time: 2023-10-01 14:30).

[1699] Step 5:

[1700] The server receives the response from the generative AI model.

[1701] Input: Prompt statement.

[1702] Data processing: A generative AI model generates personalized product recommendations and promotional information based on prompts.

[1703] Output: Generated content (e.g., recommended product lists and promotional information).

[1704] Step 6:

[1705] The terminal displays the generated content to the customer.

[1706] Input: Generated content.

[1707] Data processing: Converting content into a format suitable for the device screen.

[1708] Output: Personalized product recommendations and promotional information displayed on the customer's smartphone screen.

[1709] In this way, customers receive information tailored to their needs in real time.

[1710] Example 2

[1711] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1712] With conventional systems, it was possible to obtain customer information, location information, people flow information, and purchasing information individually, but it was difficult to integrate this information to provide personalized content and services. Furthermore, the content and services provided did not adequately address customer needs, making it difficult to improve average revenue per user (ARPU). Furthermore, data preprocessing and the use of generative AI models were insufficient, preventing efficient information processing.

[1713] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1714] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating content and services personalized by the generative AI model, and means for providing the generated content and services. This makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per unit (ARPU).

[1715] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1716] "Customer information" is data about a customer, including, for example, name, age, gender, purchase history, and the like.

[1717] "Location information" is data that indicates the geographical location of a specific object, and includes coordinate information such as latitude and longitude.

[1718] "People flow information" is data on the movements and stays of people in specific areas or locations.

[1719] "Purchase information" refers to data about products or services purchased by a customer, including the date and time of purchase, the place of purchase, and the purchase amount.

[1720] "Preprocessing" refers to a series of operations to convert acquired data into a format suitable for analysis and processing, including data cleaning and format standardization.

[1721] A "generative AI model" is an artificial intelligence model that generates new information or content based on input data.

[1722] "Personalized content and services" are information and services that are customized to meet the needs and preferences of a particular customer.

[1723] "Providing" refers to the act of presenting the generated content or service to a customer.

[1724] "Average Revenue Per Unit (ARPU)" is a metric that indicates the average revenue per customer over a specific period of time.

[1725] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[1726] The server first obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the mobile carrier's database via API and obtains the necessary data, such as the customer's name, age, gender, purchase history, current location, and place of stay.

[1727] Next, the server preprocesses the acquired data. This includes cleaning the data (filling in missing values ​​and removing outliers) and standardizing the format. For example, the Python Pandas library is used to convert the latitude and longitude data of the location information into a standard format.

[1728] The preprocessed data is input to the generative AI model. The server converts the preprocessed data into JSON format and inputs it to the generative AI model. An example of a specific prompt sentence is as follows:

[1729] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[1730] Location: {Latitude: 35.6895, Longitude: 139.6917}

[1731] People flow information: {Current location: Shinjuku Station}

[1732] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[1733] The generative AI model generates personalized content and services based on input data. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to that customer. Specifically, it might generate a suggestion such as, "If you purchase a smartphone case near Shinjuku Station, you can receive a discount at a specific store."

[1734] Finally, the server sends the generated content and services to the device. The device displays the received content to the user. For example, it displays suggestions to the user using the smartphone's push notification function. This allows the user to receive information and services that are optimized for them.

[1735] This system makes it possible to efficiently provide personalized content and services that meet customer needs, thereby improving average revenue per user (ARPU).

[1736] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1737] Step 1: Get the data

[1738] The server obtains customer information, location information, people flow information, and purchase information from the mobile carrier's database. Specifically, the server accesses the database through an API to obtain information such as the customer's name, age, gender, purchase history, current location, and where they are staying. The input is the API request, and the output is the various pieces of information obtained.

[1739] Step 2: Preprocessing the data

[1740] The server preprocesses the acquired data. Specifically, it uses Python's Pandas library to clean the data (fill in missing values ​​and remove outliers) and standardize the format. For example, it converts latitude and longitude location data into a standard format. The input is the acquired raw data, and the output is the preprocessed data.

[1741] Step 3: Input to the generative AI model

[1742] The server inputs the preprocessed data into the generative AI model. Specifically, it converts the preprocessed data into JSON format and inputs it into the generative AI model. The input is the preprocessed data, and the output is a prompt sentence for the generative AI model. An example of a prompt sentence is as follows:

[1743] Customer information: {Customer ID: 12345, Purchase history: [Smartphone]}

[1744] Location: {Latitude: 35.6895, Longitude: 139.6917}

[1745] People flow information: {Current location: Shinjuku Station}

[1746] Purchase information: {Product: Smartphone, Purchase date: 2023-10-01}

[1747] Step 4: Generate personalized content

[1748] A generative AI model generates personalized content and services based on the input prompt. For example, if a customer purchases a smartphone, the generative AI model will suggest smartphone accessories and related services to the customer. The input is the prompt, and the output is the generated personalized content.

[1749] Step 5: Providing content

[1750] The server sends the generated content to the device. The device displays the received content to the user. Specifically, the device uses the smartphone's push notification function to display suggestions to the user. The input is the generated content, and the output is a notification displayed to the user. This allows the user to receive information and services that are optimized for them.

[1751] (Application example 2)

[1752] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1753] Traditional product proposals and promotions in brick-and-mortar stores are unable to adequately meet the needs of each individual customer, making it difficult to improve customer satisfaction and maximize sales. Furthermore, providing personalized information in real time is difficult, making it difficult to effectively stimulate customers' purchasing desire.

[1754] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1755] In this invention, the server includes means using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for combining the acquired information to provide personalized content and services, and means for providing product suggestions and promotion information in real time based on the customer's location information and purchase history. This makes it possible to provide optimal product suggestions and promotion information to each individual customer in real time.

[1756] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1757] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1758] "Location information" is data that indicates a customer's current geographic location.

[1759] "People flow information" refers to data such as people's movement patterns and length of stay in a specific area.

[1760] "Purchase information" is data about products and services purchased by customers.

[1761] "Individualized content and services" refers to information and services provided tailored to the needs and preferences of each individual customer.

[1762] "Generative AI" is an artificial intelligence technology that generates new information and content based on input data.

[1763] "Means for providing product suggestions and promotional information in real time" refers to technology for instantly providing product and promotional information according to the customer's current situation.

[1764] "ARPU" stands for average revenue per user, and is an indicator of average revenue per customer.

[1765] As an embodiment of the present invention, a smart shopping assistant system for a brick-and-mortar store will be described as an example. This system provides product suggestions and promotion information in real time based on customer location information and purchase history.

[1766] System configuration

[1767] The system consists of the following main components:

[1768] 1. Server: Executes generative AI models using natural language processing technology.

[1769] 2. Terminal: A device carried by the customer, such as a smartphone or smart glasses.

[1770] 3. Database: Stores customer information, location information, people flow information, and purchasing information.

[1771] Program processing

[1772] The server implements the system using the following means:

[1773] 1. Means using natural language processing technology: Generative AI models are used to generate optimal product suggestions and promotional information for customers.

[1774] 2. How to obtain customer information: Obtain customer personal information and purchasing history from the database.

[1775] 3. Means of obtaining location information: The customer's current location is obtained using technologies such as GPS and beacons.

[1776] 4. Means of acquiring people flow information: Sensors and cameras are used to acquire people's movement patterns and stay times within the store.

[1777] 5. Means of obtaining purchasing information: Information on the products and services purchased by customers is obtained from the POS system.

[1778] 6. Means of providing personalized content and services: By combining the various pieces of information obtained, we can provide each customer with optimal product suggestions and promotional information in real time.

[1779] Data processing and calculation

[1780] The server uses a programming language such as Python to perform the following tasks:

[1781] 1. Data acquisition: Customer information, location information, people flow information, and purchasing information are acquired from databases and sensors.

[1782] 2. Sending prompts to the generative AI model: Based on the acquired data, prompts are sent to the generative AI model to generate product suggestions and promotional information.

[1783] 3. Real-time provision: The generated information is sent to the terminal and displayed to the customer in real time.

[1784] Specific examples

[1785] For example, if a customer is in "Aisle 5" and has previously purchased "item1" and "item2," we can use this information to send the following prompt to our generative AI model:

[1786] Prompt Sentence Examples

[1787] Customer ID: 12345, Current Location: Aisle 5, Purchase History: ['item1', 'item2']

[1788] Based on this prompt, the generative AI model will return recommended products related to "Aisle 5" or based on past purchase history. For example, "item 3" or "item 4" might be suggested. This way, the model can provide personalized product suggestions to improve the shopping experience.

[1789] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1790] Step 1:

[1791] The server retrieves customer information from a database. The input is the customer ID, and the output is the customer's personal information and purchase history. Specifically, it sends a query to the database using the customer ID as a key to retrieve the corresponding customer information.

[1792] Step 2:

[1793] The terminal acquires the customer's current location information. The input is a signal from a GPS or beacon, and the output is the customer's current location. Specifically, the terminal periodically sends the location information to the server.

[1794] Step 3:

[1795] The server acquires people flow information from sensors and cameras. The input is data from the sensors and cameras, and the output is people's movement patterns and stay times within the store. Specifically, the server analyzes the data from the sensors and cameras to generate people flow information.

[1796] Step 4:

[1797] The server obtains purchase information from the POS system. The input is data from the POS system, and the output is information about the products and services purchased by the customer. Specifically, it analyzes the transaction data from the POS system and obtains the purchase information.

[1798] Step 5:

[1799] The server combines the acquired information to generate a prompt. The input is customer information, location information, people flow information, and purchasing information, and the output is a prompt to be sent to the generative AI model. Specifically, each piece of information is compiled in text format to generate the prompt.

[1800] Step 6:

[1801] The server sends prompt sentences to the generative AI model to generate product suggestions and promotion information. The input is the prompt sentence, and the output is the generated product suggestions and promotion information. Specifically, the server sends prompt sentences to the generative AI model and receives the returned information.

[1802] Step 7:

[1803] The server sends the generated product suggestions and promotion information to the terminal. The input is the generated information, and the output is the information displayed on the terminal. Specifically, the generated information is sent to the terminal, and the terminal displays it to the customer.

[1804] Step 8:

[1805] The user checks the product suggestions and promotion information displayed on the device and considers purchasing if necessary. The input is the information displayed on the device, and the output is the user's purchasing behavior. Specifically, the user operates the device to check the product information and decides to purchase.

[1806] Example 3

[1807] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1808] Conventional systems can collect customer information, location information, people flow information, and purchasing information individually, but it is difficult to integrate this information to provide personalized content and services in real time. Furthermore, the content and services provided often do not match customer needs, making it difficult to improve ARPU (average revenue per user).

[1809] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1810] In this invention, the server includes means for using natural language processing technology, means for acquiring customer information, means for acquiring location information, means for acquiring people flow information, means for acquiring purchase information, means for preprocessing each piece of acquired information, means for inputting the preprocessed information into a generative AI model, means for generating personalized content and services using the generative AI model, and means for delivering the generated content and services to user terminals. This makes it possible to provide personalized content and services that meet customer needs in real time, thereby improving ARPU.

[1811] "Natural language processing technology" is a technology that enables computers to understand, generate, and manipulate human language.

[1812] "Customer information" refers to data such as a customer's personal information, purchasing history, preferences, etc.

[1813] "Location information" refers to data related to a customer's current location and past movement history.

[1814] "People flow information" is data on people's movement patterns and length of stay within a particular area or facility.

[1815] "Purchase information" is data about products and services that customers have purchased in the past.

[1816] "Preprocessing" is the process of converting acquired data into a format suitable for generative AI models by cleaning, normalizing, and filling in missing values.

[1817] A "generative AI model" is an artificial intelligence model that generates new content or information based on input data.

[1818] "Personalized content and services" are information and services that are customized to meet a customer's specific needs and preferences.

[1819] A "user terminal" is a device that is directly operated by a user, such as a smartphone, tablet, or PC.

[1820] "Delivery" is the process of transmitting generated content or services to user terminals.

[1821] "ARPU" stands for Average Revenue Per User and refers to the average revenue per user.

[1822] This invention is a system that uses a generative AI model to integrate customer information, location information, people flow information, and purchasing information to provide personalized content and services. Specific embodiments of this system are described below.

[1823] First, the server acquires customer information, location information, foot traffic information, and purchase information. This information is collected in real time from the mobile carrier's database using API. For example, the moment a user enters a shopping mall, their location information is sent to the server.

[1824] The server then preprocesses the acquired data, which includes cleaning, normalizing, and imputing missing values. For example, if location information is missing, it imputes the closest past location information.

[1825] The preprocessed data is input into a generative AI model. For example, OpenAI's GPT-4 is used as a generative AI model. The server inputs the pr...

Claims

1. a means for obtaining customer information regarding the user; means for acquiring location information of the user; A means for acquiring people flow information regarding the movement or stay of people at the user's location; A means for acquiring purchase information relating to purchases by the user; A means for recommending content or services that can be provided to the user based on the acquired customer information, location information, people flow information, and purchase information, using a prompt sentence that instructs the recommendation of content or services that can be provided to the user and a generative AI model; A system including:

2. means for recognizing an emotional state of the user using an emotion engine; The system according to claim 1, wherein the recommending means recommends content or services that can be provided to the user based on the acquired customer information, the location information, the people flow information, the purchasing information, and the emotional state of the user, using a prompt sentence that instructs the system to recommend content or services that can be provided to the user, and the generative AI model.

3. 2. The system according to claim 1, wherein the recommending means, when the user is located within a commercial facility, recommends discount information or product information of stores within the commercial facility as content or services that can be provided to the user.

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