System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and authenticating login information of a user; means for obtaining purchase history and evaluation information of the user; means for analyzing the obtained purchase history and evaluation information to identify interests and needs of the user; means for suggesting an optimal product to the user based on the analysis result; means for receiving a start point and a transportation means specified by the user and calculating a nearest store and a route thereof; and means for providing the calculated store and route information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The vast amount of information and products available on the Internet presents a challenge for users, making it difficult to quickly find products and information that meet their needs. In particular, frequent online shoppers and those searching for the right products and services seek optimal suggestions that match their interests and preferences. Furthermore, when users want to visit a specific store, they need efficient route guidance. To address these challenges, there is a demand for a system that utilizes user login information and purchase history, and uses generative AI to provide users with the most appropriate information and product discovery support. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and rating information, means for analyzing the acquired purchase history and rating information to identify the user's interests and needs, means for proposing optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and a route thereto, and means for providing the calculated store and route information to the user, thereby enabling the user to receive personalized product suggestions and efficient store guidance, thereby realizing an efficient and satisfying shopping experience.

[0006] ---

[0007] "User" refers to an individual or corporation that uses this system to search for and purchase information or products.

[0008] "Login Information" means the identification and authentication information (e.g., user ID, password) used by a User to access the System.

[0009] "Purchase history" refers to data on products (product name, purchase date, price, etc.) that a user has previously purchased from an online shopping site, etc.

[0010] "Rating information" refers to data on ratings and reviews (rating scores, comments, etc.) made by users regarding purchased products.

[0011] "Generative AI" refers to artificial intelligence algorithms and systems used to analyze purchasing and evaluation data to identify user preferences and needs.

[0012] "Interests and needs" refers to the product categories that the user has purchasing intentions or is interested in.

[0013] "Optimal products" refers to recommended products suggested by the system based on the user's interests and needs.

[0014] The "starting point" refers to the departure location (address or current location) specified by the user.

[0015] "Transportation" refers to the means of transportation used by the user (car, walking, public transportation, etc.).

[0016] The "nearest store" refers to the store that is closest to the starting point specified by the user.

[0017] A "route" refers to the route from a starting point specified by the user to the nearest store.

[0018] "Means for providing" refers to a method or device for presenting information from the system to the user.

[0019] ---

[0020] This allows for clear definitions of important terms included in the claims. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] ---

[0043] The system of the present invention utilizes the user's login information, purchase history, and rating information to suggest the most suitable products for the user and guide the user to the nearest route to the store they want to visit, thereby providing a more personalized and efficient shopping experience.

[0044] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0045] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0046] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[0047] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[0048] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0049] The main feature of this system is that it proposes products tailored to each user's individual preferences, and also uses location information to provide optimal routes and store information, allowing users to easily find products that match their interests and needs, and facilitating smooth visits to physical stores.

[0050] ---

[0051] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0052] The processing flow will be explained below.

[0053] ---

[0054] Step 1:

[0055] A user logs in to the system.

[0056] The user enters their account information (user ID and password) and presses the login button.

[0057] The terminal transmits the login information entered by the user to the authentication server.

[0058] Step 2:

[0059] The server authenticates the login information.

[0060] The server queries the authentication database to verify the consistency of the entered ID and password.

[0061] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[0062] Step 3:

[0063] The server acquires the user's purchase history and rating information.

[0064] The server retrieves past purchase history and rating information from the database based on the user's ID.

[0065] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[0066] Step 4:

[0067] The acquired data is sent to the generation AI.

[0068] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[0069] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[0070] Step 5:

[0071] Generative AI analyzes the data.

[0072] The generative AI analyzes the received data to identify the user's interests and needs.

[0073] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[0074] Step 6:

[0075] Generative AI creates a list of recommended products.

[0076] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[0077] Recommended products include new products and products from brands that are highly rated by users.

[0078] Step 7:

[0079] The server sends a list of recommended products to the terminal.

[0080] The server sends the recommended product list received from the generation AI to the user's device.

[0081] The user's terminal displays the received list on the screen.

[0082] Step 8:

[0083] The user inputs the starting point, transportation method, and store name.

[0084] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[0085] Once you have completed the input, press the search button.

[0086] Step 9:

[0087] The server calculates store and route information.

[0088] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[0089] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[0090] Step 10:

[0091] The server provides the calculation results to the user.

[0092] The server transmits the received route guidance information and store information to the user's terminal.

[0093] The user's terminal displays this and provides route guidance and store information to the user.

[0094] Step 11:

[0095] The user decides on the action.

[0096] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[0097] ---

[0098] The above is a description of the process flow of the system broken down into specific steps, which will enable a detailed understanding of the process flow for implementing the present invention.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] In modern society, it is important to make personalized product suggestions that meet the diverse purchasing needs of users. However, conventional systems are unable to effectively utilize users' purchase history and rating information, resulting in insufficient product suggestions that match individual preferences. Furthermore, when visiting a store, optimal route guidance is not provided, resulting in an inefficient shopping experience for users.

[0102] 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.

[0103] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and rating information, means for transmitting the acquired purchase history and rating information to a generative AI model and identifying the user's interests and needs, means for proposing optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and its route using a geographic information system, and means for providing the calculated store and route information to the user. This allows the user to receive personalized product suggestions and enjoy efficient route guidance to make a comfortable and smooth visit to a physical store.

[0104] "User login information" means the authentication information (e.g., user ID and password) entered by a user to access the system.

[0105] "Means of authentication" refers to the process and function of verifying the login information entered by the user and confirming that the user is a legitimate user.

[0106] "User purchase history" is data on a list of products that the user has purchased in the past and related information.

[0107] "Rating information" is information including the details of ratings and reviews given by users on products they have purchased in the past.

[0108] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and uses the results to identify user interests and needs.

[0109] "Geographic Information System (GIS)" refers to systems and methods for collecting, managing, and analyzing geographic data.

[0110] The "nearest store" refers to the store located closest to the starting point designated by the user.

[0111] The "means for calculating a route" refers to the process and function for calculating the optimal route from the starting point specified by the user to the store, which is the destination.

[0112] The system of the present invention utilizes a user's login information, purchase history, and rating information to suggest optimal products to the user and also provides route guidance to the nearest store. A specific embodiment of this system will be described below.

[0113] The system is configured as follows:

[0114] 1. Receiving and authenticating login information:

[0115] The user enters the user ID and password on the login screen.

[0116] The server receives the entered login information and authenticates the user.

[0117] If the authentication is successful, the home page is displayed on the user's terminal.

[0118] 2. Obtaining user data:

[0119] The server accesses the database to obtain the user's purchase history and rating information.

[0120] The acquired data is temporarily stored on the server.

[0121] 3. Data transmission and analysis:

[0122] The server sends the acquired purchase history and rating information to the generative AI model.

[0123] A generative AI model analyzes this data to identify user interests and needs.

[0124] The analysis results are sent back to the server.

[0125] 4. Product proposal generation:

[0126] The server will suggest the best products to the user based on the analysis results from the generated AI.

[0127] The proposed product list is sent to the user terminal and displayed on the terminal.

[0128] 5. Store Locator and Route Guidance:

[0129] To search for a store the user wants to visit, they enter a starting point (e.g., home address) and a mode of transportation (e.g., car, walking, public transportation).

[0130] The server uses a geographic information system (GIS) to search for the nearest store.

[0131] The server calculates the nearest store and the optimal route and sends it to the user's device.

[0132] 6. Display and Use of Information:

[0133] The recommended product list and route guidance sent from the server are displayed on the user's terminal.

[0134] Based on this information, users decide to select and purchase products or visit a physical store.

[0135] Hardware and Software

[0136] The system uses the following hardware and software:

[0137] Server: Includes database management system (e.g. MySQL) and authentication system.

[0138] Generative AI models: Data analysis models (e.g., machine learning models using TensorFlow or PyTorch).

[0139] Geographic Information System (GIS): Map data provision services (e.g., Google Maps API).

[0140] User devices: smartphones, tablets, PCs, etc.

[0141] Specific examples

[0142] For example, if a user searches for a certain electronics retailer near Shinjuku Station, the server will suggest stores near Shinjuku Station and the optimal walking route. It will also display a list of recommended electronics products based on the user's past purchase data and ratings. The user can use the displayed route guidance to travel efficiently to their destination and consider the presented products.

[0143] Prompt Sentence Examples

[0144] "Please suggest the latest technology models that might interest me based on my past purchase history and reviews. Also, please calculate the shortest route from my home to the electronics store of my choice."

[0145] The above is a specific embodiment of the present invention. By using this system, users can receive personalized product suggestions and efficient store guidance, resulting in a more satisfying shopping experience.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] Enter and authenticate your login information

[0149] User input: The user enters their user ID and password on the login screen.

[0150] Data processing and calculation: The server receives the input information and queries the database to verify it.

[0151] Output: Obtain the matching result, and if authentication is successful, send the user's homepage information to the user's device.

[0152] Specific operation: The user submits the input information, and the server obtains the authentication result and returns it to the user.

[0153] Step 2:

[0154] Retrieving User Data

[0155] Input: Session information for a successfully authenticated user.

[0156] Data processing and calculation: The server accesses the database and obtains the user's purchase history and rating information.

[0157] Output: The acquired purchase history and rating information is temporarily stored on the server.

[0158] Specific operation: The server issues a query to the database based on the session information to retrieve relevant data.

[0159] Step 3:

[0160] Data transmission and analysis

[0161] Input: Retrieved purchase history and rating information.

[0162] Data processing and calculation: The server sends this data to the generative AI model, which then analyzes the data.

[0163] Output: Analysis results that identify user interests and needs.

[0164] Specific operation: The server sends purchase history and rating information to the generative AI model, which then analyzes the data and returns the results.

[0165] Step 4:

[0166] Generate product suggestions

[0167] Input: Analysis results of the generative AI model.

[0168] Data processing and calculation: The server selects the most suitable products for the user based on the analysis results and generates a list of recommended products.

[0169] Output: Sends the recommended product list to the user's device.

[0170] Specific operation: The server generates a list of recommended products and sends it to the user's device for display.

[0171] Step 5:

[0172] Store search and route guidance

[0173] Input: User-specified starting point and mode of transportation.

[0174] Data processing and calculation: The server sends this information to the geographic information system, which calculates the nearest store and its route.

[0175] Output: Gets the nearest store information and route directions and sends them back to the server.

[0176] Specific operation: The user inputs the starting point and mode of transportation, and the server works with the GIS to calculate the optimal route.

[0177] Step 6:

[0178] Display and Use of Information

[0179] Input: Recommended product list and directions sent from the server.

[0180] Data processing and calculation: The user terminal receives this information and displays it on the screen.

[0181] Output: Based on the displayed information, the user decides to select a product, purchase it, or visit a physical store.

[0182] Specific operation: The user's device displays recommended products and route guidance, and the user acts based on these.

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] Conventional shopping systems rarely make full use of users' purchase history and rating information to make product suggestions, resulting in the problem of not being able to make optimal product suggestions tailored to each user's individual preferences. Furthermore, systems that provide optimal route guidance when users visit physical stores are also inadequate, resulting in users often wasting time before reaching the store. To solve these problems, a system is needed that analyzes each user's data, accurately suggests products, and provides guidance on the optimal route to the physical store.

[0186] 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.

[0187] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and review information, means for analyzing the acquired purchase history and review information to identify the user's interests and needs, means for suggesting optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and route thereto, means for providing the calculated store and route information to the user, means for generating a product suggestion prompt using a generative artificial intelligence model, and means for calculating a route to the nearest store using a geographic information system. This enables optimal product suggestions tailored to each user's individual preferences and efficient store guidance.

[0188] "Means for receiving and authenticating user login information" refers to means for receiving the user ID and password required when a user accesses the system and for authenticating the user based on that information.

[0189] The "means for acquiring user purchase history and evaluation information" refers to a means for accumulating products purchased by users in the past and their evaluations, and acquiring that data.

[0190] The "means for analyzing the acquired purchase history and evaluation information to identify the user's interests and needs" is a means for analyzing the user's past purchase history and evaluation information to clarify the user's preferences and interests.

[0191] "Means for proposing optimal products to users based on analysis results" refers to means for selecting and recommending products that are suitable for users based on analyzed data.

[0192] "Means for receiving the starting point and transportation means specified by the user and calculating the nearest store and its route" refers to means for receiving the starting point and transportation means entered by the user and calculating the optimal store and route accordingly.

[0193] The "means for providing the user with calculated store and route information" is a means for displaying the calculated store location and the optimal route to reach the store to the user.

[0194] "Means for generating prompt sentences for product proposals using a generative artificial intelligence model" refers to means for using generative AI to create prompt sentences for product proposals based on user data.

[0195] "Means for calculating the route to the nearest store using a geographic information system" refers to a means for using a geographic information system (GIS) to calculate the optimal route from a starting point specified by the user to a destination (store).

[0196] The system of the present invention utilizes a user's login information, purchase history, and rating information to provide individually customized product suggestions and optimal route guidance to the nearest physical store. The main means for realizing this system include login authentication, data acquisition, data analysis, product suggestions, route calculation, prompt generation using generation AI, and route calculation using a geographic information system (GIS).

[0197] First, the server receives and authenticates the user's login information. This process is done using the user ID and password, and if authentication is successful, the server retrieves the purchase history and rating information associated with the user's account.

[0198] The server then sends the acquired purchase history and review information to the generative AI model, which analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased technology-related products such as smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0199] Based on the results of this analysis, the generative AI model generates prompts for product recommendations. For example, the following prompts are generated:

[0200] Based on the user's data: {'Purchase history': ['Smartphone', 'Laptop', 'Wireless earphones'], 'Rating information': {'Smartphone': 5, 'Laptop': 4}}, please suggest the following products:

[0201] Furthermore, users can input their starting point and mode of transportation through the system. The server receives this information and calculates the nearest store and its route using a geographic information system (GIS). For example, if a user searches for "electronics retailer" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. This allows users to visit stores efficiently.

[0202] The system uses the following main hardware and software:

[0203] requests: A library for API communication between the server and external services

[0204] geopy: A library for geographical calculations

[0205] openai: A library for using generative AI models

[0206] The introduction of this system allows users to experience personalized product suggestions and efficient store visits, saving time and improving the shopping experience. In this way, users can easily find products that match their interests and needs, and can also smoothly visit physical stores.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] The server receives the user's login information (user ID, password) and performs authentication. Specifically, the server sends the login information sent by the user to the authentication API and receives the authentication result. The input is the user ID and password, and the output is the authentication result (success or failure).

[0210] Step 2:

[0211] If authentication is successful, the server obtains the user's purchase history and rating information. Specifically, the server uses the authenticated user ID to obtain the user's purchase history data and rating data from the database. The input is the user ID, and the output is the purchase history and rating information.

[0212] Step 3:

[0213] The server sends the acquired purchase history and review information to the generative AI model to identify the user's interests and needs. Specifically, the server converts the user's data into prompt sentences for the generative AI model and sends them to the generative AI model to obtain analysis results. The input is the purchase history and review information, and the output is the analysis results that indicate the user's interests and needs.

[0214] Step 4:

[0215] The server uses a generative AI model to generate a product suggestion prompt based on the analysis results. Specifically, the server inputs the user's analysis results into the generative AI model, which then generates a product suggestion prompt. The input is the user's analysis results, and the output is the product suggestion prompt.

[0216] Step 5:

[0217] The server sends the generated prompt sentence to the generative AI model to obtain a list of recommended products. Specifically, the server sends a request to the generative AI model using the prompt sentence and receives a list of recommended products. The input is the prompt sentence, and the output is the list of recommended products.

[0218] Step 6:

[0219] The user inputs the starting point and the mode of transportation. The terminal receives the starting point (e.g., address, location information) and the mode of transportation (e.g., car, walking, public transportation) from the user. The input is the starting point and the mode of transportation, and the output is this information.

[0220] Step 7:

[0221] The server receives the starting point and mode of transportation specified by the user and calculates the nearest store and its route using a geographic information system (GIS). Specifically, the server uses GIS to calculate the optimal route from the starting point to the destination (store). The input is the starting point and mode of transportation, and the output is the optimal route.

[0222] Step 8:

[0223] The server provides the calculated store and route information to the terminal. Specifically, the server sends the calculated route and information on the nearest store to the user's terminal and displays it. The input is the optimal route, and the output is the route information displayed on the user's terminal.

[0224] Step 9:

[0225] The user visits the specified store based on the route guidance and recommended product list they received and makes a purchase. Since this is a user action, there are no inputs or outputs to the system.

[0226] The above is a detailed explanation of the processing steps in the "Personal Shopping Guide" system of the present invention. In this way, users can efficiently receive optimal product suggestions and route guidance to physical stores.

[0227] 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.

[0228] ---

[0229] The present invention relates to a system that provides a more personalized and efficient shopping experience by suggesting optimal products to users and guiding them on the nearest route to stores they want to visit, using the user's login information, purchase history, and rating information. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[0230] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0231] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0232] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a webcam can be used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, in the case of voice input, the user's tone of voice can be analyzed to similarly recognize emotions.

[0233] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[0234] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[0235] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[0236] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0237] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[0238] ---

[0239] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0240] The processing flow will be explained below.

[0241] ---

[0242] Step 1:

[0243] A user logs in to the system.

[0244] The user enters their account information (user ID and password) and presses the login button.

[0245] The terminal transmits the login information entered by the user to the authentication server.

[0246] Step 2:

[0247] The server authenticates the login information.

[0248] The server queries the authentication database to verify the consistency of the entered ID and password.

[0249] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[0250] Step 3:

[0251] The server acquires the user's purchase history and rating information.

[0252] The server retrieves past purchase history and rating information from the database based on the user's ID.

[0253] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[0254] Step 4:

[0255] The acquired data is sent to the generation AI.

[0256] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[0257] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[0258] Step 5:

[0259] Generative AI analyzes the data.

[0260] The generative AI analyzes the received data to identify the user's interests and needs.

[0261] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[0262] Step 6:

[0263] Generative AI creates a list of recommended products.

[0264] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[0265] Recommended products include new products and products from brands that are highly rated by users.

[0266] Step 7:

[0267] The emotion engine recognizes the user's real-time emotions.

[0268] It uses the camera and microphone connected to the user's device to capture facial expressions and tone of voice.

[0269] The emotion engine analyzes the collected data and identifies the user's emotional state (e.g., happy, excited, sad).

[0270] Step 8:

[0271] The emotion engine's recognition results are fed back to the generative AI.

[0272] The emotion engine sends the recognized emotion data to the generation AI.

[0273] The generative AI takes this emotional data into account to refine the recommended product list.

[0274] Step 9:

[0275] The server transmits the recommended product list to the user terminal.

[0276] The server sends the final recommended product list received from the generation AI to the user's device.

[0277] The user's terminal displays the received list on the screen.

[0278] Step 10:

[0279] The user inputs the starting point, transportation method, and store name.

[0280] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[0281] Once you have completed the input, press the search button.

[0282] Step 11:

[0283] The server calculates store and route information.

[0284] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[0285] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[0286] Step 12:

[0287] The server provides the calculation results to the user.

[0288] The server transmits the received route guidance information and store information to the user's terminal.

[0289] The user's terminal displays this and provides route guidance and store information to the user.

[0290] Step 13:

[0291] The user decides on the action.

[0292] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[0293] ---

[0294] The above is a description of the process flow of a system that combines emotion engines, broken down into specific steps, which will enable a detailed understanding of the process for implementing the present invention.

[0295] Example 2

[0296] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0297] In today's shopping environment, users have difficulty finding the right product among the vast number of products available. Furthermore, there is a lack of mechanisms to accurately understand users' interests and needs and suggest appropriate products in real time. Furthermore, there are no systems that suggest products based on the user's emotional state or provide efficient route guidance to the store they want to visit. This makes it difficult for users to have a satisfying shopping experience.

[0298] 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.

[0299] In this invention, the server includes means for receiving and authenticating login information, means for acquiring purchase history and user evaluation information, means for analyzing the acquired purchase history and user evaluation information to identify interests and needs, means for suggesting optimal products, means for recognizing emotions in real time by analyzing the user's facial expressions, tone of voice, or text content, means for adjusting the suggested products in consideration of the emotions, means for receiving a specified starting point and transportation means, and calculating the nearest store and the optimal route thereto, and means for providing the calculated store and route information to the user. This enables product suggestions and efficient store guidance that take into consideration the user's individual preferences and real-time emotions.

[0300] "Login information" refers to information used to identify and authenticate a user, and typically refers to a combination of a user ID and password.

[0301] "Authentication" is the process of verifying whether the login information provided is correct and authenticating the user.

[0302] "Purchase history" is a record of products purchased by a user in the past, and includes data such as purchase date, product name, and price.

[0303] "User evaluation information" is information about evaluations and reviews made by users on products they have purchased in the past.

[0304] "Generative AI" is a type of artificial intelligence that uses machine learning to analyze input data and perform specific tasks.

[0305] An "emotion engine" is a technology that recognizes emotions by analyzing a user's facial expressions, tone of voice, or text content.

[0306] The "starting point" is information about the location from which the user departs, and typically refers to an address or place name.

[0307] "Transportation" refers to the method a user uses to travel, including car, walking, public transportation, etc.

[0308] The "nearest store" refers to the store located closest to the starting point specified by the user.

[0309] An "optimal route" is the most efficient and quickest route from a starting point to a destination.

[0310] A "geographic information system (GIS)" is an information system for managing geographic data and performing spatial analysis, and is used for route calculations.

[0311] "Real time" means that processing is executed immediately the moment the user performs an operation.

[0312] "Product suggestion" refers to selecting and offering products that are deemed optimal based on the user's interests, needs, and emotional state.

[0313] "Feedback" is the process of reflecting the data and recognition results acquired by the system in the next processing or proposal.

[0314] MODE FOR CARRYING OUT THE INVENTION

[0315] This invention relates to a system that uses a user's login information, purchase history, and rating information to suggest optimal products to the user and also guides them on an efficient route from a specified starting point to the nearest store. This system incorporates a generative AI model and an emotion engine to realize product suggestions that take into account the user's interests, needs, and real-time emotional state.

[0316] First, a user logs in to the system by entering their account information into a terminal. The user's account information (user ID and password) is sent to the server, which receives it and performs authentication. If authentication is successful, the server retrieves the purchase history and rating information associated with the user's account from the database.

[0317] The server then sends the acquired purchase history and review information to the generative AI model. The generative AI model analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased and reviewed smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology-related products and suggest the latest smartphone models and related accessories.

[0318] The system also incorporates an emotion engine, which can recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, the system uses a webcam to analyze their facial expressions to determine whether they are excited or interested. In addition, when using voice input, the system analyzes the user's tone of voice to similarly recognize emotions.

[0319] The emotion data recognized by the emotion engine is fed back to the generative AI model, which then adjusts the product recommendations accordingly. For example, if the user is feeling depressed, it will suggest products and services that help them relax, and if they are excited, it will suggest products related to active activities.

[0320] Furthermore, users can enter their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system and search for the name of the store they want to visit. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions.

[0321] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the information presented or visit a physical store.

[0322] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0323] Examples of prompt statements

[0324] User ID: user1234

[0325] Password: password5678

[0326] Purchase History:

[0327] January 2023: Smartphones

[0328] March 2023: Laptop

[0329] May 2023: Wireless earphones

[0330] Evaluation Information:

[0331] Smartphone: Highly rated

[0332] Laptop: Medium rating

[0333] Wireless earphones: highly rated

[0334] Starting point: Shinjuku Station

[0335] Transportation: Walking

[0336] Store to visit: A certain electronics retail store

[0337] Using this system, users can receive product suggestions that take into account their individual preferences and real-time emotions, providing a satisfying shopping experience through efficient store navigation.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Program processing flow

[0340] Step 1: Enter your login details

[0341] The user enters their user ID and password on the login screen.

[0342] Input: User ID, Password

[0343] Output: Login information

[0344] Specific operation: The terminal sends the login information entered by the user to the server.

[0345] Step 2: User authentication

[0346] The server receives the login information and authenticates it against a database.

[0347] Input: Login information

[0348] Output: Authentication result (success / failure)

[0349] Specific operation: The server uses the database to check whether the user ID and password match and returns the authentication result to the terminal.

[0350] Step 3: Obtain purchase history and rating information

[0351] The server obtains the purchase history and rating information of the successfully authenticated user from the database.

[0352] Input: User ID

[0353] Output: Purchase history, rating information

[0354] Specific operation: The server queries the database to obtain the user's purchase history and rating information, and sends it to the generation AI.

[0355] Step 4: Analyze the data

[0356] The generation AI analyzes the purchase history and review information it receives to identify the user's interests and needs.

[0357] Input: purchase history, evaluation information

[0358] Output: Analysis results (interests and needs)

[0359] How it works: Generative AI uses machine learning algorithms to analyze past purchase and review data to identify users' interests and needs.

[0360] Step 5: Product proposal

[0361] Based on the analysis results, the generative AI will list the most suitable products for the user.

[0362] Input: Analysis results (interests and needs)

[0363] Output: Suggested product list

[0364] How it works: Generative AI uses pre-trained models to suggest products that match the user's interests and needs.

[0365] Step 6: Collect emotion data

[0366] The device activates a webcam and microphone to capture the user's facial expressions and voice.

[0367] Input: User's facial expression data, voice data

[0368] Output: Emotion data

[0369] Specific operation: The device transmits the user's facial expression data and voice data to the server in real time via the webcam and microphone.

[0370] Step 7: Sentiment Analysis

[0371] The emotion engine analyzes the collected facial expression and voice data to recognize the user's emotions.

[0372] Input: facial expression data, voice data

[0373] Output: Real-time emotion data

[0374] Specific operation: The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize the user's emotional state.

[0375] Step 8: Adjust the product offering

[0376] The generative AI adjusts the suggested product list based on real-time emotional data.

[0377] Input: Real-time emotion data, suggested product list

[0378] Output: Adjusted suggested product list

[0379] Specific operation: The generative AI takes into account the emotional data, reevaluates the selection of products to suggest, and adds or removes products from the list that are appropriate for the user's emotional state.

[0380] Step 9: Enter your starting point and transportation

[0381] The user inputs the starting point and mode of transportation into the terminal and searches for the name of the store they want to visit.

[0382] Input: Starting point, transportation method, store name

[0383] Output: Starting point information, transportation information, store name

[0384] Specific operation: The terminal sends input data from the user to the server.

[0385] Step 10: Route calculation

[0386] The server calculates the route using a geographic information system (GIS) based on the starting point information, transportation information, and store names.

[0387] Input: Starting point information, transportation information, store name

[0388] Output: Optimal route information

[0389] Specific operation: The server uses GIS to calculate the optimal route from the specified starting point to the nearest store.

[0390] Step 11: Viewing Information

[0391] The server transmits the calculated optimum route information and the adjusted suggested product list to the user's terminal.

[0392] Input: Optimal route information, adjusted suggested product list

[0393] Output: Data for display

[0394] Specific operation: The terminal displays the received information on the user interface.

[0395] This allows users to receive personalized product suggestions and efficient store guidance, resulting in a highly satisfying shopping experience.

[0396] (Application example 2)

[0397] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0398] Modern shopping experiences are becoming more diverse, and personalized product recommendations based on users' purchase history and ratings are becoming increasingly important. However, existing systems have not been able to adequately consider users' real-time emotions when making product recommendations, and they have not provided smooth guidance to physical stores. This has led to challenges that make it difficult for users to have a satisfying shopping experience.

[0399] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating user login information, means for acquiring the user's purchase history and rating information, means for analyzing the acquired purchase history and rating information and identifying the user's interests and needs, means for recognizing the user's emotions, means for suggesting optimal products to the user based on the analysis results and emotion recognition, means for receiving the starting point and transportation means specified by the user and calculating the nearest store and route thereto, and means for providing the calculated store and route information to the user. This enables highly accurate product suggestions that take into account the user's real-time emotions and efficient route guidance to physical stores.

[0400] "User login information" means authentication information such as a user ID and password entered by a user to access the system.

[0401] "Purchase history" is a collection of data on products purchased by a user in the past.

[0402] "Rating information" is data relating to ratings and reviews given by users on products they have purchased in the past.

[0403] "Analysis" is the process of using acquired purchase history and rating information to identify user interests and needs.

[0404] "Emotion recognition" is a technology that analyzes a user's facial expressions, tone of voice, etc. to determine their emotional state in real time.

[0405] "Optimal product proposal" means proposing the most suitable product for the user based on the user's purchasing history, evaluation information, and emotion recognition results.

[0406] The "starting point" is the starting point designated by the user.

[0407] "Transportation" refers to the means of transportation used by the user (e.g., walking, driving, public transportation).

[0408] The "nearest store" is the physical store closest to the user's current location or the specified starting point.

[0409] "Route calculation" refers to calculating the optimal route from a starting point to a destination specified by the user.

[0410] "Route guidance" refers to providing calculated route information to a user and showing them how to get to their destination.

[0411] The present invention relates to a system that uses a user's login information, purchase history, and rating information to suggest the most suitable products to the user and guides the user to the nearest route to the store they want to visit. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[0412] The system works as follows: The process begins when a user logs in to the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0413] The server then sends the acquired purchase history and evaluation information to the generation AI, which then analyzes this data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the generation AI will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0414] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a camera is used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, when using voice input, the tone of the user's voice can be analyzed to similarly recognize emotions.

[0415] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[0416] Furthermore, users can search for store names by entering their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system. The server receives this input information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions. The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the presented information or visit a physical store.

[0417] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0418] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[0419] Example prompts for generative AI models:

[0420] User purchase history:

[0421] Smartphone: 5 times

[0422] Laptop: 3 times

[0423] Wireless earphones: 2 times

[0424] User rating information:

[0425] Smartphone: ★★★★☆

[0426] Laptop: ★★★☆☆

[0427] Wireless Earphones: ★★★★☆

[0428] Current emotional state: Positive

[0429] Products suggested to users:

[0430] 1. The latest smartwatches

[0431] 2. High-quality protective cases for smartphones

[0432] 3. Wireless Charger

[0433] This approach allows users to receive consistent, personalized service and a highly satisfying shopping experience.

[0434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0435] Step 1:

[0436] A user logs into the system from a terminal. The user enters their user ID and password, and this login information is sent to the server. The server receives the login information and authenticates the user. It returns a success message to the identified user.

[0437] Step 2:

[0438] When a user successfully logs in, the server retrieves the user's purchase history and rating information. To retrieve this information, the server queries the database based on the user ID and retrieves the relevant purchase history and rating information. The retrieved data is then sent to the user's device.

[0439] Step 3:

[0440] The server sends the acquired purchase history and rating information to the generation AI. The generation AI analyzes this data to identify the user's interests and needs. Specifically, it estimates the user's preferences based on past purchase types, product ratings, and purchase frequency. The analysis results are generated and sent back to the server.

[0441] Step 4:

[0442] The server uses the device's camera and microphone to recognize the user's emotions. The device sends facial expression data from the camera and voice data from the microphone to the server. The server inputs this data into an emotion recognition engine and analyzes the user's current emotional state. The analysis results are sent back to the server.

[0443] Step 5:

[0444] The server combines the analysis results of the generative AI with those of the emotion recognition engine. Based on this, it selects the most suitable products for the user and generates a list of products. For example, if the user is in a positive mood, it will suggest products that stimulate purchasing desire, such as related accessories. Conversely, if the user is in a negative mood, it will suggest products that will help them relax.

[0445] Step 6:

[0446] The user inputs the starting point and transportation method from the terminal. This information is sent to the server. Based on the input information, the server uses a geographic information system (GIS) to calculate the nearest store and its route. The calculated route includes factors such as travel time and traffic conditions.

[0447] Step 7:

[0448] The server sends the calculated route guidance and recommended product information to the user's device. The device displays this information, and the user can check the presented products and proceed with the purchase if interested. The user can also visit a physical store based on the route guidance to the nearest store.

[0449] Step 8:

[0450] When a user selects from the list of products presented, the user's selection information is sent to the server, which stores it in a database as purchase history and rating information for the next purchase and uses it to improve the accuracy of future suggestions.

[0451] This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0452] 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.

[0453] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0454] 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.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0468] ---

[0469] The system of the present invention utilizes the user's login information, purchase history, and rating information to suggest the most suitable products for the user and guide the user to the nearest route to the store they want to visit, thereby providing a more personalized and efficient shopping experience.

[0470] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0471] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0472] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[0473] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[0474] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0475] The main feature of this system is that it proposes products tailored to each user's individual preferences, and also uses location information to provide optimal routes and store information, allowing users to easily find products that match their interests and needs, and facilitating smooth visits to physical stores.

[0476] ---

[0477] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0478] The processing flow will be explained below.

[0479] ---

[0480] Step 1:

[0481] A user logs in to the system.

[0482] The user enters their account information (user ID and password) and presses the login button.

[0483] The terminal transmits the login information entered by the user to the authentication server.

[0484] Step 2:

[0485] The server authenticates the login information.

[0486] The server queries the authentication database to verify the consistency of the entered ID and password.

[0487] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[0488] Step 3:

[0489] The server acquires the user's purchase history and rating information.

[0490] The server retrieves past purchase history and rating information from the database based on the user's ID.

[0491] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[0492] Step 4:

[0493] The acquired data is sent to the generation AI.

[0494] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[0495] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[0496] Step 5:

[0497] Generative AI analyzes the data.

[0498] The generative AI analyzes the received data to identify the user's interests and needs.

[0499] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[0500] Step 6:

[0501] Generative AI creates a list of recommended products.

[0502] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[0503] Recommended products include new products and products from brands that are highly rated by users.

[0504] Step 7:

[0505] The server sends a list of recommended products to the terminal.

[0506] The server sends the recommended product list received from the generation AI to the user's device.

[0507] The user's terminal displays the received list on the screen.

[0508] Step 8:

[0509] The user inputs the starting point, transportation method, and store name.

[0510] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[0511] Once you have completed the input, press the search button.

[0512] Step 9:

[0513] The server calculates store and route information.

[0514] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[0515] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[0516] Step 10:

[0517] The server provides the calculation results to the user.

[0518] The server transmits the received route guidance information and store information to the user's terminal.

[0519] The user's terminal displays this and provides route guidance and store information to the user.

[0520] Step 11:

[0521] The user decides on the action.

[0522] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[0523] ---

[0524] The above is a description of the process flow of the system broken down into specific steps, which will enable a detailed understanding of the process flow for implementing the present invention.

[0525] Example 1

[0526] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0527] In modern society, it is important to make personalized product suggestions that meet the diverse purchasing needs of users. However, conventional systems are unable to effectively utilize users' purchase history and rating information, resulting in insufficient product suggestions that match individual preferences. Furthermore, when visiting a store, optimal route guidance is not provided, resulting in an inefficient shopping experience for users.

[0528] 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.

[0529] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and rating information, means for transmitting the acquired purchase history and rating information to a generative AI model and identifying the user's interests and needs, means for proposing optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and its route using a geographic information system, and means for providing the calculated store and route information to the user. This allows the user to receive personalized product suggestions and enjoy efficient route guidance to make a comfortable and smooth visit to a physical store.

[0530] "User login information" means the authentication information (e.g., user ID and password) entered by a user to access the system.

[0531] "Means of authentication" refers to the process and function of verifying the login information entered by the user and confirming that the user is a legitimate user.

[0532] "User purchase history" is data on a list of products that the user has purchased in the past and related information.

[0533] "Rating information" is information including the details of ratings and reviews given by users on products they have purchased in the past.

[0534] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and uses the results to identify user interests and needs.

[0535] "Geographic Information System (GIS)" refers to systems and methods for collecting, managing, and analyzing geographic data.

[0536] The "nearest store" refers to the store located closest to the starting point designated by the user.

[0537] The "means for calculating a route" refers to the process and function for calculating the optimal route from the starting point specified by the user to the store, which is the destination.

[0538] The system of the present invention utilizes a user's login information, purchase history, and rating information to suggest optimal products to the user and also provides route guidance to the nearest store. A specific embodiment of this system will be described below.

[0539] The system is configured as follows:

[0540] 1. Receiving and authenticating login information:

[0541] The user enters the user ID and password on the login screen.

[0542] The server receives the entered login information and authenticates the user.

[0543] If the authentication is successful, the home page is displayed on the user's terminal.

[0544] 2. Obtaining user data:

[0545] The server accesses the database to obtain the user's purchase history and rating information.

[0546] The acquired data is temporarily stored on the server.

[0547] 3. Data transmission and analysis:

[0548] The server sends the acquired purchase history and rating information to the generative AI model.

[0549] A generative AI model analyzes this data to identify user interests and needs.

[0550] The analysis results are sent back to the server.

[0551] 4. Product proposal generation:

[0552] The server will suggest the best products to the user based on the analysis results from the generated AI.

[0553] The proposed product list is sent to the user terminal and displayed on the terminal.

[0554] 5. Store Locator and Route Guidance:

[0555] To search for a store the user wants to visit, they enter a starting point (e.g., home address) and a mode of transportation (e.g., car, walking, public transportation).

[0556] The server uses a geographic information system (GIS) to search for the nearest store.

[0557] The server calculates the nearest store and the optimal route and sends it to the user's device.

[0558] 6. Display and Use of Information:

[0559] The recommended product list and route guidance sent from the server are displayed on the user's terminal.

[0560] Based on this information, users decide to select and purchase products or visit a physical store.

[0561] Hardware and Software

[0562] The system uses the following hardware and software:

[0563] Server: Includes database management system (e.g. MySQL) and authentication system.

[0564] Generative AI models: Data analysis models (e.g., machine learning models using TensorFlow or PyTorch).

[0565] Geographic Information System (GIS): Map data provision services (e.g., Google Maps API).

[0566] User devices: smartphones, tablets, PCs, etc.

[0567] Specific examples

[0568] For example, if a user searches for a certain electronics retailer near Shinjuku Station, the server will suggest stores near Shinjuku Station and the optimal walking route. It will also display a list of recommended electronics products based on the user's past purchase data and ratings. The user can use the displayed route guidance to travel efficiently to their destination and consider the presented products.

[0569] Prompt Sentence Examples

[0570] "Please suggest the latest technology models that might interest me based on my past purchase history and reviews. Also, please calculate the shortest route from my home to the electronics store of my choice."

[0571] The above is a specific embodiment of the present invention. By using this system, users can receive personalized product suggestions and efficient store guidance, resulting in a more satisfying shopping experience.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Enter and authenticate your login information

[0575] User input: The user enters their user ID and password on the login screen.

[0576] Data processing and calculation: The server receives the input information and queries the database to verify it.

[0577] Output: Obtain the matching result, and if authentication is successful, send the user's homepage information to the user's device.

[0578] Specific operation: The user submits the input information, and the server obtains the authentication result and returns it to the user.

[0579] Step 2:

[0580] Retrieving User Data

[0581] Input: Session information for a successfully authenticated user.

[0582] Data processing and calculation: The server accesses the database and obtains the user's purchase history and rating information.

[0583] Output: The acquired purchase history and rating information is temporarily stored on the server.

[0584] Specific operation: The server issues a query to the database based on the session information to retrieve relevant data.

[0585] Step 3:

[0586] Data transmission and analysis

[0587] Input: Retrieved purchase history and rating information.

[0588] Data processing and calculation: The server sends this data to the generative AI model, which then analyzes the data.

[0589] Output: Analysis results that identify user interests and needs.

[0590] Specific operation: The server sends purchase history and rating information to the generative AI model, which then analyzes the data and returns the results.

[0591] Step 4:

[0592] Generate product suggestions

[0593] Input: Analysis results of the generative AI model.

[0594] Data processing and calculation: The server selects the most suitable products for the user based on the analysis results and generates a list of recommended products.

[0595] Output: Sends the recommended product list to the user's device.

[0596] Specific operation: The server generates a list of recommended products and sends it to the user's device for display.

[0597] Step 5:

[0598] Store search and route guidance

[0599] Input: User-specified starting point and mode of transportation.

[0600] Data processing and calculation: The server sends this information to the geographic information system, which calculates the nearest store and its route.

[0601] Output: Gets the nearest store information and route directions and sends them back to the server.

[0602] Specific operation: The user inputs the starting point and mode of transportation, and the server works with the GIS to calculate the optimal route.

[0603] Step 6:

[0604] Display and Use of Information

[0605] Input: Recommended product list and directions sent from the server.

[0606] Data processing and calculation: The user terminal receives this information and displays it on the screen.

[0607] Output: Based on the displayed information, the user decides to select a product, purchase it, or visit a physical store.

[0608] Specific operation: The user's device displays recommended products and route guidance, and the user acts based on these.

[0609] (Application example 1)

[0610] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0611] Conventional shopping systems rarely make full use of users' purchase history and rating information to make product suggestions, resulting in the problem of not being able to make optimal product suggestions tailored to each user's individual preferences. Furthermore, systems that provide optimal route guidance when users visit physical stores are also inadequate, resulting in users often wasting time before reaching the store. To solve these problems, a system is needed that analyzes each user's data, accurately suggests products, and provides guidance on the optimal route to the physical store.

[0612] 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.

[0613] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and review information, means for analyzing the acquired purchase history and review information to identify the user's interests and needs, means for suggesting optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and route thereto, means for providing the calculated store and route information to the user, means for generating a product suggestion prompt using a generative artificial intelligence model, and means for calculating a route to the nearest store using a geographic information system. This enables optimal product suggestions tailored to each user's individual preferences and efficient store guidance.

[0614] "Means for receiving and authenticating user login information" refers to means for receiving the user ID and password required when a user accesses the system and for authenticating the user based on that information.

[0615] The "means for acquiring user purchase history and evaluation information" refers to a means for accumulating products purchased by users in the past and their evaluations, and acquiring that data.

[0616] The "means for analyzing the acquired purchase history and evaluation information to identify the user's interests and needs" is a means for analyzing the user's past purchase history and evaluation information to clarify the user's preferences and interests.

[0617] "Means for proposing optimal products to users based on analysis results" refers to means for selecting and recommending products that are suitable for users based on analyzed data.

[0618] "Means for receiving the starting point and transportation means specified by the user and calculating the nearest store and its route" refers to means for receiving the starting point and transportation means entered by the user and calculating the optimal store and route accordingly.

[0619] The "means for providing the user with calculated store and route information" is a means for displaying the calculated store location and the optimal route to reach the store to the user.

[0620] "Means for generating prompt sentences for product proposals using a generative artificial intelligence model" refers to means for using generative AI to create prompt sentences for product proposals based on user data.

[0621] "Means for calculating the route to the nearest store using a geographic information system" refers to a means for using a geographic information system (GIS) to calculate the optimal route from a starting point specified by the user to a destination (store).

[0622] The system of the present invention utilizes a user's login information, purchase history, and rating information to provide individually customized product suggestions and optimal route guidance to the nearest physical store. The main means for realizing this system include login authentication, data acquisition, data analysis, product suggestions, route calculation, prompt generation using generation AI, and route calculation using a geographic information system (GIS).

[0623] First, the server receives and authenticates the user's login information. This process is done using the user ID and password, and if authentication is successful, the server retrieves the purchase history and rating information associated with the user's account.

[0624] The server then sends the acquired purchase history and review information to the generative AI model, which analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased technology-related products such as smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0625] Based on the results of this analysis, the generative AI model generates prompts for product recommendations. For example, the following prompts are generated:

[0626] Based on the user's data: {'Purchase history': ['Smartphone', 'Laptop', 'Wireless earphones'], 'Rating information': {'Smartphone': 5, 'Laptop': 4}}, please suggest the following products:

[0627] Furthermore, users can input their starting point and mode of transportation through the system. The server receives this information and calculates the nearest store and its route using a geographic information system (GIS). For example, if a user searches for "electronics retailer" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. This allows users to visit stores efficiently.

[0628] The system uses the following main hardware and software:

[0629] requests: A library for API communication between the server and external services

[0630] geopy: A library for geographical calculations

[0631] openai: A library for using generative AI models

[0632] The introduction of this system allows users to experience personalized product suggestions and efficient store visits, saving time and improving the shopping experience. In this way, users can easily find products that match their interests and needs, and can also smoothly visit physical stores.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] The server receives the user's login information (user ID, password) and performs authentication. Specifically, the server sends the login information sent by the user to the authentication API and receives the authentication result. The input is the user ID and password, and the output is the authentication result (success or failure).

[0636] Step 2:

[0637] If authentication is successful, the server obtains the user's purchase history and rating information. Specifically, the server uses the authenticated user ID to obtain the user's purchase history data and rating data from the database. The input is the user ID, and the output is the purchase history and rating information.

[0638] Step 3:

[0639] The server sends the acquired purchase history and review information to the generative AI model to identify the user's interests and needs. Specifically, the server converts the user's data into prompt sentences for the generative AI model and sends them to the generative AI model to obtain analysis results. The input is the purchase history and review information, and the output is the analysis results that indicate the user's interests and needs.

[0640] Step 4:

[0641] The server uses a generative AI model to generate a product suggestion prompt based on the analysis results. Specifically, the server inputs the user's analysis results into the generative AI model, which then generates a product suggestion prompt. The input is the user's analysis results, and the output is the product suggestion prompt.

[0642] Step 5:

[0643] The server sends the generated prompt sentence to the generative AI model to obtain a list of recommended products. Specifically, the server sends a request to the generative AI model using the prompt sentence and receives a list of recommended products. The input is the prompt sentence, and the output is the list of recommended products.

[0644] Step 6:

[0645] The user inputs the starting point and the mode of transportation. The terminal receives the starting point (e.g., address, location information) and the mode of transportation (e.g., car, walking, public transportation) from the user. The input is the starting point and the mode of transportation, and the output is this information.

[0646] Step 7:

[0647] The server receives the starting point and mode of transportation specified by the user and calculates the nearest store and its route using a geographic information system (GIS). Specifically, the server uses GIS to calculate the optimal route from the starting point to the destination (store). The input is the starting point and mode of transportation, and the output is the optimal route.

[0648] Step 8:

[0649] The server provides the calculated store and route information to the terminal. Specifically, the server sends the calculated route and information on the nearest store to the user's terminal and displays it. The input is the optimal route, and the output is the route information displayed on the user's terminal.

[0650] Step 9:

[0651] The user visits the specified store based on the route guidance and recommended product list they received and makes a purchase. Since this is a user action, there are no inputs or outputs to the system.

[0652] The above is a detailed explanation of the processing steps in the "Personal Shopping Guide" system of the present invention. In this way, users can efficiently receive optimal product suggestions and route guidance to physical stores.

[0653] 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.

[0654] ---

[0655] The present invention relates to a system that provides a more personalized and efficient shopping experience by suggesting optimal products to users and guiding them on the nearest route to stores they want to visit, using the user's login information, purchase history, and rating information. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[0656] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0657] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0658] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a webcam can be used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, in the case of voice input, the user's tone of voice can be analyzed to similarly recognize emotions.

[0659] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[0660] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[0661] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[0662] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0663] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[0664] ---

[0665] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0666] The processing flow will be explained below.

[0667] ---

[0668] Step 1:

[0669] A user logs in to the system.

[0670] The user enters their account information (user ID and password) and presses the login button.

[0671] The terminal transmits the login information entered by the user to the authentication server.

[0672] Step 2:

[0673] The server authenticates the login information.

[0674] The server queries the authentication database to verify the consistency of the entered ID and password.

[0675] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[0676] Step 3:

[0677] The server acquires the user's purchase history and rating information.

[0678] The server retrieves past purchase history and rating information from the database based on the user's ID.

[0679] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[0680] Step 4:

[0681] The acquired data is sent to the generation AI.

[0682] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[0683] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[0684] Step 5:

[0685] Generative AI analyzes the data.

[0686] The generative AI analyzes the received data to identify the user's interests and needs.

[0687] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[0688] Step 6:

[0689] Generative AI creates a list of recommended products.

[0690] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[0691] Recommended products include new products and products from brands that are highly rated by users.

[0692] Step 7:

[0693] The emotion engine recognizes the user's real-time emotions.

[0694] It uses the camera and microphone connected to the user's device to capture facial expressions and tone of voice.

[0695] The emotion engine analyzes the collected data and identifies the user's emotional state (e.g., happy, excited, sad).

[0696] Step 8:

[0697] The emotion engine's recognition results are fed back to the generative AI.

[0698] The emotion engine sends the recognized emotion data to the generation AI.

[0699] The generative AI takes this emotional data into account to refine the recommended product list.

[0700] Step 9:

[0701] The server transmits the recommended product list to the user terminal.

[0702] The server sends the final recommended product list received from the generation AI to the user's device.

[0703] The user's terminal displays the received list on the screen.

[0704] Step 10:

[0705] The user inputs the starting point, transportation method, and store name.

[0706] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[0707] Once you have completed the input, press the search button.

[0708] Step 11:

[0709] The server calculates store and route information.

[0710] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[0711] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[0712] Step 12:

[0713] The server provides the calculation results to the user.

[0714] The server transmits the received route guidance information and store information to the user's terminal.

[0715] The user's terminal displays this and provides route guidance and store information to the user.

[0716] Step 13:

[0717] The user decides on the action.

[0718] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[0719] ---

[0720] The above is a description of the process flow of a system that combines emotion engines, broken down into specific steps, which will enable a detailed understanding of the process for implementing the present invention.

[0721] Example 2

[0722] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0723] In today's shopping environment, users have difficulty finding the right product among the vast number of products available. Furthermore, there is a lack of mechanisms to accurately understand users' interests and needs and suggest appropriate products in real time. Furthermore, there are no systems that suggest products based on the user's emotional state or provide efficient route guidance to the store they want to visit. This makes it difficult for users to have a satisfying shopping experience.

[0724] 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.

[0725] In this invention, the server includes means for receiving and authenticating login information, means for acquiring purchase history and user evaluation information, means for analyzing the acquired purchase history and user evaluation information to identify interests and needs, means for suggesting optimal products, means for recognizing emotions in real time by analyzing the user's facial expressions, tone of voice, or text content, means for adjusting the suggested products in consideration of the emotions, means for receiving a specified starting point and transportation means, and calculating the nearest store and the optimal route thereto, and means for providing the calculated store and route information to the user. This enables product suggestions and efficient store guidance that take into consideration the user's individual preferences and real-time emotions.

[0726] "Login information" refers to information used to identify and authenticate a user, and typically refers to a combination of a user ID and password.

[0727] "Authentication" is the process of verifying whether the login information provided is correct and authenticating the user.

[0728] "Purchase history" is a record of products purchased by a user in the past, and includes data such as purchase date, product name, and price.

[0729] "User evaluation information" is information about evaluations and reviews made by users on products they have purchased in the past.

[0730] "Generative AI" is a type of artificial intelligence that uses machine learning to analyze input data and perform specific tasks.

[0731] An "emotion engine" is a technology that recognizes emotions by analyzing a user's facial expressions, tone of voice, or text content.

[0732] The "starting point" is information about the location from which the user departs, and typically refers to an address or place name.

[0733] "Transportation" refers to the method a user uses to travel, including car, walking, public transportation, etc.

[0734] The "nearest store" refers to the store located closest to the starting point specified by the user.

[0735] An "optimal route" is the most efficient and quickest route from a starting point to a destination.

[0736] A "geographic information system (GIS)" is an information system for managing geographic data and performing spatial analysis, and is used for route calculations.

[0737] "Real time" means that processing is executed immediately the moment the user performs an operation.

[0738] "Product suggestion" refers to selecting and offering products that are deemed optimal based on the user's interests, needs, and emotional state.

[0739] "Feedback" is the process of reflecting the data and recognition results acquired by the system in the next processing or proposal.

[0740] MODE FOR CARRYING OUT THE INVENTION

[0741] This invention relates to a system that uses a user's login information, purchase history, and rating information to suggest optimal products to the user and also guides them on an efficient route from a specified starting point to the nearest store. This system incorporates a generative AI model and an emotion engine to realize product suggestions that take into account the user's interests, needs, and real-time emotional state.

[0742] First, a user logs in to the system by entering their account information into a terminal. The user's account information (user ID and password) is sent to the server, which receives it and performs authentication. If authentication is successful, the server retrieves the purchase history and rating information associated with the user's account from the database.

[0743] The server then sends the acquired purchase history and review information to the generative AI model. The generative AI model analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased and reviewed smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology-related products and suggest the latest smartphone models and related accessories.

[0744] The system also incorporates an emotion engine, which can recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, the system uses a webcam to analyze their facial expressions to determine whether they are excited or interested. In addition, when using voice input, the system analyzes the user's tone of voice to similarly recognize emotions.

[0745] The emotion data recognized by the emotion engine is fed back to the generative AI model, which then adjusts the product recommendations accordingly. For example, if the user is feeling depressed, it will suggest products and services that help them relax, and if they are excited, it will suggest products related to active activities.

[0746] Furthermore, users can enter their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system and search for the name of the store they want to visit. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions.

[0747] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the information presented or visit a physical store.

[0748] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0749] Examples of prompt statements

[0750] User ID: user1234

[0751] Password: password5678

[0752] Purchase History:

[0753] January 2023: Smartphones

[0754] March 2023: Laptop

[0755] May 2023: Wireless earphones

[0756] Evaluation Information:

[0757] Smartphone: Highly rated

[0758] Laptop: Medium rating

[0759] Wireless earphones: highly rated

[0760] Starting point: Shinjuku Station

[0761] Transportation: Walking

[0762] Store to visit: A certain electronics retail store

[0763] Using this system, users can receive product suggestions that take into account their individual preferences and real-time emotions, providing a satisfying shopping experience through efficient store navigation.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Program processing flow

[0766] Step 1: Enter your login details

[0767] The user enters their user ID and password on the login screen.

[0768] Input: User ID, Password

[0769] Output: Login information

[0770] Specific operation: The terminal sends the login information entered by the user to the server.

[0771] Step 2: User authentication

[0772] The server receives the login information and authenticates it against a database.

[0773] Input: Login information

[0774] Output: Authentication result (success / failure)

[0775] Specific operation: The server uses the database to check whether the user ID and password match and returns the authentication result to the terminal.

[0776] Step 3: Obtain purchase history and rating information

[0777] The server obtains the purchase history and rating information of the successfully authenticated user from the database.

[0778] Input: User ID

[0779] Output: Purchase history, rating information

[0780] Specific operation: The server queries the database to obtain the user's purchase history and rating information, and sends it to the generation AI.

[0781] Step 4: Analyze the data

[0782] The generation AI analyzes the purchase history and review information it receives to identify the user's interests and needs.

[0783] Input: purchase history, evaluation information

[0784] Output: Analysis results (interests and needs)

[0785] How it works: Generative AI uses machine learning algorithms to analyze past purchase and review data to identify users' interests and needs.

[0786] Step 5: Product proposal

[0787] Based on the analysis results, the generative AI will list the most suitable products for the user.

[0788] Input: Analysis results (interests and needs)

[0789] Output: Suggested product list

[0790] How it works: Generative AI uses pre-trained models to suggest products that match the user's interests and needs.

[0791] Step 6: Collect emotion data

[0792] The device activates a webcam and microphone to capture the user's facial expressions and voice.

[0793] Input: User's facial expression data, voice data

[0794] Output: Emotion data

[0795] Specific operation: The device transmits the user's facial expression data and voice data to the server in real time via the webcam and microphone.

[0796] Step 7: Sentiment Analysis

[0797] The emotion engine analyzes the collected facial expression and voice data to recognize the user's emotions.

[0798] Input: facial expression data, voice data

[0799] Output: Real-time emotion data

[0800] Specific operation: The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize the user's emotional state.

[0801] Step 8: Adjust the product offering

[0802] The generative AI adjusts the suggested product list based on real-time emotional data.

[0803] Input: Real-time emotion data, suggested product list

[0804] Output: Adjusted suggested product list

[0805] Specific operation: The generative AI takes into account the emotional data, reevaluates the selection of products to suggest, and adds or removes products from the list that are appropriate for the user's emotional state.

[0806] Step 9: Enter your starting point and transportation

[0807] The user inputs the starting point and mode of transportation into the terminal and searches for the name of the store they want to visit.

[0808] Input: Starting point, transportation method, store name

[0809] Output: Starting point information, transportation information, store name

[0810] Specific operation: The terminal sends input data from the user to the server.

[0811] Step 10: Route calculation

[0812] The server calculates the route using a geographic information system (GIS) based on the starting point information, transportation information, and store names.

[0813] Input: Starting point information, transportation information, store name

[0814] Output: Optimal route information

[0815] Specific operation: The server uses GIS to calculate the optimal route from the specified starting point to the nearest store.

[0816] Step 11: Viewing Information

[0817] The server transmits the calculated optimum route information and the adjusted suggested product list to the user's terminal.

[0818] Input: Optimal route information, adjusted suggested product list

[0819] Output: Data for display

[0820] Specific operation: The terminal displays the received information on the user interface.

[0821] This allows users to receive personalized product suggestions and efficient store guidance, resulting in a highly satisfying shopping experience.

[0822] (Application example 2)

[0823] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0824] Modern shopping experiences are becoming more diverse, and personalized product recommendations based on users' purchase history and ratings are becoming increasingly important. However, existing systems have not been able to adequately consider users' real-time emotions when making product recommendations, and they have not provided smooth guidance to physical stores. This has led to challenges that make it difficult for users to have a satisfying shopping experience.

[0825] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating user login information, means for acquiring the user's purchase history and rating information, means for analyzing the acquired purchase history and rating information and identifying the user's interests and needs, means for recognizing the user's emotions, means for suggesting optimal products to the user based on the analysis results and emotion recognition, means for receiving the starting point and transportation means specified by the user and calculating the nearest store and route thereto, and means for providing the calculated store and route information to the user. This enables highly accurate product suggestions that take into account the user's real-time emotions and efficient route guidance to physical stores.

[0826] "User login information" means authentication information such as a user ID and password entered by a user to access the system.

[0827] "Purchase history" is a collection of data on products purchased by a user in the past.

[0828] "Rating information" is data relating to ratings and reviews given by users on products they have purchased in the past.

[0829] "Analysis" is the process of using acquired purchase history and rating information to identify user interests and needs.

[0830] "Emotion recognition" is a technology that analyzes a user's facial expressions, tone of voice, etc. to determine their emotional state in real time.

[0831] "Optimal product proposal" means proposing the most suitable product for the user based on the user's purchasing history, evaluation information, and emotion recognition results.

[0832] The "starting point" is the starting point designated by the user.

[0833] "Transportation" refers to the means of transportation used by the user (e.g., walking, driving, public transportation).

[0834] The "nearest store" is the physical store closest to the user's current location or the specified starting point.

[0835] "Route calculation" refers to calculating the optimal route from a starting point to a destination specified by the user.

[0836] "Route guidance" refers to providing calculated route information to a user and showing them how to get to their destination.

[0837] The present invention relates to a system that uses a user's login information, purchase history, and rating information to suggest the most suitable products to the user and guides the user to the nearest route to the store they want to visit. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[0838] The system works as follows: The process begins when a user logs in to the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0839] The server then sends the acquired purchase history and evaluation information to the generation AI, which then analyzes this data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the generation AI will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0840] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a camera is used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, when using voice input, the tone of the user's voice can be analyzed to similarly recognize emotions.

[0841] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[0842] Furthermore, users can search for store names by entering their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system. The server receives this input information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions. The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the presented information or visit a physical store.

[0843] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0844] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[0845] Example prompts for generative AI models:

[0846] User purchase history:

[0847] Smartphone: 5 times

[0848] Laptop: 3 times

[0849] Wireless earphones: 2 times

[0850] User rating information:

[0851] Smartphone: ★★★★☆

[0852] Laptop: ★★★☆☆

[0853] Wireless Earphones: ★★★★☆

[0854] Current emotional state: Positive

[0855] Products suggested to users:

[0856] 1. The latest smartwatches

[0857] 2. High-quality protective cases for smartphones

[0858] 3. Wireless Charger

[0859] This approach allows users to receive consistent, personalized service and a highly satisfying shopping experience.

[0860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0861] Step 1:

[0862] A user logs into the system from a terminal. The user enters their user ID and password, and this login information is sent to the server. The server receives the login information and authenticates the user. It returns a success message to the identified user.

[0863] Step 2:

[0864] When a user successfully logs in, the server retrieves the user's purchase history and rating information. To retrieve this information, the server queries the database based on the user ID and retrieves the relevant purchase history and rating information. The retrieved data is then sent to the user's device.

[0865] Step 3:

[0866] The server sends the acquired purchase history and rating information to the generation AI. The generation AI analyzes this data to identify the user's interests and needs. Specifically, it estimates the user's preferences based on past purchase types, product ratings, and purchase frequency. The analysis results are generated and sent back to the server.

[0867] Step 4:

[0868] The server uses the device's camera and microphone to recognize the user's emotions. The device sends facial expression data from the camera and voice data from the microphone to the server. The server inputs this data into an emotion recognition engine and analyzes the user's current emotional state. The analysis results are sent back to the server.

[0869] Step 5:

[0870] The server combines the analysis results of the generative AI with those of the emotion recognition engine. Based on this, it selects the most suitable products for the user and generates a list of products. For example, if the user is in a positive mood, it will suggest products that stimulate purchasing desire, such as related accessories. Conversely, if the user is in a negative mood, it will suggest products that will help them relax.

[0871] Step 6:

[0872] The user inputs the starting point and transportation method from the terminal. This information is sent to the server. Based on the input information, the server uses a geographic information system (GIS) to calculate the nearest store and its route. The calculated route includes factors such as travel time and traffic conditions.

[0873] Step 7:

[0874] The server sends the calculated route guidance and recommended product information to the user's device. The device displays this information, and the user can check the presented products and proceed with the purchase if interested. The user can also visit a physical store based on the route guidance to the nearest store.

[0875] Step 8:

[0876] When a user selects from the list of products presented, the user's selection information is sent to the server, which stores it in a database as purchase history and rating information for the next purchase and uses it to improve the accuracy of future suggestions.

[0877] This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0878] 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.

[0879] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0894] ---

[0895] The system of the present invention utilizes the user's login information, purchase history, and rating information to suggest the most suitable products for the user and guide the user to the nearest route to the store they want to visit, thereby providing a more personalized and efficient shopping experience.

[0896] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[0897] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[0898] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[0899] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[0900] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[0901] The main feature of this system is that it proposes products tailored to each user's individual preferences, and also uses location information to provide optimal routes and store information, allowing users to easily find products that match their interests and needs, and facilitating smooth visits to physical stores.

[0902] ---

[0903] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[0904] The processing flow will be explained below.

[0905] ---

[0906] Step 1:

[0907] A user logs in to the system.

[0908] The user enters their account information (user ID and password) and presses the login button.

[0909] The terminal transmits the login information entered by the user to the authentication server.

[0910] Step 2:

[0911] The server authenticates the login information.

[0912] The server queries the authentication database to verify the consistency of the entered ID and password.

[0913] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[0914] Step 3:

[0915] The server acquires the user's purchase history and rating information.

[0916] The server retrieves past purchase history and rating information from the database based on the user's ID.

[0917] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[0918] Step 4:

[0919] The acquired data is sent to the generation AI.

[0920] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[0921] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[0922] Step 5:

[0923] Generative AI analyzes the data.

[0924] The generative AI analyzes the received data to identify the user's interests and needs.

[0925] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[0926] Step 6:

[0927] Generative AI creates a list of recommended products.

[0928] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[0929] Recommended products include new products and products from brands that are highly rated by users.

[0930] Step 7:

[0931] The server sends a list of recommended products to the terminal.

[0932] The server sends the recommended product list received from the generation AI to the user's device.

[0933] The user's terminal displays the received list on the screen.

[0934] Step 8:

[0935] The user inputs the starting point, transportation method, and store name.

[0936] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[0937] Once you have completed the input, press the search button.

[0938] Step 9:

[0939] The server calculates store and route information.

[0940] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[0941] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[0942] Step 10:

[0943] The server provides the calculation results to the user.

[0944] The server transmits the received route guidance information and store information to the user's terminal.

[0945] The user's terminal displays this and provides route guidance and store information to the user.

[0946] Step 11:

[0947] The user decides on the action.

[0948] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[0949] ---

[0950] The above is a description of the process flow of the system broken down into specific steps, which will enable a detailed understanding of the process flow for implementing the present invention.

[0951] Example 1

[0952] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0953] In modern society, it is important to make personalized product suggestions that meet the diverse purchasing needs of users. However, conventional systems are unable to effectively utilize users' purchase history and rating information, resulting in insufficient product suggestions that match individual preferences. Furthermore, when visiting a store, optimal route guidance is not provided, resulting in an inefficient shopping experience for users.

[0954] 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.

[0955] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and rating information, means for transmitting the acquired purchase history and rating information to a generative AI model and identifying the user's interests and needs, means for proposing optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and its route using a geographic information system, and means for providing the calculated store and route information to the user. This allows the user to receive personalized product suggestions and enjoy efficient route guidance to make a comfortable and smooth visit to a physical store.

[0956] "User login information" means the authentication information (e.g., user ID and password) entered by a user to access the system.

[0957] "Means of authentication" refers to the process and function of verifying the login information entered by the user and confirming that the user is a legitimate user.

[0958] "User purchase history" is data on a list of products that the user has purchased in the past and related information.

[0959] "Rating information" is information including the details of ratings and reviews given by users on products they have purchased in the past.

[0960] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and uses the results to identify user interests and needs.

[0961] "Geographic Information System (GIS)" refers to systems and methods for collecting, managing, and analyzing geographic data.

[0962] The "nearest store" refers to the store located closest to the starting point designated by the user.

[0963] The "means for calculating a route" refers to the process and function for calculating the optimal route from the starting point specified by the user to the store, which is the destination.

[0964] The system of the present invention utilizes a user's login information, purchase history, and rating information to suggest optimal products to the user and also provides route guidance to the nearest store. A specific embodiment of this system will be described below.

[0965] The system is configured as follows:

[0966] 1. Receiving and authenticating login information:

[0967] The user enters the user ID and password on the login screen.

[0968] The server receives the entered login information and authenticates the user.

[0969] If the authentication is successful, the home page is displayed on the user's terminal.

[0970] 2. Obtaining user data:

[0971] The server accesses the database to obtain the user's purchase history and rating information.

[0972] The acquired data is temporarily stored on the server.

[0973] 3. Data transmission and analysis:

[0974] The server sends the acquired purchase history and rating information to the generative AI model.

[0975] A generative AI model analyzes this data to identify user interests and needs.

[0976] The analysis results are sent back to the server.

[0977] 4. Product proposal generation:

[0978] The server will suggest the best products to the user based on the analysis results from the generated AI.

[0979] The proposed product list is sent to the user terminal and displayed on the terminal.

[0980] 5. Store Locator and Route Guidance:

[0981] To search for a store the user wants to visit, they enter a starting point (e.g., home address) and a mode of transportation (e.g., car, walking, public transportation).

[0982] The server uses a geographic information system (GIS) to search for the nearest store.

[0983] The server calculates the nearest store and the optimal route and sends it to the user's device.

[0984] 6. Display and Use of Information:

[0985] The recommended product list and route guidance sent from the server are displayed on the user's terminal.

[0986] Based on this information, users decide to select and purchase products or visit a physical store.

[0987] Hardware and Software

[0988] The system uses the following hardware and software:

[0989] Server: Includes database management system (e.g. MySQL) and authentication system.

[0990] Generative AI models: Data analysis models (e.g., machine learning models using TensorFlow or PyTorch).

[0991] Geographic Information System (GIS): Map data provision services (e.g., Google Maps API).

[0992] User devices: smartphones, tablets, PCs, etc.

[0993] Specific examples

[0994] For example, if a user searches for a certain electronics retailer near Shinjuku Station, the server will suggest stores near Shinjuku Station and the optimal walking route. It will also display a list of recommended electronics products based on the user's past purchase data and ratings. The user can use the displayed route guidance to travel efficiently to their destination and consider the presented products.

[0995] Prompt Sentence Examples

[0996] "Please suggest the latest technology models that might interest me based on my past purchase history and reviews. Also, please calculate the shortest route from my home to the electronics store of my choice."

[0997] The above is a specific embodiment of the present invention. By using this system, users can receive personalized product suggestions and efficient store guidance, resulting in a more satisfying shopping experience.

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] Enter and authenticate your login information

[1001] User input: The user enters their user ID and password on the login screen.

[1002] Data processing and calculation: The server receives the input information and queries the database to verify it.

[1003] Output: Obtain the matching result, and if authentication is successful, send the user's homepage information to the user's device.

[1004] Specific operation: The user submits the input information, and the server obtains the authentication result and returns it to the user.

[1005] Step 2:

[1006] Retrieving User Data

[1007] Input: Session information for a successfully authenticated user.

[1008] Data processing and calculation: The server accesses the database and obtains the user's purchase history and rating information.

[1009] Output: The acquired purchase history and rating information is temporarily stored on the server.

[1010] Specific operation: The server issues a query to the database based on the session information to retrieve relevant data.

[1011] Step 3:

[1012] Data transmission and analysis

[1013] Input: Retrieved purchase history and rating information.

[1014] Data processing and calculation: The server sends this data to the generative AI model, which then analyzes the data.

[1015] Output: Analysis results that identify user interests and needs.

[1016] Specific operation: The server sends purchase history and rating information to the generative AI model, which then analyzes the data and returns the results.

[1017] Step 4:

[1018] Generate product suggestions

[1019] Input: Analysis results of the generative AI model.

[1020] Data processing and calculation: The server selects the most suitable products for the user based on the analysis results and generates a list of recommended products.

[1021] Output: Sends the recommended product list to the user's device.

[1022] Specific operation: The server generates a list of recommended products and sends it to the user's device for display.

[1023] Step 5:

[1024] Store search and route guidance

[1025] Input: User-specified starting point and mode of transportation.

[1026] Data processing and calculation: The server sends this information to the geographic information system, which calculates the nearest store and its route.

[1027] Output: Gets the nearest store information and route directions and sends them back to the server.

[1028] Specific operation: The user inputs the starting point and mode of transportation, and the server works with the GIS to calculate the optimal route.

[1029] Step 6:

[1030] Display and Use of Information

[1031] Input: Recommended product list and directions sent from the server.

[1032] Data processing and calculation: The user terminal receives this information and displays it on the screen.

[1033] Output: Based on the displayed information, the user decides to select a product, purchase it, or visit a physical store.

[1034] Specific operation: The user's device displays recommended products and route guidance, and the user acts based on these.

[1035] (Application example 1)

[1036] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1037] Conventional shopping systems rarely make full use of users' purchase history and rating information to make product suggestions, resulting in the problem of not being able to make optimal product suggestions tailored to each user's individual preferences. Furthermore, systems that provide optimal route guidance when users visit physical stores are also inadequate, resulting in users often wasting time before reaching the store. To solve these problems, a system is needed that analyzes each user's data, accurately suggests products, and provides guidance on the optimal route to the physical store.

[1038] 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.

[1039] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and review information, means for analyzing the acquired purchase history and review information to identify the user's interests and needs, means for suggesting optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and route thereto, means for providing the calculated store and route information to the user, means for generating a product suggestion prompt using a generative artificial intelligence model, and means for calculating a route to the nearest store using a geographic information system. This enables optimal product suggestions tailored to each user's individual preferences and efficient store guidance.

[1040] "Means for receiving and authenticating user login information" refers to means for receiving the user ID and password required when a user accesses the system and for authenticating the user based on that information.

[1041] The "means for acquiring user purchase history and evaluation information" refers to a means for accumulating products purchased by users in the past and their evaluations, and acquiring that data.

[1042] The "means for analyzing the acquired purchase history and evaluation information to identify the user's interests and needs" is a means for analyzing the user's past purchase history and evaluation information to clarify the user's preferences and interests.

[1043] "Means for proposing optimal products to users based on analysis results" refers to means for selecting and recommending products that are suitable for users based on analyzed data.

[1044] "Means for receiving the starting point and transportation means specified by the user and calculating the nearest store and its route" refers to means for receiving the starting point and transportation means entered by the user and calculating the optimal store and route accordingly.

[1045] The "means for providing the user with calculated store and route information" is a means for displaying the calculated store location and the optimal route to reach the store to the user.

[1046] "Means for generating prompt sentences for product proposals using a generative artificial intelligence model" refers to means for using generative AI to create prompt sentences for product proposals based on user data.

[1047] "Means for calculating the route to the nearest store using a geographic information system" refers to a means for using a geographic information system (GIS) to calculate the optimal route from a starting point specified by the user to a destination (store).

[1048] The system of the present invention utilizes a user's login information, purchase history, and rating information to provide individually customized product suggestions and optimal route guidance to the nearest physical store. The main means for realizing this system include login authentication, data acquisition, data analysis, product suggestions, route calculation, prompt generation using generation AI, and route calculation using a geographic information system (GIS).

[1049] First, the server receives and authenticates the user's login information. This process is done using the user ID and password, and if authentication is successful, the server retrieves the purchase history and rating information associated with the user's account.

[1050] The server then sends the acquired purchase history and review information to the generative AI model, which analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased technology-related products such as smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1051] Based on the results of this analysis, the generative AI model generates prompts for product recommendations. For example, the following prompts are generated:

[1052] Based on the user's data: {'Purchase history': ['Smartphone', 'Laptop', 'Wireless earphones'], 'Rating information': {'Smartphone': 5, 'Laptop': 4}}, please suggest the following products:

[1053] Furthermore, users can input their starting point and mode of transportation through the system. The server receives this information and calculates the nearest store and its route using a geographic information system (GIS). For example, if a user searches for "electronics retailer" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. This allows users to visit stores efficiently.

[1054] The system uses the following main hardware and software:

[1055] requests: A library for API communication between the server and external services

[1056] geopy: A library for geographical calculations

[1057] openai: A library for using generative AI models

[1058] The introduction of this system allows users to experience personalized product suggestions and efficient store visits, saving time and improving the shopping experience. In this way, users can easily find products that match their interests and needs, and can also smoothly visit physical stores.

[1059] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1060] Step 1:

[1061] The server receives the user's login information (user ID, password) and performs authentication. Specifically, the server sends the login information sent by the user to the authentication API and receives the authentication result. The input is the user ID and password, and the output is the authentication result (success or failure).

[1062] Step 2:

[1063] If authentication is successful, the server obtains the user's purchase history and rating information. Specifically, the server uses the authenticated user ID to obtain the user's purchase history data and rating data from the database. The input is the user ID, and the output is the purchase history and rating information.

[1064] Step 3:

[1065] The server sends the acquired purchase history and review information to the generative AI model to identify the user's interests and needs. Specifically, the server converts the user's data into prompt sentences for the generative AI model and sends them to the generative AI model to obtain analysis results. The input is the purchase history and review information, and the output is the analysis results that indicate the user's interests and needs.

[1066] Step 4:

[1067] The server uses a generative AI model to generate a product suggestion prompt based on the analysis results. Specifically, the server inputs the user's analysis results into the generative AI model, which then generates a product suggestion prompt. The input is the user's analysis results, and the output is the product suggestion prompt.

[1068] Step 5:

[1069] The server sends the generated prompt sentence to the generative AI model to obtain a list of recommended products. Specifically, the server sends a request to the generative AI model using the prompt sentence and receives a list of recommended products. The input is the prompt sentence, and the output is the list of recommended products.

[1070] Step 6:

[1071] The user inputs the starting point and the mode of transportation. The terminal receives the starting point (e.g., address, location information) and the mode of transportation (e.g., car, walking, public transportation) from the user. The input is the starting point and the mode of transportation, and the output is this information.

[1072] Step 7:

[1073] The server receives the starting point and mode of transportation specified by the user and calculates the nearest store and its route using a geographic information system (GIS). Specifically, the server uses GIS to calculate the optimal route from the starting point to the destination (store). The input is the starting point and mode of transportation, and the output is the optimal route.

[1074] Step 8:

[1075] The server provides the calculated store and route information to the terminal. Specifically, the server sends the calculated route and information on the nearest store to the user's terminal and displays it. The input is the optimal route, and the output is the route information displayed on the user's terminal.

[1076] Step 9:

[1077] The user visits the specified store based on the route guidance and recommended product list they received and makes a purchase. Since this is a user action, there are no inputs or outputs to the system.

[1078] The above is a detailed explanation of the processing steps in the "Personal Shopping Guide" system of the present invention. In this way, users can efficiently receive optimal product suggestions and route guidance to physical stores.

[1079] 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.

[1080] ---

[1081] The present invention relates to a system that provides a more personalized and efficient shopping experience by suggesting optimal products to users and guiding them on the nearest route to stores they want to visit, using the user's login information, purchase history, and rating information. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[1082] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[1083] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1084] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a webcam can be used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, in the case of voice input, the user's tone of voice can be analyzed to similarly recognize emotions.

[1085] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[1086] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[1087] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[1088] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1089] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[1090] ---

[1091] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[1092] The processing flow will be explained below.

[1093] ---

[1094] Step 1:

[1095] A user logs in to the system.

[1096] The user enters their account information (user ID and password) and presses the login button.

[1097] The terminal transmits the login information entered by the user to the authentication server.

[1098] Step 2:

[1099] The server authenticates the login information.

[1100] The server queries the authentication database to verify the consistency of the entered ID and password.

[1101] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[1102] Step 3:

[1103] The server acquires the user's purchase history and rating information.

[1104] The server retrieves past purchase history and rating information from the database based on the user's ID.

[1105] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[1106] Step 4:

[1107] The acquired data is sent to the generation AI.

[1108] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[1109] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[1110] Step 5:

[1111] Generative AI analyzes the data.

[1112] The generative AI analyzes the received data to identify the user's interests and needs.

[1113] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[1114] Step 6:

[1115] Generative AI creates a list of recommended products.

[1116] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[1117] Recommended products include new products and products from brands that are highly rated by users.

[1118] Step 7:

[1119] The emotion engine recognizes the user's real-time emotions.

[1120] It uses the camera and microphone connected to the user's device to capture facial expressions and tone of voice.

[1121] The emotion engine analyzes the collected data and identifies the user's emotional state (e.g., happy, excited, sad).

[1122] Step 8:

[1123] The emotion engine's recognition results are fed back to the generative AI.

[1124] The emotion engine sends the recognized emotion data to the generation AI.

[1125] The generative AI takes this emotional data into account to refine the recommended product list.

[1126] Step 9:

[1127] The server transmits the recommended product list to the user terminal.

[1128] The server sends the final recommended product list received from the generation AI to the user's device.

[1129] The user's terminal displays the received list on the screen.

[1130] Step 10:

[1131] The user inputs the starting point, transportation method, and store name.

[1132] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[1133] Once you have completed the input, press the search button.

[1134] Step 11:

[1135] The server calculates store and route information.

[1136] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[1137] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[1138] Step 12:

[1139] The server provides the calculation results to the user.

[1140] The server transmits the received route guidance information and store information to the user's terminal.

[1141] The user's terminal displays this and provides route guidance and store information to the user.

[1142] Step 13:

[1143] The user decides on the action.

[1144] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[1145] ---

[1146] The above is a description of the process flow of a system that combines emotion engines, broken down into specific steps, which will enable a detailed understanding of the process for implementing the present invention.

[1147] Example 2

[1148] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1149] In today's shopping environment, users have difficulty finding the right product among the vast number of products available. Furthermore, there is a lack of mechanisms to accurately understand users' interests and needs and suggest appropriate products in real time. Furthermore, there are no systems that suggest products based on the user's emotional state or provide efficient route guidance to the store they want to visit. This makes it difficult for users to have a satisfying shopping experience.

[1150] 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.

[1151] In this invention, the server includes means for receiving and authenticating login information, means for acquiring purchase history and user evaluation information, means for analyzing the acquired purchase history and user evaluation information to identify interests and needs, means for suggesting optimal products, means for recognizing emotions in real time by analyzing the user's facial expressions, tone of voice, or text content, means for adjusting the suggested products in consideration of the emotions, means for receiving a specified starting point and transportation means, and calculating the nearest store and the optimal route thereto, and means for providing the calculated store and route information to the user. This enables product suggestions and efficient store guidance that take into consideration the user's individual preferences and real-time emotions.

[1152] "Login information" refers to information used to identify and authenticate a user, and typically refers to a combination of a user ID and password.

[1153] "Authentication" is the process of verifying whether the login information provided is correct and authenticating the user.

[1154] "Purchase history" is a record of products purchased by a user in the past, and includes data such as purchase date, product name, and price.

[1155] "User evaluation information" is information about evaluations and reviews made by users on products they have purchased in the past.

[1156] "Generative AI" is a type of artificial intelligence that uses machine learning to analyze input data and perform specific tasks.

[1157] An "emotion engine" is a technology that recognizes emotions by analyzing a user's facial expressions, tone of voice, or text content.

[1158] The "starting point" is information about the location from which the user departs, and typically refers to an address or place name.

[1159] "Transportation" refers to the method a user uses to travel, including car, walking, public transportation, etc.

[1160] The "nearest store" refers to the store located closest to the starting point specified by the user.

[1161] An "optimal route" is the most efficient and quickest route from a starting point to a destination.

[1162] A "geographic information system (GIS)" is an information system for managing geographic data and performing spatial analysis, and is used for route calculations.

[1163] "Real time" means that processing is executed immediately the moment the user performs an operation.

[1164] "Product suggestion" refers to selecting and offering products that are deemed optimal based on the user's interests, needs, and emotional state.

[1165] "Feedback" is the process of reflecting the data and recognition results acquired by the system in the next processing or proposal.

[1166] MODE FOR CARRYING OUT THE INVENTION

[1167] This invention relates to a system that uses a user's login information, purchase history, and rating information to suggest optimal products to the user and also guides them on an efficient route from a specified starting point to the nearest store. This system incorporates a generative AI model and an emotion engine to realize product suggestions that take into account the user's interests, needs, and real-time emotional state.

[1168] First, a user logs in to the system by entering their account information into a terminal. The user's account information (user ID and password) is sent to the server, which receives it and performs authentication. If authentication is successful, the server retrieves the purchase history and rating information associated with the user's account from the database.

[1169] The server then sends the acquired purchase history and review information to the generative AI model. The generative AI model analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased and reviewed smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology-related products and suggest the latest smartphone models and related accessories.

[1170] The system also incorporates an emotion engine, which can recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, the system uses a webcam to analyze their facial expressions to determine whether they are excited or interested. In addition, when using voice input, the system analyzes the user's tone of voice to similarly recognize emotions.

[1171] The emotion data recognized by the emotion engine is fed back to the generative AI model, which then adjusts the product recommendations accordingly. For example, if the user is feeling depressed, it will suggest products and services that help them relax, and if they are excited, it will suggest products related to active activities.

[1172] Furthermore, users can enter their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system and search for the name of the store they want to visit. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions.

[1173] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the information presented or visit a physical store.

[1174] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1175] Examples of prompt statements

[1176] User ID: user1234

[1177] Password: password5678

[1178] Purchase History:

[1179] January 2023: Smartphones

[1180] March 2023: Laptop

[1181] May 2023: Wireless earphones

[1182] Evaluation Information:

[1183] Smartphone: Highly rated

[1184] Laptop: Medium rating

[1185] Wireless earphones: highly rated

[1186] Starting point: Shinjuku Station

[1187] Transportation: Walking

[1188] Store to visit: A certain electronics retail store

[1189] Using this system, users can receive product suggestions that take into account their individual preferences and real-time emotions, providing a satisfying shopping experience through efficient store navigation.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Program processing flow

[1192] Step 1: Enter your login details

[1193] The user enters their user ID and password on the login screen.

[1194] Input: User ID, Password

[1195] Output: Login information

[1196] Specific operation: The terminal sends the login information entered by the user to the server.

[1197] Step 2: User authentication

[1198] The server receives the login information and authenticates it against a database.

[1199] Input: Login information

[1200] Output: Authentication result (success / failure)

[1201] Specific operation: The server uses the database to check whether the user ID and password match and returns the authentication result to the terminal.

[1202] Step 3: Obtain purchase history and rating information

[1203] The server obtains the purchase history and rating information of the successfully authenticated user from the database.

[1204] Input: User ID

[1205] Output: Purchase history, rating information

[1206] Specific operation: The server queries the database to obtain the user's purchase history and rating information, and sends it to the generation AI.

[1207] Step 4: Analyze the data

[1208] The generation AI analyzes the purchase history and review information it receives to identify the user's interests and needs.

[1209] Input: purchase history, evaluation information

[1210] Output: Analysis results (interests and needs)

[1211] How it works: Generative AI uses machine learning algorithms to analyze past purchase and review data to identify users' interests and needs.

[1212] Step 5: Product proposal

[1213] Based on the analysis results, the generative AI will list the most suitable products for the user.

[1214] Input: Analysis results (interests and needs)

[1215] Output: Suggested product list

[1216] How it works: Generative AI uses pre-trained models to suggest products that match the user's interests and needs.

[1217] Step 6: Collect emotion data

[1218] The device activates a webcam and microphone to capture the user's facial expressions and voice.

[1219] Input: User's facial expression data, voice data

[1220] Output: Emotion data

[1221] Specific operation: The device transmits the user's facial expression data and voice data to the server in real time via the webcam and microphone.

[1222] Step 7: Sentiment Analysis

[1223] The emotion engine analyzes the collected facial expression and voice data to recognize the user's emotions.

[1224] Input: facial expression data, voice data

[1225] Output: Real-time emotion data

[1226] Specific operation: The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize the user's emotional state.

[1227] Step 8: Adjust the product offering

[1228] The generative AI adjusts the suggested product list based on real-time emotional data.

[1229] Input: Real-time emotion data, suggested product list

[1230] Output: Adjusted suggested product list

[1231] Specific operation: The generative AI takes into account the emotional data, reevaluates the selection of products to suggest, and adds or removes products from the list that are appropriate for the user's emotional state.

[1232] Step 9: Enter your starting point and transportation

[1233] The user inputs the starting point and mode of transportation into the terminal and searches for the name of the store they want to visit.

[1234] Input: Starting point, transportation method, store name

[1235] Output: Starting point information, transportation information, store name

[1236] Specific operation: The terminal sends input data from the user to the server.

[1237] Step 10: Route calculation

[1238] The server calculates the route using a geographic information system (GIS) based on the starting point information, transportation information, and store names.

[1239] Input: Starting point information, transportation information, store name

[1240] Output: Optimal route information

[1241] Specific operation: The server uses GIS to calculate the optimal route from the specified starting point to the nearest store.

[1242] Step 11: Viewing Information

[1243] The server transmits the calculated optimum route information and the adjusted suggested product list to the user's terminal.

[1244] Input: Optimal route information, adjusted suggested product list

[1245] Output: Data for display

[1246] Specific operation: The terminal displays the received information on the user interface.

[1247] This allows users to receive personalized product suggestions and efficient store guidance, resulting in a highly satisfying shopping experience.

[1248] (Application example 2)

[1249] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1250] Modern shopping experiences are becoming more diverse, and personalized product recommendations based on users' purchase history and ratings are becoming increasingly important. However, existing systems have not been able to adequately consider users' real-time emotions when making product recommendations, and they have not provided smooth guidance to physical stores. This has led to challenges that make it difficult for users to have a satisfying shopping experience.

[1251] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating user login information, means for acquiring the user's purchase history and rating information, means for analyzing the acquired purchase history and rating information and identifying the user's interests and needs, means for recognizing the user's emotions, means for suggesting optimal products to the user based on the analysis results and emotion recognition, means for receiving the starting point and transportation means specified by the user and calculating the nearest store and route thereto, and means for providing the calculated store and route information to the user. This enables highly accurate product suggestions that take into account the user's real-time emotions and efficient route guidance to physical stores.

[1252] "User login information" means authentication information such as a user ID and password entered by a user to access the system.

[1253] "Purchase history" is a collection of data on products purchased by a user in the past.

[1254] "Rating information" is data relating to ratings and reviews given by users on products they have purchased in the past.

[1255] "Analysis" is the process of using acquired purchase history and rating information to identify user interests and needs.

[1256] "Emotion recognition" is a technology that analyzes a user's facial expressions, tone of voice, etc. to determine their emotional state in real time.

[1257] "Optimal product proposal" means proposing the most suitable product for the user based on the user's purchasing history, evaluation information, and emotion recognition results.

[1258] The "starting point" is the starting point designated by the user.

[1259] "Transportation" refers to the means of transportation used by the user (e.g., walking, driving, public transportation).

[1260] The "nearest store" is the physical store closest to the user's current location or the specified starting point.

[1261] "Route calculation" refers to calculating the optimal route from a starting point to a destination specified by the user.

[1262] "Route guidance" refers to providing calculated route information to a user and showing them how to get to their destination.

[1263] The present invention relates to a system that uses a user's login information, purchase history, and rating information to suggest the most suitable products to the user and guides the user to the nearest route to the store they want to visit. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[1264] The system works as follows: The process begins when a user logs in to the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[1265] The server then sends the acquired purchase history and evaluation information to the generation AI, which then analyzes this data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the generation AI will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1266] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a camera is used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, when using voice input, the tone of the user's voice can be analyzed to similarly recognize emotions.

[1267] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[1268] Furthermore, users can search for store names by entering their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system. The server receives this input information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions. The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the presented information or visit a physical store.

[1269] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1270] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[1271] Example prompts for generative AI models:

[1272] User purchase history:

[1273] Smartphone: 5 times

[1274] Laptop: 3 times

[1275] Wireless earphones: 2 times

[1276] User rating information:

[1277] Smartphone: ★★★★☆

[1278] Laptop: ★★★☆☆

[1279] Wireless Earphones: ★★★★☆

[1280] Current emotional state: Positive

[1281] Products suggested to users:

[1282] 1. The latest smartwatches

[1283] 2. High-quality protective cases for smartphones

[1284] 3. Wireless Charger

[1285] This approach allows users to receive consistent, personalized service and a highly satisfying shopping experience.

[1286] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1287] Step 1:

[1288] A user logs into the system from a terminal. The user enters their user ID and password, and this login information is sent to the server. The server receives the login information and authenticates the user. It returns a success message to the identified user.

[1289] Step 2:

[1290] When a user successfully logs in, the server retrieves the user's purchase history and rating information. To retrieve this information, the server queries the database based on the user ID and retrieves the relevant purchase history and rating information. The retrieved data is then sent to the user's device.

[1291] Step 3:

[1292] The server sends the acquired purchase history and rating information to the generation AI. The generation AI analyzes this data to identify the user's interests and needs. Specifically, it estimates the user's preferences based on past purchase types, product ratings, and purchase frequency. The analysis results are generated and sent back to the server.

[1293] Step 4:

[1294] The server uses the device's camera and microphone to recognize the user's emotions. The device sends facial expression data from the camera and voice data from the microphone to the server. The server inputs this data into an emotion recognition engine and analyzes the user's current emotional state. The analysis results are sent back to the server.

[1295] Step 5:

[1296] The server combines the analysis results of the generative AI with those of the emotion recognition engine. Based on this, it selects the most suitable products for the user and generates a list of products. For example, if the user is in a positive mood, it will suggest products that stimulate purchasing desire, such as related accessories. Conversely, if the user is in a negative mood, it will suggest products that will help them relax.

[1297] Step 6:

[1298] The user inputs the starting point and transportation method from the terminal. This information is sent to the server. Based on the input information, the server uses a geographic information system (GIS) to calculate the nearest store and its route. The calculated route includes factors such as travel time and traffic conditions.

[1299] Step 7:

[1300] The server sends the calculated route guidance and recommended product information to the user's device. The device displays this information, and the user can check the presented products and proceed with the purchase if interested. The user can also visit a physical store based on the route guidance to the nearest store.

[1301] Step 8:

[1302] When a user selects from the list of products presented, the user's selection information is sent to the server, which stores it in a database as purchase history and rating information for the next purchase and uses it to improve the accuracy of future suggestions.

[1303] This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[1304] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1305] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1306] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1307] [Fourth embodiment]

[1308] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1309] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1310] 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).

[1311] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1316] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1317] 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.

[1318] 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.

[1319] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1320] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1321] ---

[1322] The system of the present invention utilizes the user's login information, purchase history, and rating information to suggest the most suitable products for the user and guide the user to the nearest route to the store they want to visit, thereby providing a more personalized and efficient shopping experience.

[1323] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[1324] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1325] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[1326] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[1327] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1328] The main feature of this system is that it proposes products tailored to each user's individual preferences, and also uses location information to provide optimal routes and store information, allowing users to easily find products that match their interests and needs, and facilitating smooth visits to physical stores.

[1329] ---

[1330] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[1331] The processing flow will be explained below.

[1332] ---

[1333] Step 1:

[1334] A user logs in to the system.

[1335] The user enters their account information (user ID and password) and presses the login button.

[1336] The terminal transmits the login information entered by the user to the authentication server.

[1337] Step 2:

[1338] The server authenticates the login information.

[1339] The server queries the authentication database to verify the consistency of the entered ID and password.

[1340] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[1341] Step 3:

[1342] The server acquires the user's purchase history and rating information.

[1343] The server retrieves past purchase history and rating information from the database based on the user's ID.

[1344] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[1345] Step 4:

[1346] The acquired data is sent to the generation AI.

[1347] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[1348] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[1349] Step 5:

[1350] Generative AI analyzes the data.

[1351] The generative AI analyzes the received data to identify the user's interests and needs.

[1352] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[1353] Step 6:

[1354] Generative AI creates a list of recommended products.

[1355] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[1356] Recommended products include new products and products from brands that are highly rated by users.

[1357] Step 7:

[1358] The server sends a list of recommended products to the terminal.

[1359] The server sends the recommended product list received from the generation AI to the user's device.

[1360] The user's terminal displays the received list on the screen.

[1361] Step 8:

[1362] The user inputs the starting point, transportation method, and store name.

[1363] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[1364] Once you have completed the input, press the search button.

[1365] Step 9:

[1366] The server calculates store and route information.

[1367] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[1368] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[1369] Step 10:

[1370] The server provides the calculation results to the user.

[1371] The server transmits the received route guidance information and store information to the user's terminal.

[1372] The user's terminal displays this and provides route guidance and store information to the user.

[1373] Step 11:

[1374] The user decides on the action.

[1375] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[1376] ---

[1377] The above is a description of the process flow of the system broken down into specific steps, which will enable a detailed understanding of the process flow for implementing the present invention.

[1378] Example 1

[1379] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1380] In modern society, it is important to make personalized product suggestions that meet the diverse purchasing needs of users. However, conventional systems are unable to effectively utilize users' purchase history and rating information, resulting in insufficient product suggestions that match individual preferences. Furthermore, when visiting a store, optimal route guidance is not provided, resulting in an inefficient shopping experience for users.

[1381] 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.

[1382] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and rating information, means for transmitting the acquired purchase history and rating information to a generative AI model and identifying the user's interests and needs, means for proposing optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and its route using a geographic information system, and means for providing the calculated store and route information to the user. This allows the user to receive personalized product suggestions and enjoy efficient route guidance to make a comfortable and smooth visit to a physical store.

[1383] "User login information" means the authentication information (e.g., user ID and password) entered by a user to access the system.

[1384] "Means of authentication" refers to the process and function of verifying the login information entered by the user and confirming that the user is a legitimate user.

[1385] "User purchase history" is data on a list of products that the user has purchased in the past and related information.

[1386] "Rating information" is information including the details of ratings and reviews given by users on products they have purchased in the past.

[1387] A "generative AI model" is an artificial intelligence algorithm that analyzes large amounts of data and uses the results to identify user interests and needs.

[1388] "Geographic Information System (GIS)" refers to systems and methods for collecting, managing, and analyzing geographic data.

[1389] The "nearest store" refers to the store located closest to the starting point designated by the user.

[1390] The "means for calculating a route" refers to the process and function for calculating the optimal route from the starting point specified by the user to the store, which is the destination.

[1391] The system of the present invention utilizes a user's login information, purchase history, and rating information to suggest optimal products to the user and also provides route guidance to the nearest store. A specific embodiment of this system will be described below.

[1392] The system is configured as follows:

[1393] 1. Receiving and authenticating login information:

[1394] The user enters the user ID and password on the login screen.

[1395] The server receives the entered login information and authenticates the user.

[1396] If the authentication is successful, the home page is displayed on the user's terminal.

[1397] 2. Obtaining user data:

[1398] The server accesses the database to obtain the user's purchase history and rating information.

[1399] The acquired data is temporarily stored on the server.

[1400] 3. Data transmission and analysis:

[1401] The server sends the acquired purchase history and rating information to the generative AI model.

[1402] A generative AI model analyzes this data to identify user interests and needs.

[1403] The analysis results are sent back to the server.

[1404] 4. Product proposal generation:

[1405] The server will suggest the best products to the user based on the analysis results from the generated AI.

[1406] The proposed product list is sent to the user terminal and displayed on the terminal.

[1407] 5. Store Locator and Route Guidance:

[1408] To search for a store the user wants to visit, they enter a starting point (e.g., home address) and a mode of transportation (e.g., car, walking, public transportation).

[1409] The server uses a geographic information system (GIS) to search for the nearest store.

[1410] The server calculates the nearest store and the optimal route and sends it to the user's device.

[1411] 6. Display and Use of Information:

[1412] The recommended product list and route guidance sent from the server are displayed on the user's terminal.

[1413] Based on this information, users decide to select and purchase products or visit a physical store.

[1414] Hardware and Software

[1415] The system uses the following hardware and software:

[1416] Server: Includes database management system (e.g. MySQL) and authentication system.

[1417] Generative AI models: Data analysis models (e.g., machine learning models using TensorFlow or PyTorch).

[1418] Geographic Information System (GIS): Map data provision services (e.g., Google Maps API).

[1419] User devices: smartphones, tablets, PCs, etc.

[1420] Specific examples

[1421] For example, if a user searches for a certain electronics retailer near Shinjuku Station, the server will suggest stores near Shinjuku Station and the optimal walking route. It will also display a list of recommended electronics products based on the user's past purchase data and ratings. The user can use the displayed route guidance to travel efficiently to their destination and consider the presented products.

[1422] Prompt Sentence Examples

[1423] "Please suggest the latest technology models that might interest me based on my past purchase history and reviews. Also, please calculate the shortest route from my home to the electronics store of my choice."

[1424] The above is a specific embodiment of the present invention. By using this system, users can receive personalized product suggestions and efficient store guidance, resulting in a more satisfying shopping experience.

[1425] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1426] Step 1:

[1427] Enter and authenticate your login information

[1428] User input: The user enters their user ID and password on the login screen.

[1429] Data processing and calculation: The server receives the input information and queries the database to verify it.

[1430] Output: Obtain the matching result, and if authentication is successful, send the user's homepage information to the user's device.

[1431] Specific operation: The user submits the input information, and the server obtains the authentication result and returns it to the user.

[1432] Step 2:

[1433] Retrieving User Data

[1434] Input: Session information for a successfully authenticated user.

[1435] Data processing and calculation: The server accesses the database and obtains the user's purchase history and rating information.

[1436] Output: The acquired purchase history and rating information is temporarily stored on the server.

[1437] Specific operation: The server issues a query to the database based on the session information to retrieve relevant data.

[1438] Step 3:

[1439] Data transmission and analysis

[1440] Input: Retrieved purchase history and rating information.

[1441] Data processing and calculation: The server sends this data to the generative AI model, which then analyzes the data.

[1442] Output: Analysis results that identify user interests and needs.

[1443] Specific operation: The server sends purchase history and rating information to the generative AI model, which then analyzes the data and returns the results.

[1444] Step 4:

[1445] Generate product suggestions

[1446] Input: Analysis results of the generative AI model.

[1447] Data processing and calculation: The server selects the most suitable products for the user based on the analysis results and generates a list of recommended products.

[1448] Output: Sends the recommended product list to the user's device.

[1449] Specific operation: The server generates a list of recommended products and sends it to the user's device for display.

[1450] Step 5:

[1451] Store search and route guidance

[1452] Input: User-specified starting point and mode of transportation.

[1453] Data processing and calculation: The server sends this information to the geographic information system, which calculates the nearest store and its route.

[1454] Output: Gets the nearest store information and route directions and sends them back to the server.

[1455] Specific operation: The user inputs the starting point and mode of transportation, and the server works with the GIS to calculate the optimal route.

[1456] Step 6:

[1457] Display and Use of Information

[1458] Input: Recommended product list and directions sent from the server.

[1459] Data processing and calculation: The user terminal receives this information and displays it on the screen.

[1460] Output: Based on the displayed information, the user decides to select a product, purchase it, or visit a physical store.

[1461] Specific operation: The user's device displays recommended products and route guidance, and the user acts based on these.

[1462] (Application example 1)

[1463] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1464] Conventional shopping systems rarely make full use of users' purchase history and rating information to make product suggestions, resulting in the problem of not being able to make optimal product suggestions tailored to each user's individual preferences. Furthermore, systems that provide optimal route guidance when users visit physical stores are also inadequate, resulting in users often wasting time before reaching the store. To solve these problems, a system is needed that analyzes each user's data, accurately suggests products, and provides guidance on the optimal route to the physical store.

[1465] 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.

[1466] In this invention, the server includes means for receiving and authenticating a user's login information, means for acquiring the user's purchase history and review information, means for analyzing the acquired purchase history and review information to identify the user's interests and needs, means for suggesting optimal products to the user based on the analysis results, means for receiving a starting point and transportation means specified by the user and calculating the nearest store and route thereto, means for providing the calculated store and route information to the user, means for generating a product suggestion prompt using a generative artificial intelligence model, and means for calculating a route to the nearest store using a geographic information system. This enables optimal product suggestions tailored to each user's individual preferences and efficient store guidance.

[1467] "Means for receiving and authenticating user login information" refers to means for receiving the user ID and password required when a user accesses the system and for authenticating the user based on that information.

[1468] The "means for acquiring user purchase history and evaluation information" refers to a means for accumulating products purchased by users in the past and their evaluations, and acquiring that data.

[1469] The "means for analyzing the acquired purchase history and evaluation information to identify the user's interests and needs" is a means for analyzing the user's past purchase history and evaluation information to clarify the user's preferences and interests.

[1470] "Means for proposing optimal products to users based on analysis results" refers to means for selecting and recommending products that are suitable for users based on analyzed data.

[1471] "Means for receiving the starting point and transportation means specified by the user and calculating the nearest store and its route" refers to means for receiving the starting point and transportation means entered by the user and calculating the optimal store and route accordingly.

[1472] The "means for providing the user with calculated store and route information" is a means for displaying the calculated store location and the optimal route to reach the store to the user.

[1473] "Means for generating prompt sentences for product proposals using a generative artificial intelligence model" refers to means for using generative AI to create prompt sentences for product proposals based on user data.

[1474] "Means for calculating the route to the nearest store using a geographic information system" refers to a means for using a geographic information system (GIS) to calculate the optimal route from a starting point specified by the user to a destination (store).

[1475] The system of the present invention utilizes a user's login information, purchase history, and rating information to provide individually customized product suggestions and optimal route guidance to the nearest physical store. The main means for realizing this system include login authentication, data acquisition, data analysis, product suggestions, route calculation, prompt generation using generation AI, and route calculation using a geographic information system (GIS).

[1476] First, the server receives and authenticates the user's login information. This process is done using the user ID and password, and if authentication is successful, the server retrieves the purchase history and rating information associated with the user's account.

[1477] The server then sends the acquired purchase history and review information to the generative AI model, which analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased technology-related products such as smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1478] Based on the results of this analysis, the generative AI model generates prompts for product recommendations. For example, the following prompts are generated:

[1479] Based on the user's data: {'Purchase history': ['Smartphone', 'Laptop', 'Wireless earphones'], 'Rating information': {'Smartphone': 5, 'Laptop': 4}}, please suggest the following products:

[1480] Furthermore, users can input their starting point and mode of transportation through the system. The server receives this information and calculates the nearest store and its route using a geographic information system (GIS). For example, if a user searches for "electronics retailer" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. This allows users to visit stores efficiently.

[1481] The system uses the following main hardware and software:

[1482] requests: A library for API communication between the server and external services

[1483] geopy: A library for geographical calculations

[1484] openai: A library for using generative AI models

[1485] The introduction of this system allows users to experience personalized product suggestions and efficient store visits, saving time and improving the shopping experience. In this way, users can easily find products that match their interests and needs, and can also smoothly visit physical stores.

[1486] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1487] Step 1:

[1488] The server receives the user's login information (user ID, password) and performs authentication. Specifically, the server sends the login information sent by the user to the authentication API and receives the authentication result. The input is the user ID and password, and the output is the authentication result (success or failure).

[1489] Step 2:

[1490] If authentication is successful, the server obtains the user's purchase history and rating information. Specifically, the server uses the authenticated user ID to obtain the user's purchase history data and rating data from the database. The input is the user ID, and the output is the purchase history and rating information.

[1491] Step 3:

[1492] The server sends the acquired purchase history and review information to the generative AI model to identify the user's interests and needs. Specifically, the server converts the user's data into prompt sentences for the generative AI model and sends them to the generative AI model to obtain analysis results. The input is the purchase history and review information, and the output is the analysis results that indicate the user's interests and needs.

[1493] Step 4:

[1494] The server uses a generative AI model to generate a product suggestion prompt based on the analysis results. Specifically, the server inputs the user's analysis results into the generative AI model, which then generates a product suggestion prompt. The input is the user's analysis results, and the output is the product suggestion prompt.

[1495] Step 5:

[1496] The server sends the generated prompt sentence to the generative AI model to obtain a list of recommended products. Specifically, the server sends a request to the generative AI model using the prompt sentence and receives a list of recommended products. The input is the prompt sentence, and the output is the list of recommended products.

[1497] Step 6:

[1498] The user inputs the starting point and the mode of transportation. The terminal receives the starting point (e.g., address, location information) and the mode of transportation (e.g., car, walking, public transportation) from the user. The input is the starting point and the mode of transportation, and the output is this information.

[1499] Step 7:

[1500] The server receives the starting point and mode of transportation specified by the user and calculates the nearest store and its route using a geographic information system (GIS). Specifically, the server uses GIS to calculate the optimal route from the starting point to the destination (store). The input is the starting point and mode of transportation, and the output is the optimal route.

[1501] Step 8:

[1502] The server provides the calculated store and route information to the terminal. Specifically, the server sends the calculated route and information on the nearest store to the user's terminal and displays it. The input is the optimal route, and the output is the route information displayed on the user's terminal.

[1503] Step 9:

[1504] The user visits the specified store based on the route guidance and recommended product list they received and makes a purchase. Since this is a user action, there are no inputs or outputs to the system.

[1505] The above is a detailed explanation of the processing steps in the "Personal Shopping Guide" system of the present invention. In this way, users can efficiently receive optimal product suggestions and route guidance to physical stores.

[1506] 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.

[1507] ---

[1508] The present invention relates to a system that provides a more personalized and efficient shopping experience by suggesting optimal products to users and guiding them on the nearest route to stores they want to visit, using the user's login information, purchase history, and rating information. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[1509] The system works as follows: The process begins when a user first logs into the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[1510] The server then sends the acquired purchase history and evaluation information to the AI, which analyzes the data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the AI ​​will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1511] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a webcam can be used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, in the case of voice input, the user's tone of voice can be analyzed to similarly recognize emotions.

[1512] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[1513] Additionally, users can search for store names by entering their starting point (e.g., home address) and mode of transportation (e.g., car, walking, public transportation) through the system. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and route, taking into account travel time and traffic conditions.

[1514] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the device. The user can then purchase the product based on the information presented or visit a physical store.

[1515] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1516] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[1517] ---

[1518] The above is an embodiment of the present invention. This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[1519] The processing flow will be explained below.

[1520] ---

[1521] Step 1:

[1522] A user logs in to the system.

[1523] The user enters their account information (user ID and password) and presses the login button.

[1524] The terminal transmits the login information entered by the user to the authentication server.

[1525] Step 2:

[1526] The server authenticates the login information.

[1527] The server queries the authentication database to verify the consistency of the entered ID and password.

[1528] If the authentication is successful, the server starts a session for the user, generates a session ID, and sends it to the terminal.

[1529] Step 3:

[1530] The server acquires the user's purchase history and rating information.

[1531] The server retrieves past purchase history and rating information from the database based on the user's ID.

[1532] The purchase history includes the product name, purchase date, price, etc., and the evaluation information includes evaluation scores and comments from users.

[1533] Step 4:

[1534] The acquired data is sent to the generation AI.

[1535] The server preprocesses the acquired purchase history and evaluation information and sends it to the generation AI.

[1536] Preprocessing involves formatting the data so that it is easy for AI to analyze.

[1537] Step 5:

[1538] Generative AI analyzes the data.

[1539] The generative AI analyzes the received data to identify the user's interests and needs.

[1540] Specifically, patterns are found by clustering purchased product categories and evaluation data.

[1541] Step 6:

[1542] Generative AI creates a list of recommended products.

[1543] Based on the interests and needs identified by AI, the system selects the most suitable products for the user and creates a recommended product list.

[1544] Recommended products include new products and products from brands that are highly rated by users.

[1545] Step 7:

[1546] The emotion engine recognizes the user's real-time emotions.

[1547] It uses the camera and microphone connected to the user's device to capture facial expressions and tone of voice.

[1548] The emotion engine analyzes the collected data and identifies the user's emotional state (e.g., happy, excited, sad).

[1549] Step 8:

[1550] The emotion engine's recognition results are fed back to the generative AI.

[1551] The emotion engine sends the recognized emotion data to the generation AI.

[1552] The generative AI takes this emotional data into account to refine the recommended product list.

[1553] Step 9:

[1554] The server transmits the recommended product list to the user terminal.

[1555] The server sends the final recommended product list received from the generation AI to the user's device.

[1556] The user's terminal displays the received list on the screen.

[1557] Step 10:

[1558] The user inputs the starting point, transportation method, and store name.

[1559] Users enter their starting point (address or current location), mode of transportation (car, walking, public transportation) into the search form, and then enter the name of the store they want to visit.

[1560] Once you have completed the input, press the search button.

[1561] Step 11:

[1562] The server calculates store and route information.

[1563] The server makes a request to a geographic information system (GIS) based on the user's input information and calculates the nearest store and its route.

[1564] The GIS generates the optimal route from the origin to the destination and returns the results to the server.

[1565] Step 12:

[1566] The server provides the calculation results to the user.

[1567] The server transmits the received route guidance information and store information to the user's terminal.

[1568] The user's terminal displays this and provides route guidance and store information to the user.

[1569] Step 13:

[1570] The user decides on the action.

[1571] Users can view the recommended product list provided, select and purchase products that interest them, and efficiently visit the desired store based on route guidance.

[1572] ---

[1573] The above is a description of the process flow of a system that combines emotion engines, broken down into specific steps, which will enable a detailed understanding of the process for implementing the present invention.

[1574] Example 2

[1575] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1576] In today's shopping environment, users have difficulty finding the right product among the vast number of products available. Furthermore, there is a lack of mechanisms to accurately understand users' interests and needs and suggest appropriate products in real time. Furthermore, there are no systems that suggest products based on the user's emotional state or provide efficient route guidance to the store they want to visit. This makes it difficult for users to have a satisfying shopping experience.

[1577] 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.

[1578] In this invention, the server includes means for receiving and authenticating login information, means for acquiring purchase history and user evaluation information, means for analyzing the acquired purchase history and user evaluation information to identify interests and needs, means for suggesting optimal products, means for recognizing emotions in real time by analyzing the user's facial expressions, tone of voice, or text content, means for adjusting the suggested products in consideration of the emotions, means for receiving a specified starting point and transportation means, and calculating the nearest store and the optimal route thereto, and means for providing the calculated store and route information to the user. This enables product suggestions and efficient store guidance that take into consideration the user's individual preferences and real-time emotions.

[1579] "Login information" refers to information used to identify and authenticate a user, and typically refers to a combination of a user ID and password.

[1580] "Authentication" is the process of verifying whether the login information provided is correct and authenticating the user.

[1581] "Purchase history" is a record of products purchased by a user in the past, and includes data such as purchase date, product name, and price.

[1582] "User evaluation information" is information about evaluations and reviews made by users on products they have purchased in the past.

[1583] "Generative AI" is a type of artificial intelligence that uses machine learning to analyze input data and perform specific tasks.

[1584] An "emotion engine" is a technology that recognizes emotions by analyzing a user's facial expressions, tone of voice, or text content.

[1585] The "starting point" is information about the location from which the user departs, and typically refers to an address or place name.

[1586] "Transportation" refers to the method a user uses to travel, including car, walking, public transportation, etc.

[1587] The "nearest store" refers to the store located closest to the starting point specified by the user.

[1588] An "optimal route" is the most efficient and quickest route from a starting point to a destination.

[1589] A "geographic information system (GIS)" is an information system for managing geographic data and performing spatial analysis, and is used for route calculations.

[1590] "Real time" means that processing is executed immediately the moment the user performs an operation.

[1591] "Product suggestion" refers to selecting and offering products that are deemed optimal based on the user's interests, needs, and emotional state.

[1592] "Feedback" is the process of reflecting the data and recognition results acquired by the system in the next processing or proposal.

[1593] MODE FOR CARRYING OUT THE INVENTION

[1594] This invention relates to a system that uses a user's login information, purchase history, and rating information to suggest optimal products to the user and also guides them on an efficient route from a specified starting point to the nearest store. This system incorporates a generative AI model and an emotion engine to realize product suggestions that take into account the user's interests, needs, and real-time emotional state.

[1595] First, a user logs in to the system by entering their account information into a terminal. The user's account information (user ID and password) is sent to the server, which receives it and performs authentication. If authentication is successful, the server retrieves the purchase history and rating information associated with the user's account from the database.

[1596] The server then sends the acquired purchase history and review information to the generative AI model. The generative AI model analyzes this data to identify the user's interests and needs. For example, if the user has previously purchased and reviewed smartphones, laptops, and wireless earphones, the generative AI model will determine that the user is interested in technology-related products and suggest the latest smartphone models and related accessories.

[1597] The system also incorporates an emotion engine, which can recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, the system uses a webcam to analyze their facial expressions to determine whether they are excited or interested. In addition, when using voice input, the system analyzes the user's tone of voice to similarly recognize emotions.

[1598] The emotion data recognized by the emotion engine is fed back to the generative AI model, which then adjusts the product recommendations accordingly. For example, if the user is feeling depressed, it will suggest products and services that help them relax, and if they are excited, it will suggest products related to active activities.

[1599] Furthermore, users can enter their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system and search for the name of the store they want to visit. The server receives this information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions.

[1600] The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the information presented or visit a physical store.

[1601] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1602] Examples of prompt statements

[1603] User ID: user1234

[1604] Password: password5678

[1605] Purchase History:

[1606] January 2023: Smartphones

[1607] March 2023: Laptop

[1608] May 2023: Wireless earphones

[1609] Evaluation Information:

[1610] Smartphone: Highly rated

[1611] Laptop: Medium rating

[1612] Wireless earphones: highly rated

[1613] Starting point: Shinjuku Station

[1614] Transportation: Walking

[1615] Store to visit: A certain electronics retail store

[1616] Using this system, users can receive product suggestions that take into account their individual preferences and real-time emotions, providing a satisfying shopping experience through efficient store navigation.

[1617] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1618] Program processing flow

[1619] Step 1: Enter your login details

[1620] The user enters their user ID and password on the login screen.

[1621] Input: User ID, Password

[1622] Output: Login information

[1623] Specific operation: The terminal sends the login information entered by the user to the server.

[1624] Step 2: User authentication

[1625] The server receives the login information and authenticates it against a database.

[1626] Input: Login information

[1627] Output: Authentication result (success / failure)

[1628] Specific operation: The server uses the database to check whether the user ID and password match and returns the authentication result to the terminal.

[1629] Step 3: Obtain purchase history and rating information

[1630] The server obtains the purchase history and rating information of the successfully authenticated user from the database.

[1631] Input: User ID

[1632] Output: Purchase history, rating information

[1633] Specific operation: The server queries the database to obtain the user's purchase history and rating information, and sends it to the generation AI.

[1634] Step 4: Analyze the data

[1635] The generation AI analyzes the purchase history and review information it receives to identify the user's interests and needs.

[1636] Input: purchase history, evaluation information

[1637] Output: Analysis results (interests and needs)

[1638] How it works: Generative AI uses machine learning algorithms to analyze past purchase and review data to identify users' interests and needs.

[1639] Step 5: Product proposal

[1640] Based on the analysis results, the generative AI will list the most suitable products for the user.

[1641] Input: Analysis results (interests and needs)

[1642] Output: Suggested product list

[1643] How it works: Generative AI uses pre-trained models to suggest products that match the user's interests and needs.

[1644] Step 6: Collect emotion data

[1645] The device activates a webcam and microphone to capture the user's facial expressions and voice.

[1646] Input: User's facial expression data, voice data

[1647] Output: Emotion data

[1648] Specific operation: The device transmits the user's facial expression data and voice data to the server in real time via the webcam and microphone.

[1649] Step 7: Sentiment Analysis

[1650] The emotion engine analyzes the collected facial expression and voice data to recognize the user's emotions.

[1651] Input: facial expression data, voice data

[1652] Output: Real-time emotion data

[1653] Specific operation: The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize the user's emotional state.

[1654] Step 8: Adjust the product offering

[1655] The generative AI adjusts the suggested product list based on real-time emotional data.

[1656] Input: Real-time emotion data, suggested product list

[1657] Output: Adjusted suggested product list

[1658] Specific operation: The generative AI takes into account the emotional data, reevaluates the selection of products to suggest, and adds or removes products from the list that are appropriate for the user's emotional state.

[1659] Step 9: Enter your starting point and transportation

[1660] The user inputs the starting point and mode of transportation into the terminal and searches for the name of the store they want to visit.

[1661] Input: Starting point, transportation method, store name

[1662] Output: Starting point information, transportation information, store name

[1663] Specific operation: The terminal sends input data from the user to the server.

[1664] Step 10: Route calculation

[1665] The server calculates the route using a geographic information system (GIS) based on the starting point information, transportation information, and store names.

[1666] Input: Starting point information, transportation information, store name

[1667] Output: Optimal route information

[1668] Specific operation: The server uses GIS to calculate the optimal route from the specified starting point to the nearest store.

[1669] Step 11: Viewing Information

[1670] The server transmits the calculated optimum route information and the adjusted suggested product list to the user's terminal.

[1671] Input: Optimal route information, adjusted suggested product list

[1672] Output: Data for display

[1673] Specific operation: The terminal displays the received information on the user interface.

[1674] This allows users to receive personalized product suggestions and efficient store guidance, resulting in a highly satisfying shopping experience.

[1675] (Application example 2)

[1676] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1677] Modern shopping experiences are becoming more diverse, and personalized product recommendations based on users' purchase history and ratings are becoming increasingly important. However, existing systems have not been able to adequately consider users' real-time emotions when making product recommendations, and they have not provided smooth guidance to physical stores. This has led to challenges that make it difficult for users to have a satisfying shopping experience.

[1678] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and authenticating user login information, means for acquiring the user's purchase history and rating information, means for analyzing the acquired purchase history and rating information and identifying the user's interests and needs, means for recognizing the user's emotions, means for suggesting optimal products to the user based on the analysis results and emotion recognition, means for receiving the starting point and transportation means specified by the user and calculating the nearest store and route thereto, and means for providing the calculated store and route information to the user. This enables highly accurate product suggestions that take into account the user's real-time emotions and efficient route guidance to physical stores.

[1679] "User login information" means authentication information such as a user ID and password entered by a user to access the system.

[1680] "Purchase history" is a collection of data on products purchased by a user in the past.

[1681] "Rating information" is data relating to ratings and reviews given by users on products they have purchased in the past.

[1682] "Analysis" is the process of using acquired purchase history and rating information to identify user interests and needs.

[1683] "Emotion recognition" is a technology that analyzes a user's facial expressions, tone of voice, etc. to determine their emotional state in real time.

[1684] "Optimal product proposal" means proposing the most suitable product for the user based on the user's purchasing history, evaluation information, and emotion recognition results.

[1685] The "starting point" is the starting point designated by the user.

[1686] "Transportation" refers to the means of transportation used by the user (e.g., walking, driving, public transportation).

[1687] The "nearest store" is the physical store closest to the user's current location or the specified starting point.

[1688] "Route calculation" refers to calculating the optimal route from a starting point to a destination specified by the user.

[1689] "Route guidance" refers to providing calculated route information to a user and showing them how to get to their destination.

[1690] The present invention relates to a system that uses a user's login information, purchase history, and rating information to suggest the most suitable products to the user and guides the user to the nearest route to the store they want to visit. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can more accurately select the products to suggest.

[1691] The system works as follows: The process begins when a user logs in to the system. The user enters their account information (user ID and password) and is authenticated. After successful authentication, the server retrieves the purchase history and rating information associated with the user's account.

[1692] The server then sends the acquired purchase history and evaluation information to the generation AI, which then analyzes this data. As a result of the analysis, the user's interests and needs are identified. For example, if the user has previously purchased technology-related products such as a smartphone, laptop, or wireless earphones, the generation AI will determine that the user is interested in technology products and suggest the latest smartphone models and related accessories.

[1693] Furthermore, by incorporating an emotion engine, it is possible to recognize emotions in real time by analyzing the user's facial expressions, tone of voice, or text content. For example, when a user receives a product proposal, a camera is used to analyze the user's facial expressions to determine whether the user is excited or interested. In addition, when using voice input, the tone of the user's voice can be analyzed to similarly recognize emotions.

[1694] The emotion data recognized by the emotion engine is fed back to the generative AI, which adjusts the selection of products to be recommended based on the user's emotions. For example, if the user is feeling depressed, it will suggest products and services that will help them relax, and conversely, if the user is excited, it will suggest products related to active activities.

[1695] Furthermore, users can search for store names by entering their starting point (e.g., home address) and transportation method (e.g., car, walking, public transportation) through the system. The server receives this input information and uses a geographic information system (GIS) to calculate the nearest store and its route, taking into account travel time and traffic conditions. The calculated route guidance and recommended product information are sent from the server to the user's device and displayed on the user's device. The user can then purchase the product based on the presented information or visit a physical store.

[1696] For example, if a user searches for "a certain electronics retail store" from "Shinjuku Station (walking distance)," the server will suggest stores near Shinjuku Station and the optimal walking route. The user can use this route guidance to travel efficiently to their destination. They can also consider additional purchases based on the recommended products.

[1697] The main features of this system are that it proposes products tailored to each user's individual preferences, and by utilizing an emotion engine, it makes more accurate product proposals by taking into account the user's real-time emotions, and it also uses location information to provide optimal routes and store guidance. This allows users to easily find products that match their interests, needs, and emotions, and makes visiting physical stores smoother.

[1698] Example prompts for generative AI models:

[1699] User purchase history:

[1700] Smartphone: 5 times

[1701] Laptop: 3 times

[1702] Wireless earphones: 2 times

[1703] User rating information:

[1704] Smartphone: ★★★★☆

[1705] Laptop: ★★★☆☆

[1706] Wireless Earphones: ★★★★☆

[1707] Current emotional state: Positive

[1708] Products suggested to users:

[1709] 1. The latest smartwatches

[1710] 2. High-quality protective cases for smartphones

[1711] 3. Wireless Charger

[1712] This approach allows users to receive consistent, personalized service and a highly satisfying shopping experience.

[1713] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1714] Step 1:

[1715] A user logs into the system from a terminal. The user enters their user ID and password, and this login information is sent to the server. The server receives the login information and authenticates the user. It returns a success message to the identified user.

[1716] Step 2:

[1717] When a user successfully logs in, the server retrieves the user's purchase history and rating information. To retrieve this information, the server queries the database based on the user ID and retrieves the relevant purchase history and rating information. The retrieved data is then sent to the user's device.

[1718] Step 3:

[1719] The server sends the acquired purchase history and rating information to the generation AI. The generation AI analyzes this data to identify the user's interests and needs. Specifically, it estimates the user's preferences based on past purchase types, product ratings, and purchase frequency. The analysis results are generated and sent back to the server.

[1720] Step 4:

[1721] The server uses the device's camera and microphone to recognize the user's emotions. The device sends facial expression data from the camera and voice data from the microphone to the server. The server inputs this data into an emotion recognition engine and analyzes the user's current emotional state. The analysis results are sent back to the server.

[1722] Step 5:

[1723] The server combines the analysis results of the generative AI with those of the emotion recognition engine. Based on this, it selects the most suitable products for the user and generates a list of products. For example, if the user is in a positive mood, it will suggest products that stimulate purchasing desire, such as related accessories. Conversely, if the user is in a negative mood, it will suggest products that will help them relax.

[1724] Step 6:

[1725] The user inputs the starting point and transportation method from the terminal. This information is sent to the server. Based on the input information, the server uses a geographic information system (GIS) to calculate the nearest store and its route. The calculated route includes factors such as travel time and traffic conditions.

[1726] Step 7:

[1727] The server sends the calculated route guidance and recommended product information to the user's device. The device displays this information, and the user can check the presented products and proceed with the purchase if interested. The user can also visit a physical store based on the route guidance to the nearest store.

[1728] Step 8:

[1729] When a user selects from the list of products presented, the user's selection information is sent to the server, which stores it in a database as purchase history and rating information for the next purchase and uses it to improve the accuracy of future suggestions.

[1730] This system allows users to receive personalized product suggestions and efficient store guidance, resulting in an efficient and satisfying shopping experience.

[1731] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1732] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1733] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1734] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1735] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1736] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1737] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1738] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1739] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1740] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1741] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1742] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1743] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1744] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1745] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1746] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1747] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1748] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1749] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1750] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1751] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1752] The following is further disclosed regarding the above embodiment.

[1753] (Claim 1)

[1754] means for receiving and authenticating user login information;

[1755] A means for acquiring user purchase history and evaluation information;

[1756] A means for analyzing the acquired purchase history and evaluation information to identify the interests and needs of users;

[1757] A means of proposing optimal products to users based on the analysis results;

[1758] A means for receiving a starting point and a means of transportation designated by a user and calculating the nearest store and a route therefor;

[1759] The system includes a means for providing calculated store and route information to a user.

[1760] (Claim 2)

[1761] 10. The system of claim 1, wherein the means for identifying user interests and needs uses generative artificial intelligence.

[1762] (Claim 3)

[1763] 2. The system according to claim 1, wherein the user's evaluation information includes the user's evaluation of past purchased products.

[1764] "Example 1"

[1765] (Claim 1)

[1766] means for receiving and authenticating user login information;

[1767] A means for acquiring user purchase history and evaluation information;

[1768] A means for transmitting the acquired purchase history and rating information to a generative AI model to identify user interests and needs;

[1769] A means of proposing optimal products to users based on the analysis results;

[1770] a means for receiving a starting point and a means of transportation designated by a user and calculating the nearest store and a route therefor using a geographic information system;

[1771] The system includes a means for providing calculated store and route information to a user.

[1772] (Claim 2)

[1773] 2. The system of claim 1, wherein the means for identifying user interests and needs uses a generative AI model.

[1774] (Claim 3)

[1775] 2. The system according to claim 1, wherein the user's evaluation information includes the user's evaluation of past purchased products.

[1776] "Application Example 1"

[1777] (Claim 1)

[1778] means for receiving and authenticating user login information;

[1779] A means for acquiring user purchase history and evaluation information;

[1780] A means for analyzing the acquired purchase history and evaluation information to identify the interests and needs of users;

[1781] A means of proposing optimal products to users based on the analysis results;

[1782] A means for receiving a starting point and a means of transportation designated by a user and calculating the nearest store and a route therefor;

[1783] means for providing the calculated store and route information to a user;

[1784] A means for generating a product suggestion prompt sentence using a generative artificial intelligence model;

[1785] A system including means for calculating a route to the nearest store using a geographic information system.

[1786] (Claim 2)

[1787] 10. The system of claim 1, wherein the means for identifying user interests and needs uses generative artificial intelligence.

[1788] (Claim 3)

[1789] 2. The system according to claim 1, wherein the user's evaluation information includes the user's evaluation of past purchased products.

[1790] "Example 2: Combining Emotion Engines"

[1791] (Claim 1)

[1792] a means for receiving and authenticating login information;

[1793] A means for acquiring purchase history and user evaluation information;

[1794] A means for analyzing the acquired purchase history and user rating information to identify interests and needs;

[1795] Based on the results of that analysis, we will propose the most suitable products.

[1796] A means for recognizing emotions in real time by analyzing a user's facial expressions, tone of voice, or text content;

[1797] A means to adjust product recommendations based on emotions;

[1798] A means for receiving a designated starting point and transportation means and calculating the nearest store and the optimal route therefor;

[1799] The system includes a means for providing calculated store and route information to a user.

[1800] (Claim 2)

[1801] 10. The system of claim 1, wherein the means for identifying interests and needs uses generative artificial intelligence.

[1802] (Claim 3)

[1803] 2. The system of claim 1, wherein the evaluation information includes evaluations of past purchased products.

[1804] "Application example 2 when combining emotion engines"

[1805] (Claim 1)

[1806] means for receiving and authenticating user login information;

[1807] A means for acquiring user purchase history and evaluation information;

[1808] A means for analyzing the acquired purchase history and evaluation information to identify the interests and needs of users;

[1809] means for recognizing a user's emotion;

[1810] A means for suggesting optimal products to users based on the analysis results and emotion recognition;

[1811] A means for receiving a starting point and a transportation means designated by a user and calculating the nearest store and a route therefor;

[1812] The system includes a means for providing calculated store and route information to a user.

[1813] (Claim 2)

[1814] 10. The system of claim 1, wherein the means for identifying user interests and needs uses generative artificial intelligence.

[1815] (Claim 3)

[1816] 2. The system according to claim 1, wherein the user's evaluation information includes the user's evaluation of past purchased products. [Explanation of symbols]

[1817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving and authenticating user login information; A means for acquiring user purchase history and evaluation information; A means for analyzing the acquired purchase history and evaluation information to identify the interests and needs of users; A means of proposing optimal products to users based on the analysis results; A means for receiving a starting point and a means of transportation designated by a user and calculating the nearest store and a route therefor; The system includes a means for providing calculated store and route information to a user.

2. 10. The system of claim 1, wherein the means for identifying user interests and needs uses generative artificial intelligence.

3. The system according to claim 1 , wherein the user's evaluation information includes the user's evaluation of past purchased products.

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