System

Abstract

To provide a system.SOLUTION: A system includes: first means configured to input preference information which is information indicating food, hobby, and lifestyle preferred by a user; second means configured to generate a first prompt sentence for outputting an analysis result of preference of the user, based on the preference information; third means configured to input the generated first prompt sentence into a generative AI model to generate a preference analysis result of the user; and fourth means configured to transmit filtered property information to a terminal of the user according to the generated preference analysis result of 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] Traditional property search services provide basic information such as floor plan, price, and location, but do not take into account more personalized lifestyle information such as the user's hobbies, favorite foods, shopping preferences, etc. As a result, many users end up regretting or failing after selecting a property because "●● is far away" or "●● is inconvenient." [Means for solving the problem]

[0005] The present invention provides a means for showing properties based on the user's hobbies, favorite foods, and shopping preferences, a means for identifying the area (prefecture, city, town) or location where the user lives, and a means for simultaneously displaying routes and distances on a map based on the identified area or location. It also provides a means for showing properties tailored to the user's life plan. This helps users select a property that suits their lifestyle, reducing regrets and mistakes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0014] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0027] "Example 1"

[0028] The system of the present invention collects user input information and behavioral history, and based on this information, understands the user's hobbies, favorite foods, and shopping preferences. For example, it analyzes the user's hobbies and preferences from the websites and apps the user frequently visits, the content of social media posts, and purchase history.

[0029] "Example 2"

[0030] Next, the system will recommend properties based on the user's preferences. For example, if the system determines that the user's favorite food is ramen, it will prioritize properties in areas with many ramen restaurants. If the system also determines that the user enjoys running, it will recommend properties near parks and riverbanks.

[0031] "Example 3"

[0032] Furthermore, the system identifies the area (prefecture, city, town) or location where the user lives and displays the route and distance on a map based on that area or location. For example, if the user identifies the area where they live as "Shinjuku Ward, Tokyo," the system displays the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.) on a map.

[0033] "Example 4"

[0034] The service also provides property information tailored to the user's life plan. For example, if the user is planning to have a child in the near future, the service will provide information on properties with an environment suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: Collect user input and behavioral history, such as information collected from websites and apps frequently visited, social media posts, and purchase history.

[0038] Step 2: Understand the user's hobbies, favorite foods, and shopping preferences based on the collected information. For example, analyze the user's hobbies and preferences based on the websites and apps they frequently visit, their social media posts, and their purchase history.

[0039] "Example 2"

[0040] Step 1: Based on the user's preferences, properties are shown. For example, if the user's favorite food is ramen, properties in areas with many ramen restaurants are shown first.

[0041] Step 2: If the system determines that the user enjoys running as a hobby, it will recommend properties near parks and riverbanks.

[0042] "Example 3"

[0043] Step 1: Identify the area (prefecture, city, town) or location where the user lives. For example, identify the area where the user lives as "Shinjuku Ward, Tokyo."

[0044] Step 2: Display the route and distance on a map based on the identified area or location. For example, display the route and distance between properties in the area and the user's frequently visited places (workplace, school, hobby facilities, etc.) on a map.

[0045] "Example 4"

[0046] Step 1: Understand the user's life plan. For example, if the user is planning to have a child in the near future, find out that information.

[0047] Step 2: We will show you properties that fit your life plan. For example, we will show you properties that are suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[0048] Example 1

[0049] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] Conventional property guide systems have difficulty in providing property information that fully reflects the user's hobbies and preferences, and have been unable to increase user satisfaction. In addition, there is a lack of means to effectively utilize the user's behavioral history, making it difficult to provide property information that matches the user's lifestyle.

[0051] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history data, a means for preprocessing the collected data, and a means for integrating the preprocessed data. This makes it possible to analyze the user's hobbies and preferences in detail and provide property information based on the analysis.

[0052] "User's hobbies, favorite foods, and shopping preference information" is information about the user's favorite activities, meals, and products they purchase.

[0053] A "means for showing properties" is a method or system for presenting suitable real estate properties to users.

[0054] "Means for identifying the area or location where a user lives" refers to a method or system for identifying the area or specific location where a user lives.

[0055] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route between specific points and the distance on a map.

[0056] "Means for collecting user behavioral history data" refers to methods or systems for collecting user behavioral data such as website visit history, social media posts, and purchase history.

[0057] "Means for preprocessing collected data" refers to a method or system for formatting collected data into a format that is easy to analyze.

[0058] A "means for integrating pre-processed data" is a method or system for combining pre-processed data from different data sources into a single data set.

[0059] The "means for analyzing the integrated data" refers to a method or system for analyzing the integrated data and extracting the user's hobbies and preferences.

[0060] "Means for generating user preference information based on analysis results" refers to a method or system for specifically identifying a user's hobbies and preferences based on the results of data analysis.

[0061] "Means for creating prompt sentences based on generated preference information" refers to a method or system for creating prompt sentences to be input into a generative AI model based on generated user preference information.

[0062] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences. The system collects user behavior history data, preprocesses, integrates, and analyzes it to generate user preference information and create prompt sentences.

[0063] Hardware and software used

[0064] server:

[0065] A central processing unit for data collection, preprocessing, integration, analysis, and generation.

[0066] The software used includes Python (registered trademark), Java (registered trademark), Google (registered trademark) Analytics API, Twitter (registered trademark) API, Facebook (registered trademark) Graph API, e-commerce site API, and TENSORFLOW (registered trademark).

[0067] Data collection

[0068] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[0069] Website access logs: We use the Google Analytics API to collect information about the websites that users visit.

[0070] Social media posts: Use the Twitter API or Facebook Graph API to retrieve user posts.

[0071] Purchase history: Use the e-commerce site's API to obtain the user's purchase history.

[0072] Data Preprocessing

[0073] The server preprocesses the collected data. Specifically, it performs the following processes:

[0074] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[0075] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[0076] Data Integration

[0077] The server integrates the preprocessed data. Specifically, it performs the following processes:

[0078] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[0079] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[0080] Data analysis

[0081] The server analyzes the integrated data. Specifically, it performs the following processes:

[0082] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[0083] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[0084] Generate results

[0085] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[0086] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[0087] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[0088] Specific examples

[0089] If a user frequently visits a cooking recipe site, posts on social media about trying new recipes, and buys cooking equipment on an e-commerce site, the server will operate as follows:

[0090] 1. Data Collection:

[0091] The server uses the Google Analytics API to obtain access logs of cooking recipe sites visited by users.

[0092] The server uses the Twitter API to retrieve posts such as "I tried a new recipe."

[0093] The server uses the API of the EC site to obtain the purchase history of cooking utensils.

[0094] 2. Data Preprocessing:

[0095] The server deletes unnecessary information from the acquired access log and extracts the number of visits and duration of stay.

[0096] The server extracts posts containing specific keywords (e.g., recipes, cooking) from social media posts.

[0097] The server extracts data related to the cookware from the purchase history.

[0098] 3. Data integration:

[0099] The server compiles access logs, social media posts, and purchase history into a single data set.

[0100] The server converts the dataset into a data frame format, formatting it in a way that is easy to analyze.

[0101] 4. Data Analysis:

[0102] The server analyzes the text of the social media post and extracts that the user is interested in cooking.

[0103] The server learns the user's behavioral patterns and predicts their food preferences.

[0104] 5. Generate results:

[0105] The server creates and visualizes reports summarizing the user's hobbies and preferences.

[0106] The server creates a prompt to be input to the generative AI model, for example, "Please analyze the user's hobbies and preferences based on their behavioral history data. We will provide the following data: website access log: cooking recipe site, social media posts: tried new recipes, purchase history: cooking utensils."

[0107] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1: Data collection

[0110] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[0111] Input: Website access logs, social media posts, purchase history

[0112] Operation:

[0113] Use the Google Analytics API to obtain access logs of websites visited by users.

[0114] Use the Twitter API or Facebook Graph API to retrieve users' social media posts.

[0115] Use the EC site's API to obtain the user's purchase history.

[0116] Output: Collected behavioral history data

[0117] Step 2: Data Preprocessing

[0118] The server preprocesses the collected data. Specifically, it performs the following processes:

[0119] Input: Collected behavioral history data

[0120] Operation:

[0121] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[0122] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[0123] Output: Preprocessed data

[0124] Step 3: Data Integration

[0125] The server integrates the preprocessed data. Specifically, it performs the following processes:

[0126] Input: Preprocessed data

[0127] Operation:

[0128] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[0129] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[0130] Output: A consolidated dataset

[0131] Step 4: Data analysis

[0132] The server analyzes the integrated data. Specifically, it performs the following processes:

[0133] Input: Integrated dataset

[0134] Operation:

[0135] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[0136] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[0137] Output: Analysis results (user's hobbies and preferences)

[0138] Step 5: Generate results

[0139] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[0140] Input: Analysis results (user hobbies and preferences)

[0141] Operation:

[0142] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[0143] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[0144] Output: User preference information, prompt

[0145] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional property information systems do not adequately provide personalized information based on users' hobbies and preferences. Furthermore, they lack the functionality to recommend the best products for each individual user by utilizing the user's behavioral history and input information, making it difficult to increase user satisfaction. Furthermore, there is a lack of a way to notify users of discount information or new product information in a timely manner, making it difficult to effectively list products that users may be interested in.

[0149] 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.

[0150] In this invention, the server includes means for providing property guidance based on information about the user's hobbies, favorite foods, and shopping preferences, means for identifying the region (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map at once based on the identified region or location, means for collecting the user's behavior history and input information and recommending products that are optimal for each user, means for notifying the user of discount information and new product information for specific products based on the user's hobbies and preferences, and means for listing products that the user may be interested in based on the purchase history and browsing history. This enables personalized property guidance and product recommendations based on the user's hobbies and preferences, thereby increasing user satisfaction.

[0151] "User hobbies" are activities or areas of interest that a user engages in for enjoyment.

[0152] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[0153] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[0154] "Means for showing properties" refers to methods or systems for providing users with appropriate property information and showing them around.

[0155] "User's geographic area" means the specific geographic area (state, city, town) where the user resides.

[0156] "Location determination means" means a method or system for identifying and locating a user's current location or a specific location.

[0157] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route and distance from the user's current location to the destination on a map.

[0158] "Behavioral history" refers to a record of a user's past actions and activities.

[0159] "Input information" refers to data or information provided by a user to a system.

[0160] A "means for recommending products" refers to a method or system for suggesting appropriate products to users based on their preferences and behavioral history.

[0161] "Discount Information" means information regarding price discounts offered on specific products or services.

[0162] "New product information" refers to information about products that have been newly introduced to the market.

[0163] "Purchase history" refers to a record of products and services a user has purchased in the past.

[0164] "Browsing history" refers to a record of web pages and product information that a user has viewed in the past.

[0165] A "listing means" refers to a method or system for displaying related items in list form based on specific conditions.

[0166] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0167] System Configuration

[0168] This system consists of a server that collects user behavior history and input information and recommends the best products for each user, and a terminal used by the user. Specifically, the following hardware and software are used:

[0169] Hardware

[0170] Server: A powerful computer used to collect, process, and analyze data.

[0171] Smartphone: A device where users enter information and view recommended products.

[0172] software

[0173] Python: A programming language used for data processing and implementing machine learning models.

[0174] Libraries for loading, preprocessing, and manipulating data.

[0175] Libraries used to build and evaluate machine learning models.

[0176] A tool for extracting features from user behavior history.

[0177] A function for calculating cosine similarity.

[0178] Data processing and calculation

[0179] The server processes and calculates the data in the following procedure.

[0180] 1. Data collection: The server collects user behavior history data (e.g., browsing history, purchase history) as a CSV file.

[0181] 2. Data preprocessing: The server reads the data and performs the necessary preprocessing.

[0182] 3. Feature extraction: The server extracts features from the user's behavior history.

[0183] 4. Similarity calculation: The server calculates the cosine similarity.

[0184] 5. Product recommendation: The server lists similar products and recommends them to the user.

[0185] Specific examples

[0186] For example, if a user has previously purchased "sports equipment," the server will notify them of new products and discounts in the same category. Also, if a user frequently visits websites for "outdoor equipment stores," the server will generate a list of related product recommendations.

[0187] Prompt Sentence Examples

[0188] "Create a Python program that recommends optimal products based on a user's behavioral history. User behavioral history data will be provided as a CSV file, and product data will also be provided as a CSV file. Implement a function to analyze user preferences, calculate cosine similarity, and recommend products."

[0189] In this way, personalized property information and product recommendations based on the user's hobbies and preferences become possible, thereby increasing user satisfaction.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] The server collects user behavior history data (e.g., browsing history, purchase history).

[0193] Input: User behavior history data (CSV file)

[0194] Output: Collected behavioral history data

[0195] Specific operation: The server periodically collects the user's activity history on smartphones and other devices and saves it as a CSV file.

[0196] Step 2:

[0197] The server reads the collected behavior history data and performs the necessary preprocessing.

[0198] Input: Collected behavioral history data (CSV file)

[0199] Output: Preprocessed data frame

[0200] Specific operation: The server reads the CSV file using the Pandas library and performs preprocessing such as filling in missing values ​​and deleting unnecessary data.

[0201] Step 3:

[0202] The server extracts features from the user's behavior history.

[0203] Input: Preprocessed data frame

[0204] Output: Feature vector

[0205] Specific operation: The server extracts text features from the user's behavior history data and converts them into vector format.

[0206] Step 4:

[0207] The server calculates the cosine similarity.

[0208] Input: Feature vector

[0209] Output: Cosine similarity matrix

[0210] Specific operation: The server calculates the cosine similarity between feature vectors and generates a similarity matrix.

[0211] Step 5:

[0212] The server lists similar products and recommends them to the user.

[0213] Input: Cosine similarity matrix, product data (CSV file)

[0214] Output: Recommended products list

[0215] Specific operation: Based on the cosine similarity matrix, the server selects the products that are most similar to the user, and compares them with the product data to create a recommended product list.

[0216] Step 6:

[0217] The server notifies the user of discount information and new product information for specific products based on the user's hobbies and preferences.

[0218] Input: Recommended product list, discount information, new product information

[0219] Output: Notification message

[0220] Specific operation: The server extracts relevant discount information and new product information based on the recommended product list and sends notification messages to the user.

[0221] Step 7:

[0222] Users can use their smartphones to check the recommended products and purchase them if necessary.

[0223] Input: Notification message

[0224] Output: Purchase history

[0225] Specific operation: The user receives a notification message on their smartphone, checks the recommended products, and completes the purchase process. The purchase history is sent to the server and used for future recommendations.

[0226] Example 2

[0227] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0228] Conventional property guide systems have difficulty in recommending properties that fully reflect the user's preferences, and have been unable to increase user satisfaction. In addition, since property guides are not tailored to the user's lifestyle or hobbies, it takes a long time for the user to find the perfect property.

[0229] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting user preference information, a means for transmitting the input preference information to the server, a means for the server to store the preference information in a database, a means for the server to input the stored preference information to a generative AI model and analyze it, a means for the server to acquire property information from an external information providing service, a means for the server to filter the property information based on the analysis results of the generative AI model, a means for transmitting the filtered property information to a terminal, and a means for the terminal to display the property information to the user. This enables highly accurate property recommendations based on the user's preference information, thereby increasing user satisfaction.

[0230] "User preference information" is information that indicates a user's personal preferences and interests, such as preferred foods, hobbies, and lifestyle.

[0231] "Input means" refers to the interface or device through which a user provides preference information to a system, such as a web form or a mobile application.

[0232] "Means of transmission" refers to the communication protocol or technology used to transmit the preference information entered by the user to the server, such as the HTTPS protocol.

[0233] The term "means for storing in a database" refers to a data storage system for persistently storing the received preference information, such as a relational database management system.

[0234] A "generative AI model" refers to an artificial intelligence model that analyzes user preference information and recommends appropriate properties. For example, a model that uses natural language processing technology falls into this category.

[0235] "External information provider" refers to an external data source or API that provides property information. For example, a real estate information provider.

[0236] "Filtering means" refers to the process of selecting acquired property information to suit the user's preferences based on the analysis results of the generated AI model.

[0237] "Terminal" refers to the device used by the user to view property information, such as a computer or smartphone.

[0238] "Display means" refers to the technology or method used to visually present filtered property information to a user on a device, such as a web page using HTML and CSS.

[0239] The present invention relates to a system for recommending properties based on user preference information. A specific embodiment of this system will be described below.

[0240] System configuration

[0241] Input of user preference information

[0242] Users access the system using a terminal, such as a PC or smartphone, and enter their preference information. Users enter information such as their favorite foods and hobbies through a web form or mobile application.

[0243] Sending preference information

[0244] The device sends the user's input preference information to the server using the HTTPS protocol. The data is in JSON format.

[0245] Saving your preferences

[0246] The server analyzes the received JSON data and extracts preference information. The extracted information is stored in a database. A relational database management system (RDBMS) is used for the database. For example, MySQL (registered trademark) is used.

[0247] Analysis of preference information

[0248] The server inputs the stored preference information into a generative AI model to analyze the user's preferences. The generative AI model uses natural language processing technology (for example, GPT-4 (registered trademark)). The generative AI model returns analysis results such as "I like ramen" or "I like running."

[0249] Acquiring property information

[0250] The server retrieves property information from an external information service. The external information service may be the API of a real estate information service. For example, the server sends an API request such as "GET / properties?location=Tokyo."

[0251] Filtering property information

[0252] The server filters the acquired property information based on the analysis results of the generated AI model. A Python library is used for filtering. For example, properties in areas with many ramen restaurants or properties near parks or riverbanks are selected.

[0253] Sending filtering results

[0254] The server sends the filtered property information to the terminal in JSON format, for example by sending the data to the endpoint "POST / filtered_properties".

[0255] Displaying property information

[0256] The property information received by the device is displayed to the user using HTML and CSS. For example, a list of properties can be displayed in a table format, with each property having a link to detailed information.

[0257] Specific examples

[0258] A user accesses the system and enters the following preferences:

[0259] Favorite food: Ramen

[0260] Hobbies: Running

[0261] The device sends this information to a server, which stores the information in a database and analyzes it using a generative AI model. As a result of the analysis, the user is classified as a "ramen lover" and a "running lover." The server retrieves property information from the API of a real estate information service and filters properties in areas with many ramen restaurants or near parks or riverbanks. The filtered property information is sent to the device and displayed to the user.

[0262] Prompt Sentence Examples

[0263] "Design a system that recommends optimal properties based on the user's preferences. For example, if it is analyzed that the user likes ramen, it should prioritize properties in areas with many ramen restaurants. Also, if it is analyzed that the user enjoys running, it should recommend properties near parks and riverbanks."

[0264] By inputting this prompt sentence into a generative AI model, it is possible to design a system that recommends properties based on the user's preferences.

[0265] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0266] Step 1:

[0267] Users access the system using their devices and enter their preferences, such as their favorite foods and hobbies, through web forms or mobile applications. The entered information is stored on the device as JSON-formatted data.

[0268] Input: User preference information (favorite foods, hobbies, etc.)

[0269] Output: Preference information data in JSON format

[0270] Step 2:

[0271] The device sends the preference information entered by the user to the server using the HTTPS protocol. Specifically, the device sends JSON format data to the server as an HTTPS request.

[0272] Input: Preference information data in JSON format

[0273] Output: Preference information data sent to the server

[0274] Step 3:

[0275] The server analyzes the received JSON data and extracts preference information. Specifically, the server uses a JSON parser to analyze the data and extract information such as favorite foods and hobbies. The extracted information is stored in a relational database management system (RDBMS).

[0276] Input: Preference information data sent to the server

[0277] Output: Preference information stored in a database

[0278] Step 4:

[0279] The server inputs the saved preference information into a generative AI model to analyze the user's preferences. Specifically, the server inputs the preference information into a generative AI model (e.g., GPT-4), and the model returns an analysis result such as "I like ramen" or "I like running."

[0280] Input: Preference information stored in a database

[0281] Output: Preference analysis results from the generative AI model

[0282] Step 5:

[0283] The server retrieves property information from an external information provider. Specifically, the server calls the API of the real estate information provider to retrieve property information. For example, it sends an API request such as "GET / properties?location=Tokyo".

[0284] Input: API request

[0285] Output: Retrieved property information

[0286] Step 6:

[0287] The server filters the acquired property information based on the analysis results of the generated AI model. Specifically, the server uses a Python library to select properties in areas with many ramen restaurants, or properties near parks or riverbanks.

[0288] Input: Preference analysis results by the generated AI model, acquired property information

[0289] Output: Filtered property information

[0290] Step 7:

[0291] The server sends the filtered property information to the terminal in JSON format. Specifically, it sends the data to the endpoint "POST / filtered_properties".

[0292] Input: Filtered property information

[0293] Output: Property information sent to the terminal

[0294] Step 8:

[0295] The device displays the received property information to the user. Specifically, the device uses HTML and CSS to display a list of properties in a table format, with each property provided with a link to detailed information.

[0296] Input: Property information sent to the terminal

[0297] Output: Property information displayed to the user

[0298] (Application example 2)

[0299] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0300] Conventional property information systems do not adequately provide personalized property recommendations based on users' preferences and lifestyles. Furthermore, they lack the functionality to improve their recommendations based on user feedback, resulting in a decline in user satisfaction.

[0301] 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 introducing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the region (prefecture, city, town) or location where the user lives, means for simultaneously displaying routes and distances on a map based on the identified region or location, means for collecting and analyzing the user's preference information and proposing optimal properties, means for displaying information about the areas surrounding the proposed properties (e.g., restaurants, parks, transportation), and means for improving the proposal content based on user feedback. This enables personalized property proposals based on the user's preferences and lifestyle, and is expected to improve user satisfaction.

[0302] "User hobbies" are activities or areas of interest that a user is interested in and enjoys.

[0303] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[0304] "Shopping preference information" refers to information such as the products and brands that users prefer when shopping, and the frequency of purchases.

[0305] "Means for showing properties" refers to methods or systems for providing users with appropriate property information and showing them around.

[0306] "Means for identifying the area (prefecture, city, town) or location where the user lives" refers to a method or system for identifying and specifying the area or location where the user currently lives.

[0307] "Means for simultaneously displaying route and distance on a map" refers to a method or system for simultaneously displaying route and distance to a destination on a map based on a specified area or location.

[0308] "Means for collecting and analyzing user preference information and proposing optimal properties" refers to a method or system for collecting user preference information, analyzing it, and proposing optimal properties to users.

[0309] "Means for displaying surrounding information (restaurants, parks, transportation, etc.) of the proposed property" refers to a method or system for displaying information about restaurants, parks, transportation, etc. in the vicinity of the proposed property.

[0310] "Means for improving proposal content based on user feedback" refers to a method or system for collecting feedback from users and improving the content of property proposals based on that feedback.

[0311] The system for carrying out the present invention includes a plurality of means for providing property information based on user preference information. Specific embodiments thereof are described below.

[0312] System Configuration

[0313] The system mainly consists of a server, user terminals, and a database. The server is responsible for collecting and analyzing user preference information and proposing the most suitable properties. User terminals are devices such as smartphones and tablets that provide an interface for users to interact with the system. The database is used to store user preference information and property information.

[0314] Program processing

[0315] The server performs the process using the following means.

[0316] 1. How we collect and analyze user preferences:

[0317] The server collects information about the user's hobbies, favorite foods, and shopping preferences. This information is collected automatically from user input and user behavior history.

[0318] The collected information is stored in a database and analyzed using generative AI models.

[0319] 2. How we suggest the best property for you:

[0320] Based on the analysis results, the server proposes the most suitable property to the user, which matches the user's preferences.

[0321] For example, if a user likes ramen, the system will prioritize properties in areas with many ramen restaurants. If a user enjoys running, the system will suggest properties near parks or riverbanks.

[0322] 3. How to view the surrounding area of ​​the proposed property:

[0323] The server displays information about restaurants, parks, transportation, and other areas near the proposed property on the user's device.

[0324] This allows users to get a detailed understanding of the property's surroundings.

[0325] 4. How we improve our offerings based on user feedback:

[0326] The server collects feedback from users and uses it to improve the content of property suggestions.

[0327] Feedback is collected in the form of ratings and comments users make on proposed properties.

[0328] Hardware and software used

[0329] Hardware: Smartphone (ANDROID (registered trademark)), server

[0330] Software: Python, database, generative AI models

[0331] Specific examples

[0332] For example, if a user likes ramen and has running as a hobby, the server will prioritize suggesting properties in areas with many ramen restaurants or near parks or riverbanks. If the user provides feedback on the suggested properties, the server will use that feedback to improve the next suggestion.

[0333] Prompt Sentence Examples

[0334] Develop an application that suggests optimal properties based on the user's preferences. Analyze the user's favorite foods and hobbies and show properties based on those. For example, if the user likes ramen, show properties in areas with many ramen restaurants first. Or, if the user enjoys running, show properties near parks and riverbanks.

[0335] In this way, it becomes possible to make personalized property suggestions based on the user's preferences and lifestyle, which is expected to improve user satisfaction.

[0336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0337] Step 1:

[0338] The server collects user preference information. It stores input information from the user's device and the user's behavioral history in a database. Specifically, it collects information about hobbies, favorite foods, and shopping preferences entered by the user into the application, and automatically records the user's behavioral history (e.g., places visited, products purchased). The input data is saved in the database as information about the user's hobbies, favorite foods, and shopping preferences.

[0339] Step 2:

[0340] The server analyzes the collected preference information using a generative AI model. It obtains the user's preference information from the database and inputs it into the generative AI model. The generative AI model analyzes the user's preference patterns and extracts the characteristics of properties that are best suited to the user. The output is the property conditions that are best suited to the user.

[0341] Step 3:

[0342] The server searches for the most suitable property from a property database based on the analysis results. The property database stores detailed information about each property (e.g., location, surrounding facilities, price). The server filters properties that match the conditions obtained from the generative AI model and creates an optimal property list. The output is a property list to suggest to the user.

[0343] Step 4:

[0344] The server retrieves surrounding information for the proposed property and displays it on the user's device. The server retrieves surrounding information (e.g., restaurants, parks, and transportation) for each property included in the property list from the database and sends it to the user's device. The user's device displays the received surrounding information on a map. As an output, the user can visually check the surrounding environment of the property.

[0345] Step 5:

[0346] The user provides feedback on the proposed property. The user inputs their rating and comments on the proposed property through their terminal. The input feedback is sent to the server and stored in the database. The user's feedback data is obtained as the output.

[0347] Step 6:

[0348] The server improves the suggestions based on user feedback. It retrieves the feedback data from the database and inputs it into the generative AI model. The generative AI model analyzes the feedback and adjusts the property suggestion algorithm, so that the next property suggestions will better match the user's preferences. The output is an improved property suggestion algorithm.

[0349] Example 3

[0350] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] Conventional property guide systems do not adequately guide users to properties based on their hobbies, preferences, and life plans. Furthermore, they lack a function to simultaneously display routes and distances between the user's area and frequently visited places, making it difficult for users to obtain sufficient information when selecting a property. Furthermore, they lack a function to identify properties suitable for raising children, preventing them from providing appropriate property guidance according to the user's life stage.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0353] In this invention, the server includes: means for providing property information based on the user's hobbies, favorite foods, and shopping preferences; means for identifying the user's area (prefecture, city, town) or location; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for providing property information based on the user's life plan; means for calculating routes and distances from the user's area of ​​residence to frequently visited places; means for identifying properties suitable for raising children based on the user's life plan; and means for displaying the location, route, and distance of properties on a map. This allows users to receive property information based on their hobbies, preferences, and life plan, and further allows them to simultaneously check the routes and distances between the user's area of ​​residence and frequently visited places. Furthermore, identifying properties suitable for raising children allows users to select properties appropriate for their life stage.

[0354] "User" means an individual or organization that uses the system to receive property information.

[0355] "Hobbies" are activities or areas of interest that a user personally enjoys.

[0356] "Favorite foods" refers to the foods and dishes that the user likes to eat.

[0357] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[0358] "Property" means a building or land for the User to live in or use.

[0359] "Area of ​​residence" refers to the administrative division (prefecture, city, town) where the user currently resides.

[0360] A "place" is a specific location or facility that a user frequently visits.

[0361] A "map" is a drawing or digital representation intended to visually represent a particular area or place.

[0362] A "route" is the path a user takes from one point to another.

[0363] "Distance" refers to the physical length from the starting point to the destination point when a user travels.

[0364] "Life plan" refers to the user's future lifestyle planning and plans.

[0365] A "property suitable for raising children" is a property that has a good environment for raising children and is located near parks and childcare support facilities.

[0366] A "server" is a computer system that receives information from users, processes it, and provides the results.

[0367] A "terminal" is a device that allows a user to receive and display information provided by a server.

[0368] "Identify" means to clearly identify a specific area or location based on the information entered by the user.

[0369] "Guiding" means providing information to users and helping them make appropriate choices.

[0370] "Calculating" means deriving the required result based on specific numbers or data.

[0371] "Displaying" means providing information visually to a user.

[0372] MODE FOR CARRYING OUT THE INVENTION

[0373] This invention is a system that guides users to properties based on their hobbies, favorite foods, shopping preferences, living area and location, and life plan. The system exchanges information between the server, terminal, and user, and provides users with the most suitable property information.

[0374] Server Roles

[0375] The server receives and analyzes the information provided by the user and generates appropriate property information. Specifically, it uses the following hardware and software:

[0376] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[0377] Software: Database Management System (DBMS), generative AI models

[0378] The server identifies the area where the user lives and the places they frequently visit, and then obtains the latitude and longitude from the user's input information to identify the area.

[0379] Next, the server collects property information from real estate databases and property information websites. For example, property information for Shinjuku Ward is obtained via API and stored in the database.

[0380] In addition, the server uses a route calculation function to calculate the route and distance from the user's area to frequently visited places, allowing the user to know from which property and how long it will take to reach the destination.

[0381] The server also identifies suitable properties for raising children based on the user's life plan, including database queries to select properties near parks and childcare facilities.

[0382] Device Role

[0383] The terminal receives the data sent from the server and visually displays it to the user. Specifically, it uses the following hardware and software:

[0384] Hardware: User interface devices such as computers, smartphones, and tablets

[0385] Software: Web browser, map display application

[0386] The device provides an interface for displaying property locations, routes, and distances on a map, allowing users to visually confirm information. Property information based on life plans is also displayed in list format and on a map.

[0387] User Roles

[0388] Users input information about the area where they live and the places they frequently visit into the system. For example, they provide information such as "Shinjuku Ward, Tokyo" or "My workplace is in Shibuya Ward." Users can then check the route and distance on the map displayed on their device to understand the relative locations of properties.

[0389] Users also input information about their life plans, such as "I plan to have a child in the near future." Based on this information, users can receive information about properties suitable for raising children.

[0390] Examples and prompts

[0391] For example, if a user lives in Shinjuku Ward, Tokyo, and their workplace is in Shibuya Ward, the server will collect property information within Shinjuku Ward and calculate the route and distance to their workplace in Shibuya Ward. The device will then display this information on a map, providing a visual representation to the user.

[0392] If a user enters "I'm planning to have a child in the near future," the server will identify properties in Shinjuku Ward that are suitable for raising children and send them to the device, which will then display them in a list or on a map and guide the user.

[0393] An example of a prompt is as follows:

[0394] "I live in Shinjuku Ward, Tokyo, and my workplace is in Shibuya Ward. Please show me properties in Shinjuku Ward and the route and distance to my workplace in Shibuya Ward. I'm also planning to have a child in the near future, so please also show me properties that are suitable for raising children."

[0395] By inputting this prompt sentence into the generative AI model, the system provides information according to the user's request. The flow of the identification process in Example 3 will be described with reference to FIG.

[0396] Step 1:

[0397] Enter information about the area where the user lives and the places they frequently visit.

[0398] Input: The user inputs information about the area where they live (e.g., "Shinjuku-ku, Tokyo") and the places they frequently visit (e.g., "My workplace is in Shibuya-ku") through the system interface.

[0399] Specific operation: The user enters "Shinjuku-ku, Tokyo" and "Shibuya-ku" and clicks the send button.

[0400] Output: The user's input information is sent to the server.

[0401] Step 2:

[0402] The server identifies the region and location.

[0403] Input: The server receives region and location information submitted by the user.

[0404] Data processing: Obtain latitude and longitude from the user's input information and identify the region.

[0405] Specific operation: The server obtains the latitude and longitude of "Shinjuku Ward, Tokyo" and also obtains the latitude and longitude of "Shibuya Ward."

[0406] Output: The latitude and longitude of the identified region and location is generated.

[0407] Step 3:

[0408] The server collects property information.

[0409] Input: The server collects property information based on the latitude and longitude information of the specified area.

[0410] Data processing: Property information within the specified area is obtained from real estate databases and property information sites via API and stored in a database.

[0411] Specific operation: The server obtains property information within Shinjuku Ward and stores it in a database.

[0412] Output: Property information within Shinjuku Ward is saved in the database.

[0413] Step 4:

[0414] The server calculates the route and distance.

[0415] Input: The server calculates the route and distance based on the property information in the identified area and the latitude and longitude information of frequently visited places.

[0416] Data calculation: Use the route calculation function to calculate the route and distance from the user's area to frequently visited places.

[0417] Specific operation: The server calculates the route and distance from each property in Shinjuku Ward to the workplace in Shibuya Ward and saves the results in a database.

[0418] Output: Route and distance information from each property to the workplace is generated.

[0419] Step 5:

[0420] The server generates a property guide based on the life plan.

[0421] Input: The server receives information about the user's life plans (e.g., "I plan to have a child in the near future").

[0422] Data processing: Run a database query to find properties near parks and childcare facilities.

[0423] Specific operation: The server filters properties in Shinjuku Ward that are near parks and childcare support facilities and generates a list.

[0424] Output: A list of properties suitable for raising children is generated.

[0425] Step 6:

[0426] The device displays a map and property information.

[0427] Input: The device receives property information, route, and distance data sent from the server.

[0428] Data processing: Use the map display function to display the property location, route, and distance on a map.

[0429] Specific operation: The device displays on a map properties in Shinjuku Ward and routes to workplaces in Shibuya Ward, as well as a list of properties suitable for raising children.

[0430] Output: Information is presented visually to the user.

[0431] (Application example 3)

[0432] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0433] Conventional property guide systems were unable to display the route and distance to the user's area or frequently visited places at once, making it inconvenient for users when selecting a property. Furthermore, property guides were not adequately based on the user's life plan, making it difficult for users to find a property that met their needs. This resulted in a decrease in the efficiency and satisfaction of users when selecting a property.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0435] In this invention, the server includes means for showing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the area (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map based on the identified area or location, means for identifying places the user frequently visits and displaying routes and distances to those locations on a map, and means for filtering properties based on the user's life plan, thereby enabling the user to efficiently find properties that fit their lifestyle and future plans.

[0436] "Hobbies" are activities or areas of interest that a user personally enjoys.

[0437] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[0438] "Shopping preference information" refers to information about the products, brands, and purchasing trends that users prefer when shopping.

[0439] "Area of ​​residence" refers to the geographical area, such as the prefecture, city, or town where the user resides.

[0440] A "place" is a location or facility that a user visits to perform a particular activity.

[0441] "Means for displaying routes and distances on a map" means a method or device for visually showing routes and distances between specific points on a map.

[0442] "Frequently visited places" are locations or facilities that users frequently visit.

[0443] "Life plan" refers to the user's future lifestyle planning and plans, including events such as the birth of a child or changing jobs.

[0444] A "means for filtering properties" refers to a method or device for sorting property information based on specific conditions and extracting properties suitable for the user.

[0445] A system for implementing this invention includes means for guiding users to properties based on information about their hobbies, favorite foods, and shopping preferences; means for identifying the area or location where the user lives; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for identifying locations frequently visited by the user and displaying routes and distances to those locations on a map; and means for filtering properties based on the user's life plan.

[0446] 1. System Program

[0447] The program of this system performs the following processing.

[0448] 2. Program processing explanation

[0449] The server collects information about the user's hobbies, favorite foods, and shopping preferences, and then guides them to properties based on that information. It identifies the area or location where the user lives, and displays the route and distance on a map based on the identified area or location. It also identifies places the user frequently visits, and displays the route and distance to those places on a map. It filters properties based on the user's life plan.

[0450] Specifically, the server uses a Python program to display routes and distances on a map. User preferences and hobbies are automatically collected from user input and behavioral history, allowing users to efficiently find properties that fit their lifestyle and future plans.

[0451] 3. Specific Examples

[0452] For example, if a user lives in Shinjuku Ward, Tokyo, their workplace is near Shinjuku Station, and their hobby facility is near Yoyogi Park, the system will use this information to suggest the most suitable property. The map will also show the route and distance between the property and their workplace or hobby facility.

[0453] Example prompts to input to a generative AI model:

[0454] Create an application that identifies the area where the user lives as "Shinjuku-ku, Tokyo," and displays on a map the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.). Also, add a function to guide users to properties that fit their life plans.

[0455] This prompt can be used to communicate specific application requirements to the generative AI model.

[0456] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0457] Step 1:

[0458] The server collects information about the user's hobbies, favorite foods, and shopping preferences.

[0459] Input: User input information or behavioral history

[0460] Data processing: Collected information is stored in a database and user preference patterns are analyzed.

[0461] Output: User preference information data

[0462] Step 2:

[0463] The server identifies the region or location where the user lives.

[0464] Input: User's address information

[0465] Data processing: Address information is analyzed using a geographic information system (GIS) and converted into latitude and longitude information.

[0466] Output: Latitude and longitude information of the user's area

[0467] Step 3:

[0468] The server displays the route and distance on a map at once based on the identified region or location.

[0469] Input: Latitude and longitude information of the area where the user lives, and latitude and longitude information of frequently visited places

[0470] Data processing: Uses map API to calculate route and distance and display on the map

[0471] Output: Route and distance displayed on a map

[0472] Step 4:

[0473] The server identifies the user's frequently visited locations and displays the route and distance to those locations on a map.

[0474] Input: Information about places the user frequently visits (e.g., workplace, school, hobby facilities)

[0475] Data processing: Obtain the latitude and longitude information of these locations, use the map API to calculate the route and distance, and display it on the map.

[0476] Output: Route and distance displayed on a map

[0477] Step 5:

[0478] The server filters properties based on the user's life plan.

[0479] Input: User's life plan information (e.g., planned childbirth, planned job change)

[0480] Data processing: Selecting suitable properties from the property database based on life plan information

[0481] Output: Filtered property list

[0482] Step 6:

[0483] The server presents the filtered property list to the user.

[0484] Input: Filtered property list

[0485] Data processing: Converting property listings into a format for display in the user interface

[0486] Output: List of properties displayed in the user interface

[0487] In this way, users can efficiently find properties that suit their lifestyle and future plans.

[0488] 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.

[0489] "Example 1"

[0490] One embodiment of the invention that combines an emotion engine is a system that includes a means for recognizing a user's emotion. This system recognizes emotions from the user's facial expressions, voice, text input, etc. For example, the system captures the user's facial expressions and voice while browsing property information through a camera or microphone, and recognizes the user's emotion by analyzing that data.

[0491] "Example 2"

[0492] The system also includes a means for adjusting property information based on emotions recognized by the emotion engine. For example, if a user is recognized as expressing joy while browsing property information, properties with similar characteristics to that property will be prioritized. Conversely, if a user is recognized as expressing displeasure while browsing property information, properties with different characteristics from that property will be prioritized.

[0493] "Example 3"

[0494] The system also guides users to properties based on their hobbies, favorite foods, and shopping preferences. This information is collected automatically from user input or from their behavioral history. For example, the system collects information from the websites and apps the user frequently visits, social media posts, and purchase history, and uses this information to analyze the user's hobbies and preferences.

[0495] "Example 4"

[0496] It also identifies the area (prefecture, city, town) or location where the user lives and displays the route and distance on a map based on the identified area or location. For example, it identifies the area where the user lives as "Shinjuku Ward, Tokyo" and displays the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.) on a map.

[0497] The processing flow of each embodiment will be described below.

[0498] "Example 1"

[0499] Step 1: User views property information.

[0500] Step 2: The system captures the user's facial expressions, voice, text input, etc. through the camera and microphone.

[0501] Step 3: Analyze the acquired data and recognize the user's emotions.

[0502] "Example 2"

[0503] Step 1: User views property information.

[0504] Step 2: The emotion engine recognizes the user's emotion.

[0505] Step 3: The system adjusts the property guide based on the recognized emotion.

[0506] "Example 3"

[0507] Step 1: Collect information about hobbies, favorite foods, and shopping preferences from user input or user behavioral history.

[0508] Step 2: Based on the collected information, the system analyzes the user's hobbies and preferences.

[0509] Step 3: Based on the analysis results, the system will guide you to properties.

[0510] "Example 4"

[0511] Step 1: Identify the region (state, city, town) or location where the user lives.

[0512] Step 2: Based on the identified area or location, the system will display the route and distance on the map at once.

[0513] Step 3: The system will guide you to the property based on the route and distance displayed.

[0514] Example 1

[0515] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0516] Conventional property information systems do not adequately provide personalized suggestions based on the user's hobbies and preferences. In addition, they do not provide services that take the user's emotions into consideration, making it difficult to increase user satisfaction.

[0517] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for providing property guidance based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the area or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified area or location, a means for recognizing emotions from the user's facial expression, voice, text input, etc., and a means for integrating emotion data and preference data to provide personalized services to the user. This makes it possible to provide property guidance based on the user's hobbies and preferences, and further makes it possible to provide services that take the user's emotions into consideration.

[0518] "User's hobbies, favorite foods, and shopping preference information" is information about the activities, foods, and products that the user is interested in.

[0519] "Means for showing properties" refers to methods or systems for providing users with appropriate property information.

[0520] "Means for identifying the area or location where a user lives" refers to a method or system for identifying the area or specific location where a user lives.

[0521] A "means for simultaneously displaying a route and distance on a map" is a method or system for simultaneously displaying a route and its distance on a map based on a specified region or location.

[0522] "Means for recognizing emotions from a user's facial expressions, voice, text input, etc." refers to a method or system for analyzing data such as a user's facial expressions, voice, text input, etc., and identifying the user's emotions.

[0523] "Means for integrating emotional data and preference data to provide users with personalized services" refers to methods and systems for combining and analyzing a user's emotional data and preference data to provide individually optimized services to the user.

[0524] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences. The system identifies the area or location where the user lives and displays routes and distances on a map based on the identified area or location. It also recognizes emotions from the user's facial expressions, voice, text input, etc., and provides personalized services to the user by integrating the emotion data with the preference data.

[0525] Hardware and software used

[0526] Hardware

[0527] Server: A central processing unit for collecting, analyzing, and integrating data.

[0528] Devices: PCs, smartphones, tablets, etc. used by users

[0529] Camera: A device for capturing the user's facial expressions

[0530] Microphone: A device used to collect the user's voice.

[0531] software

[0532] Google Analytics: A tool for collecting user behavior history

[0533] Facebook API: A tool for collecting social media posts

[0534] Python: A programming language for data organization and analysis

[0535] Libraries for organizing data in dataframe format

[0536] Libraries for building machine learning models

[0537] OpenCV (registered trademark): A library for facial expression analysis

[0538] Google Cloud Speech-to-Text API: An API for converting voice data into text.

[0539] Specific examples

[0540] Example 1: Understanding user hobbies

[0541] If a user frequently visits a cooking recipe website, the server can analyze the site's data to determine the types of cuisine the user is particularly interested in, such as keywords like "pasta" or "curry."

[0542] Example 2: Emotion recognition

[0543] When a user browses property information, facial expression data captured by the camera is analyzed using OpenCV, and if the user smiles, it is determined that the user has a positive emotion toward the property. For example, if a user says, "I like this property," the voice data is converted to text using the Google Cloud Speech-to-Text API and recorded as a positive emotion.

[0544] Prompt Sentence Examples

[0545] "Write a program that collects data on websites frequently visited by users and identifies their interests. Specifically, you will use Python to analyze the content of web pages and identify topics that interest users."

[0546] "Create a program that captures the user's facial expression while they are browsing property information and analyzes it using OpenCV. If the user smiles, it will be determined that they have a positive attitude toward the property."

[0547] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0548] Step 1:

[0549] A user uses a website or app.

[0550] Users use various websites and apps on a daily basis, for example, frequently visiting cooking recipe sites or shopping apps, which creates a user behavior history.

[0551] Step 2:

[0552] The server collects user behavior history.

[0553] The server uses Google Analytics and Facebook APIs to collect the user's behavioral history of the websites and apps they visit. Specifically, it records information about the links the user clicks and the pages they view. The input is the user's behavioral history data, and the output is organized behavioral history data.

[0554] Step 3:

[0555] The server collects social media posts and purchase history.

[0556] The server collects users' social media posts and online shopping purchase history. For example, it uses a social media API to obtain users' posts and an online shopping API to obtain their purchase history. The input is the social media post data and purchase history data, and the output is the organized post and purchase history data.

[0557] Step 4:

[0558] The server organizes the collected data.

[0559] The server uses Python libraries to organize the collected data in data frame format. For example, it can combine a user's visit history and purchase history into a single dataset. The input is the collected behavioral history data, posting data, and purchase history data, and the output is the integrated dataset.

[0560] Step 5:

[0561] The server analyzes the data using machine learning models.

[0562] The server builds a machine learning model to analyze the user's hobbies and preferences. For example, it uses a clustering algorithm to group the user's behavioral patterns. The input is the integrated dataset, and the output is the analysis results of the user's hobbies and preferences.

[0563] Step 6:

[0564] Users use the camera and microphone.

[0565] When a user browses property information, they use a camera or microphone. For example, facial expressions and voice are recorded using the built-in camera or microphone of a PC. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[0566] Step 7:

[0567] The server collects the user's facial expression and voice data.

[0568] The server collects the user's facial expression and voice data acquired through a camera and microphone. Specifically, it uses OpenCV to capture the facial expression data and convert the voice data into text. The input is the captured facial expression and voice data, and the output is an analyzable data format.

[0569] Step 8:

[0570] The server analyzes the data it has obtained.

[0571] The server analyzes the acquired facial and voice data to recognize the user's emotions. For example, using a facial expression analysis algorithm, if the user smiles, it can determine that the user has positive feelings toward the property. The input is analyzable facial and voice data, and the output is the analysis result regarding the user's emotions.

[0572] Step 9:

[0573] The server integrates the emotion data and preference data.

[0574] The server integrates the analyzed emotional data and preference data to understand the user's behavioral patterns and emotional changes. For example, if a user smiles while browsing a particular cooking recipe, it can determine that the user is particularly interested in that dish. The input is emotional data and preference data, and the output is an integrated user profile.

[0575] Step 10:

[0576] The server provides personalized services to the user.

[0577] The server provides personalized services to users based on the integrated data. For example, it prioritizes the display of cooking recipes or property information that the user is interested in. The input is an integrated user profile, and the output is the provision of personalized services.

[0578] (Application example 1)

[0579] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0580] Conventional property information systems do not provide optimal product recommendations that take into account the user's hobbies, preferences, and emotions, making it difficult to increase user satisfaction. Furthermore, there is a lack of effective ways to utilize the user's behavioral history and input information, which means that the system is unable to provide suggestions that meet the user's needs.

[0581] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for guiding users to properties based on information about their hobbies, favorite foods, and shopping preferences; means for identifying the area (prefecture, city, town) or location where the user lives; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for collecting the user's behavior history and input information and understanding the user's hobbies and preferences; means for recognizing the user's emotions using an emotion engine; and means for recommending optimal products based on the user's preferences and emotions. This enables optimal product recommendations that take the user's hobbies, preferences, and emotions into consideration, thereby increasing user satisfaction.

[0582] "User Hobbies" refers to the activities and areas of interest that a user finds interesting and enjoys.

[0583] "Favorite foods" refers to the foods and dishes that a user likes to eat.

[0584] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[0585] "Means for showing properties" refers to methods or systems that provide users with appropriate property information.

[0586] "Means for identifying the area (prefecture, city, town) or location where the user lives" refers to a method or system for identifying the area or specific location where the user lives.

[0587] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route to a destination and the distance on a map.

[0588] "Means for collecting user behavior history and input information" refers to methods or systems for collecting the actions taken by users and the information entered.

[0589] "Means for understanding user hobbies and preferences" refers to methods or systems for understanding user interests and preferences based on collected data.

[0590] An "emotion engine" is a technology or system for analyzing and recognizing user emotions.

[0591] "Means for recognizing user emotions" refers to methods or systems that identify emotions from a user's facial expressions, voice, text, etc.

[0592] "Means for recommending optimal products" refers to methods or systems that suggest the most suitable products based on the user's hobbies, preferences, and emotions.

[0593] A system for implementing this invention includes means for collecting a user's behavioral history and input information to understand the user's hobbies and preferences, means for recognizing the user's emotions using an emotion engine, and means for recommending optimal products based on the user's preferences and emotions.

[0594] System configuration

[0595] The system consists of the following main components:

[0596] 1. Data collection module: Collects user behavioral history and input information, such as website visit history, app usage history, social media posts, and purchase history.

[0597] 2. Preference analysis module: Analyzes the collected data to understand the user's hobbies and preferences. Here, algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency) and KMeans clustering are used.

[0598] 3. Emotion Recognition Module: Recognizes emotions from the user's facial expressions, voice, text input, etc. Specifically, it performs facial expression recognition using OpenCV and text analysis using TextBlob (registered trademark).

[0599] 4. Product recommendation module: Recommends the best products based on the user's preferences and emotions. Product information is obtained through API and filtered based on the user's emotion tags.

[0600] Hardware and Software

[0601] Hardware: Camera, microphone, user device (smartphone, tablet, PC)

[0602] Software: OpenCV, TextBlob, TF-IDF, KMeans clustering, API communication library

[0603] Processing flow

[0604] 1. Data collection: When users visit websites or use apps, the data collection module collects their behavioral history and input information.

[0605] 2. Preference analysis: The preference analysis module analyzes the collected data to understand the user's hobbies and preferences.

[0606] 3. Emotion recognition: When the user inputs facial expressions and voice through the camera or microphone, the emotion recognition module analyzes them and recognizes the user's emotions.

[0607] 4. Product recommendation: Based on the results of preference analysis and emotion recognition, the product recommendation module retrieves the most suitable products from the API and recommends them to the user.

[0608] Specific examples

[0609] For example, if a user is searching for a "smartphone case," and the user has searched for "smartphone case" and visited related websites, and if the user expresses "happy" emotion, the system will recommend the most popular products in the "smartphone case" category.

[0610] Prompt Sentence Examples

[0611] Example prompts to input to a generative AI model:

[0612] Recommend the best smartphone case based on the user's behavioral history and emotions. The user recently searched for "smartphone case" and currently has the emotion "happy."

[0613] In this way, optimal product recommendations can be made taking into account the user's hobbies, preferences, and emotions, thereby increasing user satisfaction.

[0614] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0615] Step 1:

[0616] When a user visits a website or uses an app, the device collects behavioral history and input information. Specifically, it collects data such as the URLs of web pages visited by the user, search keywords, links clicked, and purchase history. This data is sent to a server and stored in a database. The input is the user's behavioral history and input information, and the output is the collected data.

[0617] Step 2:

[0618] The server analyzes the collected data to understand the user's hobbies and preferences. Specifically, it vectorizes the text data using TF-IDF (Term Frequency-Inverse Document Frequency) and clusters the user's preferences using KMeans clustering. The input is the collected data, and the output is clusters that indicate the user's hobbies and preferences.

[0619] Step 3:

[0620] When a user inputs facial expressions and voice through a camera or microphone, the device acquires this data and sends it to the server. Specifically, it acquires image data captured by the camera and voice data recorded by the microphone. The input is the user's facial expressions and voice data, and the output is the acquired data.

[0621] Step 4:

[0622] The server analyzes the acquired facial and voice data to recognize the user's emotions. Specifically, it uses OpenCV for facial recognition and TextBlob for text analysis of the voice data. The input is facial and voice data, and the output is a label indicating the user's emotion.

[0623] Step 5:

[0624] The server recommends optimal products based on the user's preferences and emotions. Specifically, it retrieves product information through an API and filters it based on the user's emotion tags. The input is the user's preference cluster and emotion label, and the output is a list of recommended products.

[0625] Step 6:

[0626] The terminal displays the recommended product list received from the server to the user. Specifically, the recommended products are displayed as a list on the screen of a smartphone or tablet, allowing the user to select a product. The input is the recommended product list, and the output is the product information displayed to the user.

[0627] In this way, optimal product recommendations are realized that take into account the user's behavioral history, input information, and emotions.

[0628] Example 2

[0629] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0630] Conventional property guide systems do not provide property information that fully takes into account the user's preferences and emotions, making it difficult to increase user satisfaction. Furthermore, they lack the functionality to adjust property information based on the user's emotions, which means they are unable to provide properties that meet the user's needs.

[0631] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for providing property guidance based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the area or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified area or location, a means for recognizing the user's emotions and adjusting the property guidance based on the emotions, and a means for saving and analyzing the user's preference information in a database. This enables optimal property guidance based on the user's preference information and emotions.

[0632] "User hobbies" refer to the activities and areas of interest that a user enjoys on a daily basis.

[0633] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[0634] "Shopping preference information" refers to information about the products and brands that users prefer when shopping, as well as their purchasing tendencies.

[0635] "Means for showing properties" refers to methods or systems for presenting suitable real estate properties to users.

[0636] "Means for identifying the area or place where a user lives" refers to methods or technologies for identifying the area or place where a user lives.

[0637] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route and distance between specific points on a map.

[0638] "Means for recognizing a user's emotions and adjusting property listings based on those emotions" refers to a method or system for analyzing a user's emotions and changing the content of property listings based on the results.

[0639] "Means for storing and analyzing user preference information in a database" refers to a method or system for storing information about user preferences in a database and analyzing that information.

[0640] MODE FOR CARRYING OUT THE INVENTION

[0641] The present invention relates to a system for providing optimal property information based on a user's preference information and emotions. A specific embodiment of this system will be described below.

[0642] 1. Collecting user preference information

[0643] When a user accesses the system, the server collects the user's preference information. Specifically, the server collects data based on the property information the user has viewed in the past and the preferences the user has entered (for example, favorite foods or hobbies). This data collection is done using a database management system (for example, MySQL).

[0644] 2. Property information based on user preferences

[0645] The server guides the user to properties suitable for them based on the collected preference information. For example, if the analysis shows that the user likes ramen, the server will prioritize displaying properties in areas with many ramen restaurants. Also, if the user's hobby is running, the server will prioritize displaying properties near parks and riverbanks. This process uses a geographic information system (GIS) and map API.

[0646] 3. Emotion Recognition by Emotion Engine

[0647] While the user is browsing property information, the device analyzes the user's facial expressions and voice using an emotion engine (for example, Microsoft® Azure® Emotion API or IBM Watson® Tone Analyzer). Based on the analysis results, the device recognizes the user's emotions.

[0648] 4. Adjust property listings based on emotions

[0649] The server receives data from the emotion engine and adjusts the property guide based on the user's emotions. For example, if the emotion of joy is recognized while the user is browsing property information, properties with similar characteristics to the property will be prioritized. Conversely, if the emotion of displeasure is recognized, properties with different characteristics from the property will be introduced.

[0650] Specific examples

[0651] Example 1: A user who likes ramen

[0652] If a user types "I like ramen," the server will prioritize displaying properties in areas with many ramen restaurants. For example, properties in Shinjuku Ward, Tokyo will be displayed.

[0653] Example 2: A user whose hobby is running

[0654] If a user enters "running is my hobby," the server will prioritize displaying properties near parks and riverbanks. For example, properties in Setagaya Ward, Tokyo will be displayed.

[0655] Example 3: Using the Emotion Engine

[0656] When a user is browsing property information, if the emotion engine recognizes the emotion of "happiness," it will prioritize properties with similar characteristics to the property in question. Conversely, if the emotion engine recognizes the emotion of "discomfort," it will prioritize properties with different characteristics from the property in question.

[0657] Prompt Sentence Examples

[0658] "Please explain the system that prioritizes showing properties in areas with many ramen restaurants if it is analyzed that the user likes ramen."

[0659] "Please explain a system that, when a user is browsing property information and an emotion engine recognizes the emotion of happiness, prioritizes showing properties with similar characteristics to the property."

[0660] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0661] Program processing flow

[0662] Step 1: Collecting user preferences

[0663] 1.1. A user logs into the system.

[0664] Input: User ID and password

[0665] Output: Login success or failure message

[0666] Specific operation: The user enters their ID and password, and the terminal sends this to the server. The server performs authentication and returns a response to the terminal indicating whether the login was successful.

[0667] 1.2. The server collects the user's past browsing history and input preferences.

[0668] Input: User ID

[0669] Output: User preference information

[0670] What it does: The server queries the database to retrieve the user's past browsing history and input preferences.

[0671] 1.3. The server stores the collected data in a database.

[0672] Input: Newly collected preference information

[0673] Output: Preference information stored in a database

[0674] Specific operation: The server adds the newly collected data to a database using a database management system (e.g., MySQL).

[0675] Step 2: Property information based on user preferences

[0676] 2.1. The server retrieves the user's preference information from the database.

[0677] Input: User ID

[0678] Output: User preference information

[0679] Specific operation: The server queries the database to obtain the user's preference information.

[0680] 2.2. The server filters suitable properties based on the acquired preference information.

[0681] Input: User preference information

[0682] Output: Filtered property information

[0683] Specific operation: The server analyzes the preference information and selects properties that match the conditions. For example, if the user likes ramen, it selects properties in an area with many ramen restaurants.

[0684] 2.3. The server displays the filtered property information to the user.

[0685] Input: Filtered property information

[0686] Output: Property information displayed to the user

[0687] Specific operation: The server sends the selected property information to the terminal, which then displays the property information to the user.

[0688] Step 3: Emotion recognition by the emotion engine

[0689] 3.1. The user views the property information.

[0690] Input: Property information

[0691] Output: User browsing behavior

[0692] Specific operation: The user views property information through the terminal.

[0693] 3.2. The device sends the user's facial expressions and voice to the emotion engine.

[0694] Input: User's facial expressions and voice data

[0695] Output: Data sent to the emotion engine

[0696] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the captured data to the emotion engine.

[0697] 3.3. The emotion engine analyzes the user's emotions and returns the results to the device.

[0698] Input: User's facial expressions and voice data

[0699] Output: Emotion analysis results

[0700] Specific operation: The emotion engine analyzes the received data, determines the type and intensity of the emotion, and sends the analysis results back to the device.

[0701] Step 4: Adjust your listing based on emotions

[0702] 4.1. The device sends the analysis results from the emotion engine to the server.

[0703] Input: Sentiment analysis results

[0704] Output: Data sent to the server

[0705] Specific operation: The device sends the analysis results received from the emotion engine to the server.

[0706] 4.2. The server adjusts the property information based on the analysis results.

[0707] Input: Sentiment analysis results

[0708] Output: Adjusted property information

[0709] Specific operation: The server analyzes the analysis results and adjusts the property guide based on the user's emotions. For example, it prioritizes properties with similar characteristics to the property for which the user expressed happy emotions.

[0710] 4.3. The server displays the adjusted property information to the user.

[0711] Input: Adjusted property information

[0712] Output: Adjusted property information displayed to the user

[0713] Specific operation: The server sends the adjusted property information to the terminal, and the terminal displays the adjusted property information to the user.

[0714] (Application example 2)

[0715] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0716] Conventional property guide systems do not fully consider the user's preferences and emotions when recommending properties, making it difficult for users to find the property that best suits them. Furthermore, there is a lack of a means to analyze the user's emotions in real time and recommend properties based on that analysis, which makes it difficult to increase user satisfaction.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0718] In this invention, the server includes means for showing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the region (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map at once based on the identified region or location, emotion recognition means for analyzing the user's emotions in real time, and means for recommending properties based on the user's preference information and emotions. This makes it possible to recommend optimal properties taking the user's preference information and emotions into consideration, thereby increasing user satisfaction.

[0719] "User hobbies" refer to the activities and areas of interest that a user regularly enjoys.

[0720] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[0721] "Shopping preference information" refers to information about the products, brands, and purchasing trends that users prefer when shopping.

[0722] "Means for showing properties" refers to methods or systems for providing users with appropriate property information.

[0723] "Means for identifying the region (prefecture, city, town) or location where the user lives" refers to a method or system for identifying the region or specific location where the user lives.

[0724] "Means for simultaneously displaying a route and distance on a map" means a method or system for simultaneously displaying a route and its distance on a map based on a specified area or location.

[0725] An "emotion recognition means" is a method or system for analyzing and recognizing emotions from a user's facial expressions and behavior in real time.

[0726] A "means for recommending properties" refers to a method or system for selecting and suggesting the most suitable properties based on the user's preference information and emotions.

[0727] In order to implement the present invention, the following system configuration and program are required.

[0728] System Configuration

[0729] 1. Hardware:

[0730] Smartphone: Use a smartphone with a camera.

[0731] Server: We use a server for data processing and property recommendations.

[0732] 2. Software:

[0733] EmotionRecognition Library: A library for analyzing user emotions in real time.

[0734] PropertyRecommendation Library: A library for recommending properties based on user preferences and sentiment.

[0735] Python: A programming language used to implement the program.

[0736] Program processing explanation

[0737] Collecting user preference information

[0738] Users can input their hobbies, favorite foods, and shopping preferences through a smartphone application, and preference information can also be automatically collected from the user's behavioral history.

[0739] emotion recognition

[0740] The smartphone camera captures the user's facial expressions while browsing property information, and the captured images are analyzed in real time using the EmotionRecognition library to recognize the user's emotions.

[0741] Property recommendation

[0742] The server uses the PropertyRecommendation library to recommend optimal properties based on the user's preference information and recognized emotions. The recommended properties are presented to the user through a smartphone application.

[0743] Specific examples

[0744] For example, if a user likes ramen and has a hobby of running, the system will prioritize showing properties with many ramen restaurants and close to parks and riverbanks.Also, if the system recognizes the emotion of joy while the user is browsing property information, it will prioritize showing properties with similar characteristics.

[0745] Prompt Sentence Examples

[0746] An example of a prompt sentence to input to the generative AI model is as follows:

[0747] Build a system that analyzes a user's preferences (favorite foods, hobbies, etc.) and their emotions while browsing property information to recommend the most suitable properties. For example, if a user loves ramen and enjoys running, prioritize properties with many ramen restaurants and close to parks and riverbanks. Also, if the system recognizes the emotion of joy while the user is browsing property information, prioritize properties with similar characteristics.

[0748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0749] Step 1:

[0750] Users enter information about their hobbies, favorite foods, and shopping preferences through a smartphone application, which is then sent to a server and stored in a database.

[0751] Input: User's hobbies, favorite foods, and shopping preferences

[0752] Output: User preference information stored on the server

[0753] Step 2:

[0754] Preference information is automatically collected from the user's behavioral history. The smartphone application collects the user's location information, past search history, purchase history, etc. and sends them to a server. The server analyzes this data and updates the user's preference information.

[0755] Input: User location, search history, purchase history

[0756] Output: Updated user preferences

[0757] Step 3:

[0758] When a user is browsing property information, the smartphone camera captures the user's facial expression. The captured image is sent to the server and analyzed in real time using the EmotionRecognition library. The server recognizes the user's emotions and saves the results.

[0759] Input: User facial expression image

[0760] Output: Perceived user emotion

[0761] Step 4:

[0762] The server recommends the most suitable property based on the user's preference information and recognized emotions using the PropertyRecommendation library. The server receives the user's preference information and emotion data as input and selects appropriate properties from the property database.

[0763] Input: User preference information, recognized emotions

[0764] Output: Recommended property list

[0765] Step 5:

[0766] The server sends the recommended property list to a smartphone application, where users can view the recommended property information. The property information also includes a function that displays the route and distance on a map at the same time.

[0767] Input: Recommended property list

[0768] Output: Property information displayed on the user's smartphone

[0769] Step 6:

[0770] If the user's emotion is recognized again while browsing the recommended property information, the server updates the property recommendation based on the emotion data, thereby always recommending the most suitable property according to the user's emotion.

[0771] Input: User emotion data

[0772] Output: Updated list of recommended properties

[0773] Example 3

[0774] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0775] Conventional property guide systems do not adequately suggest properties based on the user's hobbies, preferences, and life plans. They also lack the functionality to simultaneously display routes and distances on a map based on the user's area of ​​residence and frequently visited places. This makes it difficult for users to find properties that best suit their lifestyle.

[0776] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for introducing properties based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the region (prefecture, city, town) or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified region or location, a means for collecting the user's behavior history and analyzing the user's hobbies and preferences using a generative AI model, and a means for introducing properties based on the user's life plan. This makes it possible to efficiently suggest properties that are optimal for the user's lifestyle.

[0777] "User's hobbies, favorite foods, and shopping preference information" is information about the user's favorite activities, meals, and products they purchase.

[0778] "Means for showing properties" refers to methods or systems for suggesting suitable real estate properties to users.

[0779] "Means for identifying the user's area (prefecture, city, town) or location" refers to a method or system for identifying the area where a user lives or the places they frequently visit.

[0780] A "means for simultaneously displaying a route and distance on a map" is a method or system for simultaneously displaying a route and its distance on a map based on a specified region or location.

[0781] "Means for collecting user behavioral history" refers to methods or systems for collecting data such as users' website visit history, app usage history, social media posts, and purchase history.

[0782] "Means for analyzing user hobbies and preferences using a generative AI model" refers to a method or system for analyzing user hobbies and preferences using a generative AI model based on collected user behavioral history data.

[0783] "Means for introducing properties based on the user's life plan" refers to a method or system for suggesting appropriate real estate properties according to the user's future plans and lifestyle design.

[0784] MODE FOR CARRYING OUT THE INVENTION

[0785] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences, identifies the area or location where the user lives, and displays routes and distances on a map. It also includes a function to guide users to properties based on their life plans.

[0786] Hardware and software used

[0787] Hardware: Servers, user devices (PCs, smartphones)

[0788] Software: Map APIs (e.g., Google Maps API), database management systems (e.g., MySQL), generative AI models (e.g., GPT-3®)

[0789] Specific operation of the system

[0790] 1. Enter your user information

[0791] User: Using the device, the user enters the area of ​​residence (prefecture, city, town) and frequently visited places (workplace, school, hobby facilities, etc.). For example, the user enters information such as "Shinjuku Ward, Tokyo," "workplace is Shibuya Ward," and "hobby is visiting cafes" into the application's input form.

[0792] 2. Storage of User Information

[0793] Server: Receives the information entered by the user and saves it in a database. The server receives the entered information in JSON format and saves it in a MySQL database. For example, it saves the information "Shinjuku Ward, Tokyo," "Shibuya Ward," and "cafe hopping" along with the user ID.

[0794] 3. Identifying the area and displaying the map

[0795] Server: Identifies the area where the user lives and uses a map API to calculate the route and distance between properties in that area and places the user frequently visits. The server obtains coordinate information for "Shinjuku Ward, Tokyo" and "Shibuya Ward," and uses the Google Maps API to calculate the route and distance to properties in Shinjuku Ward and workplaces in Shibuya Ward. The server generates data to display the calculation results on a map and sends it to the user's device.

[0796] 4. Property information based on your life plan

[0797] Server: If a user inputs that they are expecting a baby in the near future, the server searches the database for properties with an environment suitable for raising children and provides guidance. Based on the information that a baby is expected to be born, the server searches the database for properties that meet criteria such as "close to a park" and "well-equipped with childcare support facilities." The search results are sent to the user's device.

[0798] 5. Property information based on your interests and tastes

[0799] Server: Collects information about the user's hobbies, favorite foods, and shopping preferences, and uses a generative AI model to analyze the user's hobbies and preferences. The server collects the user's behavioral history (frequently visited websites and apps, social media posts, purchase history, etc.) and inputs this into a generative AI model (such as GPT-3). The generative AI model analyzes this data and suggests properties that match the user's hobbies and preferences. For example, if a user enjoys visiting cafes, it will recommend properties in areas with many cafes.

[0800] Specific examples

[0801] User input information: "I live in Shinjuku, Tokyo, work in Shibuya, and my hobby is visiting cafes."

[0802] System Action:

[0803] 1. User: Enter "Shinjuku Ward, Tokyo," "Shibuya Ward," and "cafe hopping."

[0804] 2. Server: Stores this information in a database.

[0805] 3. Server: Uses the Google Maps API to calculate the route and distance to properties in Shinjuku Ward and workplaces in Shibuya Ward, and generates data to display on the map.

[0806] 4. Server: Based on the information that a child is expected to be born, the server searches for and provides guidance on properties suitable for raising children.

[0807] 5. Server: Using a generative AI model, it suggests properties suitable for a user who enjoys cafe hopping.

[0808] Prompt Sentence Examples

[0809] "Please show me properties suitable for a user who lives in Shinjuku Ward, Tokyo, works in Shibuya Ward, and whose hobby is cafe hopping."

[0810] "Please guide users who are planning to have children in the near future to properties with suitable environments for raising children."

[0811] In this way, the system guides the user to the most suitable property based on the information input by the user and the user's behavior history. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0812] Step 1:

[0813] Entering user information

[0814] User: Uses the device to enter the area where they live (state, city, town) and places they frequently visit (workplace, school, recreational facilities, etc.).

[0815] Input: Information such as "Shinjuku-ku, Tokyo," "Workplace is Shibuya-ku," and "Hobby is visiting cafes."

[0816] Specific behavior: The user enters information into an application's input form and presses the submit button.

[0817] Output: The entered user information is sent to the server.

[0818] Step 2:

[0819] Saving user information

[0820] Server: Receives the information entered by the user and stores it in a database.

[0821] Input: Location and frequently visited places entered by the user.

[0822] Specific operation: The server receives the entered information in JSON format and saves it in a MySQL database.

[0823] Output: User information stored in the database.

[0824] Step 3:

[0825] Identifying the area and displaying the map

[0826] Server: Identifies the area where the user lives and uses a map API to calculate routes and distances between properties in that area and the user's most frequently visited locations.

[0827] Input: User's location and frequent places information stored in a database.

[0828] Specific operation: The server obtains the coordinate information for "Shinjuku Ward, Tokyo" and "Shibuya Ward," and uses the Google Maps API to calculate the route and distance to a property in Shinjuku Ward and a workplace in Shibuya Ward.

[0829] Output: Data is generated to display the calculation results on a map and sent to the user's device.

[0830] Step 4:

[0831] Property information based on your life plan

[0832] Server: If a user inputs that they are planning to have a child in the near future, the server searches the database for properties with suitable environments for raising children and provides guidance.

[0833] Input: Information about the user's life plans (e.g., "I'm planning to have a child").

[0834] Specific operation: Based on the information that "a child is expected to be born," the server searches the database for properties that meet conditions such as "close to a park" and "with ample childcare support facilities."

[0835] Output: Search results are sent to the user's device.

[0836] Step 5:

[0837] Property information based on your interests and tastes

[0838] Server: Collects information about users' hobbies, favorite foods, and shopping preferences, and analyzes their hobbies and preferences using a generative AI model.

[0839] Input: User behavior history (frequently visited websites and apps, social media posts, purchase history, etc.).

[0840] Specific operation: The server inputs the collected behavioral history data into a generative AI model (such as GPT-3) and analyzes the user's hobbies and preferences.

[0841] Output: Based on the analysis results, properties that match the user's tastes and preferences are suggested and sent to the user's device.

[0842] (Application example 3)

[0843] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0844] Conventional property guide systems do not adequately guide users to properties based on their hobbies, preferences, or life plans. They also lack the functionality to simultaneously display on a map the route and distance to the user's area of ​​residence or frequently visited places. Furthermore, they lack the functionality to analyze users' hobbies and preferences from their behavioral history, posts on social networking services, and purchase history, making it difficult to guide users to the properties and stores that are best suited to them.

[0845] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for providing property guidance based on the user's hobbies, favorite food, and shopping preference information; means for identifying the area (prefecture, city, town) or location where the user lives; means for displaying routes and distances on a map based on the identified area or location; means for providing store guidance tailored to the user's life plan; means for analyzing the user's hobbies and preferences from the user's behavior history, posts on social networking services, and purchase history; and means for calculating routes and distances between the user's area of ​​residence and frequently visited places and displaying them on a map. This makes it possible to provide guidance to the most suitable properties and stores based on the user's hobbies, preferences, and life plan.

[0846] "User hobbies" are activities or interests that a user is interested in and enjoys.

[0847] "Favorite foods" refers to the types of foods or dishes that a user likes to eat.

[0848] "Shopping preference information" is information about a user's preferences and tendencies regarding the products and services they purchase.

[0849] "Means for showing properties" refers to methods or systems for suggesting suitable properties to users and providing information about them.

[0850] "User's geographic area" means the specific geographic area (state, city, town) where the user resides.

[0851] "Location determination methods" are methods or technologies used to identify and locate a user's current location or frequently visited locations.

[0852] "Means for simultaneously displaying route and distance on a map" refers to a method or system for simultaneously displaying the route and distance between a user's starting point and destination on a map.

[0853] "Means for providing store guidance tailored to a user's life plan" refers to a method or system for suggesting and providing guidance on appropriate stores based on the user's future plans and lifestyle designs.

[0854] "Behavioral history" is a record of a user's past actions and activities.

[0855] "Social networking service posts" refers to information such as messages, photos, and videos that users post publicly on social media.

[0856] "Purchase history" is a record of products and services a user has purchased in the past.

[0857] "Means for analyzing hobbies and preferences" refers to methods and technologies for analyzing and understanding users' hobbies and preferences.

[0858] "Means for calculating route and distance and displaying it on a map" refers to a method or system for calculating the route and distance between the user's starting point and destination and displaying it on a map.

[0859] A system for implementing this invention has the function of guiding users to properties and stores based on their hobbies, favorite foods, and shopping preferences. The system identifies the user's area of ​​residence and frequently visited places, and displays routes and distances on a map. The system also provides store guidance tailored to the user's life plan, analyzing the user's hobbies and preferences from their behavioral history, posts on social networking services, and purchase history.

[0860] Hardware and software used

[0861] Hardware: Smartphone

[0862] Software: Python, Google Maps API

[0863] Data processing and calculation

[0864] 1. Collecting information about your hobbies, favorite foods, and shopping preferences:

[0865] The server collects information entered by users, their behavioral history, posts on social networking services, and purchase history.

[0866] Based on the collected data, users' hobbies and preferences are analyzed.

[0867] 2. Identifying your region or location:

[0868] The server obtains location information to identify the user's current location and frequently visited locations.

[0869] Location information is obtained using the smartphone's GPS function and user input information.

[0870] 3. View route and distance on the map:

[0871] The server uses the Google Maps API to calculate routes and distances between the user's area and frequently visited places.

[0872] The calculated route and distance are displayed as a map on the smartphone screen.

[0873] 4. Store guidance tailored to your life plan:

[0874] The server suggests appropriate stores based on the user's life plan information.

[0875] For example, if a user is planning to have a child in the near future, the service will guide them to stores that have a wide selection of childcare products.

[0876] Specific examples

[0877] If a user lives in Shinjuku Ward, Tokyo, and frequently visits Shinjuku Station, Shinjuku Gyoen National Garden, and Shinjuku Sanchome Station, the system will display the route and distance to these locations on a map. If the user is expecting a child in the near future, the system will guide the user to stores with a wide selection of childcare products. Furthermore, if the user's hobby is visiting cafes, the system will recommend cafes.

[0878] Prompt Sentence Examples

[0879] Identify the area where the user lives as "Shinjuku Ward, Tokyo," and display on a map the route and distance between properties in that area and places the user frequently visits (Shinjuku Station, Shinjuku Gyoen National Garden, Shinjuku Sanchome Station). Also, if the user is planning to have a child in the near future, guide them to properties in an environment suitable for raising children. Furthermore, recommend cafes based on the user's hobby (cafe hopping).

[0880] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0881] Step 1:

[0882] The server collects information about the user's hobbies, favorite foods, and shopping preferences. As input, it receives user input information, behavioral history, social networking service posts, and purchase history. It analyzes this data to identify the user's hobbies and preferences. As output, it obtains analysis results about the user's hobbies and preferences.

[0883] Step 2:

[0884] The server identifies the user's area of ​​residence and frequently visited locations. As input, it receives the user's current location and frequently visited locations, including the smartphone's GPS and user input. As output, it obtains the user's area of ​​residence and frequently visited locations.

[0885] Step 3:

[0886] The server uses the Google Maps API to calculate the route and distance between the user's home location and frequently visited locations. As input, it uses the user's home location and frequently visited locations identified in step 2. As output, it receives the calculated route and distance information.

[0887] Step 4:

[0888] The server displays the calculated route and distance information as a map on the smartphone screen. As input, it uses the route and distance information obtained in step 3. As output, the map is displayed on the user's smartphone.

[0889] Step 5:

[0890] The server suggests appropriate stores based on the user's life plan information. As input, it receives the user's life plan information. For example, if the user plans to have a child in the near future, it uses that information. As output, it obtains a list of stores that fit the user's life plan.

[0891] Step 6:

[0892] The server recommends stores based on the user's hobbies and preferences. As input, it uses the analysis results of the user's hobbies and preferences obtained in step 1. As output, it obtains a list of stores that suit the user's hobbies and preferences.

[0893] Step 7:

[0894] The server calculates the route and distance between the user's area and frequently visited places and displays it on a map. As input, it uses the results of identifying the user's area and frequently visited places obtained in step 2. As output, it displays the calculated route and distance information on a map.

[0895] 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.

[0896] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other 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.

[0897] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0898] 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.

[0899] [Second embodiment]

[0900] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0901] 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.

[0902] 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).

[0903] 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.

[0904] 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.

[0905] 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).

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0912] "Example 1"

[0913] The system of the present invention collects user input information and behavioral history, and based on this information, understands the user's hobbies, favorite foods, and shopping preferences. For example, it analyzes the user's hobbies and preferences from the websites and apps the user frequently visits, the content of social media posts, and purchase history.

[0914] "Example 2"

[0915] Next, the system will recommend properties based on the user's preferences. For example, if the system determines that the user's favorite food is ramen, it will prioritize properties in areas with many ramen restaurants. If the system also determines that the user enjoys running, it will recommend properties near parks and riverbanks.

[0916] "Example 3"

[0917] Furthermore, the system identifies the area (prefecture, city, town) or location where the user lives and displays the route and distance on a map based on that area or location. For example, if the user identifies the area where they live as "Shinjuku Ward, Tokyo," the system displays the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.) on a map.

[0918] "Example 4"

[0919] The service also provides property information tailored to the user's life plan. For example, if the user is planning to have a child in the near future, the service will provide information on properties with an environment suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[0920] The processing flow of each embodiment will be described below.

[0921] "Example 1"

[0922] Step 1: Collect user input and behavioral history, such as information collected from websites and apps frequently visited, social media posts, and purchase history.

[0923] Step 2: Understand the user's hobbies, favorite foods, and shopping preferences based on the collected information. For example, analyze the user's hobbies and preferences based on the websites and apps they frequently visit, their social media posts, and their purchase history.

[0924] "Example 2"

[0925] Step 1: Based on the user's preferences, properties are shown. For example, if the user's favorite food is ramen, properties in areas with many ramen restaurants are shown first.

[0926] Step 2: If the system determines that the user enjoys running as a hobby, it will recommend properties near parks and riverbanks.

[0927] "Example 3"

[0928] Step 1: Identify the area (prefecture, city, town) or location where the user lives. For example, identify the area where the user lives as "Shinjuku Ward, Tokyo."

[0929] Step 2: Display the route and distance on a map based on the identified area or location. For example, display the route and distance between properties in the area and the user's frequently visited places (workplace, school, hobby facilities, etc.) on a map.

[0930] "Example 4"

[0931] Step 1: Understand the user's life plan. For example, if the user is planning to have a child in the near future, find out that information.

[0932] Step 2: We will show you properties that fit your life plan. For example, we will show you properties that are suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[0933] Example 1

[0934] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] Conventional property guide systems have difficulty in providing property information that fully reflects the user's hobbies and preferences, and have been unable to increase user satisfaction. In addition, there is a lack of means to effectively utilize the user's behavioral history, making it difficult to provide property information that matches the user's lifestyle.

[0936] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history data, a means for preprocessing the collected data, and a means for integrating the preprocessed data. This makes it possible to analyze the user's hobbies and preferences in detail and provide property information based on the analysis.

[0937] "User's hobbies, favorite foods, and shopping preference information" is information about the user's favorite activities, meals, and products they purchase.

[0938] A "means for showing properties" is a method or system for presenting suitable real estate properties to users.

[0939] "Means for identifying the area or location where a user lives" refers to a method or system for identifying the area or specific location where a user lives.

[0940] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route between specific points and the distance on a map.

[0941] "Means for collecting user behavioral history data" refers to methods or systems for collecting user behavioral data such as website visit history, social media posts, and purchase history.

[0942] "Means for preprocessing collected data" refers to a method or system for formatting collected data into a format that is easy to analyze.

[0943] A "means for integrating pre-processed data" is a method or system for combining pre-processed data from different data sources into a single data set.

[0944] The "means for analyzing the integrated data" refers to a method or system for analyzing the integrated data and extracting the user's hobbies and preferences.

[0945] "Means for generating user preference information based on analysis results" refers to a method or system for specifically identifying a user's hobbies and preferences based on the results of data analysis.

[0946] "Means for creating prompt sentences based on generated preference information" refers to a method or system for creating prompt sentences to be input into a generative AI model based on generated user preference information.

[0947] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences. The system collects user behavior history data, preprocesses, integrates, and analyzes it to generate user preference information and create prompt sentences.

[0948] Hardware and software used

[0949] server:

[0950] A central processing unit for data collection, preprocessing, integration, analysis, and generation.

[0951] The software used includes Python, Java, Google Analytics API, Twitter API, Facebook Graph API, e-commerce site API, and TensorFlow.

[0952] Data collection

[0953] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[0954] Website access logs: We use the Google Analytics API to collect information about the websites that users visit.

[0955] Social media posts: Use the Twitter API or Facebook Graph API to retrieve user posts.

[0956] Purchase history: Use the e-commerce site's API to obtain the user's purchase history.

[0957] Data Preprocessing

[0958] The server preprocesses the collected data. Specifically, it performs the following processes:

[0959] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[0960] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[0961] Data Integration

[0962] The server integrates the preprocessed data. Specifically, it performs the following processes:

[0963] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[0964] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[0965] Data analysis

[0966] The server analyzes the integrated data. Specifically, it performs the following processes:

[0967] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[0968] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[0969] Generate results

[0970] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[0971] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[0972] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[0973] Specific examples

[0974] If a user frequently visits a cooking recipe site, posts on social media about trying new recipes, and buys cooking equipment on an e-commerce site, the server will operate as follows:

[0975] 1. Data Collection:

[0976] The server uses the Google Analytics API to obtain access logs of cooking recipe sites visited by users.

[0977] The server uses the Twitter API to retrieve posts such as "I tried a new recipe."

[0978] The server uses the API of the EC site to obtain the purchase history of cooking utensils.

[0979] 2. Data Preprocessing:

[0980] The server deletes unnecessary information from the acquired access log and extracts the number of visits and duration of stay.

[0981] The server extracts posts containing specific keywords (e.g., recipes, cooking) from social media posts.

[0982] The server extracts data related to the cookware from the purchase history.

[0983] 3. Data integration:

[0984] The server compiles access logs, social media posts, and purchase history into a single data set.

[0985] The server converts the dataset into a data frame format, formatting it in a way that is easy to analyze.

[0986] 4. Data Analysis:

[0987] The server analyzes the text of the social media post and extracts that the user is interested in cooking.

[0988] The server learns the user's behavioral patterns and predicts their food preferences.

[0989] 5. Generate results:

[0990] The server creates and visualizes reports summarizing the user's hobbies and preferences.

[0991] The server creates a prompt to be input to the generative AI model, for example, "Please analyze the user's hobbies and preferences based on their behavioral history data. We will provide the following data: website access log: cooking recipe site, social media posts: tried new recipes, purchase history: cooking utensils."

[0992] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[0993] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0994] Step 1: Data collection

[0995] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[0996] Input: Website access logs, social media posts, purchase history

[0997] Operation:

[0998] Use the Google Analytics API to obtain access logs of websites visited by users.

[0999] Use the Twitter API or Facebook Graph API to retrieve users' social media posts.

[1000] Use the EC site's API to obtain the user's purchase history.

[1001] Output: Collected behavioral history data

[1002] Step 2: Data Preprocessing

[1003] The server preprocesses the collected data. Specifically, it performs the following processes:

[1004] Input: Collected behavioral history data

[1005] Operation:

[1006] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[1007] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[1008] Output: Preprocessed data

[1009] Step 3: Data Integration

[1010] The server integrates the preprocessed data. Specifically, it performs the following processes:

[1011] Input: Preprocessed data

[1012] Operation:

[1013] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[1014] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[1015] Output: A consolidated dataset

[1016] Step 4: Data analysis

[1017] The server analyzes the integrated data. Specifically, it performs the following processes:

[1018] Input: Integrated dataset

[1019] Operation:

[1020] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[1021] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[1022] Output: Analysis results (user's hobbies and preferences)

[1023] Step 5: Generate results

[1024] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[1025] Input: Analysis results (user hobbies and preferences)

[1026] Operation:

[1027] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[1028] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[1029] Output: User preference information, prompt

[1030] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[1031] (Application example 1)

[1032] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1033] Conventional property information systems do not adequately provide personalized information based on users' hobbies and preferences. Furthermore, they lack the functionality to recommend the best products for each individual user by utilizing the user's behavioral history and input information, making it difficult to increase user satisfaction. Furthermore, there is a lack of a way to notify users of discount information or new product information in a timely manner, making it difficult to effectively list products that users may be interested in.

[1034] 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.

[1035] In this invention, the server includes means for providing property guidance based on information about the user's hobbies, favorite foods, and shopping preferences, means for identifying the region (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map at once based on the identified region or location, means for collecting the user's behavior history and input information and recommending products that are optimal for each user, means for notifying the user of discount information and new product information for specific products based on the user's hobbies and preferences, and means for listing products that the user may be interested in based on the purchase history and browsing history. This enables personalized property guidance and product recommendations based on the user's hobbies and preferences, thereby increasing user satisfaction.

[1036] "User hobbies" are activities or areas of interest that a user engages in for enjoyment.

[1037] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1038] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[1039] "Means for showing properties" refers to methods or systems for providing users with appropriate property information and showing them around.

[1040] "User's geographic area" means the specific geographic area (state, city, town) where the user resides.

[1041] "Location determination means" means a method or system for identifying and locating a user's current location or a specific location.

[1042] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route and distance from the user's current location to the destination on a map.

[1043] "Behavioral history" refers to a record of a user's past actions and activities.

[1044] "Input information" refers to data or information provided by a user to a system.

[1045] A "means for recommending products" refers to a method or system for suggesting appropriate products to users based on their preferences and behavioral history.

[1046] "Discount Information" means information regarding price discounts offered on specific products or services.

[1047] "New product information" refers to information about products that have been newly introduced to the market.

[1048] "Purchase history" refers to a record of products and services a user has purchased in the past.

[1049] "Browsing history" refers to a record of web pages and product information that a user has viewed in the past.

[1050] A "listing means" refers to a method or system for displaying related items in list form based on specific conditions.

[1051] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1052] System Configuration

[1053] This system consists of a server that collects user behavior history and input information and recommends the best products for each user, and a terminal used by the user. Specifically, the following hardware and software are used:

[1054] Hardware

[1055] Server: A powerful computer used to collect, process, and analyze data.

[1056] Smartphone: A device where users enter information and view recommended products.

[1057] software

[1058] Python: A programming language used for data processing and implementing machine learning models.

[1059] Libraries for loading, preprocessing, and manipulating data.

[1060] Libraries used to build and evaluate machine learning models.

[1061] A tool for extracting features from user behavior history.

[1062] A function for calculating cosine similarity.

[1063] Data processing and calculation

[1064] The server processes and calculates the data in the following procedure.

[1065] 1. Data collection: The server collects user behavior history data (e.g., browsing history, purchase history) as a CSV file.

[1066] 2. Data preprocessing: The server reads the data and performs the necessary preprocessing.

[1067] 3. Feature extraction: The server extracts features from the user's behavior history.

[1068] 4. Similarity calculation: The server calculates the cosine similarity.

[1069] 5. Product recommendation: The server lists similar products and recommends them to the user.

[1070] Specific examples

[1071] For example, if a user has previously purchased "sports equipment," the server will notify them of new products and discounts in the same category. Also, if a user frequently visits websites for "outdoor equipment stores," the server will generate a list of related product recommendations.

[1072] Prompt Sentence Examples

[1073] "Create a Python program that recommends optimal products based on a user's behavioral history. User behavioral history data will be provided as a CSV file, and product data will also be provided as a CSV file. Implement a function to analyze user preferences, calculate cosine similarity, and recommend products."

[1074] In this way, personalized property information and product recommendations based on the user's hobbies and preferences become possible, thereby increasing user satisfaction.

[1075] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1076] Step 1:

[1077] The server collects user behavior history data (e.g., browsing history, purchase history).

[1078] Input: User behavior history data (CSV file)

[1079] Output: Collected behavioral history data

[1080] Specific operation: The server periodically collects the user's activity history on smartphones and other devices and saves it as a CSV file.

[1081] Step 2:

[1082] The server reads the collected behavior history data and performs the necessary preprocessing.

[1083] Input: Collected behavioral history data (CSV file)

[1084] Output: Preprocessed data frame

[1085] Specific operation: The server reads the CSV file using the Pandas library and performs preprocessing such as filling in missing values ​​and deleting unnecessary data.

[1086] Step 3:

[1087] The server extracts features from the user's behavior history.

[1088] Input: Preprocessed data frame

[1089] Output: Feature vector

[1090] Specific operation: The server extracts text features from the user's behavior history data and converts them into vector format.

[1091] Step 4:

[1092] The server calculates the cosine similarity.

[1093] Input: Feature vector

[1094] Output: Cosine similarity matrix

[1095] Specific operation: The server calculates the cosine similarity between feature vectors and generates a similarity matrix.

[1096] Step 5:

[1097] The server lists similar products and recommends them to the user.

[1098] Input: Cosine similarity matrix, product data (CSV file)

[1099] Output: Recommended products list

[1100] Specific operation: Based on the cosine similarity matrix, the server selects the products that are most similar to the user, and compares them with the product data to create a recommended product list.

[1101] Step 6:

[1102] The server notifies the user of discount information and new product information for specific products based on the user's hobbies and preferences.

[1103] Input: Recommended product list, discount information, new product information

[1104] Output: Notification message

[1105] Specific operation: The server extracts relevant discount information and new product information based on the recommended product list and sends notification messages to the user.

[1106] Step 7:

[1107] Users can use their smartphones to check the recommended products and purchase them if necessary.

[1108] Input: Notification message

[1109] Output: Purchase history

[1110] Specific operation: The user receives a notification message on their smartphone, checks the recommended products, and completes the purchase process. The purchase history is sent to the server and used for future recommendations.

[1111] Example 2

[1112] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1113] Conventional property guide systems have difficulty in recommending properties that fully reflect the user's preferences, and have been unable to increase user satisfaction. In addition, since property guides are not tailored to the user's lifestyle or hobbies, it takes a long time for the user to find the perfect property.

[1114] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting user preference information, a means for transmitting the input preference information to the server, a means for the server to store the preference information in a database, a means for the server to input the stored preference information to a generative AI model and analyze it, a means for the server to acquire property information from an external information providing service, a means for the server to filter the property information based on the analysis results of the generative AI model, a means for transmitting the filtered property information to a terminal, and a means for the terminal to display the property information to the user. This enables highly accurate property recommendations based on the user's preference information, thereby increasing user satisfaction.

[1115] "User preference information" is information that indicates a user's personal preferences and interests, such as preferred foods, hobbies, and lifestyle.

[1116] "Input means" refers to the interface or device through which a user provides preference information to a system, such as a web form or a mobile application.

[1117] "Means of transmission" refers to the communication protocol or technology used to transmit the preference information entered by the user to the server, such as the HTTPS protocol.

[1118] The term "means for storing in a database" refers to a data storage system for persistently storing the received preference information, such as a relational database management system.

[1119] A "generative AI model" refers to an artificial intelligence model that analyzes user preference information and recommends appropriate properties. For example, a model that uses natural language processing technology falls into this category.

[1120] "External information provider" refers to an external data source or API that provides property information. For example, a real estate information provider.

[1121] "Filtering means" refers to the process of selecting acquired property information to suit the user's preferences based on the analysis results of the generated AI model.

[1122] "Terminal" refers to the device used by the user to view property information, such as a computer or smartphone.

[1123] "Display means" refers to the technology or method used to visually present filtered property information to a user on a device, such as a web page using HTML and CSS.

[1124] The present invention relates to a system for recommending properties based on user preference information. A specific embodiment of this system will be described below.

[1125] System configuration

[1126] Input of user preference information

[1127] Users access the system using a terminal, such as a PC or smartphone, and enter their preference information. Users enter information such as their favorite foods and hobbies through a web form or mobile application.

[1128] Sending preference information

[1129] The device sends the user's input preference information to the server using the HTTPS protocol. The data is in JSON format.

[1130] Saving your preferences

[1131] The server analyzes the received JSON data and extracts preference information. The extracted information is stored in a database. A relational database management system (RDBMS) is used for the database, such as MySQL.

[1132] Analysis of preference information

[1133] The server inputs the stored preference information into a generative AI model to analyze the user's preferences. The generative AI model uses natural language processing technology (e.g., GPT-4). The generative AI model returns analysis results such as "I like ramen" or "I like running."

[1134] Acquiring property information

[1135] The server retrieves property information from an external information service. The external information service may be the API of a real estate information service. For example, the server sends an API request such as "GET / properties?location=Tokyo."

[1136] Filtering property information

[1137] The server filters the acquired property information based on the analysis results of the generated AI model. A Python library is used for filtering. For example, properties in areas with many ramen restaurants or properties near parks or riverbanks are selected.

[1138] Sending filtering results

[1139] The server sends the filtered property information to the terminal in JSON format, for example by sending the data to the endpoint "POST / filtered_properties".

[1140] Displaying property information

[1141] The property information received by the device is displayed to the user using HTML and CSS. For example, a list of properties can be displayed in a table format, with each property having a link to detailed information.

[1142] Specific examples

[1143] A user accesses the system and enters the following preferences:

[1144] Favorite food: Ramen

[1145] Hobbies: Running

[1146] The device sends this information to a server, which stores the information in a database and analyzes it using a generative AI model. As a result of the analysis, the user is classified as a "ramen lover" and a "running lover." The server retrieves property information from the API of a real estate information service and filters properties in areas with many ramen restaurants or near parks or riverbanks. The filtered property information is sent to the device and displayed to the user.

[1147] Prompt Sentence Examples

[1148] "Design a system that recommends optimal properties based on the user's preferences. For example, if it is analyzed that the user likes ramen, it should prioritize properties in areas with many ramen restaurants. Also, if it is analyzed that the user enjoys running, it should recommend properties near parks and riverbanks."

[1149] By inputting this prompt sentence into a generative AI model, it is possible to design a system that recommends properties based on the user's preferences.

[1150] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] Users access the system using their devices and enter their preferences, such as their favorite foods and hobbies, through web forms or mobile applications. The entered information is stored on the device as JSON-formatted data.

[1153] Input: User preference information (favorite foods, hobbies, etc.)

[1154] Output: Preference information data in JSON format

[1155] Step 2:

[1156] The device sends the preference information entered by the user to the server using the HTTPS protocol. Specifically, the device sends JSON format data to the server as an HTTPS request.

[1157] Input: Preference information data in JSON format

[1158] Output: Preference information data sent to the server

[1159] Step 3:

[1160] The server analyzes the received JSON data and extracts preference information. Specifically, the server uses a JSON parser to analyze the data and extract information such as favorite foods and hobbies. The extracted information is stored in a relational database management system (RDBMS).

[1161] Input: Preference information data sent to the server

[1162] Output: Preference information stored in a database

[1163] Step 4:

[1164] The server inputs the saved preference information into a generative AI model to analyze the user's preferences. Specifically, the server inputs the preference information into a generative AI model (e.g., GPT-4), and the model returns an analysis result such as "I like ramen" or "I like running."

[1165] Input: Preference information stored in a database

[1166] Output: Preference analysis results from the generative AI model

[1167] Step 5:

[1168] The server retrieves property information from an external information provider. Specifically, the server calls the API of the real estate information provider to retrieve property information. For example, it sends an API request such as "GET / properties?location=Tokyo".

[1169] Input: API request

[1170] Output: Retrieved property information

[1171] Step 6:

[1172] The server filters the acquired property information based on the analysis results of the generated AI model. Specifically, the server uses a Python library to select properties in areas with many ramen restaurants, or properties near parks or riverbanks.

[1173] Input: Preference analysis results by the generated AI model, acquired property information

[1174] Output: Filtered property information

[1175] Step 7:

[1176] The server sends the filtered property information to the terminal in JSON format. Specifically, it sends the data to the endpoint "POST / filtered_properties".

[1177] Input: Filtered property information

[1178] Output: Property information sent to the terminal

[1179] Step 8:

[1180] The device displays the received property information to the user. Specifically, the device uses HTML and CSS to display a list of properties in a table format, with each property provided with a link to detailed information.

[1181] Input: Property information sent to the terminal

[1182] Output: Property information displayed to the user

[1183] (Application example 2)

[1184] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1185] Conventional property information systems do not adequately provide personalized property recommendations based on users' preferences and lifestyles. Furthermore, they lack the functionality to improve their recommendations based on user feedback, resulting in a decline in user satisfaction.

[1186] 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 introducing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the region (prefecture, city, town) or location where the user lives, means for simultaneously displaying routes and distances on a map based on the identified region or location, means for collecting and analyzing the user's preference information and proposing optimal properties, means for displaying information about the areas surrounding the proposed properties (e.g., restaurants, parks, transportation), and means for improving the proposal content based on user feedback. This enables personalized property proposals based on the user's preferences and lifestyle, and is expected to improve user satisfaction.

[1187] "User hobbies" are activities or areas of interest that a user is interested in and enjoys.

[1188] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1189] "Shopping preference information" refers to information such as the products and brands that users prefer when shopping, and the frequency of purchases.

[1190] "Means for showing properties" refers to methods or systems for providing users with appropriate property information and showing them around.

[1191] "Means for identifying the area (prefecture, city, town) or location where the user lives" refers to a method or system for identifying and specifying the area or location where the user currently lives.

[1192] "Means for simultaneously displaying route and distance on a map" refers to a method or system for simultaneously displaying route and distance to a destination on a map based on a specified area or location.

[1193] "Means for collecting and analyzing user preference information and proposing optimal properties" refers to a method or system for collecting user preference information, analyzing it, and proposing optimal properties to users.

[1194] "Means for displaying surrounding information (restaurants, parks, transportation, etc.) of the proposed property" refers to a method or system for displaying information about restaurants, parks, transportation, etc. in the vicinity of the proposed property.

[1195] "Means for improving proposal content based on user feedback" refers to a method or system for collecting feedback from users and improving the content of property proposals based on that feedback.

[1196] The system for carrying out the present invention includes a plurality of means for providing property information based on user preference information. Specific embodiments thereof are described below.

[1197] System Configuration

[1198] The system mainly consists of a server, user terminals, and a database. The server is responsible for collecting and analyzing user preference information and proposing the most suitable properties. User terminals are devices such as smartphones and tablets that provide an interface for users to interact with the system. The database is used to store user preference information and property information.

[1199] Program processing

[1200] The server performs the process using the following means.

[1201] 1. How we collect and analyze user preferences:

[1202] The server collects information about the user's hobbies, favorite foods, and shopping preferences. This information is collected automatically from user input and user behavior history.

[1203] The collected information is stored in a database and analyzed using generative AI models.

[1204] 2. How we suggest the best property for you:

[1205] Based on the analysis results, the server proposes the most suitable property to the user, which matches the user's preferences.

[1206] For example, if a user likes ramen, the system will prioritize properties in areas with many ramen restaurants. If a user enjoys running, the system will suggest properties near parks or riverbanks.

[1207] 3. How to view the surrounding area of ​​the proposed property:

[1208] The server displays information about restaurants, parks, transportation, and other areas near the proposed property on the user's device.

[1209] This allows users to get a detailed understanding of the property's surroundings.

[1210] 4. How we improve our offerings based on user feedback:

[1211] The server collects feedback from users and uses it to improve the content of property suggestions.

[1212] Feedback is collected in the form of ratings and comments users make on proposed properties.

[1213] Hardware and software used

[1214] Hardware: Smartphone (Android), server

[1215] Software: Python, database, generative AI models

[1216] Specific examples

[1217] For example, if a user likes ramen and has running as a hobby, the server will prioritize suggesting properties in areas with many ramen restaurants or near parks or riverbanks. If the user provides feedback on the suggested properties, the server will use that feedback to improve the next suggestion.

[1218] Prompt Sentence Examples

[1219] Develop an application that suggests optimal properties based on the user's preferences. Analyze the user's favorite foods and hobbies and show properties based on those. For example, if the user likes ramen, show properties in areas with many ramen restaurants first. Or, if the user enjoys running, show properties near parks and riverbanks.

[1220] In this way, it becomes possible to make personalized property suggestions based on the user's preferences and lifestyle, which is expected to improve user satisfaction.

[1221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1222] Step 1:

[1223] The server collects user preference information. It stores input information from the user's device and the user's behavioral history in a database. Specifically, it collects information about hobbies, favorite foods, and shopping preferences entered by the user into the application, and automatically records the user's behavioral history (e.g., places visited, products purchased). The input data is saved in the database as information about the user's hobbies, favorite foods, and shopping preferences.

[1224] Step 2:

[1225] The server analyzes the collected preference information using a generative AI model. It obtains the user's preference information from the database and inputs it into the generative AI model. The generative AI model analyzes the user's preference patterns and extracts the characteristics of properties that are best suited to the user. The output is the property conditions that are best suited to the user.

[1226] Step 3:

[1227] The server searches for the most suitable property from a property database based on the analysis results. The property database stores detailed information about each property (e.g., location, surrounding facilities, price). The server filters properties that match the conditions obtained from the generative AI model and creates an optimal property list. The output is a property list to suggest to the user.

[1228] Step 4:

[1229] The server retrieves surrounding information for the proposed property and displays it on the user's device. The server retrieves surrounding information (e.g., restaurants, parks, and transportation) for each property included in the property list from the database and sends it to the user's device. The user's device displays the received surrounding information on a map. As an output, the user can visually check the surrounding environment of the property.

[1230] Step 5:

[1231] The user provides feedback on the proposed property. The user inputs their rating and comments on the proposed property through their terminal. The input feedback is sent to the server and stored in the database. The user's feedback data is obtained as the output.

[1232] Step 6:

[1233] The server improves the suggestions based on user feedback. It retrieves the feedback data from the database and inputs it into the generative AI model. The generative AI model analyzes the feedback and adjusts the property suggestion algorithm, so that the next property suggestions will better match the user's preferences. The output is an improved property suggestion algorithm.

[1234] Example 3

[1235] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1236] Conventional property guide systems do not adequately guide users to properties based on their hobbies, preferences, and life plans. Furthermore, they lack a function to simultaneously display routes and distances between the user's area and frequently visited places, making it difficult for users to obtain sufficient information when selecting a property. Furthermore, they lack a function to identify properties suitable for raising children, preventing them from providing appropriate property guidance according to the user's life stage.

[1237] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1238] In this invention, the server includes: means for providing property information based on the user's hobbies, favorite foods, and shopping preferences; means for identifying the user's area (prefecture, city, town) or location; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for providing property information based on the user's life plan; means for calculating routes and distances from the user's area of ​​residence to frequently visited places; means for identifying properties suitable for raising children based on the user's life plan; and means for displaying the location, route, and distance of properties on a map. This allows users to receive property information based on their hobbies, preferences, and life plan, and further allows them to simultaneously check the routes and distances between the user's area of ​​residence and frequently visited places. Furthermore, identifying properties suitable for raising children allows users to select properties appropriate for their life stage.

[1239] "User" means an individual or organization that uses the system to receive property information.

[1240] "Hobbies" are activities or areas of interest that a user personally enjoys.

[1241] "Favorite foods" refers to the foods and dishes that the user likes to eat.

[1242] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[1243] "Property" means a building or land for the User to live in or use.

[1244] "Area of ​​residence" refers to the administrative division (prefecture, city, town) where the user currently resides.

[1245] A "place" is a specific location or facility that a user frequently visits.

[1246] A "map" is a drawing or digital representation intended to visually represent a particular area or place.

[1247] A "route" is the path a user takes from one point to another.

[1248] "Distance" refers to the physical length from the starting point to the destination point when a user travels.

[1249] "Life plan" refers to the user's future lifestyle planning and plans.

[1250] A "property suitable for raising children" is a property that has a good environment for raising children and is located near parks and childcare support facilities.

[1251] A "server" is a computer system that receives information from users, processes it, and provides the results.

[1252] A "terminal" is a device that allows a user to receive and display information provided by a server.

[1253] "Identify" means to clearly identify a specific area or location based on the information entered by the user.

[1254] "Guiding" means providing information to users and helping them make appropriate choices.

[1255] "Calculating" means deriving the required result based on specific numbers or data.

[1256] "Displaying" means providing information visually to the user.

[1257] MODE FOR CARRYING OUT THE INVENTION

[1258] This invention is a system that guides users to properties based on their hobbies, favorite foods, shopping preferences, living area and location, and life plan. The system exchanges information between the server, terminal, and user, and provides users with the most suitable property information.

[1259] Server Roles

[1260] The server receives and analyzes the information provided by the user and generates appropriate property information. Specifically, it uses the following hardware and software:

[1261] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[1262] Software: Database Management System (DBMS), generative AI models

[1263] The server identifies the area where the user lives and the places they frequently visit, and then obtains the latitude and longitude from the user's input information to identify the area.

[1264] Next, the server collects property information from real estate databases and property information websites. For example, property information for Shinjuku Ward is obtained via API and stored in the database.

[1265] In addition, the server uses a route calculation function to calculate the route and distance from the user's area to frequently visited places, allowing the user to know from which property and how long it will take to reach the destination.

[1266] The server also identifies suitable properties for raising children based on the user's life plan, including database queries to select properties near parks and childcare facilities.

[1267] Device Role

[1268] The terminal receives the data sent from the server and visually displays it to the user. Specifically, it uses the following hardware and software:

[1269] Hardware: User interface devices such as computers, smartphones, and tablets

[1270] Software: Web browser, map display application

[1271] The device provides an interface for displaying property locations, routes, and distances on a map, allowing users to visually confirm information. Property information based on life plans is also displayed in list format and on a map.

[1272] User Roles

[1273] Users input information about the area where they live and the places they frequently visit into the system. For example, they provide information such as "Shinjuku Ward, Tokyo" or "My workplace is in Shibuya Ward." Users can then check the route and distance on the map displayed on their device to understand the relative locations of properties.

[1274] Users also input information about their life plans, such as "I plan to have a child in the near future." Based on this information, users can receive information about properties suitable for raising children.

[1275] Examples and prompts

[1276] For example, if a user lives in Shinjuku Ward, Tokyo, and their workplace is in Shibuya Ward, the server will collect property information within Shinjuku Ward and calculate the route and distance to their workplace in Shibuya Ward. The device will then display this information on a map, providing a visual representation to the user.

[1277] If a user inputs that they are planning to have a child in the near future, the server will identify properties in Shinjuku Ward that are suitable for raising children and send them to the device, which will then display them in a list or on a map and guide the user.

[1278] An example of a prompt is as follows:

[1279] "I live in Shinjuku Ward, Tokyo, and my workplace is in Shibuya Ward. Please show me the route and distance to properties in Shinjuku Ward and my workplace in Shibuya Ward. I'm also planning to have a child in the near future, so please also show me properties that are suitable for raising children."

[1280] By inputting this prompt sentence into the generative AI model, the system provides information according to the user's request. The flow of the identification process in Example 3 will be described with reference to FIG.

[1281] Step 1:

[1282] Enter information about the area where the user lives and the places they frequently visit.

[1283] Input: The user inputs information about the area where they live (e.g., "Shinjuku-ku, Tokyo") and the places they frequently visit (e.g., "My workplace is in Shibuya-ku") through the system interface.

[1284] Specific operation: The user enters "Shinjuku-ku, Tokyo" and "Shibuya-ku" and clicks the send button.

[1285] Output: The user's input information is sent to the server.

[1286] Step 2:

[1287] The server identifies the region and location.

[1288] Input: The server receives region and location information submitted by the user.

[1289] Data processing: Obtain latitude and longitude from the user's input information and identify the region.

[1290] Specific operation: The server obtains the latitude and longitude of "Shinjuku Ward, Tokyo" and also obtains the latitude and longitude of "Shibuya Ward."

[1291] Output: The latitude and longitude of the identified region and location is generated.

[1292] Step 3:

[1293] The server collects property information.

[1294] Input: The server collects property information based on the latitude and longitude information of the specified area.

[1295] Data processing: Property information within the specified area is obtained from real estate databases and property information sites via API and stored in a database.

[1296] Specific operation: The server obtains property information within Shinjuku Ward and stores it in a database.

[1297] Output: Property information within Shinjuku Ward is saved in the database.

[1298] Step 4:

[1299] The server calculates the route and distance.

[1300] Input: The server calculates the route and distance based on the property information in the identified area and the latitude and longitude information of frequently visited places.

[1301] Data calculation: Use the route calculation function to calculate the route and distance from the user's area to frequently visited places.

[1302] Specific operation: The server calculates the route and distance from each property in Shinjuku Ward to the workplace in Shibuya Ward and saves the results in a database.

[1303] Output: Route and distance information from each property to the workplace is generated.

[1304] Step 5:

[1305] The server generates a property guide based on the life plan.

[1306] Input: The server receives information about the user's life plans (e.g., "I plan to have a child in the near future").

[1307] Data processing: Run a database query to find properties near parks and childcare facilities.

[1308] Specific operation: The server filters properties in Shinjuku Ward that are near parks and childcare support facilities and generates a list.

[1309] Output: A list of properties suitable for raising children is generated.

[1310] Step 6:

[1311] The device displays a map and property information.

[1312] Input: The device receives property information, route, and distance data sent from the server.

[1313] Data processing: Use the map display function to display the property location, route, and distance on a map.

[1314] Specific operation: The device displays on a map properties in Shinjuku Ward and routes to workplaces in Shibuya Ward, as well as a list of properties suitable for raising children.

[1315] Output: Information is presented visually to the user.

[1316] (Application example 3)

[1317] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1318] Conventional property guide systems were unable to display the route and distance to the user's area or frequently visited places at once, making it inconvenient for users when selecting a property. Furthermore, property guides were not adequately based on the user's life plan, making it difficult for users to find a property that met their needs. This resulted in a decrease in the efficiency and satisfaction of users when selecting a property.

[1319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1320] In this invention, the server includes means for showing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the area (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map based on the identified area or location, means for identifying places the user frequently visits and displaying routes and distances to those locations on a map, and means for filtering properties based on the user's life plan, thereby enabling the user to efficiently find properties that fit their lifestyle and future plans.

[1321] "Hobbies" are activities or areas of interest that a user personally enjoys.

[1322] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1323] "Shopping preference information" refers to information about the products, brands, and purchasing trends that users prefer when shopping.

[1324] "Area of ​​residence" refers to the geographical area, such as the prefecture, city, or town where the user resides.

[1325] A "place" is a location or facility that a user visits to perform a particular activity.

[1326] "Means for displaying routes and distances on a map" means a method or device for visually showing routes and distances between specific points on a map.

[1327] "Frequently visited places" are locations or facilities that users frequently visit.

[1328] "Life plan" refers to the user's future lifestyle planning and plans, including events such as the birth of a child or changing jobs.

[1329] A "means for filtering properties" refers to a method or device for sorting property information based on specific conditions and extracting properties suitable for the user.

[1330] A system for implementing this invention includes means for guiding users to properties based on information about their hobbies, favorite foods, and shopping preferences; means for identifying the area or location where the user lives; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for identifying locations frequently visited by the user and displaying routes and distances to those locations on a map; and means for filtering properties based on the user's life plan.

[1331] 1. System Program

[1332] The program of this system performs the following processing.

[1333] 2. Program processing explanation

[1334] The server collects information about the user's hobbies, favorite foods, and shopping preferences, and then guides them to properties based on that information. It identifies the area or location where the user lives, and displays the route and distance on a map based on the identified area or location. It also identifies places the user frequently visits, and displays the route and distance to those places on a map. It filters properties based on the user's life plan.

[1335] Specifically, the server uses a Python program to display routes and distances on a map. User preferences and hobbies are automatically collected from user input and behavioral history, allowing users to efficiently find properties that fit their lifestyle and future plans.

[1336] 3. Specific Examples

[1337] For example, if a user lives in Shinjuku Ward, Tokyo, their workplace is near Shinjuku Station, and their hobby facility is near Yoyogi Park, the system will use this information to suggest the most suitable property. The map will also show the route and distance between the property and their workplace or hobby facility.

[1338] Example prompts to input to the generative AI model:

[1339] Create an application that identifies the area where the user lives as "Shinjuku-ku, Tokyo," and displays on a map the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.). Also, add a function to guide users to properties that fit their life plans.

[1340] This prompt can be used to communicate specific application requirements to the generative AI model.

[1341] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1342] Step 1:

[1343] The server collects information about the user's hobbies, favorite foods, and shopping preferences.

[1344] Input: User input information or behavioral history

[1345] Data processing: Collected information is stored in a database and user preference patterns are analyzed.

[1346] Output: User preference information data

[1347] Step 2:

[1348] The server identifies the region or location where the user lives.

[1349] Input: User's address information

[1350] Data processing: Address information is analyzed using a geographic information system (GIS) and converted into latitude and longitude information.

[1351] Output: Latitude and longitude information of the user's area

[1352] Step 3:

[1353] The server displays the route and distance on a map at once based on the identified region or location.

[1354] Input: Latitude and longitude information of the area where the user lives, and latitude and longitude information of frequently visited places

[1355] Data processing: Uses map API to calculate route and distance and display on the map

[1356] Output: Route and distance displayed on a map

[1357] Step 4:

[1358] The server identifies the user's frequently visited locations and displays the route and distance to those locations on a map.

[1359] Input: Information about places the user frequently visits (e.g., workplace, school, hobby facilities)

[1360] Data processing: Obtain the latitude and longitude information of these locations, use the map API to calculate the route and distance, and display it on the map.

[1361] Output: Route and distance displayed on a map

[1362] Step 5:

[1363] The server filters properties based on the user's life plan.

[1364] Input: User's life plan information (e.g., planned childbirth, planned job change)

[1365] Data processing: Selecting suitable properties from the property database based on life plan information

[1366] Output: Filtered property list

[1367] Step 6:

[1368] The server presents the filtered property list to the user.

[1369] Input: Filtered property list

[1370] Data processing: Converting property listings into a format for display in the user interface

[1371] Output: List of properties displayed in the user interface

[1372] In this way, users can efficiently find properties that suit their lifestyle and future plans.

[1373] 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.

[1374] "Example 1"

[1375] One embodiment of the invention that combines an emotion engine is a system that includes a means for recognizing a user's emotion. This system recognizes emotions from the user's facial expressions, voice, text input, etc. For example, the system captures the user's facial expressions and voice while browsing property information through a camera or microphone, and recognizes the user's emotion by analyzing that data.

[1376] "Example 2"

[1377] The system also includes a means for adjusting property information based on emotions recognized by the emotion engine. For example, if a user is recognized as expressing joy while browsing property information, properties with similar characteristics to that property will be prioritized. Conversely, if a user is recognized as expressing displeasure while browsing property information, properties with different characteristics from that property will be prioritized.

[1378] "Example 3"

[1379] The system also guides users to properties based on their hobbies, favorite foods, and shopping preferences. This information is collected automatically from user input or from their behavioral history. For example, the system collects information from the websites and apps the user frequently visits, social media posts, and purchase history, and uses this information to analyze the user's hobbies and preferences.

[1380] "Example 4"

[1381] It also identifies the area (prefecture, city, town) or location where the user lives and displays the route and distance on a map based on the identified area or location. For example, it identifies the area where the user lives as "Shinjuku Ward, Tokyo" and displays the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.) on a map.

[1382] The processing flow of each embodiment will be described below.

[1383] "Example 1"

[1384] Step 1: User views property information.

[1385] Step 2: The system captures the user's facial expressions, voice, text input, etc. through the camera and microphone.

[1386] Step 3: Analyze the acquired data and recognize the user's emotions.

[1387] "Example 2"

[1388] Step 1: User views property information.

[1389] Step 2: The emotion engine recognizes the user's emotion.

[1390] Step 3: The system adjusts the property guide based on the recognized emotion.

[1391] "Example 3"

[1392] Step 1: Collect information about hobbies, favorite foods, and shopping preferences from user input or user behavioral history.

[1393] Step 2: Based on the collected information, the system analyzes the user's hobbies and preferences.

[1394] Step 3: Based on the analysis results, the system will guide you to properties.

[1395] "Example 4"

[1396] Step 1: Identify the region (state, city, town) or location where the user lives.

[1397] Step 2: Based on the identified area or location, the system will display the route and distance on the map at once.

[1398] Step 3: The system will guide you to the property based on the route and distance displayed.

[1399] Example 1

[1400] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1401] Conventional property information systems do not adequately provide personalized suggestions based on the user's hobbies and preferences. In addition, they do not provide services that take the user's emotions into consideration, making it difficult to increase user satisfaction.

[1402] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for providing property guidance based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the area or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified area or location, a means for recognizing emotions from the user's facial expression, voice, text input, etc., and a means for integrating emotion data and preference data to provide personalized services to the user. This makes it possible to provide property guidance based on the user's hobbies and preferences, and further makes it possible to provide services that take the user's emotions into consideration.

[1403] "User's hobbies, favorite foods, and shopping preference information" is information about the activities, foods, and products that the user is interested in.

[1404] "Means for showing properties" refers to methods or systems for providing users with appropriate property information.

[1405] "Means for identifying the area or location where a user lives" refers to a method or system for identifying the area or specific location where a user lives.

[1406] A "means for simultaneously displaying a route and distance on a map" is a method or system for simultaneously displaying a route and its distance on a map based on a specified region or location.

[1407] "Means for recognizing emotions from a user's facial expressions, voice, text input, etc." refers to a method or system for analyzing data such as a user's facial expressions, voice, text input, etc., and identifying the user's emotions.

[1408] "Means for integrating emotional data and preference data to provide users with personalized services" refers to methods and systems for combining and analyzing a user's emotional data and preference data to provide individually optimized services to the user.

[1409] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences. The system identifies the area or location where the user lives and displays routes and distances on a map based on the identified area or location. It also recognizes emotions from the user's facial expressions, voice, text input, etc., and provides personalized services to the user by integrating the emotion data with the preference data.

[1410] Hardware and software used

[1411] Hardware

[1412] Server: A central processing unit for collecting, analyzing, and integrating data.

[1413] Devices: PCs, smartphones, tablets, etc. used by users

[1414] Camera: A device for capturing the user's facial expressions

[1415] Microphone: A device used to collect the user's voice.

[1416] software

[1417] Google Analytics: A tool for collecting user behavior history

[1418] Facebook API: A tool for collecting social media posts

[1419] Python: A programming language for data organization and analysis

[1420] Libraries for organizing data in dataframe format

[1421] Libraries for building machine learning models

[1422] OpenCV: A library for facial expression analysis

[1423] Google Cloud Speech-to-Text API: An API for converting voice data into text.

[1424] Specific examples

[1425] Example 1: Understanding user hobbies

[1426] If a user frequently visits a cooking recipe website, the server can analyze the site's data to determine the types of cuisine the user is particularly interested in, such as keywords like "pasta" or "curry."

[1427] Example 2: Emotion recognition

[1428] When a user browses property information, facial expression data captured by the camera is analyzed using OpenCV, and if the user smiles, it is determined that the user has a positive emotion toward the property. For example, if a user says, "I like this property," the voice data is converted to text using the Google Cloud Speech-to-Text API and recorded as a positive emotion.

[1429] Prompt Sentence Examples

[1430] "Write a program that collects data on websites frequently visited by users and identifies their interests. Specifically, you will use Python to analyze the content of web pages and identify topics that interest users."

[1431] "Create a program that captures the user's facial expression while they are browsing property information and uses OpenCV to analyze it. If the user smiles, it will be determined that they have a positive attitude toward the property."

[1432] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1433] Step 1:

[1434] A user uses a website or app.

[1435] Users use various websites and apps on a daily basis, for example, frequently visiting cooking recipe sites or shopping apps, which creates a user behavior history.

[1436] Step 2:

[1437] The server collects user behavior history.

[1438] The server uses Google Analytics and Facebook APIs to collect the user's behavioral history of the websites and apps they visit. Specifically, it records information about the links the user clicks and the pages they view. The input is the user's behavioral history data, and the output is organized behavioral history data.

[1439] Step 3:

[1440] The server collects social media posts and purchase history.

[1441] The server collects users' social media posts and online shopping purchase history. For example, it uses a social media API to obtain users' posts and an online shopping API to obtain their purchase history. The input is the social media post data and purchase history data, and the output is the organized post and purchase history data.

[1442] Step 4:

[1443] The server organizes the collected data.

[1444] The server uses Python libraries to organize the collected data in data frame format. For example, it can combine a user's visit history and purchase history into a single dataset. The input is the collected behavioral history data, posting data, and purchase history data, and the output is the integrated dataset.

[1445] Step 5:

[1446] The server analyzes the data using machine learning models.

[1447] The server builds a machine learning model to analyze the user's hobbies and preferences. For example, it uses a clustering algorithm to group the user's behavioral patterns. The input is the integrated dataset, and the output is the analysis results of the user's hobbies and preferences.

[1448] Step 6:

[1449] Users use the camera and microphone.

[1450] When a user browses property information, they use a camera or microphone. For example, facial expressions and voice are recorded using the built-in camera or microphone of a PC. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[1451] Step 7:

[1452] The server collects the user's facial expression and voice data.

[1453] The server collects the user's facial expression and voice data acquired through a camera and microphone. Specifically, it uses OpenCV to capture the facial expression data and convert the voice data into text. The input is the captured facial expression and voice data, and the output is an analyzable data format.

[1454] Step 8:

[1455] The server analyzes the data it has obtained.

[1456] The server analyzes the acquired facial and voice data to recognize the user's emotions. For example, using a facial expression analysis algorithm, if the user smiles, it can determine that the user has positive feelings toward the property. The input is analyzable facial and voice data, and the output is the analysis result regarding the user's emotions.

[1457] Step 9:

[1458] The server integrates the emotion data and preference data.

[1459] The server integrates the analyzed emotional data and preference data to understand the user's behavioral patterns and emotional changes. For example, if a user smiles while browsing a particular cooking recipe, it can determine that the user is particularly interested in that dish. The input is emotional data and preference data, and the output is an integrated user profile.

[1460] Step 10:

[1461] The server provides personalized services to the user.

[1462] The server provides personalized services to users based on the integrated data. For example, it prioritizes the display of cooking recipes or property information that the user is interested in. The input is an integrated user profile, and the output is the provision of personalized services.

[1463] (Application example 1)

[1464] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1465] Conventional property information systems do not provide optimal product recommendations that take into account the user's hobbies, preferences, and emotions, making it difficult to increase user satisfaction. Furthermore, there is a lack of effective ways to utilize the user's behavioral history and input information, which means that the system is unable to provide suggestions that meet the user's needs.

[1466] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for guiding users to properties based on information about their hobbies, favorite foods, and shopping preferences; means for identifying the area (prefecture, city, town) or location where the user lives; means for simultaneously displaying routes and distances on a map based on the identified area or location; means for collecting the user's behavior history and input information and understanding the user's hobbies and preferences; means for recognizing the user's emotions using an emotion engine; and means for recommending optimal products based on the user's preferences and emotions. This enables optimal product recommendations that take the user's hobbies, preferences, and emotions into consideration, thereby increasing user satisfaction.

[1467] "User Hobbies" refers to the activities and areas of interest that a user finds interesting and enjoys.

[1468] "Favorite foods" refers to the foods and dishes that a user likes to eat.

[1469] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[1470] "Means for showing properties" refers to methods or systems that provide users with appropriate property information.

[1471] "Means for identifying the area (prefecture, city, town) or location where the user lives" refers to a method or system for identifying the area or specific location where the user lives.

[1472] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route to a destination and the distance on a map.

[1473] "Means for collecting user behavior history and input information" refers to methods or systems for collecting the actions taken by users and the information entered.

[1474] "Means for understanding user hobbies and preferences" refers to methods or systems for understanding user interests and preferences based on collected data.

[1475] An "emotion engine" is a technology or system for analyzing and recognizing user emotions.

[1476] "Means for recognizing user emotions" refers to methods or systems that identify emotions from a user's facial expressions, voice, text, etc.

[1477] "Means for recommending optimal products" refers to methods or systems that suggest the most suitable products based on the user's hobbies, preferences, and emotions.

[1478] A system for implementing this invention includes means for collecting a user's behavioral history and input information, understanding the user's hobbies and preferences, means for recognizing the user's emotions using an emotion engine, and means for recommending optimal products based on the user's preferences and emotions.

[1479] System configuration

[1480] The system consists of the following main components:

[1481] 1. Data collection module: Collects user behavioral history and input information, such as website visit history, app usage history, social media posts, and purchase history.

[1482] 2. Preference analysis module: Analyzes the collected data to understand the user's hobbies and preferences. Here, algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency) and KMeans clustering are used.

[1483] 3. Emotion Recognition Module: Recognizes emotions from the user's facial expressions, voice, text input, etc. Specifically, it performs facial expression recognition using OpenCV and text analysis using TextBlob.

[1484] 4. Product recommendation module: Recommends the best products based on the user's preferences and emotions. Product information is obtained through API and filtered based on the user's emotion tags.

[1485] Hardware and Software

[1486] Hardware: Camera, microphone, user device (smartphone, tablet, PC)

[1487] Software: OpenCV, TextBlob, TF-IDF, KMeans clustering, API communication library

[1488] Processing flow

[1489] 1. Data collection: When users visit websites or use apps, the data collection module collects their behavioral history and input information.

[1490] 2. Preference analysis: The preference analysis module analyzes the collected data to understand the user's hobbies and preferences.

[1491] 3. Emotion recognition: When the user inputs facial expressions and voice through the camera or microphone, the emotion recognition module analyzes them and recognizes the user's emotions.

[1492] 4. Product recommendation: Based on the results of preference analysis and emotion recognition, the product recommendation module retrieves the most suitable products from the API and recommends them to the user.

[1493] Specific examples

[1494] For example, if a user is searching for a "smartphone case," and the user has searched for "smartphone case" and visited related websites, and if the user expresses "happy" emotion, the system will recommend the most popular products in the "smartphone case" category.

[1495] Prompt Sentence Examples

[1496] Example prompts to input to a generative AI model:

[1497] Recommend the best smartphone case based on the user's behavioral history and emotions. The user recently searched for "smartphone case" and currently has the emotion "happy."

[1498] In this way, optimal product recommendations can be made taking into account the user's hobbies, preferences, and emotions, thereby increasing user satisfaction.

[1499] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1500] Step 1:

[1501] When a user visits a website or uses an app, the device collects behavioral history and input information. Specifically, it collects data such as the URLs of web pages visited by the user, search keywords, links clicked, and purchase history. This data is sent to a server and stored in a database. The input is the user's behavioral history and input information, and the output is the collected data.

[1502] Step 2:

[1503] The server analyzes the collected data to understand the user's hobbies and preferences. Specifically, it vectorizes the text data using TF-IDF (Term Frequency-Inverse Document Frequency) and clusters the user's preferences using KMeans clustering. The input is the collected data, and the output is clusters that indicate the user's hobbies and preferences.

[1504] Step 3:

[1505] When a user inputs facial expressions and voice through a camera or microphone, the device acquires this data and sends it to the server. Specifically, it acquires image data captured by the camera and voice data recorded by the microphone. The input is the user's facial expressions and voice data, and the output is the acquired data.

[1506] Step 4:

[1507] The server analyzes the acquired facial and voice data to recognize the user's emotions. Specifically, it uses OpenCV for facial recognition and TextBlob for text analysis of the voice data. The input is facial and voice data, and the output is a label indicating the user's emotion.

[1508] Step 5:

[1509] The server recommends optimal products based on the user's preferences and emotions. Specifically, it retrieves product information through an API and filters it based on the user's emotion tags. The input is the user's preference cluster and emotion label, and the output is a list of recommended products.

[1510] Step 6:

[1511] The terminal displays the recommended product list received from the server to the user. Specifically, the recommended products are displayed as a list on the screen of a smartphone or tablet, allowing the user to select a product. The input is the recommended product list, and the output is the product information displayed to the user.

[1512] In this way, optimal product recommendations are realized that take into account the user's behavioral history, input information, and emotions.

[1513] Example 2

[1514] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1515] Conventional property guide systems do not provide property information that fully takes into account the user's preferences and emotions, making it difficult to increase user satisfaction. Furthermore, they lack the functionality to adjust property information based on the user's emotions, which means they are unable to provide properties that meet the user's needs.

[1516] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for providing property guidance based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the area or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified area or location, a means for recognizing the user's emotions and adjusting the property guidance based on the emotions, and a means for saving and analyzing the user's preference information in a database. This enables optimal property guidance based on the user's preference information and emotions.

[1517] "User hobbies" refer to the activities and areas of interest that a user enjoys on a daily basis.

[1518] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1519] "Shopping preference information" refers to information about the products and brands that users prefer when shopping, as well as their purchasing tendencies.

[1520] "Means for showing properties" refers to methods or systems for presenting suitable real estate properties to users.

[1521] "Means for identifying the area or place where a user lives" refers to methods or technologies for identifying the area or place where a user lives.

[1522] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route and distance between specific points on a map.

[1523] "Means for recognizing a user's emotions and adjusting property listings based on those emotions" refers to a method or system for analyzing a user's emotions and changing the content of property listings based on the results.

[1524] "Means for storing and analyzing user preference information in a database" refers to a method or system for storing information about user preferences in a database and analyzing that information.

[1525] MODE FOR CARRYING OUT THE INVENTION

[1526] The present invention relates to a system for providing optimal property information based on a user's preference information and emotions. A specific embodiment of this system will be described below.

[1527] 1. Collecting user preference information

[1528] When a user accesses the system, the server collects the user's preference information. Specifically, the server collects data based on the property information the user has viewed in the past and the preferences the user has entered (for example, favorite foods or hobbies). This data collection is done using a database management system (for example, MySQL).

[1529] 2. Property information based on user preferences

[1530] The server guides the user to properties suitable for them based on the collected preference information. For example, if the analysis shows that the user likes ramen, the server will prioritize displaying properties in areas with many ramen restaurants. Also, if the user's hobby is running, the server will prioritize displaying properties near parks and riverbanks. This process uses a geographic information system (GIS) and map API.

[1531] 3. Emotion Recognition by Emotion Engine

[1532] While the user is browsing property information, the device analyzes the user's facial expressions and voice using an emotion engine (for example, Microsoft Azure Emotion API or IBM Watson Tone Analyzer). Based on the analysis results, the device recognizes the user's emotions.

[1533] 4. Adjust property listings based on emotions

[1534] The server receives data from the emotion engine and adjusts the property guide based on the user's emotions. For example, if the emotion of joy is recognized while the user is browsing property information, properties with similar characteristics to the property will be prioritized. Conversely, if the emotion of displeasure is recognized, properties with different characteristics from the property will be introduced.

[1535] Specific examples

[1536] Example 1: A user who likes ramen

[1537] If a user types "I like ramen," the server will prioritize displaying properties in areas with many ramen restaurants. For example, properties in Shinjuku Ward, Tokyo will be displayed.

[1538] Example 2: A user whose hobby is running

[1539] If a user enters "running is my hobby," the server will prioritize displaying properties near parks and riverbanks. For example, properties in Setagaya Ward, Tokyo will be displayed.

[1540] Example 3: Using the Emotion Engine

[1541] When a user is browsing property information, if the emotion engine recognizes the emotion of "happiness," it will prioritize properties with similar characteristics to the property in question. Conversely, if the emotion engine recognizes the emotion of "discomfort," it will prioritize properties with different characteristics from the property in question.

[1542] Prompt Sentence Examples

[1543] "Please explain the system that prioritizes showing properties in areas with many ramen restaurants if it is analyzed that the user likes ramen."

[1544] "Please explain a system that, when a user is browsing property information and an emotion engine recognizes the emotion of happiness, prioritizes showing properties with similar characteristics to the property."

[1545] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1546] Program processing flow

[1547] Step 1: Collecting user preferences

[1548] 1.1. A user logs into the system.

[1549] Input: User ID and password

[1550] Output: Login success or failure message

[1551] Specific operation: The user enters their ID and password, and the terminal sends this to the server. The server performs authentication and returns a response to the terminal indicating whether the login was successful.

[1552] 1.2. The server collects the user's past browsing history and input preferences.

[1553] Input: User ID

[1554] Output: User preference information

[1555] What it does: The server queries the database to retrieve the user's past browsing history and input preferences.

[1556] 1.3. The server stores the collected data in a database.

[1557] Input: Newly collected preference information

[1558] Output: Preference information stored in a database

[1559] Specific operation: The server adds the newly collected data to a database using a database management system (e.g., MySQL).

[1560] Step 2: Property information based on user preferences

[1561] 2.1. The server retrieves the user's preference information from the database.

[1562] Input: User ID

[1563] Output: User preference information

[1564] Specific operation: The server queries the database to obtain the user's preference information.

[1565] 2.2. The server filters suitable properties based on the acquired preference information.

[1566] Input: User preference information

[1567] Output: Filtered property information

[1568] Specific operation: The server analyzes the preference information and selects properties that match the conditions. For example, if the user likes ramen, it selects properties in an area with many ramen restaurants.

[1569] 2.3. The server displays the filtered property information to the user.

[1570] Input: Filtered property information

[1571] Output: Property information displayed to the user

[1572] Specific operation: The server sends the selected property information to the terminal, which then displays the property information to the user.

[1573] Step 3: Emotion recognition by the emotion engine

[1574] 3.1. The user views the property information.

[1575] Input: Property information

[1576] Output: User browsing behavior

[1577] Specific operation: The user views property information through the terminal.

[1578] 3.2. The device sends the user's facial expressions and voice to the emotion engine.

[1579] Input: User's facial expressions and voice data

[1580] Output: Data sent to the emotion engine

[1581] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the captured data to the emotion engine.

[1582] 3.3. The emotion engine analyzes the user's emotions and returns the results to the device.

[1583] Input: User's facial expressions and voice data

[1584] Output: Emotion analysis results

[1585] Specific operation: The emotion engine analyzes the received data, determines the type and intensity of the emotion, and sends the analysis results back to the device.

[1586] Step 4: Adjust your listing based on emotions

[1587] 4.1. The device sends the analysis results from the emotion engine to the server.

[1588] Input: Sentiment analysis results

[1589] Output: Data sent to the server

[1590] Specific operation: The device sends the analysis results received from the emotion engine to the server.

[1591] 4.2. The server adjusts the property information based on the analysis results.

[1592] Input: Sentiment analysis results

[1593] Output: Adjusted property information

[1594] Specific operation: The server analyzes the analysis results and adjusts the property guide based on the user's emotions. For example, it prioritizes properties with similar characteristics to the property for which the user expressed happy emotions.

[1595] 4.3. The server displays the adjusted property information to the user.

[1596] Input: Adjusted property information

[1597] Output: Adjusted property information displayed to the user

[1598] Specific operation: The server sends the adjusted property information to the terminal, and the terminal displays the adjusted property information to the user.

[1599] (Application example 2)

[1600] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1601] Conventional property guide systems do not fully consider the user's preferences and emotions when recommending properties, making it difficult for users to find the property that best suits them. Furthermore, there is a lack of a means to analyze the user's emotions in real time and recommend properties based on that analysis, which makes it difficult to increase user satisfaction.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1603] In this invention, the server includes means for showing properties based on the user's hobbies, favorite foods, and shopping preference information, means for identifying the region (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map at once based on the identified region or location, emotion recognition means for analyzing the user's emotions in real time, and means for recommending properties based on the user's preference information and emotions. This makes it possible to recommend optimal properties taking the user's preference information and emotions into consideration, thereby increasing user satisfaction.

[1604] "User hobbies" refer to the activities and areas of interest that a user regularly enjoys.

[1605] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1606] "Shopping preference information" refers to information about the products, brands, and purchasing trends that users prefer when shopping.

[1607] "Means for showing properties" refers to methods or systems for providing users with appropriate property information.

[1608] "Means for identifying the region (prefecture, city, town) or location where the user lives" refers to a method or system for identifying the region or specific location where the user lives.

[1609] "Means for simultaneously displaying a route and distance on a map" means a method or system for simultaneously displaying a route and its distance on a map based on a specified area or location.

[1610] An "emotion recognition means" is a method or system for analyzing and recognizing emotions from a user's facial expressions and behavior in real time.

[1611] A "means for recommending properties" refers to a method or system for selecting and suggesting the most suitable properties based on the user's preference information and emotions.

[1612] In order to implement the present invention, the following system configuration and program are required.

[1613] System Configuration

[1614] 1. Hardware:

[1615] Smartphone: Use a smartphone with a camera.

[1616] Server: We use a server for data processing and property recommendations.

[1617] 2. Software:

[1618] EmotionRecognition Library: A library for analyzing user emotions in real time.

[1619] PropertyRecommendation Library: A library for recommending properties based on user preferences and sentiment.

[1620] Python: A programming language used to implement the program.

[1621] Program processing explanation

[1622] Collecting user preference information

[1623] Users can input their hobbies, favorite foods, and shopping preferences through a smartphone application, and preference information can also be automatically collected from the user's behavioral history.

[1624] emotion recognition

[1625] The smartphone camera captures the user's facial expressions while browsing property information, and the captured images are analyzed in real time using the EmotionRecognition library to recognize the user's emotions.

[1626] Property recommendation

[1627] The server uses the PropertyRecommendation library to recommend optimal properties based on the user's preference information and recognized emotions. The recommended properties are presented to the user through a smartphone application.

[1628] Specific examples

[1629] For example, if a user likes ramen and has a hobby of running, the system will prioritize showing properties with many ramen restaurants and close to parks and riverbanks.Also, if the system recognizes the emotion of joy while the user is browsing property information, it will prioritize showing properties with similar characteristics.

[1630] Prompt Sentence Examples

[1631] An example of a prompt sentence to input to the generative AI model is as follows:

[1632] Build a system that analyzes a user's preferences (favorite foods, hobbies, etc.) and their emotions while browsing property information to recommend the most suitable properties. For example, if a user loves ramen and enjoys running, prioritize properties with many ramen restaurants and close to parks and riverbanks. Also, if the system recognizes the emotion of joy while the user is browsing property information, prioritize properties with similar characteristics.

[1633] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1634] Step 1:

[1635] Users enter information about their hobbies, favorite foods, and shopping preferences through a smartphone application, which is then sent to a server and stored in a database.

[1636] Input: User's hobbies, favorite foods, and shopping preferences

[1637] Output: User preference information stored on the server

[1638] Step 2:

[1639] Preference information is automatically collected from the user's behavioral history. The smartphone application collects the user's location information, past search history, purchase history, etc. and sends them to a server. The server analyzes this data and updates the user's preference information.

[1640] Input: User location, search history, purchase history

[1641] Output: Updated user preferences

[1642] Step 3:

[1643] When a user is browsing property information, the smartphone camera captures the user's facial expression. The captured image is sent to the server and analyzed in real time using the EmotionRecognition library. The server recognizes the user's emotions and saves the results.

[1644] Input: User facial expression image

[1645] Output: Perceived user emotion

[1646] Step 4:

[1647] The server recommends the most suitable property based on the user's preference information and recognized emotions using the PropertyRecommendation library. The server receives the user's preference information and emotion data as input and selects appropriate properties from the property database.

[1648] Input: User preference information, recognized emotions

[1649] Output: Recommended property list

[1650] Step 5:

[1651] The server sends the recommended property list to a smartphone application, where users can view the recommended property information. The property information also includes a function that displays the route and distance on a map at the same time.

[1652] Input: Recommended property list

[1653] Output: Property information displayed on the user's smartphone

[1654] Step 6:

[1655] If the user's emotion is recognized again while browsing the recommended property information, the server updates the property recommendation based on the emotion data, thereby always recommending the most suitable property according to the user's emotion.

[1656] Input: User emotion data

[1657] Output: Updated list of recommended properties

[1658] Example 3

[1659] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1660] Conventional property guide systems do not adequately suggest properties based on the user's hobbies, preferences, and life plans. They also lack the functionality to simultaneously display routes and distances on a map based on the user's area of ​​residence and frequently visited places. This makes it difficult for users to find properties that best suit their lifestyle.

[1661] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for introducing properties based on the user's hobbies, favorite foods, and shopping preference information, a means for identifying the region (prefecture, city, town) or location where the user lives, a means for simultaneously displaying the route and distance on a map based on the identified region or location, a means for collecting the user's behavior history and analyzing the user's hobbies and preferences using a generative AI model, and a means for introducing properties based on the user's life plan. This makes it possible to efficiently suggest properties that are optimal for the user's lifestyle.

[1662] "User's hobbies, favorite foods, and shopping preference information" is information about the user's favorite activities, meals, and products they purchase.

[1663] "Means for showing properties" refers to methods or systems for suggesting suitable real estate properties to users.

[1664] "Means for identifying the user's area (prefecture, city, town) or location" refers to a method or system for identifying the area where a user lives or the places they frequently visit.

[1665] A "means for simultaneously displaying a route and distance on a map" is a method or system for simultaneously displaying a route and its distance on a map based on a specified region or location.

[1666] "Means for collecting user behavioral history" refers to methods or systems for collecting data such as users' website visit history, app usage history, social media posts, and purchase history.

[1667] "Means for analyzing user hobbies and preferences using a generative AI model" refers to a method or system for analyzing user hobbies and preferences using a generative AI model based on collected user behavioral history data.

[1668] "Means for introducing properties based on the user's life plan" refers to a method or system for suggesting appropriate real estate properties according to the user's future plans and lifestyle design.

[1669] MODE FOR CARRYING OUT THE INVENTION

[1670] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences, identifies the area or location where the user lives, and displays routes and distances on a map. It also includes a function to guide users to properties based on their life plans.

[1671] Hardware and software used

[1672] Hardware: Servers, user devices (PCs, smartphones)

[1673] Software: Map APIs (e.g., Google Maps API), database management systems (e.g., MySQL), generative AI models (e.g., GPT-3)

[1674] Specific operation of the system

[1675] 1. Enter your user information

[1676] User: Using the device, the user enters the area of ​​residence (prefecture, city, town) and frequently visited places (workplace, school, hobby facilities, etc.). For example, the user enters information such as "Shinjuku Ward, Tokyo," "workplace is Shibuya Ward," and "hobby is visiting cafes" into the application's input form.

[1677] 2. Storage of User Information

[1678] Server: Receives the information entered by the user and saves it in a database. The server receives the entered information in JSON format and saves it in a MySQL database. For example, it saves the information "Shinjuku Ward, Tokyo," "Shibuya Ward," and "cafe hopping" along with the user ID.

[1679] 3. Identifying the area and displaying the map

[1680] Server: Identifies the area where the user lives and uses a map API to calculate the route and distance between properties in that area and places the user frequently visits. The server obtains coordinate information for "Shinjuku Ward, Tokyo" and "Shibuya Ward," and uses the Google Maps API to calculate the route and distance to properties in Shinjuku Ward and workplaces in Shibuya Ward. The server generates data to display the calculation results on a map and sends it to the user's device.

[1681] 4. Property information based on your life plan

[1682] Server: If a user inputs that they are expecting a baby in the near future, the server searches the database for properties with an environment suitable for raising children and provides guidance. Based on the information that a baby is expected to be born, the server searches the database for properties that meet criteria such as "close to a park" and "well-equipped with childcare support facilities." The search results are sent to the user's device.

[1683] 5. Property information based on your interests and tastes

[1684] Server: Collects information about the user's hobbies, favorite foods, and shopping preferences, and uses a generative AI model to analyze the user's hobbies and preferences. The server collects the user's behavioral history (frequently visited websites and apps, social media posts, purchase history, etc.) and inputs this into a generative AI model (such as GPT-3). The generative AI model analyzes this data and suggests properties that match the user's hobbies and preferences. For example, if a user enjoys visiting cafes, it will recommend properties in areas with many cafes.

[1685] Specific examples

[1686] User input information: "I live in Shinjuku, Tokyo, work in Shibuya, and my hobby is visiting cafes."

[1687] System Action:

[1688] 1. User: Enter "Shinjuku Ward, Tokyo," "Shibuya Ward," and "cafe hopping."

[1689] 2. Server: Stores this information in a database.

[1690] 3. Server: Uses the Google Maps API to calculate the route and distance to properties in Shinjuku Ward and workplaces in Shibuya Ward, and generates data to display on the map.

[1691] 4. Server: Based on the information that a child is expected to be born, the server searches for and provides guidance on properties suitable for raising children.

[1692] 5. Server: Using a generative AI model, it suggests properties suitable for a user who enjoys cafe hopping.

[1693] Prompt Sentence Examples

[1694] "Please show me properties suitable for a user who lives in Shinjuku Ward, Tokyo, works in Shibuya Ward, and whose hobby is cafe hopping."

[1695] "Please guide users who are planning to have children in the near future to properties with suitable environments for raising children."

[1696] In this way, the system guides the user to the most suitable property based on the information input by the user and the user's behavior history. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1697] Step 1:

[1698] Entering user information

[1699] User: Uses the device to enter the area where they live (state, city, town) and places they frequently visit (workplace, school, recreational facilities, etc.).

[1700] Input: Information such as "Shinjuku-ku, Tokyo," "Workplace is Shibuya-ku," and "Hobby is visiting cafes."

[1701] Specific behavior: The user enters information into an application's input form and presses the submit button.

[1702] Output: The entered user information is sent to the server.

[1703] Step 2:

[1704] Saving user information

[1705] Server: Receives the information entered by the user and stores it in a database.

[1706] Input: Location and frequently visited places entered by the user.

[1707] Specific operation: The server receives the entered information in JSON format and saves it in a MySQL database.

[1708] Output: User information stored in the database.

[1709] Step 3:

[1710] Identifying the area and displaying the map

[1711] Server: Identifies the area where the user lives and uses a map API to calculate routes and distances between properties in that area and the user's most frequently visited locations.

[1712] Input: User's location and frequent places information stored in a database.

[1713] Specific operation: The server obtains the coordinate information for "Shinjuku Ward, Tokyo" and "Shibuya Ward," and uses the Google Maps API to calculate the route and distance to a property in Shinjuku Ward and a workplace in Shibuya Ward.

[1714] Output: Data is generated to display the calculation results on a map and sent to the user's device.

[1715] Step 4:

[1716] Property information based on your life plan

[1717] Server: If a user inputs that they are planning to have a child in the near future, the server searches the database for properties with suitable environments for raising children and provides guidance.

[1718] Input: Information about the user's life plans (e.g., "I'm planning to have a child").

[1719] Specific operation: Based on the information that "a child is expected to be born," the server searches the database for properties that meet conditions such as "close to a park" and "with ample childcare support facilities."

[1720] Output: Search results are sent to the user's device.

[1721] Step 5:

[1722] Property information based on your interests and tastes

[1723] Server: Collects information about users' hobbies, favorite foods, and shopping preferences, and analyzes their hobbies and preferences using a generative AI model.

[1724] Input: User behavior history (frequently visited websites and apps, social media posts, purchase history, etc.).

[1725] Specific operation: The server inputs the collected behavioral history data into a generative AI model (such as GPT-3) and analyzes the user's hobbies and preferences.

[1726] Output: Based on the analysis results, properties that match the user's tastes and preferences are suggested and sent to the user's device.

[1727] (Application example 3)

[1728] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1729] Conventional property guide systems do not adequately guide users to properties based on their hobbies, preferences, or life plans. They also lack the functionality to simultaneously display on a map the route and distance to the user's area of ​​residence or frequently visited places. Furthermore, they lack the functionality to analyze users' hobbies and preferences from their behavioral history, posts on social networking services, and purchase history, making it difficult to guide users to the properties and stores that are best suited to them.

[1730] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for providing property guidance based on the user's hobbies, favorite food, and shopping preference information; means for identifying the area (prefecture, city, town) or location where the user lives; means for displaying routes and distances on a map based on the identified area or location; means for providing store guidance tailored to the user's life plan; means for analyzing the user's hobbies and preferences from the user's behavior history, posts on social networking services, and purchase history; and means for calculating routes and distances between the user's area of ​​residence and frequently visited places and displaying them on a map. This makes it possible to provide guidance to the most suitable properties and stores based on the user's hobbies, preferences, and life plan.

[1731] "User hobbies" are activities or interests that a user is interested in and enjoys.

[1732] "Favorite foods" refers to the types of foods or dishes that a user likes to eat.

[1733] "Shopping preference information" is information about a user's preferences and tendencies regarding the products and services they purchase.

[1734] "Means for showing properties" refers to methods or systems for suggesting suitable properties to users and providing information about them.

[1735] "User's geographic area" means the specific geographic area (state, city, town) where the user resides.

[1736] "Location determination methods" are methods or technologies used to identify and locate a user's current location or frequently visited locations.

[1737] "Means for simultaneously displaying route and distance on a map" refers to a method or system for simultaneously displaying the route and distance between a user's starting point and destination on a map.

[1738] "Means for providing store guidance tailored to a user's life plan" refers to a method or system for suggesting and providing guidance on appropriate stores based on the user's future plans and lifestyle designs.

[1739] "Behavioral history" is a record of a user's past actions and activities.

[1740] "Social networking service posts" refers to information such as messages, photos, and videos that users post publicly on social media.

[1741] "Purchase history" is a record of products and services a user has purchased in the past.

[1742] "Means for analyzing hobbies and preferences" refers to methods and technologies for analyzing and understanding users' hobbies and preferences.

[1743] "Means for calculating route and distance and displaying it on a map" refers to a method or system for calculating the route and distance between the user's starting point and destination and displaying it on a map.

[1744] A system for implementing this invention has the function of guiding users to properties and stores based on their hobbies, favorite foods, and shopping preferences. The system identifies the user's area of ​​residence and frequently visited places, and displays routes and distances on a map. The system also provides store guidance tailored to the user's life plan, analyzing the user's hobbies and preferences from their behavioral history, posts on social networking services, and purchase history.

[1745] Hardware and software used

[1746] Hardware: Smartphone

[1747] Software: Python, Google Maps API

[1748] Data processing and calculation

[1749] 1. Collecting information about your hobbies, favorite foods, and shopping preferences:

[1750] The server collects information entered by users, their behavioral history, posts on social networking services, and purchase history.

[1751] Based on the collected data, users' hobbies and preferences are analyzed.

[1752] 2. Identifying your region or location:

[1753] The server obtains location information to identify the user's current location and frequently visited locations.

[1754] Location information is obtained using the smartphone's GPS function and user input information.

[1755] 3. View route and distance on the map:

[1756] The server uses the Google Maps API to calculate routes and distances between the user's area and frequently visited places.

[1757] The calculated route and distance are displayed as a map on the smartphone screen.

[1758] 4. Store guidance tailored to your life plan:

[1759] The server suggests appropriate stores based on the user's life plan information.

[1760] For example, if a user is planning to have a child in the near future, the service will guide them to stores that have a wide selection of childcare products.

[1761] Specific examples

[1762] If a user lives in Shinjuku Ward, Tokyo, and frequently visits Shinjuku Station, Shinjuku Gyoen National Garden, and Shinjuku Sanchome Station, the system will display the route and distance to these locations on a map. If the user is expecting a child in the near future, the system will guide the user to stores with a wide selection of childcare products. Furthermore, if the user's hobby is visiting cafes, the system will recommend cafes.

[1763] Prompt Sentence Examples

[1764] Identify the area where the user lives as "Shinjuku Ward, Tokyo," and display on a map the route and distance between properties in that area and places the user frequently visits (Shinjuku Station, Shinjuku Gyoen National Garden, Shinjuku Sanchome Station). Also, if the user is planning to have a child in the near future, guide them to properties in an environment suitable for raising children. Furthermore, recommend cafes based on the user's hobby (cafe hopping).

[1765] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1766] Step 1:

[1767] The server collects information about the user's hobbies, favorite foods, and shopping preferences. As input, it receives user input information, behavioral history, social networking service posts, and purchase history. It analyzes this data to identify the user's hobbies and preferences. As output, it obtains analysis results about the user's hobbies and preferences.

[1768] Step 2:

[1769] The server identifies the user's area of ​​residence and frequently visited locations. As input, it receives the user's current location and frequently visited locations, including the smartphone's GPS and user input. As output, it obtains the user's area of ​​residence and frequently visited locations.

[1770] Step 3:

[1771] The server uses the Google Maps API to calculate the route and distance between the user's home location and frequently visited locations. As input, it uses the user's home location and frequently visited locations identified in step 2. As output, it receives the calculated route and distance information.

[1772] Step 4:

[1773] The server displays the calculated route and distance information as a map on the smartphone screen. As input, it uses the route and distance information obtained in step 3. As output, the map is displayed on the user's smartphone.

[1774] Step 5:

[1775] The server suggests appropriate stores based on the user's life plan information. As input, it receives the user's life plan information. For example, if the user plans to have a child in the near future, it uses that information. As output, it obtains a list of stores that fit the user's life plan.

[1776] Step 6:

[1777] The server recommends stores based on the user's hobbies and preferences. As input, it uses the analysis results of the user's hobbies and preferences obtained in step 1. As output, it obtains a list of stores that suit the user's hobbies and preferences.

[1778] Step 7:

[1779] The server calculates the route and distance between the user's area and frequently visited places and displays it on a map. As input, it uses the results of identifying the user's area and frequently visited places obtained in step 2. As output, it displays the calculated route and distance information on a map.

[1780] 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.

[1781] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating 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.

[1782] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1783] 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.

[1784] [Third embodiment]

[1785] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1786] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1787] 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).

[1788] 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.

[1789] 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.

[1790] 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).

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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.

[1796] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1797] "Example 1"

[1798] The system of the present invention collects user input information and behavioral history, and based on this information, understands the user's hobbies, favorite foods, and shopping preferences. For example, it analyzes the user's hobbies and preferences from the websites and apps the user frequently visits, the content of social media posts, and purchase history.

[1799] "Example 2"

[1800] Next, the system will recommend properties based on the user's preferences. For example, if the system determines that the user's favorite food is ramen, it will prioritize properties in areas with many ramen restaurants. If the system also determines that the user enjoys running, it will recommend properties near parks and riverbanks.

[1801] "Example 3"

[1802] Furthermore, the system identifies the area (prefecture, city, town) or location where the user lives and displays the route and distance on a map based on that area or location. For example, if the user identifies the area where they live as "Shinjuku Ward, Tokyo," the system displays the route and distance between properties in that area and places the user frequently visits (workplace, school, hobby facilities, etc.) on a map.

[1803] "Example 4"

[1804] The service also provides property information tailored to the user's life plan. For example, if the user is planning to have a child in the near future, the service will provide information on properties with an environment suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[1805] The processing flow of each embodiment will be described below.

[1806] "Example 1"

[1807] Step 1: Collect user input and behavioral history, such as information collected from websites and apps frequently visited, social media posts, and purchase history.

[1808] Step 2: Understand the user's hobbies, favorite foods, and shopping preferences based on the collected information. For example, analyze the user's hobbies and preferences based on the websites and apps they frequently visit, their social media posts, and their purchase history.

[1809] "Example 2"

[1810] Step 1: Based on the user's preferences, properties are shown. For example, if the user's favorite food is ramen, properties in areas with many ramen restaurants are shown first.

[1811] Step 2: If the system determines that the user enjoys running as a hobby, it will recommend properties near parks and riverbanks.

[1812] "Example 3"

[1813] Step 1: Identify the area (prefecture, city, town) or location where the user lives. For example, identify the area where the user lives as "Shinjuku Ward, Tokyo."

[1814] Step 2: Display the route and distance on a map based on the identified area or location. For example, display the route and distance between properties in the area and the user's frequently visited places (workplace, school, hobby facilities, etc.) on a map.

[1815] "Example 4"

[1816] Step 1: Understand the user's life plan. For example, if the user is planning to have a child in the near future, find out that information.

[1817] Step 2: We will show you properties that fit your life plan. For example, we will show you properties that are suitable for raising children (near a park, with plenty of childcare support facilities, etc.).

[1818] Example 1

[1819] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1820] Conventional property guide systems have difficulty in providing property information that fully reflects the user's hobbies and preferences, and have been unable to increase user satisfaction. In addition, there is a lack of means to effectively utilize the user's behavioral history, making it difficult to provide property information that matches the user's lifestyle.

[1821] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history data, a means for preprocessing the collected data, and a means for integrating the preprocessed data. This makes it possible to analyze the user's hobbies and preferences in detail and provide property information based on the analysis.

[1822] "User's hobbies, favorite foods, and shopping preference information" is information about the user's favorite activities, meals, and products they purchase.

[1823] A "means for showing properties" is a method or system for presenting suitable real estate properties to users.

[1824] "Means for identifying the area or location where a user lives" refers to a method or system for identifying the area or specific location where a user lives.

[1825] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route between specific points and the distance on a map.

[1826] "Means for collecting user behavioral history data" refers to methods or systems for collecting user behavioral data such as website visit history, social media posts, and purchase history.

[1827] "Means for preprocessing collected data" refers to a method or system for formatting collected data into a format that is easy to analyze.

[1828] A "means for integrating pre-processed data" is a method or system for combining pre-processed data from different data sources into a single data set.

[1829] The "means for analyzing the integrated data" refers to a method or system for analyzing the integrated data and extracting the user's hobbies and preferences.

[1830] "Means for generating user preference information based on analysis results" refers to a method or system for specifically identifying a user's hobbies and preferences based on the results of data analysis.

[1831] "Means for creating prompt sentences based on generated preference information" refers to a method or system for creating prompt sentences to be input into a generative AI model based on generated user preference information.

[1832] This invention is a system that guides users to properties based on their hobbies, favorite foods, and shopping preferences. The system collects user behavior history data, preprocesses, integrates, and analyzes it to generate user preference information and create prompt sentences.

[1833] Hardware and software used

[1834] server:

[1835] A central processing unit for data collection, preprocessing, integration, analysis, and generation.

[1836] The software used includes Python, Java, Google Analytics API, Twitter API, Facebook Graph API, e-commerce site API, and TensorFlow.

[1837] Data collection

[1838] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[1839] Website access logs: We use the Google Analytics API to collect information about the websites that users visit.

[1840] Social media posts: Use the Twitter API or Facebook Graph API to retrieve user posts.

[1841] Purchase history: Use the e-commerce site's API to obtain the user's purchase history.

[1842] Data Preprocessing

[1843] The server preprocesses the collected data. Specifically, it performs the following processes:

[1844] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[1845] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[1846] Data Integration

[1847] The server integrates the preprocessed data. Specifically, it performs the following processes:

[1848] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[1849] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[1850] Data analysis

[1851] The server analyzes the integrated data. Specifically, it performs the following processes:

[1852] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[1853] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[1854] Generate results

[1855] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[1856] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[1857] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[1858] Specific examples

[1859] If a user frequently visits a cooking recipe site, posts on social media about trying new recipes, and buys cooking equipment on an e-commerce site, the server will operate as follows:

[1860] 1. Data Collection:

[1861] The server uses the Google Analytics API to obtain access logs of cooking recipe sites visited by users.

[1862] The server uses the Twitter API to retrieve posts such as "I tried a new recipe."

[1863] The server uses the API of the EC site to obtain the purchase history of cooking utensils.

[1864] 2. Data Preprocessing:

[1865] The server deletes unnecessary information from the acquired access log and extracts the number of visits and duration of stay.

[1866] The server extracts posts containing specific keywords (e.g., recipes, cooking) from social media posts.

[1867] The server extracts data related to the cookware from the purchase history.

[1868] 3. Data integration:

[1869] The server compiles access logs, social media posts, and purchase history into a single data set.

[1870] The server converts the dataset into a data frame format, formatting it in a way that is easy to analyze.

[1871] 4. Data Analysis:

[1872] The server analyzes the text of the social media post and extracts that the user is interested in cooking.

[1873] The server learns the user's behavioral patterns and predicts their food preferences.

[1874] 5. Generate results:

[1875] The server creates and visualizes reports summarizing the user's hobbies and preferences.

[1876] The server creates a prompt to be input to the generative AI model, for example, "Please analyze the user's hobbies and preferences based on their behavioral history data. We will provide the following data: website access log: cooking recipe site, social media posts: tried new recipes, purchase history: cooking utensils."

[1877] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[1878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1879] Step 1: Data collection

[1880] The server collects user behavior history data. Specifically, it obtains information from the following data sources:

[1881] Input: Website access logs, social media posts, purchase history

[1882] Operation:

[1883] Use the Google Analytics API to obtain access logs of websites visited by users.

[1884] Use the Twitter API or Facebook Graph API to retrieve users' social media posts.

[1885] Use the EC site's API to obtain the user's purchase history.

[1886] Output: Collected behavioral history data

[1887] Step 2: Data Preprocessing

[1888] The server preprocesses the collected data. Specifically, it performs the following processes:

[1889] Input: Collected behavioral history data

[1890] Operation:

[1891] Cleaning text data: Remove unnecessary characters and symbols and normalize text. Use Python's regular expressions library (re).

[1892] Data filtering: Extracting only the information you need, for example, extracting social media posts that contain specific keywords.

[1893] Output: Preprocessed data

[1894] Step 3: Data Integration

[1895] The server integrates the preprocessed data. Specifically, it performs the following processes:

[1896] Input: Preprocessed data

[1897] Operation:

[1898] Data merging: Combining data from different data sources into a single dataset using Python libraries.

[1899] Data shaping: Converting the integrated data into a format that is easier to analyze, such as into a data frame format.

[1900] Output: A consolidated dataset

[1901] Step 4: Data analysis

[1902] The server analyzes the integrated data. Specifically, it performs the following processes:

[1903] Input: Integrated dataset

[1904] Operation:

[1905] Natural Language Processing (NLP): Analyzes text data and extracts user interests and preferences. Uses Python libraries.

[1906] Applying machine learning models: Learn user behavior patterns and predict preferences. Build models using TensorFlow.

[1907] Output: Analysis results (user's hobbies and preferences)

[1908] Step 5: Generate results

[1909] The server generates user preference information based on the analysis results. Specifically, it performs the following processes.

[1910] Input: Analysis results (user hobbies and preferences)

[1911] Operation:

[1912] Report generation: Create a report summarizing user interests and preferences. Visualize it using Python libraries.

[1913] Prompt generation: Create a prompt to input to the generative AI model. For example, generate a prompt in the format "Please analyze the user's hobbies and preferences based on the user's behavioral history data."

[1914] Output: User preference information, prompt

[1915] In this way, the server can analyze the user's hobbies and preferences in detail and provide specific preference information.

[1916] (Application example 1)

[1917] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1918] Conventional property information systems do not adequately provide personalized information based on users' hobbies and preferences. Furthermore, they lack the functionality to recommend the best products for each individual user by utilizing the user's behavioral history and input information, making it difficult to increase user satisfaction. Furthermore, there is a lack of a way to notify users of discount information or new product information in a timely manner, making it difficult to effectively list products that users may be interested in.

[1919] 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.

[1920] In this invention, the server includes means for providing property guidance based on information about the user's hobbies, favorite foods, and shopping preferences, means for identifying the region (prefecture, city, town) or location where the user lives, means for displaying routes and distances on a map at once based on the identified region or location, means for collecting the user's behavior history and input information and recommending products that are optimal for each user, means for notifying the user of discount information and new product information for specific products based on the user's hobbies and preferences, and means for listing products that the user may be interested in based on the purchase history and browsing history. This enables personalized property guidance and product recommendations based on the user's hobbies and preferences, thereby increasing user satisfaction.

[1921] "User hobbies" are activities or areas of interest that a user engages in for enjoyment.

[1922] "Favorite foods" refers to foods or dishes that a user particularly enjoys eating.

[1923] "Shopping preference information" refers to a user's preferences and tendencies regarding the products and services they purchase.

[1924] "Means for showing properties" refers to methods or systems for providing users with appropriate property information and showing them around.

[1925] "User's geographic area" means the specific geographic area (state, city, town) where the user resides.

[1926] "Location determination means" means a method or system for identifying and locating a user's current location or a specific location.

[1927] "Means for displaying route and distance on a map at the same time" refers to a method or system for simultaneously displaying the route and distance from the user's current location to the destination on a map.

[1928] "Behavioral history" refers to a record of a user's past actions and activities.

[1929] "Input information" refers to data or information provided by a user to a system.

[1930] A "means for recommending products" refers to a method or system for suggesting appropriate products to users based on their preferences and behavioral history.

[1931] "Discount Information" means information regarding price discounts offered on specific products or services.

[1932] "New product information" refers to information about products that have been newly introduced to the market.

[1933] "Purchase history" refers to a record of products and services a user has purchased in the past.

[1934] "Browsing history" refers to a record of web pages and product information that a user has viewed in the past.

[1935] A "listing means" refers to a method or system for displaying related items in list form based on specific conditions.

[1936] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1937] System Configuration

[1938] This system consists of a server that collects user behavior history and input information and recommends the best products for each user, and a terminal used by the user. Specifically, the following hardware and software are used:

[1939] Hardware

[1940] Server: A powerful computer used to collect, process, and analyze data.

[1941] Smartphone: A device where users enter information and view recommended products.

[1942] software

[1943] Python: A programming language used for data processing and implementing machine learning models.

[1944] Libraries for loading, preprocessing, and manipulating data.

[1945] Libraries used to build and evaluate machine learning models.

[1946] A tool for extracting features from user behavior history.

[1947] A function for calculating cosine similarity.

[1948] Data processing and calculation

[1949] The server processes and calculates the data in the following procedure.

[1950] 1. Data collection: The server collects user behavior history data (e.g., browsing history, purchase history) as a CSV file.

[1951] 2. Data preprocessing: The server reads the data and performs the necessary preprocessing.

[1952] 3. Feature extraction: The server extracts features from the user's behavior history.

[1953] 4. Similarity calculation: The server calculates the cosine similarity.

[1954] 5. Product recommendation: The server lists similar products and recommends them to the user.

[1955] Specific examples

[1956] For example, if a user has previously purchased "sports equipment," the server will notify them of new products and discounts in the same category. Also, if a user frequently visits websites for "outdoor equipment stores," the server will generate a list of related product recommendations.

[1957] Prompt Sentence Examples

[1958] "Create a Python program that recommends optimal products based on a user's behavioral history. User behavioral history data will be provided as a CSV file, and product data will also be provided as a CSV file. Implement a function to analyze user preferences, calculate cosine similarity, and recommend products."

[1959] In this way, personalized property information and product recommendations based on the user's hobbies and preferences become possible, thereby increasing user satisfaction.

[1960] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1961] Step 1:

[1962] The server collects user behavior history data (e.g., browsing history, purchase history).

[1963] Input: User behavior history data (CSV file)

[1964] Output: Collected behavioral history data

[1965] Specific operation: The server periodically collects the user's activity history on smartphones and other devices and saves it as a CSV file.

[1966] Step 2:

[1967] The server reads the collected behavior history data and performs the necessary preprocessing.

[1968] Input: Collected behavioral history data (CSV file)

[1969] Output: Preprocessed data frame

[1970] Specific operation: The server reads the CSV file using the Pandas library and performs preprocessing such as filling in missing values ​​and deleting unnecessary data.

[1971] Step 3:

[1972] The server extracts features from the user's behavior history.

[1973] Input: Preprocessed data frame

[1974] Output: Feature vector

[1975] Specific operation: The server extracts text features from the user's behavior history data and converts them into vector format.

[1976] Step 4:

[1977] The server calculates the cosine similarity.

[1978] Input: Feature vector

[1979] Output: Cosine similarity matrix

[1980] Specific operation: The server calculates the cosine similarity between feature vectors and generates a similarity matrix.

[1981] Step 5:

[1982] The server lists similar products and recommends them to the user.

[1983] Input: Cosine similarity matrix, product data (CSV file)

[1984] Output: Recommended products list

[1985] Specific operation: Based on the cosine similarity matrix, the server selects the products that are most similar to the user, and compares them with the product data to create a recommended product list.

[1986] Step 6:

[1987] The server notifies the user of discount information and new product information for specific products based on the user's hobbies and preferences.

[1988] Input: Recommended product list, discount information, new product information

[1989] Output: Notification message

[1990] Specific operation: The server extracts relevant discount information and new product information based on the recommended product list and sends notification messages to the user.

[1991] Step 7:

[1992] Users can use their smartphones to check the recommended products and purchase them if necessary.

[1993] Input: Notification message

[1994] Output: Purchase history

[1995] Specific operation: The user receives a notification message on their smartphone, checks the recommended products, and completes the purchase process. The purchase history is sent to the server and used for future recommendations.

[1996] Example 2

[1997] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1998] Conventional property guide systems have difficulty in recommending properties that fully reflect the user's preferences, and have been unable to increase user satisfaction. In addition, since property guides are not tailored to the user's lifestyle or hobbies, it takes a long time for the user to find the perfect property.

[1999] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting user preference information, a means for transmitting the input preference information to the server, a means for the server to store the preference information in a database, a means for the server to input the stored preference information to a generative AI model and analyze it, a means for the server to acquire property information from an external information providing service, a means for the server to filter the property information based on the analysis results of the generative AI model, a means for transmitting the filtered property information to a terminal, and a means for the terminal to display the property information to the user. This enables highly accurate property recommendations based on the user's preference information, thereby increasing user satisfaction.

[2000] "User preference information" is information that indicates a user's personal preferences and interests, such as preferred foods, hobbies, and lifestyle.

[2001] "Input means" refers to the interface or device through which a user provides preference information to a system, such as a web form or a mobile application.

[2002] "Means of transmission" refers to the communication protocol or technology used to transmit the preference information entered by the user to the server, such as the HTTPS protocol.

[2003] The term "means for storing in a database" refers to a data storage system for persistently storing the received preference information, such as a relational database management system.

[2004] A "generative AI model" refers to an artificial intelligence model that analyzes user preference information and recommends appropriate properties. For example, a model that uses natural language processing technology falls into this category.

[2005] "External information provider" refers to an external data source or API that provides property information. For example, a real estate information provider.

[2006] "Filtering means" refers to the process of selecting acquired property information to suit the user's preferences based on the analysis results of the generated AI model.

[2007] "Terminal" refers to the device used by the user to view property information, such as a computer or smartphone.

[2008] "Display means" refers to the technology or method used to visually present filtered property information to a user on a device, such as a web page using HTML and CSS.

[2009] The present invention relates to a system for recommending properties based on user preference information. A specific embodiment of this system will be described below.

[2010] System configuration

[2011] Input of user preference information

[2012] Users access the system using a terminal, such as a PC or smartphone, and enter their preference information. Users enter information such as their favorite foods and hobbies through a web form or mobile application.

[2013] Sending preference information

[2014] The device sends the user's input preference information to the server using the HTTPS protocol. The data is in JSON format.

[2015] Saving your preferences

[2016] The server analyzes the received JSON data and extracts preference information. The extracted information is stored in a database. A relational database management system (RDBMS) is used for the database, such as MySQL.

[2017] Analysis of preference information

[2018] The server inputs the stored preference information into a generative AI model to analyze the user's preferences. The generative AI model uses natural language processing technology (e.g., GPT-4). The generative AI model returns analysis results such as "I like ramen" or "I like running."

[2019] Acquiring property information

[2020] The server retrieves property info...

Claims

1. A first means for inputting preference information which is information indicating a user's preferred foods, hobbies, and lifestyle; a second means for generating a first prompt sentence for outputting an analysis result of the user's preferences based on the preference information; A third means for inputting the generated first prompt sentence into a generative AI model to generate an analysis result of the user's preferences; A fourth means for transmitting property information filtered based on the generated analysis result of the user's preferences to the user's terminal; A system including:

2. A fifth means for displaying, on the user's terminal, surrounding information indicating restaurants, parks, and transportation facilities in the vicinity of the property shown in the property information. The system of claim 1 .

3. The first means receives input by the user of information on the area in which the user lives and frequently visited places, and information on the user's life plan; The second means generates a second prompt sentence for outputting a property guide tailored to the user's life plan based on the information input by the user, The third means inputs the generated second prompt sentence into a generative AI model to generate a property guide tailored to the user's life plan.

3. A system according to claim 1 or 2.

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