system

Abstract

We provide the system. [Solution] A terminal as a means of receiving information allows the user to input conditions and obtain the relevant information. A means of analyzing acquired conditional information through queries against a database and extracting property information based on those conditions, A means for using generative artificial intelligence to propose properties that meet the criteria based on extracted property information and to generate additional information, The terminal provides a means for displaying proposed property information and additional information to the user, A means of sending trend information and vacancy forecasts to the management company, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern real estate market, it is difficult to efficiently find a property that meets the conditions desired by users, and there is a sense of unfairness in that the rent paid by users includes a fee for the real estate agent. Furthermore, in management companies, it is difficult to appropriately grasp market fluctuations and vacancy situations, and there is a problem that it is difficult to formulate an efficient vacancy management and marketing strategy.

Means for Solving the Problems

[0005] This invention provides a means for receiving information from a user via a terminal acting as an information receiving means and acquiring relevant property information. It then integrates with a database based on the acquired information to extract property information that matches the user's criteria, and also provides optimal property suggestions through data analysis using artificial intelligence. This system displays additional information to facilitate user-satisfying choices. Furthermore, it solves the aforementioned problems by providing management companies with trend information and vacancy forecasts, thereby promoting the development of appropriate marketing strategies.

[0006] An "information receiving means" is a device that has the function of acquiring input information from a user and transmitting it as data for processing within the system.

[0007] A "terminal" is a computer device used by users to input information and to confirm displayed information.

[0008] A "database" is a collection of information that systematically stores property information and allows for searching and retrieval based on specific criteria.

[0009] A "query" is a command or question sent to a database to retrieve specific information.

[0010] "Generative artificial intelligence" refers to algorithms designed to analyze data and suggest suitable properties based on user criteria.

[0011] "Additional information" refers to additional information provided in relation to the property, including the surrounding environment and its reputation.

[0012] A "management company" refers to a corporation or organization that operates and manages real estate properties.

[0013] "Trend information" refers to data that shows market trends and consumer preferences derived from users' search and selection history.

[0014] "Vacancy forecast" refers to data that predicts the future vacancy rate of a property and can be used by property management companies to create efficient operational plans. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0036] This invention is a system for streamlining the search and suggestion of real estate properties. The system begins with the user entering their desired property criteria via a terminal. The terminal is responsible for analyzing the user's input and transmitting it to a server. The server then sends a query to a database based on the transmitted criteria data, retrieving property information that matches the criteria. This database stores information on numerous real estate properties, and relevant information is retrieved based on the search.

[0037] Next, the server scrutinizes the property information acquired using generative artificial intelligence. The generative AI analyzes the characteristics of properties and ranks them in order to make the best suggestions based on the user's desired conditions. In addition to property information, it also generates supplementary information such as the surrounding environment and reviews, and builds a dataset to provide to the user with this information as well.

[0038] The generated information is returned to the terminal via the server, and the terminal is designed to display it in a user-friendly interface. Users can select from the presented properties and view further details. Furthermore, the content viewed and selection trends of the user are analyzed on the server and provided to the property management company. This analysis includes user preferences and market trends, which the management company can use to improve vacancy forecasts and marketing strategies.

[0039] To give a concrete example, let's assume a user is planning to get a cat and is looking for a pet-friendly property in Tokyo. The user enters the following conditions into their device: "Tokyo, pet-friendly, 1LDK, under 200,000 yen." The device sends these conditions to the server, which extracts relevant property information from its database. The generated artificial intelligence analyzes the extracted information, selects several optimal properties, and presents them to the user along with additional information such as nearby parks and veterinary clinics. Through this process, the user can find their ideal property in a short amount of time.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters their desired property conditions (e.g., location, rent, floor plan, pet-friendly, etc.) into an input form on the terminal's interface. Once the user has finished entering the information, they press the submit button, and the data is sent to the system.

[0043] Step 2:

[0044] The terminal forms the user's input into a single data packet. This packet is not sent in plain text, but is encrypted as needed before being sent to the server.

[0045] Step 3:

[0046] The server receives data packets sent from the terminal. The server decodes them, analyzes the user's requirements, and generates SQL queries for the database.

[0047] Step 4:

[0048] The server uses the generated SQL query to access the database where property information is stored. This query retrieves property data that may match the user's criteria.

[0049] Step 5:

[0050] The server inputs property information returned from the database into the AI. The AI ​​analyzes each property and selects and ranks properties that meet the user's criteria. It also generates additional information that takes into account surrounding environment information and user reviews.

[0051] Step 6:

[0052] The server converts the information generated by the AI ​​into a user-friendly format. Then, it creates a new data packet to return this information to the terminal.

[0053] Step 7:

[0054] The terminal interprets data packets received from the server and displays the information in a format that allows the user to easily compare and select options. Property images, detailed information, and additional information are presented on the user's screen.

[0055] Step 8:

[0056] Users select properties of interest from the presented list and view more detailed information. During this process, data on the user's selection process and preferences is collected and fed back to the server.

[0057] Step 9:

[0058] The server analyzes user history data and provides management companies with information on market trends and vacancy forecasts. This is crucial data for management companies to develop efficient asset management and marketing strategies.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Traditional real estate search systems made it difficult for users to efficiently find their ideal property, and in particular, it was challenging to quickly narrow down the large amount of property information to find the one that best suited their criteria. Furthermore, it was difficult to provide supplementary information about the surrounding area or to offer suggestions tailored to the individual preferences of the user.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for inputting the user's desired conditions and acquiring relevant information fragments via an input device acting as an information acquisition device; means for analyzing the data through query processing with a large data storage device and selecting property information based on the conditions; and means for evaluating and proposing properties that meet the conditions based on the selected property information using generative intelligence, and generating surrounding information. This enables users to quickly and efficiently find their ideal property, and further enables the proposal of value-added information tailored to the user's preferences.

[0064] An "information acquisition device" is a device or system for which a user inputs desired conditions and is capable of acquiring relevant information fragments.

[0065] An "input device" refers to hardware or an interface used by a user to input specific desired conditions, and plays the role of passing that input information to subsequent processing.

[0066] "Relevant information fragments" refer to information collected or generated based on conditions entered by the user and used for a specific purpose.

[0067] A "large-scale data storage device" refers to a system or database for storing large datasets that allows for the selection of necessary data through query processing.

[0068] "Query processing" refers to the process performed to retrieve data from a large-scale data storage device based on specific conditions, and its purpose is to search and extract data.

[0069] "Generative intelligence" refers to a system that uses artificial intelligence technology to make evaluations and suggestions based on specific conditions, and is also capable of generating further information.

[0070] "Selection" refers to the act of extracting property information that meets specific criteria from a large amount of data and classifying or organizing it.

[0071] "Evaluation and proposal" is the process of analyzing acquired property information, determining its value and suitability to the user's requirements, and then presenting the best option.

[0072] "Surrounding area information" refers to information about the environment and services that accompany the basic information about the property, and is intended to provide additional value to users.

[0073] This invention is a system that enables users to efficiently search for real estate properties and make the best selection. Specifically, it is realized by using an input device as an information acquisition device, a server for data analysis, and a terminal that provides a user interface.

[0074] The first step for the user is to use a terminal to input their desired property criteria. This terminal analyzes the entered information and sends it to the server. The terminal utilizes the latest interface technology and is designed to be intuitive for the user.

[0075] Based on the received conditions, the server selects suitable data from property information stored in a large data storage device. This process utilizes a database management system to accurately execute complex query processing. Generative intelligence running on the server further analyzes the selected property information. This generative intelligence has the ability to generate property evaluations and suggestions that best suit the user's conditions.

[0076] The evaluated property information and additional surrounding area information are returned to the terminal and presented to the user. The terminal's display device organizes this information and makes it easy to understand visually. Based on this displayed information, the user can check more detailed property information and choose the most suitable property. In addition, the user's selection history and trends are analyzed on the server and provided to the management organization as trend information.

[0077] For example, if a user enters the conditions "Tokyo, pet-friendly, 1LDK, under 200,000 yen," this information is immediately sent to the server. The server accesses the database and searches for properties that meet the criteria. A generative AI model evaluates the suitability of the properties, and this information is presented to the user. This system allows users to efficiently find their ideal home.

[0078] Example of a prompt:

[0079] "I'm looking for a pet-friendly 1LDK apartment in Tokyo. Could you recommend a suitable property under 200,000 yen?"

[0080] "Please explain what methods should be used to efficiently extract properties that match the specified criteria from a real estate database."

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user uses a terminal, which acts as an information acquisition device, to input their desired real estate conditions. At this time, they specify detailed conditions such as "Tokyo, pet-friendly, 1LDK, under 200,000 yen." Data based on the entered conditions is generated, and the terminal processes this data.

[0084] Step 2:

[0085] The terminal sends the entered condition information to the server. Data integrity is checked during transmission to prevent errors. The output is condition data in a format that the server can parse.

[0086] Step 3:

[0087] The server executes queries against a large data storage device (database) based on the condition data received from the terminal. Through query processing, property information matching the conditions is selected and received as the selection result.

[0088] Step 4:

[0089] The server inputs the acquired property information into a generating AI model for detailed analysis. Specifically, it scores information such as the property's location, price, and facilities, and creates a ranking. The generating AI model then selects the most suitable property for the user and outputs property information for suggestion.

[0090] Step 5:

[0091] The server returns the evaluated property information output from the generated AI model to the terminal. This data includes not only basic property information but also additional information such as surrounding facilities and reviews.

[0092] Step 6:

[0093] The terminal displays property information received from the server in a user-friendly format. Users can review this output, select properties they are interested in, and obtain detailed information.

[0094] Step 7:

[0095] The server collects the user's property viewing history and selection trends. The server analyzes this data, generates trend information and vacancy predictions, and provides the output to the management organization.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In modern society, efficiently and effectively finding usable spaces is challenging. In particular, there is a need for information presented based on the user's specific preferences and for immediate information acquisition on-site, but conventional systems fail to adequately address this. Therefore, there is a demand for improved efficiency in information acquisition and presentation.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for recognizing the user's preferences and displaying suitable information using a visual device as an information presentation device; means for analyzing the recognized preference information through queries against a data structure and extracting spatial information based on the preference; and means for proposing a space suitable for the preference and generating surrounding environment information based on the extracted spatial information using generative artificial intelligence. As a result, the user can quickly and accurately obtain the necessary spatial information and efficiently grasp the information through the visual device.

[0101] "Visual devices as information presentation devices" refers to hardware devices and related technologies for displaying information in real time through the user's vision.

[0102] "User preferences" refer to the conditions and requirements specified by the user when seeking spatial information.

[0103] "Displaying compatibility information" refers to making information that matches the user's preferences visible to the user through a visual device.

[0104] "Querying a data structure" refers to the process of sending data retrieval requests to databases and other information repositories based on the user's preferences.

[0105] "Spatial information" refers to data about a specific place or facility, encompassing comprehensive information including location and characteristics.

[0106] "Generative artificial intelligence" refers to computer programs and algorithms that analyze large amounts of data and perform condition-based recommendations and information generation.

[0107] "Generating surrounding environment information" refers to compiling geographical, social, or service-related information related to a specific space and providing it to the user.

[0108] To implement this invention, it is necessary to construct a system that includes a visual device, a server, a data structure, and generative artificial intelligence.

[0109] First, the user uses a visual device as an information presentation device. This device includes, for example, smart glasses or a head-mounted display, and has the function of recognizing the user's desired conditions through voice input or gesture input. By utilizing speech recognition technology such as Google® Speech-to-Text API, the user's wishes are converted into digital data in real time.

[0110] Next, the data input through the visual device is transferred to the server, which then queries the data structure. The data structure uses a database such as MongoDB, and it extracts spatial information that matches the user's conditions. In this process, the server identifies data that matches the conditions and retrieves the corresponding spatial information.

[0111] Furthermore, the server uses generative artificial intelligence to analyze the extracted spatial information, ranking and rearranging it in a way that best suits the user's preferences. Here, AI engines such as OpenAI's GPT series are utilized to enable complex data analysis and generation.

[0112] The generated information is presented to the user through a visual device. Using a 3D development environment such as Unity, the visual device displays acquired spatial information and surrounding environment information in augmented reality (AR) format, enabling the user to understand it intuitively.

[0113] As a concrete example, if a user looking to buy a new house wears smart glasses and voice-inputs the conditions "quiet residential area, 4 bedrooms, living room, dining room, kitchen, with balcony," the system will immediately visualize property information that matches those conditions, along with information about nearby parks, supermarkets, and other amenities, through a visual device.

[0114] This allows users to quickly grasp complex information and retrieve the desired information more efficiently. A prompt message that guides this process is: "Get the user's desired conditions via voice and convert them into objects. Design prompts to visualize and present property information and surrounding environment information that meet the conditions."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The device receives user requests via voice or gesture input. It utilizes the Google Speech-to-Text API for voice recognition and an accelerometer for gesture recognition. This converts the received requests into text format.

[0118] Step 2:

[0119] The terminal sends the desired conditions obtained in Step 1 to the server. The server receives this as input data and generates a query to the database. This query is performed against MongoDB and extracts spatial information that matches the user's conditions.

[0120] Step 3:

[0121] The server passes spatial information obtained from the database to the generative artificial intelligence (AI). The AI ​​analyzes this data and evaluates whether it meets the user criteria. Next, it ranks the information and sorts it by priority. This process involves analysis and generation using OpenAI's GPT series.

[0122] Step 4:

[0123] The server generates surrounding environment information based on the ranked spatial information obtained in step 3. This involves using the Google Maps API, etc., to collect and process geographical information and construct data with information highly relevant to the conditions.

[0124] Step 5:

[0125] The device receives the information generated in step 4 and visualizes it in a 3D development environment using Unity. This allows the user to view the information as augmented reality through visual devices such as smart glasses.

[0126] Step 6:

[0127] Based on the information presented, users select spaces that interest them and provide feedback to the server via their device. The server uses this feedback to analyze user biases and trends, which helps improve the accuracy of future suggestions.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention is a system for improving the user's property search experience, and in particular, by combining it with an emotion engine, it can evaluate the user's emotional state and reflect it in property suggestions. The user first inputs their desired conditions (e.g., location, price, floor plan, etc.) through a terminal. This information is received by the server, and appropriate property information is collected through queries against the database.

[0130] The server uses artificial intelligence to evaluate this property information and select the best property that meets the user's preferences. This evaluation process considers not only the user's input conditions but also emotional data acquired by the emotion engine. The emotion engine analyzes the user's emotional state in real time, determining, for example, whether the user is tense or relaxed.

[0131] This sentiment analysis data is incorporated into the property suggestion algorithm, which adjusts the order and content of the suggested properties to suit the user's emotions. For example, if the user is relaxed, more options are presented slowly, while if the user is stressed, the most likely preferred options are prioritized.

[0132] The terminal displays property information received from the server, along with suggestions generated by the emotion engine, in a user-friendly interface. Users compare the displayed properties and select those of interest. This selection is fed back to the server and used to improve future suggestions.

[0133] As a concrete example, let's assume a user is looking for an apartment for their first time living alone. The emotion engine detects when the user is feeling anxious, and the server adjusts its suggestions based on this emotional information. Specifically, it focuses on suggesting properties in safe areas with rents that do not exceed expectations. The goal is to provide suggestions that match the user's emotions and make the apartment search a more enjoyable experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user accesses the terminal interface and enters their desired property search criteria (e.g., location, rent, floor plan). Once finished, the user presses the "Search" button to submit their search criteria.

[0137] Step 2:

[0138] The terminal converts the entered conditions into data packets and sends them to the server. This data contains the user's basic preferences.

[0139] Step 3:

[0140] The server analyzes the conditions received from the terminal and sends a query to the database. The query filters property information based on the user's conditions and collects information on the relevant properties.

[0141] Step 4:

[0142] Simultaneously, an emotion engine built into the device collects emotion data in real time from the user's facial expressions and voice data. This emotion data indicates the user's emotional state when viewing the information.

[0143] Step 5:

[0144] The server retrieves property information from the database and emotion data from the emotion engine, and inputs it into the generative artificial intelligence. The generative AI integrates this information to generate property suggestions that best match the user's emotional state.

[0145] Step 6:

[0146] The server organizes the generated property suggestions and ranks them in an order deemed optimal for the user. This ranking is adjusted depending on whether the user is relaxed or stressed.

[0147] Step 7:

[0148] The server sends these suggested results to the terminal. The terminal visualizes this information in an easy-to-understand way for the user and displays it on the interface. This includes property images, detailed information, and additional supplementary information.

[0149] Step 8:

[0150] Users can browse properties of interest from the displayed information and view details. The selected information is then fed back to the server and may be used to improve future recommendations.

[0151] Step 9:

[0152] The server provides sentiment data, including user feedback, to the management company. This information is used to improve marketing and streamline vacancy management.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] Traditional asset search systems often present results based solely on input criteria, without considering the user's emotional state. This lack of consideration for the user's feelings has been a problem, as it can lead to stress and dissatisfaction during the process of discovering and selecting the assets that truly meet the user's needs.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for understanding the user's emotional state using an emotion analysis device, means for proposing assets based on the user's conditions and emotional state using generative artificial intelligence, and means for intuitively presenting asset information to the user. This makes it possible to propose assets that take the user's emotional state into consideration, resulting in a more satisfying search experience for the user.

[0158] An "information receiving device" is a device that takes user information as input and retrieves relevant information.

[0159] A "storage device" is a device that stores data in order to analyze acquired conditional information and extract asset information based on that analysis.

[0160] "Generative artificial intelligence" is an artificial intelligence technology that provides optimal asset recommendations based on the user's conditions and emotional state.

[0161] An "emotion analysis device" is a technological device that understands the emotional state of a user and reflects that in asset proposals.

[0162] "Usage trend information" refers to information about users' choices and behavioral patterns, and is data that is transmitted to the managing organization.

[0163] "Asset information" refers to detailed information about an asset, including its location, value, structure, and available options.

[0164] This invention provides an asset search system that takes into account the emotional state of the user. This system uses a terminal as an information receiving device to receive the user's search criteria. The data received by the terminal is transmitted to a server via a network.

[0165] The server analyzes the received data and compares it with information stored in its memory. This extracts asset information from the database that matches the user's criteria. The server then uses generative artificial intelligence to evaluate the extracted asset information and select the asset that best matches the user's preferences. This process utilizes commonly used generative AI models (e.g., natural language processing models).

[0166] Furthermore, the server uses an emotion analysis device to analyze the user's emotional state in real time. This emotional data is incorporated into the asset recommendation algorithm, which adjusts the recommendation order and content to match the user's emotions. For example, if the user is relaxed, a variety of options are offered, while if they are stressed, highly recommended assets are prioritized.

[0167] The terminal displays the results of these processes in a user-friendly interface. Frameworks such as React are used, and the design is optimized to allow users to intuitively compare asset information.

[0168] For example, if a user is looking for an asset for their first time living alone, the emotion analyzer will detect that the user is feeling anxious, and the server will prioritize presenting properties in safe areas and within their budget. This allows the user to smoothly make the best choice based on their emotional state.

[0169] An example of a prompt message is suggested for input into the generating AI model: "Generate optimal asset recommendations based on the user's search criteria and sentiment analysis data."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user inputs property-related conditions via a terminal. These conditions include location, price, and floor plan. The terminal converts this information into JSON format and sends it to the server over the network. The input is the user's desired conditions, and the output is the JSON data sent to the server.

[0173] Step 2:

[0174] The server parses the JSON data received from the terminal and extracts the necessary information. Based on this data, the server generates an SQL query and executes the query against the database stored in the storage device. Data processing is filtering based on conditions, and the output is asset information that matches the conditions.

[0175] Step 3:

[0176] The server uses an emotion analysis device to understand the user's emotional state. It takes user voice and text data as input, analyzes it, and generates real-time emotion data. The output is emotion data that quantifies the level of tension and relaxation.

[0177] Step 4:

[0178] The server uses a generative AI model to integrate collected asset information and sentiment data. It is given prompts such as "Suggest the best assets based on the user's conditions and sentiments," and the AI ​​model generates a list of recommended assets. The input is asset information and sentiment data, and the output is an optimized suggestion list.

[0179] Step 5:

[0180] The server adjusts the list of recommended assets obtained from the AI ​​model. Based on sentiment data, it adjusts priorities and presentation order. Specifically, it prioritizes the most helpful information for stressed users and presents a wide range of options for relaxed users. The output is the adjusted list of recommendations.

[0181] Step 6:

[0182] The terminal displays a reconciled asset list received from the server to the user. Using frontend technologies such as React, it provides information in a visually intuitive interface. This output presents asset information in a format that allows users to easily compare it.

[0183] Step 7:

[0184] Users evaluate the displayed assets and select properties that interest them. The selected information is fed back to the server via the terminal. The server stores this feedback in a database and uses it to improve future suggestions. The output is the feedback data recorded as a result of the user's selections.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In recent years, online shopping has seen a growing demand for flexible product suggestions that cater to diverse user needs. However, conventional systems have lacked the ability to consider users' emotional states when making suggestions, resulting in insufficient improvements in user satisfaction.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for inputting the user's conditions and emotional state via a terminal acting as an information receiving device and acquiring the relevant information; means for analyzing the acquired condition information and emotional data through queries with a data set and extracting target information based on the conditions; and means for using generative artificial intelligence to propose targets that fit the conditions and generate additional information based on the extracted target information and emotional data. This enables personalized product suggestions that are adapted to the user's emotional state.

[0190] An "information receiving device" is a terminal device used to input conditions and emotional states from the user.

[0191] "User conditions" refer to specific criteria or requirements desired by the user, such as location or compensation.

[0192] "Emotional state" refers to data that indicates a user's psychological state and emotional tendencies.

[0193] "Relevant information" refers to appropriate information related to the user's circumstances and emotional state.

[0194] A "data set" is a database or collection of information used to perform analysis in response to information queries.

[0195] "Target information" refers to specific information extracted based on the user's conditions and emotional state.

[0196] "Generative artificial intelligence" is an artificial intelligence technology that suggests the most suitable target based on user conditions and emotional data.

[0197] "Additional information" refers to additional information generated in relation to the proposed subject.

[0198] To implement this invention, a user's smartphone is used as an information receiving device. The smartphone is equipped with a camera and a microphone, and through these functions, the user's facial expressions and voice tone are collected in real time. This allows data that reflects the user's emotional state to be obtained.

[0199] The server receives conditional information and emotional state data sent by the user. This data is processed using an emotion analysis AI running on the server to analyze the user's psychological tendencies. For example, by using Microsoft® Azure®'s Emotion Recognition API, facial expression data is analyzed to determine whether the user is relaxed or tense.

[0200] Subsequently, the server uses a generative artificial intelligence engine, such as Google Cloud's Recommendations AI, to generate product suggestions that take into account the user's conditions and analyzed emotional state. The generated product information is sent to the smartphone as appropriate suggestions that match the user's conditions and displayed in a user-friendly interface.

[0201] For example, if a user is in a hurry to buy food for lunch, the sentiment analysis engine will detect that the user is in a hurry. Based on this, the server will prioritize suggesting instant food items that can be delivered quickly. In this way, it is possible to suggest products that are tailored to the user's emotions.

[0202] An example of a prompt to be input into the generating AI model is the text, "The user currently looks tired; recommend foods that can be prepared quickly."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user's smartphone is activated, and its camera and microphone are used to capture the user's facial expressions and voice in real time. Input includes image data of the user's face and audio data. This data is sent to an emotion analysis AI to analyze the user's emotional state. The output is data representing the analyzed emotional state of the user.

[0206] Step 2:

[0207] Along with emotional state data, the user's desired conditions (e.g., product category, price range, etc.) entered through the terminal are sent to the server. This input is centrally managed on the server side. The server receives this data and prepares for the next processing. The output is integrated data of condition information and emotional state.

[0208] Step 3:

[0209] The server processes the integrated data and executes queries against the data set. These queries are used to extract relevant subject information from the database based on user conditions. The inputs are condition information and sentiment state data. The output is the extracted relevant subject information.

[0210] Step 4:

[0211] The extracted target information is evaluated by a generative artificial intelligence model on the server. Emotional state data is reflected, and product recommendations best suited to the user's mental state are generated. The input consists of the extracted target information and emotional state data. The output is suggested product information.

[0212] Step 5:

[0213] The generated suggested product information is sent to the user's smartphone and displayed in a user-friendly interface. The user reviews the presented products and selects those of interest. The input is the suggested product information, and the output is the user's selection information.

[0214] Step 6:

[0215] The product information selected by the user is sent back to the server and stored as trend data. This information will be used to improve the suggestion algorithm in the future. The input is the suggested product information selected by the user, and the output is the updated trend data.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention is a system for streamlining the search and suggestion of real estate properties. The system begins with the user entering their desired property criteria via a terminal. The terminal is responsible for analyzing the user's input and transmitting it to a server. The server then sends a query to a database based on the transmitted criteria data, retrieving property information that matches the criteria. This database stores information on numerous real estate properties, and relevant information is retrieved based on the search.

[0233] Next, the server scrutinizes the property information acquired using generative artificial intelligence. The generative AI analyzes the characteristics of properties and ranks them in order to make the best suggestions based on the user's desired conditions. In addition to property information, it also generates supplementary information such as the surrounding environment and reviews, and builds a dataset to provide to the user with this information as well.

[0234] The generated information is returned to the terminal via the server, and the terminal is designed to display it in a user-friendly interface. Users can select from the presented properties and view further details. Furthermore, the content viewed and selection trends of the user are analyzed on the server and provided to the property management company. This analysis includes user preferences and market trends, which the management company can use to improve vacancy forecasts and marketing strategies.

[0235] To give a concrete example, let's assume a user is planning to get a cat and is looking for a pet-friendly property in Tokyo. The user enters the following conditions into their device: "Tokyo, pet-friendly, 1LDK, under 200,000 yen." The device sends these conditions to the server, which extracts relevant property information from its database. The generated artificial intelligence analyzes the extracted information, selects several optimal properties, and presents them to the user along with additional information such as nearby parks and veterinary clinics. Through this process, the user can find their ideal property in a short amount of time.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user enters their desired property conditions (e.g., location, rent, floor plan, pet-friendly, etc.) into an input form on the terminal's interface. Once the user has finished entering the information, they press the submit button, and the data is sent to the system.

[0239] Step 2:

[0240] The terminal forms the user's input into a single data packet. This packet is not sent in plain text, but is encrypted as needed before being sent to the server.

[0241] Step 3:

[0242] The server receives data packets sent from the terminal. The server decodes them, analyzes the user's requirements, and generates SQL queries for the database.

[0243] Step 4:

[0244] The server uses the generated SQL query to access the database where property information is stored. This query retrieves property data that may match the user's criteria.

[0245] Step 5:

[0246] The server inputs property information returned from the database into the AI. The AI ​​analyzes each property and selects and ranks properties that meet the user's criteria. It also generates additional information that takes into account surrounding environment information and user reviews.

[0247] Step 6:

[0248] The server converts the information generated by the AI ​​into a user-friendly format. Then, it creates a new data packet to return this information to the terminal.

[0249] Step 7:

[0250] The terminal interprets data packets received from the server and displays the information in a format that allows the user to easily compare and select options. Property images, detailed information, and additional information are presented on the user's screen.

[0251] Step 8:

[0252] Users select properties of interest from the presented list and view more detailed information. During this process, data on the user's selection process and preferences is collected and fed back to the server.

[0253] Step 9:

[0254] The server analyzes user history data and provides management companies with information on market trends and vacancy forecasts. This is crucial data for management companies to develop efficient asset management and marketing strategies.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] Traditional real estate search systems made it difficult for users to efficiently find their ideal property, and in particular, it was challenging to quickly narrow down the large amount of property information to find the one that best suited their criteria. Furthermore, it was difficult to provide supplementary information about the surrounding area or to offer suggestions tailored to the individual preferences of the user.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for inputting the user's desired conditions and acquiring relevant information fragments via an input device acting as an information acquisition device; means for analyzing the data through query processing with a large data storage device and selecting property information based on the conditions; and means for evaluating and proposing properties that meet the conditions based on the selected property information using generative intelligence, and generating surrounding information. This enables users to quickly and efficiently find their ideal property, and further enables the proposal of value-added information tailored to the user's preferences.

[0260] An "information acquisition device" is a device or system for which a user inputs desired conditions and is capable of acquiring relevant information fragments.

[0261] An "input device" refers to hardware or an interface used by a user to input specific desired conditions, and plays the role of passing that input information to subsequent processing.

[0262] "Relevant information fragments" refer to information collected or generated based on conditions entered by the user and used for a specific purpose.

[0263] A "large-scale data storage device" refers to a system or database for storing large datasets that allows for the selection of necessary data through query processing.

[0264] "Query processing" refers to the process performed to retrieve data from a large-scale data storage device based on specific conditions, and its purpose is to search and extract data.

[0265] "Generative intelligence" refers to a system that uses artificial intelligence technology to make evaluations and suggestions based on specific conditions, and is also capable of generating further information.

[0266] "Selection" refers to the act of extracting property information that meets specific criteria from a large amount of data and classifying or organizing it.

[0267] "Evaluation and proposal" is the process of analyzing acquired property information, determining its value and suitability to the user's requirements, and then presenting the best option.

[0268] "Surrounding area information" refers to information about the environment and services that accompany the basic information about the property, and is intended to provide additional value to users.

[0269] This invention is a system that enables users to efficiently search for real estate properties and make the best selection. Specifically, it is realized by using an input device as an information acquisition device, a server for data analysis, and a terminal that provides a user interface.

[0270] The first step for the user is to use a terminal to input their desired property criteria. This terminal analyzes the entered information and sends it to the server. The terminal utilizes the latest interface technology and is designed to be intuitive for the user.

[0271] Based on the received conditions, the server selects suitable data from property information stored in a large data storage device. This process utilizes a database management system to accurately execute complex query processing. Generative intelligence running on the server further analyzes the selected property information. This generative intelligence has the ability to generate property evaluations and suggestions that best suit the user's conditions.

[0272] The evaluated property information and additional surrounding area information are returned to the terminal and presented to the user. The terminal's display device organizes this information and makes it easy to understand visually. Based on this displayed information, the user can check more detailed property information and choose the most suitable property. In addition, the user's selection history and trends are analyzed on the server and provided to the management organization as trend information.

[0273] For example, if a user enters the conditions "Tokyo, pet-friendly, 1LDK, under 200,000 yen," this information is immediately sent to the server. The server accesses the database and searches for properties that meet the criteria. A generative AI model evaluates the suitability of the properties, and this information is presented to the user. This system allows users to efficiently find their ideal home.

[0274] Example of a prompt:

[0275] "Looking for a 1LDK pet-friendly apartment within Tokyo. Please show me the most suitable property within 200,000 yen."

[0276] "Please explain what methods should be used to efficiently extract properties that match the conditions from the real estate database."

[0277] The flow of the specific process in Example 1 will be described using Fig. 11.

[0278] Step 1:

[0279] The user uses a terminal as an information acquisition device to input the desired real estate conditions. At this time, detailed conditions such as "within Tokyo, pet-friendly, 1LDK, within 200,000 yen" are specified. Data of the input conditions is generated and processed by the terminal.

[0280] Step 2:

[0281] The terminal transmits the input condition information to the server. When transmitting, the data integrity is confirmed so that no error occurs. The output is condition data in a format that can be analyzed by the server.

[0282] Step 3:

[0283] The server executes a query on the large-scale data storage device (database) based on the condition data received from the terminal. Through the query process, property information that matches the conditions is selected and received as the screening result.

[0284] Step 4:

[0285] The server inputs the acquired property information into the generation AI model for detailed analysis. Specifically, information such as the location, price, and facilities of the property is scored to create a ranking. The generation AI model selects the most suitable property for the user and outputs the property information for proposal.

[0286] Step 5:

[0287] The server returns the evaluated property information output from the generative AI model to the terminal. This data includes, in addition to the basic information of the property, additional information such as surrounding facilities and reviews.

[0288] Step 6:

[0289] Based on the property information received from the server, the terminal displays it on the screen in a user-friendly format. The user can view this output and select properties of interest to obtain detailed information.

[0290] Step 7:

[0291] The server collects the history of the user's viewing of property information and selection tendencies. The server analyzes this data, generates trend information and vacancy predictions, and outputs them to provide to the management organization.

[0292] (Application Example 1)

[0293] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0294] In modern society, it is difficult for individuals to efficiently and effectively search for available space. In particular, information presentation based on the specific desired conditions of users and immediate information acquisition on-site are required, but conventional systems have not been able to adequately handle this. As a result, there is a need to improve the efficiency of information acquisition and presentation.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes means for recognizing the user's preferences and displaying suitable information using a visual device as an information presentation device; means for analyzing the recognized preference information through queries against a data structure and extracting spatial information based on the preference; and means for proposing a space suitable for the preference and generating surrounding environment information based on the extracted spatial information using generative artificial intelligence. As a result, the user can quickly and accurately obtain the necessary spatial information and efficiently grasp the information through the visual device.

[0297] "Visual devices as information presentation devices" refers to hardware devices and related technologies for displaying information in real time through the user's vision.

[0298] "User preferences" refer to the conditions and requirements specified by the user when seeking spatial information.

[0299] "Displaying compatibility information" refers to making information that matches the user's preferences visible to the user through a visual device.

[0300] "Querying a data structure" refers to the process of sending data retrieval requests to databases and other information repositories based on the user's preferences.

[0301] "Spatial information" refers to data about a specific place or facility, encompassing comprehensive information including location and characteristics.

[0302] "Generative artificial intelligence" refers to computer programs and algorithms that analyze large amounts of data and perform condition-based recommendations and information generation.

[0303] "Generating surrounding environment information" refers to compiling geographical, social, or service-related information related to a specific space and providing it to the user.

[0304] To implement this invention, it is necessary to construct a system that includes a visual device, a server, a data structure, and generative artificial intelligence.

[0305] First, the user uses a visual device as an information presentation device. This device includes, for example, smart glasses and head-mounted displays, and has a function of recognizing the desired conditions presented by the user through voice input or gesture input. By utilizing voice recognition technology such as Google Speech-to-Text API, the user's wishes are converted into digital data in real time.

[0306] Next, the data input through the visual device is transferred to the server, and the server executes a query with the data structure. A database such as MongoDB is used for the data structure, and spatial information that matches the user's conditions is extracted. In this process, the server identifies the data that matches the conditions and obtains the corresponding spatial information.

[0307] Furthermore, the server analyzes the extracted spatial information using generative artificial intelligence, ranks and sorts the information in the form that best suits the user's wishes. Here, AI engines such as OpenAI's GPT series are utilized to enable complex data analysis and generation.

[0308] The generated information is presented to the user through the visual device. The visual device uses a 三维 development environment such as Unity to display the acquired spatial information and surrounding environment information in augmented reality (AR) format, making it possible for the user to intuitively understand.

[0309] As a specific example, when a user who is about to purchase a new house wears smart glasses and inputs the condition of "a quiet residential area, 4LDK, with a balcony" by voice, this system visualizes on the spot the property information that meets the conditions through the visual device, together with information such as parks and supermarkets in the vicinity.

[0310] This allows users to quickly grasp complex information and retrieve the desired information more efficiently. A prompt message that guides this process is: "Get the user's desired conditions via voice and convert them into objects. Design prompts to visualize and present property information and surrounding environment information that meet the conditions."

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The device receives user requests via voice or gesture input. It utilizes the Google Speech-to-Text API for voice recognition and an accelerometer for gesture recognition. This converts the received requests into text format.

[0314] Step 2:

[0315] The terminal sends the desired conditions obtained in Step 1 to the server. The server receives this as input data and generates a query to the database. This query is performed against MongoDB and extracts spatial information that matches the user's conditions.

[0316] Step 3:

[0317] The server passes spatial information obtained from the database to the generative artificial intelligence (AI). The AI ​​analyzes this data and evaluates whether it meets the user criteria. Next, it ranks the information and sorts it by priority. This process involves analysis and generation using OpenAI's GPT series.

[0318] Step 4:

[0319] The server generates surrounding environment information based on the ranked spatial information obtained in step 3. This involves using the Google Maps API, etc., to collect and process geographical information and construct data with information highly relevant to the conditions.

[0320] Step 5:

[0321] The device receives the information generated in step 4 and visualizes it in a 3D development environment using Unity. This allows the user to view the information as augmented reality through visual devices such as smart glasses.

[0322] Step 6:

[0323] Based on the information presented, users select spaces that interest them and provide feedback to the server via their device. The server uses this feedback to analyze user biases and trends, which helps improve the accuracy of future suggestions.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention is a system for improving the user's property search experience, and in particular, by combining it with an emotion engine, it can evaluate the user's emotional state and reflect it in property suggestions. The user first inputs their desired conditions (e.g., location, price, floor plan, etc.) through a terminal. This information is received by the server, and appropriate property information is collected through queries against the database.

[0326] The server uses artificial intelligence to evaluate this property information and select the best property that meets the user's preferences. This evaluation process considers not only the user's input conditions but also emotional data acquired by the emotion engine. The emotion engine analyzes the user's emotional state in real time, determining, for example, whether the user is tense or relaxed.

[0327] This sentiment analysis data is incorporated into the property suggestion algorithm, which adjusts the order and content of the suggested properties to suit the user's emotions. For example, if the user is relaxed, more options are presented slowly, while if the user is stressed, the most likely preferred options are prioritized.

[0328] The terminal displays property information received from the server, along with suggestions generated by the emotion engine, in a user-friendly interface. Users compare the displayed properties and select those of interest. This selection is fed back to the server and used to improve future suggestions.

[0329] As a concrete example, let's assume a user is looking for an apartment for their first time living alone. The emotion engine detects when the user is feeling anxious, and the server adjusts its suggestions based on this emotional information. Specifically, it focuses on suggesting properties in safe areas with rents that do not exceed expectations. The goal is to provide suggestions that match the user's emotions and make the apartment search a more enjoyable experience.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user accesses the terminal interface and enters their desired property search criteria (e.g., location, rent, floor plan). Once finished, the user presses the "Search" button to submit their search criteria.

[0333] Step 2:

[0334] The terminal converts the entered conditions into data packets and sends them to the server. This data contains the user's basic preferences.

[0335] Step 3:

[0336] The server analyzes the conditions received from the terminal and sends a query to the database. The query filters property information based on the user's conditions and collects information on the relevant properties.

[0337] Step 4:

[0338] Simultaneously, an emotion engine built into the device collects emotion data in real time from the user's facial expressions and voice data. This emotion data indicates the user's emotional state when viewing the information.

[0339] Step 5:

[0340] The server retrieves property information from the database and emotion data from the emotion engine, and inputs it into the generative artificial intelligence. The generative AI integrates this information to generate property suggestions that best match the user's emotional state.

[0341] Step 6:

[0342] The server organizes the generated property suggestions and ranks them in an order deemed optimal for the user. This ranking is adjusted depending on whether the user is relaxed or stressed.

[0343] Step 7:

[0344] The server sends these suggested results to the terminal. The terminal visualizes this information in an easy-to-understand way for the user and displays it on the interface. This includes property images, detailed information, and additional supplementary information.

[0345] Step 8:

[0346] Users can browse properties of interest from the displayed information and view details. The selected information is then fed back to the server and may be used to improve future recommendations.

[0347] Step 9:

[0348] The server provides sentiment data, including user feedback, to the management company. This information is used to improve marketing and streamline vacancy management.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] Traditional asset search systems often present results based solely on input criteria, without considering the user's emotional state. This lack of consideration for the user's feelings has been a problem, as it can lead to stress and dissatisfaction during the process of discovering and selecting the assets that truly meet the user's needs.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for understanding the user's emotional state using an emotion analysis device, means for proposing assets based on the user's conditions and emotional state using generative artificial intelligence, and means for intuitively presenting asset information to the user. This makes it possible to propose assets that take the user's emotional state into consideration, resulting in a more satisfying search experience for the user.

[0354] An "information receiving device" is a device that takes user information as input and retrieves relevant information.

[0355] A "storage device" is a device that stores data in order to analyze acquired conditional information and extract asset information based on that analysis.

[0356] "Generative artificial intelligence" is an artificial intelligence technology that provides optimal asset recommendations based on the user's conditions and emotional state.

[0357] An "emotion analysis device" is a technological device that understands the emotional state of a user and reflects that in asset proposals.

[0358] "Usage trend information" refers to information about users' choices and behavioral patterns, and is data that is transmitted to the managing organization.

[0359] "Asset information" refers to detailed information about an asset, including its location, value, structure, and available options.

[0360] This invention provides an asset search system that takes into account the emotional state of the user. This system uses a terminal as an information receiving device to receive the user's search criteria. The data received by the terminal is transmitted to a server via a network.

[0361] The server analyzes the received data and compares it with information stored in its memory. This extracts asset information from the database that matches the user's criteria. The server then uses generative artificial intelligence to evaluate the extracted asset information and select the asset that best matches the user's preferences. This process utilizes commonly used generative AI models (e.g., natural language processing models).

[0362] Furthermore, the server uses an emotion analysis device to analyze the user's emotional state in real time. This emotional data is incorporated into the asset recommendation algorithm, which adjusts the recommendation order and content to match the user's emotions. For example, if the user is relaxed, a variety of options are offered, while if they are stressed, highly recommended assets are prioritized.

[0363] The terminal displays the results of these processes in a user-friendly interface. Frameworks such as React are used, and the design is optimized to allow users to intuitively compare asset information.

[0364] For example, if a user is looking for an asset for their first time living alone, the emotion analyzer will detect that the user is feeling anxious, and the server will prioritize presenting properties in safe areas and within their budget. This allows the user to smoothly make the best choice based on their emotional state.

[0365] An example of a prompt message is suggested for input into the generating AI model: "Generate optimal asset recommendations based on the user's search criteria and sentiment analysis data."

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The user inputs property-related conditions via a terminal. These conditions include location, price, and floor plan. The terminal converts this information into JSON format and sends it to the server over the network. The input is the user's desired conditions, and the output is the JSON data sent to the server.

[0369] Step 2:

[0370] The server parses the JSON data received from the terminal and extracts the necessary information. Based on this data, the server generates an SQL query and executes the query against the database stored in the storage device. Data processing is filtering based on conditions, and the output is asset information that matches the conditions.

[0371] Step 3:

[0372] The server uses an emotion analysis device to understand the user's emotional state. It takes user voice and text data as input, analyzes it, and generates real-time emotion data. The output is emotion data that quantifies the level of tension and relaxation.

[0373] Step 4:

[0374] The server uses a generative AI model to integrate collected asset information and sentiment data. It is given prompts such as "Suggest the best assets based on the user's conditions and sentiments," and the AI ​​model generates a list of recommended assets. The input is asset information and sentiment data, and the output is an optimized suggestion list.

[0375] Step 5:

[0376] The server adjusts the list of recommended assets obtained from the AI ​​model. Based on sentiment data, it adjusts priorities and presentation order. Specifically, it prioritizes the most helpful information for stressed users and presents a wide range of options for relaxed users. The output is the adjusted list of recommendations.

[0377] Step 6:

[0378] The terminal displays a reconciled asset list received from the server to the user. Using frontend technologies such as React, it provides information in a visually intuitive interface. This output presents asset information in a format that allows users to easily compare it.

[0379] Step 7:

[0380] Users evaluate the displayed assets and select properties that interest them. The selected information is fed back to the server via the terminal. The server stores this feedback in a database and uses it to improve future suggestions. The output is the feedback data recorded as a result of the user's selections.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In recent years, online shopping has seen a growing demand for flexible product suggestions that cater to diverse user needs. However, conventional systems have lacked the ability to consider users' emotional states when making suggestions, resulting in insufficient improvements in user satisfaction.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for inputting the user's conditions and emotional state via a terminal acting as an information receiving device and acquiring the relevant information; means for analyzing the acquired condition information and emotional data through queries with a data set and extracting target information based on the conditions; and means for using generative artificial intelligence to propose targets that fit the conditions and generate additional information based on the extracted target information and emotional data. This enables personalized product suggestions that are adapted to the user's emotional state.

[0386] An "information receiving device" is a terminal device used to input conditions and emotional states from the user.

[0387] "User conditions" refer to specific criteria or requirements desired by the user, such as location or compensation.

[0388] "Emotional state" refers to data that indicates a user's psychological state and emotional tendencies.

[0389] "Relevant information" refers to appropriate information related to the user's circumstances and emotional state.

[0390] A "data set" is a database or collection of information used to perform analysis in response to information queries.

[0391] "Target information" refers to specific information extracted based on the user's conditions and emotional state.

[0392] "Generative artificial intelligence" is an artificial intelligence technology that suggests the most suitable target based on user conditions and emotional data.

[0393] "Additional information" refers to additional information generated in relation to the proposed subject.

[0394] To implement this invention, a user's smartphone is used as an information receiving device. The smartphone is equipped with a camera and a microphone, and through these functions, the user's facial expressions and voice tone are collected in real time. This allows data that reflects the user's emotional state to be obtained.

[0395] The server receives conditional information and emotional state data sent by the user. This data is processed using an emotion analysis AI running on the server to analyze the user's psychological tendencies. For example, by using Microsoft Azure's Emotion Recognition API, facial expression data is analyzed to determine whether the user is relaxed or tense.

[0396] Subsequently, the server uses a generative artificial intelligence engine, such as Google Cloud's Recommendations AI, to generate product suggestions that take into account the user's conditions and analyzed emotional state. The generated product information is sent to the smartphone as appropriate suggestions that match the user's conditions and displayed in a user-friendly interface.

[0397] For example, if a user is in a hurry to buy food for lunch, the sentiment analysis engine will detect that the user is in a hurry. Based on this, the server will prioritize suggesting instant food items that can be delivered quickly. In this way, it is possible to suggest products that are tailored to the user's emotions.

[0398] An example of a prompt to be input into the generating AI model is the text, "The user currently looks tired; recommend foods that can be prepared quickly."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user's smartphone is activated, and its camera and microphone are used to capture the user's facial expressions and voice in real time. Input includes image data of the user's face and audio data. This data is sent to an emotion analysis AI to analyze the user's emotional state. The output is data representing the analyzed emotional state of the user.

[0402] Step 2:

[0403] Along with emotional state data, the user's desired conditions (e.g., product category, price range, etc.) entered through the terminal are sent to the server. This input is centrally managed on the server side. The server receives this data and prepares for the next processing. The output is integrated data of condition information and emotional state.

[0404] Step 3:

[0405] The server processes the integrated data and executes queries against the data set. These queries are used to extract relevant subject information from the database based on user conditions. The inputs are condition information and sentiment state data. The output is the extracted relevant subject information.

[0406] Step 4:

[0407] The extracted target information is evaluated by a generative artificial intelligence model on the server. Emotional state data is reflected, and product recommendations best suited to the user's mental state are generated. The input consists of the extracted target information and emotional state data. The output is suggested product information.

[0408] Step 5:

[0409] The generated suggested product information is sent to the user's smartphone and displayed in a user-friendly interface. The user reviews the presented products and selects those of interest. The input is the suggested product information, and the output is the user's selection information.

[0410] Step 6:

[0411] The product information selected by the user is sent back to the server and stored as trend data. This information will be used to improve the suggestion algorithm in the future. The input is the suggested product information selected by the user, and the output is the updated trend data.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] This invention is a system for streamlining the search and suggestion of real estate properties. The system begins with the user entering their desired property criteria via a terminal. The terminal is responsible for analyzing the user's input and transmitting it to a server. The server then sends a query to a database based on the transmitted criteria data, retrieving property information that matches the criteria. This database stores information on numerous real estate properties, and relevant information is retrieved based on the search.

[0429] Next, the server scrutinizes the property information acquired using generative artificial intelligence. The generative AI analyzes the characteristics of properties and ranks them in order to make the best suggestions based on the user's desired conditions. In addition to property information, it also generates supplementary information such as the surrounding environment and reviews, and builds a dataset to provide to the user with this information as well.

[0430] The generated information is returned to the terminal via the server, and the terminal is designed to display it in a user-friendly interface. Users can select from the presented properties and view further details. Furthermore, the content viewed and selection trends of the user are analyzed on the server and provided to the property management company. This analysis includes user preferences and market trends, which the management company can use to improve vacancy forecasts and marketing strategies.

[0431] To give a concrete example, let's assume a user is planning to get a cat and is looking for a pet-friendly property in Tokyo. The user enters the following conditions into their device: "Tokyo, pet-friendly, 1LDK, under 200,000 yen." The device sends these conditions to the server, which extracts relevant property information from its database. The generated artificial intelligence analyzes the extracted information, selects several optimal properties, and presents them to the user along with additional information such as nearby parks and veterinary clinics. Through this process, the user can find their ideal property in a short amount of time.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user enters their desired property conditions (e.g., location, rent, floor plan, pet-friendly, etc.) into an input form on the terminal's interface. Once the user has finished entering the information, they press the submit button, and the data is sent to the system.

[0435] Step 2:

[0436] The terminal forms the user's input into a single data packet. This packet is not sent in plain text, but is encrypted as needed before being sent to the server.

[0437] Step 3:

[0438] The server receives data packets sent from the terminal. The server decodes them, analyzes the user's requirements, and generates SQL queries for the database.

[0439] Step 4:

[0440] The server uses the generated SQL query to access the database where property information is stored. This query retrieves property data that may match the user's criteria.

[0441] Step 5:

[0442] The server inputs property information returned from the database into the AI. The AI ​​analyzes each property and selects and ranks properties that meet the user's criteria. It also generates additional information that takes into account surrounding environment information and user reviews.

[0443] Step 6:

[0444] The server converts the information generated by the AI ​​into a user-friendly format. Then, it creates a new data packet to return this information to the terminal.

[0445] Step 7:

[0446] The terminal interprets data packets received from the server and displays the information in a format that allows the user to easily compare and select options. Property images, detailed information, and additional information are presented on the user's screen.

[0447] Step 8:

[0448] Users select properties of interest from the presented list and view more detailed information. During this process, data on the user's selection process and preferences is collected and fed back to the server.

[0449] Step 9:

[0450] The server analyzes user history data and provides management companies with information on market trends and vacancy forecasts. This is crucial data for management companies to develop efficient asset management and marketing strategies.

[0451] (Example 1)

[0452] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] Traditional real estate search systems made it difficult for users to efficiently find their ideal property, and in particular, it was challenging to quickly narrow down the large amount of property information to find the one that best suited their criteria. Furthermore, it was difficult to provide supplementary information about the surrounding area or to offer suggestions tailored to the individual preferences of the user.

[0454] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0455] In this invention, the server includes means for inputting the user's desired conditions and acquiring relevant information fragments via an input device acting as an information acquisition device; means for analyzing the data through query processing with a large data storage device and selecting property information based on the conditions; and means for evaluating and proposing properties that meet the conditions based on the selected property information using generative intelligence, and generating surrounding information. This enables users to quickly and efficiently find their ideal property, and further enables the proposal of value-added information tailored to the user's preferences.

[0456] An "information acquisition device" is a device or system for which a user inputs desired conditions and is capable of acquiring relevant information fragments.

[0457] An "input device" refers to hardware or an interface used by a user to input specific desired conditions, and plays the role of passing that input information to subsequent processing.

[0458] "Relevant information fragments" refer to information collected or generated based on conditions entered by the user and used for a specific purpose.

[0459] A "large-scale data storage device" refers to a system or database for storing large datasets that allows for the selection of necessary data through query processing.

[0460] "Query processing" refers to the process performed to retrieve data from a large-scale data storage device based on specific conditions, and its purpose is to search and extract data.

[0461] "Generative intelligence" refers to a system that uses artificial intelligence technology to make evaluations and suggestions based on specific conditions, and is also capable of generating further information.

[0462] "Selection" refers to the act of extracting property information that meets specific criteria from a large amount of data and classifying or organizing it.

[0463] "Evaluation and proposal" is the process of analyzing acquired property information, determining its value and suitability to the user's requirements, and then presenting the best option.

[0464] "Surrounding area information" refers to information about the environment and services that accompany the basic information about the property, and is intended to provide additional value to users.

[0465] This invention is a system that enables users to efficiently search for real estate properties and make the best selection. Specifically, it is realized by using an input device as an information acquisition device, a server for data analysis, and a terminal that provides a user interface.

[0466] The first step for the user is to use a terminal to input their desired property criteria. This terminal analyzes the entered information and sends it to the server. The terminal utilizes the latest interface technology and is designed to be intuitive for the user.

[0467] Based on the received conditions, the server selects suitable data from property information stored in a large data storage device. This process utilizes a database management system to accurately execute complex query processing. Generative intelligence running on the server further analyzes the selected property information. This generative intelligence has the ability to generate property evaluations and suggestions that best suit the user's conditions.

[0468] The evaluated property information and additional surrounding area information are returned to the terminal and presented to the user. The terminal's display device organizes this information and makes it easy to understand visually. Based on this displayed information, the user can check more detailed property information and choose the most suitable property. In addition, the user's selection history and trends are analyzed on the server and provided to the management organization as trend information.

[0469] For example, if a user enters the conditions "Tokyo, pet-friendly, 1LDK, under 200,000 yen," this information is immediately sent to the server. The server accesses the database and searches for properties that meet the criteria. A generative AI model evaluates the suitability of the properties, and this information is presented to the user. This system allows users to efficiently find their ideal home.

[0470] Example of a prompt:

[0471] "I'm looking for a pet-friendly 1LDK apartment in Tokyo. Could you recommend a suitable property under 200,000 yen?"

[0472] "Please explain what methods should be used to efficiently extract properties that match the specified criteria from a real estate database."

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The user uses a terminal, which acts as an information acquisition device, to input their desired real estate conditions. At this time, they specify detailed conditions such as "Tokyo, pet-friendly, 1LDK, under 200,000 yen." Data based on the entered conditions is generated, and the terminal processes this data.

[0476] Step 2:

[0477] The terminal sends the entered condition information to the server. Data integrity is checked during transmission to prevent errors. The output is condition data in a format that the server can parse.

[0478] Step 3:

[0479] The server executes queries against a large data storage device (database) based on the condition data received from the terminal. Through query processing, property information matching the conditions is selected and received as the selection result.

[0480] Step 4:

[0481] The server inputs the acquired property information into a generating AI model for detailed analysis. Specifically, it scores information such as the property's location, price, and facilities, and creates a ranking. The generating AI model then selects the most suitable property for the user and outputs property information for suggestion.

[0482] Step 5:

[0483] The server returns the evaluated property information output from the generated AI model to the terminal. This data includes not only basic property information but also additional information such as surrounding facilities and reviews.

[0484] Step 6:

[0485] The terminal displays property information received from the server in a user-friendly format. Users can review this output, select properties they are interested in, and obtain detailed information.

[0486] Step 7:

[0487] The server collects the user's property viewing history and selection trends. The server analyzes this data, generates trend information and vacancy predictions, and provides the output to the management organization.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] In modern society, efficiently and effectively finding usable spaces is challenging. In particular, there is a need for information presented based on the user's specific preferences and for immediate information acquisition on-site, but conventional systems fail to adequately address this. Therefore, there is a demand for improved efficiency in information acquisition and presentation.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for recognizing the user's preferences and displaying suitable information using a visual device as an information presentation device; means for analyzing the recognized preference information through queries against a data structure and extracting spatial information based on the preference; and means for proposing a space suitable for the preference and generating surrounding environment information based on the extracted spatial information using generative artificial intelligence. As a result, the user can quickly and accurately obtain the necessary spatial information and efficiently grasp the information through the visual device.

[0493] "Visual devices as information presentation devices" refers to hardware devices and related technologies for displaying information in real time through the user's vision.

[0494] "User preferences" refer to the conditions and requirements specified by the user when seeking spatial information.

[0495] "Displaying compatibility information" refers to making information that matches the user's preferences visible to the user through a visual device.

[0496] "Querying a data structure" refers to the process of sending data retrieval requests to databases and other information repositories based on the user's preferences.

[0497] "Spatial information" refers to data about a specific place or facility, encompassing comprehensive information including location and characteristics.

[0498] "Generative artificial intelligence" refers to computer programs and algorithms that analyze large amounts of data and perform condition-based recommendations and information generation.

[0499] "Generating surrounding environment information" refers to compiling geographical, social, or service-related information related to a specific space and providing it to the user.

[0500] To implement this invention, it is necessary to construct a system that includes a visual device, a server, a data structure, and generative artificial intelligence.

[0501] First, the user uses a visual device as an information presentation device. This device includes, for example, smart glasses or a head-mounted display, and has the function of recognizing the user's desired conditions through voice input or gesture input. By utilizing speech recognition technology such as the Google Speech-to-Text API, the user's wishes are converted into digital data in real time.

[0502] Next, the data input through the visual device is transferred to the server, which then queries the data structure. The data structure uses a database such as MongoDB, and it extracts spatial information that matches the user's conditions. In this process, the server identifies data that matches the conditions and retrieves the corresponding spatial information.

[0503] Furthermore, the server uses generative artificial intelligence to analyze the extracted spatial information, ranking and rearranging it in a way that best suits the user's preferences. Here, AI engines such as OpenAI's GPT series are utilized to enable complex data analysis and generation.

[0504] The generated information is presented to the user through a visual device. Using a 3D development environment such as Unity, the visual device displays acquired spatial information and surrounding environment information in augmented reality (AR) format, enabling the user to understand it intuitively.

[0505] As a concrete example, if a user looking to buy a new house wears smart glasses and voice-inputs the conditions "quiet residential area, 4 bedrooms, living room, dining room, kitchen, with balcony," the system will immediately visualize property information that matches those conditions, along with information about nearby parks, supermarkets, and other amenities, through a visual device.

[0506] This allows users to quickly grasp complex information and retrieve the desired information more efficiently. A prompt message that guides this process is: "Get the user's desired conditions via voice and convert them into objects. Design prompts to visualize and present property information and surrounding environment information that meet the conditions."

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The device receives user requests via voice or gesture input. It utilizes the Google Speech-to-Text API for voice recognition and an accelerometer for gesture recognition. This converts the received requests into text format.

[0510] Step 2:

[0511] The terminal sends the desired conditions obtained in Step 1 to the server. The server receives this as input data and generates a query to the database. This query is performed against MongoDB and extracts spatial information that matches the user's conditions.

[0512] Step 3:

[0513] The server passes spatial information obtained from the database to the generative artificial intelligence (AI). The AI ​​analyzes this data and evaluates whether it meets the user criteria. Next, it ranks the information and sorts it by priority. This process involves analysis and generation using OpenAI's GPT series.

[0514] Step 4:

[0515] The server generates surrounding environment information based on the ranked spatial information obtained in step 3. This involves using the Google Maps API, etc., to collect and process geographical information and construct data with information highly relevant to the conditions.

[0516] Step 5:

[0517] The device receives the information generated in step 4 and visualizes it in a 3D development environment using Unity. This allows the user to view the information as augmented reality through visual devices such as smart glasses.

[0518] Step 6:

[0519] Based on the information presented, users select spaces that interest them and provide feedback to the server via their device. The server uses this feedback to analyze user biases and trends, which helps improve the accuracy of future suggestions.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention is a system for improving the user's property search experience, and in particular, by combining it with an emotion engine, it can evaluate the user's emotional state and reflect it in property suggestions. The user first inputs their desired conditions (e.g., location, price, floor plan, etc.) through a terminal. This information is received by the server, and appropriate property information is collected through queries against the database.

[0522] The server uses artificial intelligence to evaluate this property information and select the best property that meets the user's preferences. This evaluation process considers not only the user's input conditions but also emotional data acquired by the emotion engine. The emotion engine analyzes the user's emotional state in real time, determining, for example, whether the user is tense or relaxed.

[0523] This sentiment analysis data is incorporated into the property suggestion algorithm, which adjusts the order and content of the suggested properties to suit the user's emotions. For example, if the user is relaxed, more options are presented slowly, while if the user is stressed, the most likely preferred options are prioritized.

[0524] The terminal displays property information received from the server, along with suggestions generated by the emotion engine, in a user-friendly interface. Users compare the displayed properties and select those of interest. This selection is fed back to the server and used to improve future suggestions.

[0525] As a concrete example, let's assume a user is looking for an apartment for their first time living alone. The emotion engine detects when the user is feeling anxious, and the server adjusts its suggestions based on this emotional information. Specifically, it focuses on suggesting properties in safe areas with rents that do not exceed expectations. The goal is to provide suggestions that match the user's emotions and make the apartment search a more enjoyable experience.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user accesses the terminal interface and enters their desired property search criteria (e.g., location, rent, floor plan). Once finished, the user presses the "Search" button to submit their search criteria.

[0529] Step 2:

[0530] The terminal converts the entered conditions into data packets and sends them to the server. This data contains the user's basic preferences.

[0531] Step 3:

[0532] The server analyzes the conditions received from the terminal and sends a query to the database. The query filters property information based on the user's conditions and collects information on the relevant properties.

[0533] Step 4:

[0534] Simultaneously, an emotion engine built into the device collects emotion data in real time from the user's facial expressions and voice data. This emotion data indicates the user's emotional state when viewing the information.

[0535] Step 5:

[0536] The server retrieves property information from the database and emotion data from the emotion engine, and inputs it into the generative artificial intelligence. The generative AI integrates this information to generate property suggestions that best match the user's emotional state.

[0537] Step 6:

[0538] The server organizes the generated property suggestions and ranks them in an order deemed optimal for the user. This ranking is adjusted depending on whether the user is relaxed or stressed.

[0539] Step 7:

[0540] The server sends these suggested results to the terminal. The terminal visualizes this information in an easy-to-understand way for the user and displays it on the interface. This includes property images, detailed information, and additional supplementary information.

[0541] Step 8:

[0542] Users can browse properties of interest from the displayed information and view details. The selected information is then fed back to the server and may be used to improve future recommendations.

[0543] Step 9:

[0544] The server provides sentiment data, including user feedback, to the management company. This information is used to improve marketing and streamline vacancy management.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] Traditional asset search systems often present results based solely on input criteria, without considering the user's emotional state. This lack of consideration for the user's feelings has been a problem, as it can lead to stress and dissatisfaction during the process of discovering and selecting the assets that truly meet the user's needs.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0549] In this invention, the server includes means for understanding the user's emotional state using an emotion analysis device, means for proposing assets based on the user's conditions and emotional state using generative artificial intelligence, and means for intuitively presenting asset information to the user. This makes it possible to propose assets that take the user's emotional state into consideration, resulting in a more satisfying search experience for the user.

[0550] An "information receiving device" is a device that takes user information as input and retrieves relevant information.

[0551] A "storage device" is a device that stores data in order to analyze acquired conditional information and extract asset information based on that analysis.

[0552] "Generative artificial intelligence" is an artificial intelligence technology that provides optimal asset recommendations based on the user's conditions and emotional state.

[0553] An "emotion analysis device" is a technological device that understands the emotional state of a user and reflects that in asset proposals.

[0554] "Usage trend information" refers to information about users' choices and behavioral patterns, and is data that is transmitted to the managing organization.

[0555] "Asset information" refers to detailed information about an asset, including its location, value, structure, and available options.

[0556] This invention provides an asset search system that takes into account the emotional state of the user. This system uses a terminal as an information receiving device to receive the user's search criteria. The data received by the terminal is transmitted to a server via a network.

[0557] The server analyzes the received data and compares it with information stored in its memory. This extracts asset information from the database that matches the user's criteria. The server then uses generative artificial intelligence to evaluate the extracted asset information and select the asset that best matches the user's preferences. This process utilizes commonly used generative AI models (e.g., natural language processing models).

[0558] Furthermore, the server uses an emotion analysis device to analyze the user's emotional state in real time. This emotional data is incorporated into the asset recommendation algorithm, which adjusts the recommendation order and content to match the user's emotions. For example, if the user is relaxed, a variety of options are offered, while if they are stressed, highly recommended assets are prioritized.

[0559] The terminal displays the results of these processes in a user-friendly interface. Frameworks such as React are used, and the design is optimized to allow users to intuitively compare asset information.

[0560] For example, if a user is looking for an asset for their first time living alone, the emotion analyzer will detect that the user is feeling anxious, and the server will prioritize presenting properties in safe areas and within their budget. This allows the user to smoothly make the best choice based on their emotional state.

[0561] An example of a prompt message is suggested for input into the generating AI model: "Generate optimal asset recommendations based on the user's search criteria and sentiment analysis data."

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The user inputs property-related conditions via a terminal. These conditions include location, price, and floor plan. The terminal converts this information into JSON format and sends it to the server over the network. The input is the user's desired conditions, and the output is the JSON data sent to the server.

[0565] Step 2:

[0566] The server parses the JSON data received from the terminal and extracts the necessary information. Based on this data, the server generates an SQL query and executes the query against the database stored in the storage device. Data processing is filtering based on conditions, and the output is asset information that matches the conditions.

[0567] Step 3:

[0568] The server uses an emotion analysis device to understand the user's emotional state. It takes user voice and text data as input, analyzes it, and generates real-time emotion data. The output is emotion data that quantifies the level of tension and relaxation.

[0569] Step 4:

[0570] The server uses a generative AI model to integrate collected asset information and sentiment data. It is given prompts such as "Suggest the best assets based on the user's conditions and sentiments," and the AI ​​model generates a list of recommended assets. The input is asset information and sentiment data, and the output is an optimized suggestion list.

[0571] Step 5:

[0572] The server adjusts the list of recommended assets obtained from the AI ​​model. Based on sentiment data, it adjusts priorities and presentation order. Specifically, it prioritizes the most helpful information for stressed users and presents a wide range of options for relaxed users. The output is the adjusted list of recommendations.

[0573] Step 6:

[0574] The terminal displays a reconciled asset list received from the server to the user. Using frontend technologies such as React, it provides information in a visually intuitive interface. This output presents asset information in a format that allows users to easily compare it.

[0575] Step 7:

[0576] Users evaluate the displayed assets and select properties that interest them. The selected information is fed back to the server via the terminal. The server stores this feedback in a database and uses it to improve future suggestions. The output is the feedback data recorded as a result of the user's selections.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] In recent years, online shopping has seen a growing demand for flexible product suggestions that cater to diverse user needs. However, conventional systems have lacked the ability to consider users' emotional states when making suggestions, resulting in insufficient improvements in user satisfaction.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for inputting the user's conditions and emotional state via a terminal acting as an information receiving device and acquiring the relevant information; means for analyzing the acquired condition information and emotional data through queries with a data set and extracting target information based on the conditions; and means for using generative artificial intelligence to propose targets that fit the conditions and generate additional information based on the extracted target information and emotional data. This enables personalized product suggestions that are adapted to the user's emotional state.

[0582] An "information receiving device" is a terminal device used to input conditions and emotional states from the user.

[0583] "User conditions" refer to specific criteria or requirements desired by the user, such as location or compensation.

[0584] "Emotional state" refers to data that indicates a user's psychological state and emotional tendencies.

[0585] "Relevant information" refers to appropriate information related to the user's circumstances and emotional state.

[0586] A "data set" is a database or collection of information used to perform analysis in response to information queries.

[0587] "Target information" refers to specific information extracted based on the user's conditions and emotional state.

[0588] "Generative artificial intelligence" is an artificial intelligence technology that suggests the most suitable target based on user conditions and emotional data.

[0589] "Additional information" refers to additional information generated in relation to the proposed subject.

[0590] To implement this invention, a user's smartphone is used as an information receiving device. The smartphone is equipped with a camera and a microphone, and through these functions, the user's facial expressions and voice tone are collected in real time. This allows data that reflects the user's emotional state to be obtained.

[0591] The server receives conditional information and emotional state data sent by the user. This data is processed using an emotion analysis AI running on the server to analyze the user's psychological tendencies. For example, by using Microsoft Azure's Emotion Recognition API, facial expression data is analyzed to determine whether the user is relaxed or tense.

[0592] Subsequently, the server uses a generative artificial intelligence engine, such as Google Cloud's Recommendations AI, to generate product suggestions that take into account the user's conditions and analyzed emotional state. The generated product information is sent to the smartphone as appropriate suggestions that match the user's conditions and displayed in a user-friendly interface.

[0593] For example, if a user is in a hurry to buy food for lunch, the sentiment analysis engine will detect that the user is in a hurry. Based on this, the server will prioritize suggesting instant food items that can be delivered quickly. In this way, it is possible to suggest products that are tailored to the user's emotions.

[0594] An example of a prompt to be input into the generating AI model is the text, "The user currently looks tired; recommend foods that can be prepared quickly."

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The user's smartphone is activated, and its camera and microphone are used to capture the user's facial expressions and voice in real time. Input includes image data of the user's face and audio data. This data is sent to an emotion analysis AI to analyze the user's emotional state. The output is data representing the analyzed emotional state of the user.

[0598] Step 2:

[0599] Along with emotional state data, the user's desired conditions (e.g., product category, price range, etc.) entered through the terminal are sent to the server. This input is centrally managed on the server side. The server receives this data and prepares for the next processing. The output is integrated data of condition information and emotional state.

[0600] Step 3:

[0601] The server processes the integrated data and executes queries against the data set. These queries are used to extract relevant subject information from the database based on user conditions. The inputs are condition information and sentiment state data. The output is the extracted relevant subject information.

[0602] Step 4:

[0603] The extracted target information is evaluated by a generative artificial intelligence model on the server. Emotional state data is reflected, and product recommendations best suited to the user's mental state are generated. The input consists of the extracted target information and emotional state data. The output is suggested product information.

[0604] Step 5:

[0605] The generated suggested product information is sent to the user's smartphone and displayed in a user-friendly interface. The user reviews the presented products and selects those of interest. The input is the suggested product information, and the output is the user's selection information.

[0606] Step 6:

[0607] The product information selected by the user is sent back to the server and stored as trend data. This information will be used to improve the suggestion algorithm in the future. The input is the suggested product information selected by the user, and the output is the updated trend data.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] This invention is a system for streamlining the search and suggestion of real estate properties. The system begins with the user entering their desired property criteria via a terminal. The terminal is responsible for analyzing the user's input and transmitting it to a server. The server then sends a query to a database based on the transmitted criteria data, retrieving property information that matches the criteria. This database stores information on numerous real estate properties, and relevant information is retrieved based on the search.

[0626] Next, the server scrutinizes the property information acquired using generative artificial intelligence. The generative AI analyzes the characteristics of properties and ranks them in order to make the best suggestions based on the user's desired conditions. In addition to property information, it also generates supplementary information such as the surrounding environment and reviews, and builds a dataset to provide to the user with this information as well.

[0627] The generated information is returned to the terminal via the server, and the terminal is designed to display it in a user-friendly interface. Users can select from the presented properties and view further details. Furthermore, the content viewed and selection trends of the user are analyzed on the server and provided to the property management company. This analysis includes user preferences and market trends, which the management company can use to improve vacancy forecasts and marketing strategies.

[0628] To give a concrete example, let's assume a user is planning to get a cat and is looking for a pet-friendly property in Tokyo. The user enters the following conditions into their device: "Tokyo, pet-friendly, 1LDK, under 200,000 yen." The device sends these conditions to the server, which extracts relevant property information from its database. The generated artificial intelligence analyzes the extracted information, selects several optimal properties, and presents them to the user along with additional information such as nearby parks and veterinary clinics. Through this process, the user can find their ideal property in a short amount of time.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] The user enters their desired property conditions (e.g., location, rent, floor plan, pet-friendly, etc.) into an input form on the terminal's interface. Once the user has finished entering the information, they press the submit button, and the data is sent to the system.

[0632] Step 2:

[0633] The terminal forms the user's input into a single data packet. This packet is not sent in plain text, but is encrypted as needed before being sent to the server.

[0634] Step 3:

[0635] The server receives data packets sent from the terminal. The server decodes them, analyzes the user's requirements, and generates SQL queries for the database.

[0636] Step 4:

[0637] The server uses the generated SQL query to access the database where property information is stored. This query retrieves property data that may match the user's criteria.

[0638] Step 5:

[0639] The server inputs property information returned from the database into the AI. The AI ​​analyzes each property and selects and ranks properties that meet the user's criteria. It also generates additional information that takes into account surrounding environment information and user reviews.

[0640] Step 6:

[0641] The server converts the information generated by the AI ​​into a user-friendly format. Then, it creates a new data packet to return this information to the terminal.

[0642] Step 7:

[0643] The terminal interprets data packets received from the server and displays the information in a format that allows the user to easily compare and select options. Property images, detailed information, and additional information are presented on the user's screen.

[0644] Step 8:

[0645] Users select properties of interest from the presented list and view more detailed information. During this process, data on the user's selection process and preferences is collected and fed back to the server.

[0646] Step 9:

[0647] The server analyzes user history data and provides management companies with information on market trends and vacancy forecasts. This is crucial data for management companies to develop efficient asset management and marketing strategies.

[0648] (Example 1)

[0649] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] Traditional real estate search systems made it difficult for users to efficiently find their ideal property, and in particular, it was challenging to quickly narrow down the large amount of property information to find the one that best suited their criteria. Furthermore, it was difficult to provide supplementary information about the surrounding area or to offer suggestions tailored to the individual preferences of the user.

[0651] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0652] In this invention, the server includes means for inputting the user's desired conditions and acquiring relevant information fragments via an input device acting as an information acquisition device; means for analyzing the data through query processing with a large data storage device and selecting property information based on the conditions; and means for evaluating and proposing properties that meet the conditions based on the selected property information using generative intelligence, and generating surrounding information. This enables users to quickly and efficiently find their ideal property, and further enables the proposal of value-added information tailored to the user's preferences.

[0653] An "information acquisition device" is a device or system for which a user inputs desired conditions and is capable of acquiring relevant information fragments.

[0654] An "input device" refers to hardware or an interface used by a user to input specific desired conditions, and plays the role of passing that input information to subsequent processing.

[0655] "Relevant information fragments" refer to information collected or generated based on conditions entered by the user and used for a specific purpose.

[0656] A "large-scale data storage device" refers to a system or database for storing large datasets that allows for the selection of necessary data through query processing.

[0657] "Query processing" refers to the process performed to retrieve data from a large-scale data storage device based on specific conditions, and its purpose is to search and extract data.

[0658] "Generative intelligence" refers to a system that uses artificial intelligence technology to make evaluations and suggestions based on specific conditions, and is also capable of generating further information.

[0659] "Selection" refers to the act of extracting property information that meets specific criteria from a large amount of data and classifying or organizing it.

[0660] "Evaluation and proposal" is the process of analyzing acquired property information, determining its value and suitability to the user's requirements, and then presenting the best option.

[0661] "Surrounding area information" refers to information about the environment and services that accompany the basic information about the property, and is intended to provide additional value to users.

[0662] This invention is a system that enables users to efficiently search for real estate properties and make the best selection. Specifically, it is realized by using an input device as an information acquisition device, a server for data analysis, and a terminal that provides a user interface.

[0663] The first step for the user is to use a terminal to input their desired property criteria. This terminal analyzes the entered information and sends it to the server. The terminal utilizes the latest interface technology and is designed to be intuitive for the user.

[0664] Based on the received conditions, the server selects suitable data from property information stored in a large data storage device. This process utilizes a database management system to accurately execute complex query processing. Generative intelligence running on the server further analyzes the selected property information. This generative intelligence has the ability to generate property evaluations and suggestions that best suit the user's conditions.

[0665] The evaluated property information and additional surrounding area information are returned to the terminal and presented to the user. The terminal's display device organizes this information and makes it easy to understand visually. Based on this displayed information, the user can check more detailed property information and choose the most suitable property. In addition, the user's selection history and trends are analyzed on the server and provided to the management organization as trend information.

[0666] For example, if a user enters the conditions "Tokyo, pet-friendly, 1LDK, under 200,000 yen," this information is immediately sent to the server. The server accesses the database and searches for properties that meet the criteria. A generative AI model evaluates the suitability of the properties, and this information is presented to the user. This system allows users to efficiently find their ideal home.

[0667] Example of a prompt:

[0668] "I'm looking for a pet-friendly 1LDK apartment in Tokyo. Could you recommend a suitable property under 200,000 yen?"

[0669] "Please explain what methods should be used to efficiently extract properties that match the specified criteria from a real estate database."

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The user uses a terminal, which acts as an information acquisition device, to input their desired real estate conditions. At this time, they specify detailed conditions such as "Tokyo, pet-friendly, 1LDK, under 200,000 yen." Data based on the entered conditions is generated, and the terminal processes this data.

[0673] Step 2:

[0674] The terminal sends the entered condition information to the server. Data integrity is checked during transmission to prevent errors. The output is condition data in a format that the server can parse.

[0675] Step 3:

[0676] The server executes queries against a large data storage device (database) based on the condition data received from the terminal. Through query processing, property information matching the conditions is selected and received as the selection result.

[0677] Step 4:

[0678] The server inputs the acquired property information into a generating AI model for detailed analysis. Specifically, it scores information such as the property's location, price, and facilities, and creates a ranking. The generating AI model then selects the most suitable property for the user and outputs property information for suggestion.

[0679] Step 5:

[0680] The server returns the evaluated property information output from the generated AI model to the terminal. This data includes not only basic property information but also additional information such as surrounding facilities and reviews.

[0681] Step 6:

[0682] The terminal displays property information received from the server in a user-friendly format. Users can review this output, select properties they are interested in, and obtain detailed information.

[0683] Step 7:

[0684] The server collects the user's property viewing history and selection trends. The server analyzes this data, generates trend information and vacancy predictions, and provides the output to the management organization.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In modern society, efficiently and effectively finding usable spaces is challenging. In particular, there is a need for information presented based on the user's specific preferences and for immediate information acquisition on-site, but conventional systems fail to adequately address this. Therefore, there is a demand for improved efficiency in information acquisition and presentation.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for recognizing the user's preferences and displaying suitable information using a visual device as an information presentation device; means for analyzing the recognized preference information through queries against a data structure and extracting spatial information based on the preference; and means for proposing a space suitable for the preference and generating surrounding environment information based on the extracted spatial information using generative artificial intelligence. As a result, the user can quickly and accurately obtain the necessary spatial information and efficiently grasp the information through the visual device.

[0690] "Visual devices as information presentation devices" refers to hardware devices and related technologies for displaying information in real time through the user's vision.

[0691] "User preferences" refer to the conditions and requirements specified by the user when seeking spatial information.

[0692] "Displaying compatibility information" refers to making information that matches the user's preferences visible to the user through a visual device.

[0693] "Querying a data structure" refers to the process of sending data retrieval requests to databases and other information repositories based on the user's preferences.

[0694] "Spatial information" refers to data about a specific place or facility, encompassing comprehensive information including location and characteristics.

[0695] "Generative artificial intelligence" refers to computer programs and algorithms that analyze large amounts of data and perform condition-based recommendations and information generation.

[0696] "Generating surrounding environment information" refers to compiling geographical, social, or service-related information related to a specific space and providing it to the user.

[0697] To implement this invention, it is necessary to construct a system that includes a visual device, a server, a data structure, and generative artificial intelligence.

[0698] First, the user uses a visual device as an information presentation device. This device includes, for example, smart glasses or a head-mounted display, and has the function of recognizing the user's desired conditions through voice input or gesture input. By utilizing speech recognition technology such as the Google Speech-to-Text API, the user's wishes are converted into digital data in real time.

[0699] Next, the data input through the visual device is transferred to the server, which then queries the data structure. The data structure uses a database such as MongoDB, and it extracts spatial information that matches the user's conditions. In this process, the server identifies data that matches the conditions and retrieves the corresponding spatial information.

[0700] Furthermore, the server uses generative artificial intelligence to analyze the extracted spatial information, ranking and rearranging it in a way that best suits the user's preferences. Here, AI engines such as OpenAI's GPT series are utilized to enable complex data analysis and generation.

[0701] The generated information is presented to the user through a visual device. Using a 3D development environment such as Unity, the visual device displays acquired spatial information and surrounding environment information in augmented reality (AR) format, enabling the user to understand it intuitively.

[0702] As a concrete example, if a user looking to buy a new house wears smart glasses and voice-inputs the conditions "quiet residential area, 4 bedrooms, living room, dining room, kitchen, with balcony," the system will immediately visualize property information that matches those conditions, along with information about nearby parks, supermarkets, and other amenities, through a visual device.

[0703] This allows users to quickly grasp complex information and retrieve the desired information more efficiently. A prompt message that guides this process is: "Get the user's desired conditions via voice and convert them into objects. Design prompts to visualize and present property information and surrounding environment information that meet the conditions."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The device receives user requests via voice or gesture input. It utilizes the Google Speech-to-Text API for voice recognition and an accelerometer for gesture recognition. This converts the received requests into text format.

[0707] Step 2:

[0708] The terminal sends the desired conditions obtained in Step 1 to the server. The server receives this as input data and generates a query to the database. This query is performed against MongoDB and extracts spatial information that matches the user's conditions.

[0709] Step 3:

[0710] The server passes spatial information obtained from the database to the generative artificial intelligence (AI). The AI ​​analyzes this data and evaluates whether it meets the user criteria. Next, it ranks the information and sorts it by priority. This process involves analysis and generation using OpenAI's GPT series.

[0711] Step 4:

[0712] The server generates surrounding environment information based on the ranked spatial information obtained in step 3. This involves using the Google Maps API, etc., to collect and process geographical information and construct data with information highly relevant to the conditions.

[0713] Step 5:

[0714] The device receives the information generated in step 4 and visualizes it in a 3D development environment using Unity. This allows the user to view the information as augmented reality through visual devices such as smart glasses.

[0715] Step 6:

[0716] Based on the information presented, users select spaces that interest them and provide feedback to the server via their device. The server uses this feedback to analyze user biases and trends, which helps improve the accuracy of future suggestions.

[0717] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0718] This invention is a system for improving the user's property search experience, and in particular, by combining it with an emotion engine, it can evaluate the user's emotional state and reflect it in property suggestions. The user first inputs their desired conditions (e.g., location, price, floor plan, etc.) through a terminal. This information is received by the server, and appropriate property information is collected through queries against the database.

[0719] The server uses artificial intelligence to evaluate this property information and select the best property that meets the user's preferences. This evaluation process considers not only the user's input conditions but also emotional data acquired by the emotion engine. The emotion engine analyzes the user's emotional state in real time, determining, for example, whether the user is tense or relaxed.

[0720] This sentiment analysis data is incorporated into the property suggestion algorithm, which adjusts the order and content of the suggested properties to suit the user's emotions. For example, if the user is relaxed, more options are presented slowly, while if the user is stressed, the most likely preferred options are prioritized.

[0721] The terminal displays property information received from the server, along with suggestions generated by the emotion engine, in a user-friendly interface. Users compare the displayed properties and select those of interest. This selection is fed back to the server and used to improve future suggestions.

[0722] As a concrete example, let's assume a user is looking for an apartment for their first time living alone. The emotion engine detects when the user is feeling anxious, and the server adjusts its suggestions based on this emotional information. Specifically, it focuses on suggesting properties in safe areas with rents that do not exceed expectations. The goal is to provide suggestions that match the user's emotions and make the apartment search a more enjoyable experience.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The user accesses the terminal interface and enters their desired property search criteria (e.g., location, rent, floor plan). Once finished, the user presses the "Search" button to submit their search criteria.

[0726] Step 2:

[0727] The terminal converts the entered conditions into data packets and sends them to the server. This data contains the user's basic preferences.

[0728] Step 3:

[0729] The server analyzes the conditions received from the terminal and sends a query to the database. The query filters property information based on the user's conditions and collects information on the relevant properties.

[0730] Step 4:

[0731] Simultaneously, an emotion engine built into the device collects emotion data in real time from the user's facial expressions and voice data. This emotion data indicates the user's emotional state when viewing the information.

[0732] Step 5:

[0733] The server retrieves property information from the database and emotion data from the emotion engine, and inputs it into the generative artificial intelligence. The generative AI integrates this information to generate property suggestions that best match the user's emotional state.

[0734] Step 6:

[0735] The server organizes the generated property suggestions and ranks them in an order deemed optimal for the user. This ranking is adjusted depending on whether the user is relaxed or stressed.

[0736] Step 7:

[0737] The server sends these suggested results to the terminal. The terminal visualizes this information in an easy-to-understand way for the user and displays it on the interface. This includes property images, detailed information, and additional supplementary information.

[0738] Step 8:

[0739] Users can browse properties of interest from the displayed information and view details. The selected information is then fed back to the server and may be used to improve future recommendations.

[0740] Step 9:

[0741] The server provides sentiment data, including user feedback, to the management company. This information is used to improve marketing and streamline vacancy management.

[0742] (Example 2)

[0743] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Traditional asset search systems often present results based solely on input criteria, without considering the user's emotional state. This lack of consideration for the user's feelings has been a problem, as it can lead to stress and dissatisfaction during the process of discovering and selecting the assets that truly meet the user's needs.

[0745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0746] In this invention, the server includes means for understanding the user's emotional state using an emotion analysis device, means for proposing assets based on the user's conditions and emotional state using generative artificial intelligence, and means for intuitively presenting asset information to the user. This makes it possible to propose assets that take the user's emotional state into consideration, resulting in a more satisfying search experience for the user.

[0747] An "information receiving device" is a device that takes user information as input and retrieves relevant information.

[0748] A "storage device" is a device that stores data in order to analyze acquired conditional information and extract asset information based on that analysis.

[0749] "Generative artificial intelligence" is an artificial intelligence technology that provides optimal asset recommendations based on the user's conditions and emotional state.

[0750] An "emotion analysis device" is a technological device that understands the emotional state of a user and reflects that in asset proposals.

[0751] "Usage trend information" refers to information about users' choices and behavioral patterns, and is data that is transmitted to the managing organization.

[0752] "Asset information" refers to detailed information about an asset, including its location, value, structure, and available options.

[0753] This invention provides an asset search system that takes into account the emotional state of the user. This system uses a terminal as an information receiving device to receive the user's search criteria. The data received by the terminal is transmitted to a server via a network.

[0754] The server analyzes the received data and compares it with information stored in its memory. This extracts asset information from the database that matches the user's criteria. The server then uses generative artificial intelligence to evaluate the extracted asset information and select the asset that best matches the user's preferences. This process utilizes commonly used generative AI models (e.g., natural language processing models).

[0755] Furthermore, the server uses an emotion analysis device to analyze the user's emotional state in real time. This emotional data is incorporated into the asset recommendation algorithm, which adjusts the recommendation order and content to match the user's emotions. For example, if the user is relaxed, a variety of options are offered, while if they are stressed, highly recommended assets are prioritized.

[0756] The terminal displays the results of these processes in a user-friendly interface. Frameworks such as React are used, and the design is optimized to allow users to intuitively compare asset information.

[0757] For example, if a user is looking for an asset for their first time living alone, the emotion analyzer will detect that the user is feeling anxious, and the server will prioritize presenting properties in safe areas and within their budget. This allows the user to smoothly make the best choice based on their emotional state.

[0758] An example of a prompt message is suggested for input into the generating AI model: "Generate optimal asset recommendations based on the user's search criteria and sentiment analysis data."

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The user inputs property-related conditions via a terminal. These conditions include location, price, and floor plan. The terminal converts this information into JSON format and sends it to the server over the network. The input is the user's desired conditions, and the output is the JSON data sent to the server.

[0762] Step 2:

[0763] The server parses the JSON data received from the terminal and extracts the necessary information. Based on this data, the server generates an SQL query and executes the query against the database stored in the storage device. Data processing is filtering based on conditions, and the output is asset information that matches the conditions.

[0764] Step 3:

[0765] The server uses an emotion analysis device to understand the user's emotional state. It takes user voice and text data as input, analyzes it, and generates real-time emotion data. The output is emotion data that quantifies the level of tension and relaxation.

[0766] Step 4:

[0767] The server uses a generative AI model to integrate collected asset information and sentiment data. It is given prompts such as "Suggest the best assets based on the user's conditions and sentiments," and the AI ​​model generates a list of recommended assets. The input is asset information and sentiment data, and the output is an optimized suggestion list.

[0768] Step 5:

[0769] The server adjusts the list of recommended assets obtained from the AI ​​model. Based on sentiment data, it adjusts priorities and presentation order. Specifically, it prioritizes the most helpful information for stressed users and presents a wide range of options for relaxed users. The output is the adjusted list of recommendations.

[0770] Step 6:

[0771] The terminal displays a reconciled asset list received from the server to the user. Using frontend technologies such as React, it provides information in a visually intuitive interface. This output presents asset information in a format that allows users to easily compare it.

[0772] Step 7:

[0773] Users evaluate the displayed assets and select properties that interest them. The selected information is fed back to the server via the terminal. The server stores this feedback in a database and uses it to improve future suggestions. The output is the feedback data recorded as a result of the user's selections.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In recent years, online shopping has seen a growing demand for flexible product suggestions that cater to diverse user needs. However, conventional systems have lacked the ability to consider users' emotional states when making suggestions, resulting in insufficient improvements in user satisfaction.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for inputting the user's conditions and emotional state via a terminal acting as an information receiving device and acquiring the relevant information; means for analyzing the acquired condition information and emotional data through queries with a data set and extracting target information based on the conditions; and means for using generative artificial intelligence to propose targets that fit the conditions and generate additional information based on the extracted target information and emotional data. This enables personalized product suggestions that are adapted to the user's emotional state.

[0779] An "information receiving device" is a terminal device used to input conditions and emotional states from the user.

[0780] "User conditions" refer to specific criteria or requirements desired by the user, such as location or compensation.

[0781] "Emotional state" refers to data that indicates a user's psychological state and emotional tendencies.

[0782] "Relevant information" refers to appropriate information related to the user's circumstances and emotional state.

[0783] A "data set" is a database or collection of information used to perform analysis in response to information queries.

[0784] "Target information" refers to specific information extracted based on the user's conditions and emotional state.

[0785] "Generative artificial intelligence" is an artificial intelligence technology that suggests the most suitable target based on user conditions and emotional data.

[0786] "Additional information" refers to additional information generated in relation to the proposed subject.

[0787] To implement this invention, a user's smartphone is used as an information receiving device. The smartphone is equipped with a camera and a microphone, and through these functions, the user's facial expressions and voice tone are collected in real time. This allows data that reflects the user's emotional state to be obtained.

[0788] The server receives conditional information and emotional state data sent by the user. This data is processed using an emotion analysis AI running on the server to analyze the user's psychological tendencies. For example, by using Microsoft Azure's Emotion Recognition API, facial expression data is analyzed to determine whether the user is relaxed or tense.

[0789] Subsequently, the server uses a generative artificial intelligence engine, such as Google Cloud's Recommendations AI, to generate product suggestions that take into account the user's conditions and analyzed emotional state. The generated product information is sent to the smartphone as appropriate suggestions that match the user's conditions and displayed in a user-friendly interface.

[0790] For example, if a user is in a hurry to buy food for lunch, the sentiment analysis engine will detect that the user is in a hurry. Based on this, the server will prioritize suggesting instant food items that can be delivered quickly. In this way, it is possible to suggest products that are tailored to the user's emotions.

[0791] An example of a prompt to be input into the generating AI model is the text, "The user currently looks tired; recommend foods that can be prepared quickly."

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The user's smartphone is activated, and its camera and microphone are used to capture the user's facial expressions and voice in real time. Input includes image data of the user's face and audio data. This data is sent to an emotion analysis AI to analyze the user's emotional state. The output is data representing the analyzed emotional state of the user.

[0795] Step 2:

[0796] Along with emotional state data, the user's desired conditions (e.g., product category, price range, etc.) entered through the terminal are sent to the server. This input is centrally managed on the server side. The server receives this data and prepares for the next processing. The output is integrated data of condition information and emotional state.

[0797] Step 3:

[0798] The server processes the integrated data and executes queries against the data set. These queries are used to extract relevant subject information from the database based on user conditions. The inputs are condition information and sentiment state data. The output is the extracted relevant subject information.

[0799] Step 4:

[0800] The extracted target information is evaluated by a generative artificial intelligence model on the server. Emotional state data is reflected, and product recommendations best suited to the user's mental state are generated. The input consists of the extracted target information and emotional state data. The output is suggested product information.

[0801] Step 5:

[0802] The generated suggested product information is sent to the user's smartphone and displayed in a user-friendly interface. The user reviews the presented products and selects those of interest. The input is the suggested product information, and the output is the user's selection information.

[0803] Step 6:

[0804] The product information selected by the user is sent back to the server and stored as trend data. This information will be used to improve the suggestion algorithm in the future. The input is the suggested product information selected by the user, and the output is the updated trend data.

[0805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0806] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0807] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0808] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0809] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0810] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0811] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0812] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0813] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0814] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0815] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0816] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0817] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0818] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0819] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0820] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0821] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0822] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0823] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0824] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0825] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A terminal as a means of receiving information allows the user to input conditions and obtain the relevant information.

[0829] A means of analyzing acquired conditional information through queries against a database and extracting property information based on those conditions,

[0830] A means for using generative artificial intelligence to propose properties that meet the criteria based on extracted property information and to generate additional information,

[0831] The terminal provides a means for displaying proposed property information and additional information to the user,

[0832] A means of sending trend information and vacancy forecasts to the management company,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the property information displayed to the user includes location, price, floor plan, and available options.

[0836] (Claim 3)

[0837] The system according to claim 1, which uses generating artificial intelligence to sort and provide property information that best matches the user's conditions in order of specified priority.

[0838] "Example 1"

[0839] (Claim 1)

[0840] An input device acting as an information acquisition device provides a means for users to input their desired conditions and acquire relevant information fragments.

[0841] A means for analyzing acquired conditional information through query processing with a large data storage device and selecting property information based on the conditions,

[0842] A means for using generative intelligence to evaluate and propose properties that meet the criteria based on selected property information, and to generate surrounding information,

[0843] A means of presenting proposed property information and surrounding area information to the user via an information display device,

[0844] A means of transmitting usage trend data and vacancy predictions to the management organization,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the property information presented to the user includes location, cost, floor plan, and available selection information.

[0848] (Claim 3)

[0849] The system according to claim 1, which uses generative intelligence to sort and provide property information that best matches the user's desired conditions according to specified priority criteria.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A visual device as an information presentation device recognizes the user's preferences and provides a means of displaying suitable information.

[0853] A means for analyzing recognized preference information through queries against a data structure and extracting spatial information based on that preference,

[0854] A means of proposing a space that suits the desired conditions based on extracted spatial information using generative artificial intelligence, and generating surrounding environment information,

[0855] A means of visualizing proposed spatial information and surrounding environment information to the user using a visual device,

[0856] A means of collecting user activity and preference trend information and transmitting the analysis results to a management organization,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the spatial information visualized for the user includes location, cost, three-dimensional shape, and available selection information.

[0860] (Claim 3)

[0861] The system according to claim 1, which uses generating artificial intelligence to sort and visualize spatial information that best suits the user's preferences in order of specified priority.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] A terminal acting as an information receiving device provides a means for users to input their conditions and obtain related information.

[0865] A means for analyzing acquired condition information through comparison with a storage device and extracting asset information based on the conditions,

[0866] A means of using generative artificial intelligence to propose assets that meet the criteria based on extracted asset information and to generate supplementary information,

[0867] A means of understanding the emotional state of users using an emotion analysis device and reflecting that in asset proposals,

[0868] The terminal provides a means for displaying proposed asset information and supplementary information to the user,

[0869] A means of transmitting usage trend information to the management organization,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the asset information displayed to the user includes location, value, structure, and available options.

[0873] (Claim 3)

[0874] The system according to claim 1, which uses generating artificial intelligence to provide asset information that best matches the user's conditions and emotional state, sorted in a specified order of priority.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A terminal acting as an information receiving device provides a means for inputting the user's conditions and emotional state, and acquiring the relevant information.

[0878] A means for analyzing acquired conditional information and sentiment data through queries against a data set, and for extracting target information based on the conditions,

[0879] A means for proposing a target that meets the conditions and generating additional information based on extracted target information and emotion data using generative artificial intelligence,

[0880] The terminal provides a means for displaying proposed target information and additional information to the user,

[0881] A means of sending trend information and forecast information to the management company,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the target information displayed to the user includes location, price, specifications, and available selection information.

[0885] (Claim 3)

[0886] The system according to claim 1, which uses generating artificial intelligence to sort and provide target information that best matches the user's conditions and emotional state in order of specified priority. [Explanation of Symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal as a means of receiving information allows the user to input conditions and obtain the relevant information. A means of analyzing acquired conditional information through queries against a database and extracting property information based on those conditions, A means for using generative artificial intelligence to propose properties that meet the criteria based on extracted property information and to generate additional information, The terminal provides a means for displaying proposed property information and additional information to the user, A means of sending trend information and vacancy forecasts to the management company, A system that includes this.

2. The system according to claim 1, wherein the property information displayed to the user includes location, price, floor plan, and available options.

3. The system according to claim 1, which uses generating artificial intelligence to sort and provide property information that best matches the user's conditions in order of specified priority.

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