System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving dwelling-related data and lifestyle data from a user; means for analyzing the received data and generating a profile specific to the user; means for analyzing the received data and profile using multi-modal artificial intelligence and suggesting an optimal destination and lifestyle; and means for transmitting the suggested results to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, living environments are undergoing major changes, such as the spread of telework, the lifting of restrictions on ride-sharing, and changes in family structure. These changes are increasing the need for relocation, but finding the optimal place to move can be difficult. Many factors must be considered, such as video footage and blueprints of the current home, family opinions, income, and commute destination. Evaluating these individually and making a comprehensive decision is extremely cumbersome. Furthermore, while proposals for new lifestyles that take social changes into account are desirable, existing systems do not adequately address this. This presents a challenge for users, making it difficult to find a truly satisfying new home and lifestyle. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. The system includes a means for receiving housing-related data and lifestyle data from a user, a means for analyzing the received data and generating a user-specific profile, a means for analyzing the received data and profile using multimodal artificial intelligence to propose optimal relocation destinations and lifestyles, and a means for transmitting the proposal results to the user. The system also includes a means for receiving feedback provided by the user and transmitting it to a server, and a means for generating new proposals based on the feedback transmitted to the server. The system also includes a means for using multiple analytical methods, such as video analysis, audio analysis, and text analysis, based on data entered by the user, and a means for integrating the analysis results to generate a user profile. This system analyzes data such as the user's lifestyle patterns, income, family composition, and commute destination, proposes lifestyles, and ranks optimal relocation destinations based on a database of available homes. As a result, users can easily find the optimal relocation destination and lifestyle to improve their quality of life.

[0006] "Residence-related data" refers to information about the residence in which the user currently resides (e.g., videos, blueprints, area, floor plan, furniture layout, etc.).

[0007] "Lifestyle data" refers to information about the lifestyle of the user and their family (e.g., family composition, income, commute, daily routine, hobbies, preferences, etc.).

[0008] "Multimodal AI" refers to AI that has the ability to simultaneously analyze multiple different data input formats (e.g., text, audio, images, video, etc.) and make comprehensive judgments.

[0009] "User-Specific Profile" means a digital profile containing individual characteristics and needs that is generated based on the User's home-related and lifestyle data.

[0010] "Analysis means" refers to the methods and technologies for analyzing received data (e.g., video analysis, audio analysis, text analysis, etc.).

[0011] "Proposal results" refers to the proposed relocation destination and new lifestyle for the user generated by the multimodal artificial intelligence based on the analysis results.

[0012] "Feedback" refers to opinions, requests for additions, and evaluations provided by users regarding the proposals.

[0013] A "vacant house database" refers to a database that compiles information about available properties (vacant houses).

[0014] "Ranking" refers to the process of numerically evaluating and ranking the most suitable relocation destinations based on the user's conditions and needs. [Brief explanation of the drawings]

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

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

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0036] This invention is a system for proposing optimal relocation destinations and new lifestyles that takes into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze the housing-related data and lifestyle data entered by the user and generate optimal proposals.

[0037] Program Overview

[0038] 1. Data Entry

[0039] Users enter information such as home videos, blueprints, family composition, income, and commute destination through the device interface, which allows detailed data about the user's current residence and lifestyle to be collected.

[0040] 2. Data Transmission and Reception

[0041] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[0042] 3. Data Analysis

[0043] The server analyzes the received data. Specifically, object recognition and scene analysis are used to extract features of the current home from the video data, and keywords related to the family's opinions and lifestyle are extracted from the audio data through voice recognition. Text data and drawing data are also analyzed in the same way.

[0044] 4. Profile Generation

[0045] The server then uses the analyzed data to create a personalized profile for the user, which includes information about the user's lifestyle, income, family structure, and other essential needs.

[0046] 5. Analysis and Proposal Generation Using Multimodal AI

[0047] The server operates a multimodal artificial intelligence based on the generated user profile. The AI ​​considers current housing data, the user's requests, and future needs to suggest the optimal place to move to. It also references a database of vacant homes to extract properties that meet the user's criteria in a ranked format. It also takes into account changes in social conditions to suggest new lifestyles.

[0048] 6. Submitting the proposal results

[0049] The generated proposal results are sent from the server to the terminal, which displays the proposal results to the user and provides detailed information.

[0050] 7. Gather feedback and resubmit

[0051] The user enters feedback on the displayed suggestions, which is then sent to the server via the device. The server receives the user's feedback, reanalyzes it based on the feedback, and generates new suggestions. This process can be repeated as needed.

[0052] Specific examples

[0053] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device. The device sends this information to the server, which analyzes it and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also suggests new lifestyles by recommending ride-sharing and local community activities. User A can review the suggestions and add further specific conditions and provide feedback again.

[0054] In this way, the present invention can suggest the optimum relocation destination and lifestyle suited to the individual needs of the user, thereby improving the user's quality of life.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user uses the terminal interface to input residence-related data (videos, drawings, furniture layout, etc.) and lifestyle data (family composition, income, commute destination, etc.). After the user has completed the data input, they click the submit button.

[0058] Step 2:

[0059] The device processes the data and sends it to the server using a secure protocol, including encrypting the data and verifying that the transfer was successful.

[0060] Step 3:

[0061] The server receives the data from the device, stores it securely in a database, and verifies that all data has been stored correctly.

[0062] Step 4:

[0063] The server analyzes the received data, including:

[0064] Video analysis: Using object recognition and scene analysis, the characteristics of the current residence (type of furniture, arrangement, room size, etc.) are extracted.

[0065] Voice analysis: Uses voice recognition technology to convert family members' opinions and lifestyle characteristics into text.

[0066] Text analysis: Analyzes drawing data and other text information to understand detailed specifications of the home and user requirements.

[0067] Step 5:

[0068] The server uses the analyzed data to create a personalized profile for each user, which includes detailed information about the user's lifestyle, income, family structure, and commute.

[0069] Step 6:

[0070] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[0071] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0072] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0073] Step 7:

[0074] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[0075] Step 8:

[0076] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[0077] Step 9:

[0078] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via the terminal.

[0079] Step 10:

[0080] The server receives the feedback sent by the user, analyzes it again, and generates new suggestions, which are then sent back to the device and displayed to the user.

[0081] Through this process, users can find a new home and lifestyle that best suits their needs and home environment.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] Conventional housing selection systems have difficulty in making proposals that fully reflect the lifestyles and needs of individual users, resulting in problems in which users are unable to find the optimal place to move to that satisfies them. In addition, the display of proposal results is mainly text, and is not presented in a format that is easy for users to understand, resulting in a poor user experience.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for transmitting the received data to the server using a secure protocol, means for the server to analyze the received data and generate a profile dedicated to the user, means for analyzing the generated profile and an external database using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, and means for transmitting the proposal results to the user and displaying them in a graphical interface. This makes it possible to propose an optimal relocation destination and lifestyle that is more tailored to the individual needs of the user than ever before, thereby improving the user experience.

[0087] "User" refers to an individual or organization that uses the system and is the entity that provides housing-related data and lifestyle data.

[0088] "Residence-related data" refers to information such as videos of the residence, blueprint data, room features, and furniture layout.

[0089] "Lifestyle data" refers to data about a user's daily life, such as family composition, income, commute, hobbies, and transportation methods.

[0090] A "terminal" is a device that allows a user to input data and communicate with a server, and includes smartphones, tablets, PCs, etc.

[0091] A "secure protocol" is a communication protocol used to ensure security when transmitting data, and generally corresponds to HTTPS.

[0092] A "server" is a computer system that analyzes data received from terminals and plays a central role in storing and analyzing data and generating proposals.

[0093] A "profile" is a collection of information specific to a user that is generated based on analyzed data, and includes lifestyle patterns, income, family structure, requirements, and the like.

[0094] "Multimodal AI" refers to an AI technology that integrates and analyzes multiple data formats (video, audio, text, etc.) to assist in solving complex problems.

[0095] "External databases" refer to public or private data sources that exist separately from the user's data, such as real estate information or environmental data.

[0096] "Suggestions" refer to the optimal relocation and lifestyle options derived through analysis, and are advice provided to users.

[0097] A "graphical interface" is a user interface that displays the proposal results to the user in a visually easy-to-understand format, and includes maps, charts, card-style information displays, and the like.

[0098] "Feedback" refers to information that users input about their opinions and requests regarding the proposed results, which the system uses to reanalyze and make improvement suggestions.

[0099] These definitions will provide a clearer understanding of the invention.

[0100] The present invention is a system that proposes optimal relocation destinations and lifestyles to users, taking into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze data entered by users related to their residence and lifestyle, and generates optimal proposals.

[0101] Hardware and software used

[0102] Users access the system using a device such as a smartphone, tablet, or PC, and enter the following data through a web form or mobile app on their device:

[0103] Housing video

[0104] Drawing data

[0105] Family composition

[0106] income

[0107] Commuter destination

[0108] Video data is taken with a smartphone or tablet camera and uploaded directly to the application, while text data such as income and family composition is collected by entering it into the interface.

[0109] Sending and Receiving Data

[0110] The terminal sends all data entered by the user to the server using a secure protocol (e.g., HTTPS). During this process, the data is encrypted and transmitted securely. The server stores the received data in a database (e.g., MySQL (registered trademark) or PostgreSQL).

[0111] Data analysis

[0112] The server analyzes the received data. For video data, computer vision technology (e.g., OpenCV) is used to perform object recognition and scene analysis. For audio data, a speech recognition API (e.g., Google® Speech-to-Text) is used to convert it to text, and natural language processing (NLP) tools (e.g., NLTK or SpaCy) are used to extract keywords. Furthermore, blueprint data is analyzed using CAD software or image analysis tools to extract floor plan and area information.

[0113] Profile Generation

[0114] Based on the analysis results, the server creates a personalized profile for the user, which includes information such as the user's lifestyle, income, family structure, and essential conditions.

[0115] Proposal Generation

[0116] The server runs multimodal artificial intelligence (e.g., TENSORFLOW (registered trademark), PyTorch) based on the generated user profile. The AI ​​refers to existing external databases (e.g., real estate information, environmental data) and proposes the optimal relocation destination, taking into account the user's requests and future needs. It also lists the proposed properties in a ranking format and recommends ride-sharing options and local community activities.

[0117] Displaying the proposed results

[0118] The server sends the generated proposal results to the terminal, which provides an interface for displaying the proposal results graphically to the user. This interface includes a map display using the Google Maps API and a card-style list display of property information.

[0119] Gathering feedback and resubmitting

[0120] The user then enters feedback on the displayed suggestions into the device. For example, they enter specific requests, such as "I'd like my commute to be shorter" or "I'd prefer a property that allows pets." The device then securely transmits this feedback back to the server. The server receives the feedback, reanalyzes it based on the newly collected information, and generates improved suggestions. This process can be repeated as many times as necessary until the user is satisfied.

[0121] Specific examples

[0122] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of the home, family composition, income, and commute destination into the device. The device then sends this information to the server using a secure protocol. The server analyzes the information and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also recommends ride-sharing and local community activities.

[0123] Prompt Sentence Examples

[0124] "We want to move to the suburbs to work remotely. We are a married couple with a combined annual income of 8 million yen. Here are the videos and blueprints of our current home. Can you suggest the best place to move to?"

[0125] By entering this prompt, the system will generate a proposal for the best possible relocation location that meets User A's needs.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] Users enter data such as home video, blueprint data, family composition, income, and work destination through the device interface. This operation is performed using a dedicated web form or mobile app. The entered data includes video files (e.g., .mp4), blueprint image files (e.g., .jpg, .png), and text data (e.g., income, work destination).

[0129] Input: Video of residence, drawing data, family composition, income, commute destination

[0130] Output: User data temporarily stored in the device

[0131] Step 2:

[0132] The device sends the data entered by the user to the server using a secure protocol, with the data being sent securely using techniques such as AES encryption.

[0133] Input: User data

[0134] Output: Data sent using a secure protocol

[0135] Step 3:

[0136] The server receives the data sent from the device and stores it securely in a database using a database management system such as MySQL or PostgreSQL.

[0137] Input: Securely transmitted data

[0138] Output: User data stored in the database

[0139] Step 4:

[0140] The server analyzes the received and stored data. For video data, OpenCV and TensorFlow are used for object recognition and scene analysis to extract room features and furniture layout. If audio data is included, it is converted to text using the Google Speech-to-Text API, and keywords are extracted using natural language processing tools (NLTK and SpaCy). For blueprint data, image analysis software is used to calculate the room layout and area.

[0141] Input: Database user data

[0142] Output: Analyzed feature data (room features, furniture layout, keywords, floor plan, etc.)

[0143] Step 5:

[0144] Based on the analysis results, the server creates a user-specific profile, which includes information on lifestyle, income, family structure, and other essential conditions, providing a foundation for a detailed reflection of the user's needs and conditions.

[0145] Input: Parsed feature data

[0146] Output: User-specific profile

[0147] Step 6:

[0148] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​compares the data with real estate information and environmental databases to create a list of optimal relocation destinations that meet the user's requirements. The AI ​​also presents properties in a ranking format, taking into account social conditions and future needs.

[0149] Input: User-specific profile, external database

[0150] Output: Ranked list of optimal relocation destinations

[0151] Step 7:

[0152] The server sends the generated proposal results to the device, which uses the Google Maps API to display the candidate sites on a map and display detailed information about each property in a card format to make it easier for the user to understand.

[0153] Input: Ranked list of optimal relocation destinations

[0154] Output: Graphical proposal results displayed on the terminal

[0155] Step 8:

[0156] The user can then input their feedback on the displayed proposals into the terminal, for example, by inputting specific conditions or requests such as "I want to shorten my commute time" or "I would prefer a property where I can keep pets."

[0157] Input: User feedback

[0158] Output: Feedback data stored on the device

[0159] Step 9:

[0160] The device sends the user's feedback data to the server using a secure protocol, and the server receives the feedback and begins analyzing it again to generate suggestions based on the new conditions.

[0161] Input: Securely transmitted feedback data

[0162] Output: Feedback data stored on the server

[0163] Step 10:

[0164] The server re-analyzes the newly collected information and generates improved suggestions, which are again ranked and sent to the user.

[0165] Input: Feedback data, analysis system

[0166] Output: Regenerated improvement suggestions

[0167] By repeating this process, we can propose the optimal relocation destination and lifestyle that will satisfy the user.

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] In response to current changes in lifestyles and the spread of teleworking, systems that suggest optimal relocation and lifestyle options for users are needed. However, in addition to such systems, there is also a need for methods to improve the in-store experience based on users' profiles. In particular, systems that can eliminate the hassle and lack of information users face when selecting products in physical stores are important.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for analyzing the received data and generating a user-specific profile, means for analyzing the received data and profile using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, means for generating product and service proposals based on the proposal results and supporting purchases and experiences at physical stores, and means for the user to check the proposal results using a smart device at the physical store and receive further detailed information and advice. This not only makes it possible to propose optimal relocation destinations and lifestyles to users, but also to improve the purchasing experience at physical stores.

[0173] "Residence-related data" is data related to the place where the user currently resides, and includes information on the address, floor plan, videos, drawings, and surrounding environment.

[0174] "Lifestyle data" refers to information about a user's lifestyle habits and preferences, family composition, income, commute, hobbies, health status, etc.

[0175] A "profile" refers to a set of information that reflects a user's individual living patterns and needs, generated based on their home-related and lifestyle data.

[0176] "Multimodal artificial intelligence" refers to an artificial intelligence system that integrates and analyzes data in various formats, such as text, audio, images, and video, and makes comprehensive judgments like a human.

[0177] "Suggestion results" refers to the information proposed regarding the optimal relocation destination and lifestyle that the multimodal artificial intelligence derives from analyzing the user's profile.

[0178] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit to select, purchase, and experience products and services in person.

[0179] "Smart device" refers to a mobile terminal or wearable device (e.g., smartphone, smart glasses, head-mounted display) that is connected to the Internet and can run applications.

[0180] "Feedback" refers to evaluations and opinions on the proposal results provided by users, and is important information for making re-proposals based on this.

[0181] "Product and Service Suggestions" refers to a list of specific products and services suggested for purchase or experience based on the user's profile.

[0182] "Analysis means" refers to methods and technologies for processing and analyzing input data, specifically techniques including video analysis, audio analysis, and text analysis.

[0183] A "cloud server" is a server that can be accessed remotely via the Internet and is a system that provides computing resources for storing data, analyzing data, running AI models, and more.

[0184] This invention is a system that proposes optimal relocation destinations and new lifestyles based on the user's housing-related data and lifestyle data. Furthermore, this proposal is applied to in-store experiences to support the user's purchasing behavior.

[0185] First, the user enters information such as video of the home, blueprint data, family composition, income, and workplace via a smartphone app. This data is then sent to a cloud server via the internet and securely stored. The cloud server is equipped with a high-performance GPU server, which is used to analyze the data. Software used includes "BERT" and "GPT-4 (registered trademark)" for text analysis, "TensorFlow" for video analysis, and "PyTorch" for audio analysis.

[0186] The server then uses the received data to create a personalized profile for the user, including information about the user's lifestyle, income, family structure, and other essential needs. During the profile creation process, video data is analyzed using object recognition, audio data is analyzed using voice recognition, and text data is also analyzed.

[0187] Next, based on the generated profile, multimodal AI proposes optimal relocation and lifestyle options. This AI uses a generative AI model to analyze the user's requests and future needs. It also takes into account changes in social conditions to propose new lifestyles.

[0188] The results of the proposals are sent from the cloud server to smart devices such as smartphone apps and smart glasses. Users can use these devices to check the proposals and receive detailed information and advice. For example, in a physical store, by wearing smart glasses, product descriptions and layout proposals for the store can be displayed in real time, and store staff can use tablet devices to provide optimal customer service.

[0189] Users can also provide feedback on the suggestions through the app. The cloud server receives this feedback, analyzes the data again based on the feedback, and generates new suggestions. This process is repeated as needed to provide more accurate suggestions.

[0190] As a concrete example, let's consider the case where User B wants to choose new furniture. User B uses a smartphone app to input data in advance, such as family composition, income, videos and blueprints of the house they currently live in, and their lifestyle. The cloud server analyzes this data and suggests furniture and layouts that are recommended for User B. When visiting the store, User B can wear smart glasses and explore the store while receiving explanations of the products inside and advice on layout. At this time, store staff use a tablet to suggest the best products for User B.

[0191] An example prompt is:

[0192] Input data: I'm planning to go to a furniture store. I have a family of four, my income is 7 million yen, and I need office space at home to work remotely. I'll attach a video and blueprint of my home. Please suggest what kind of furniture would be suitable.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] Users use a smartphone app to input home-related and lifestyle data, including home videos, blueprints, family composition, income, and commute information. Once users enter this data, the device compiles it into a single file or dataset. The input data can be in a variety of formats, including text, video, images, and audio.

[0196] input:

[0197] Information such as videos of the residence, blueprint data, family composition, income, and commute destination.

[0198] output:

[0199] A collection of data files or datasets.

[0200] Step 2:

[0201] The device sends the compiled data file over the internet to a cloud server using the secure HTTPS protocol, which then stores the data securely in a database.

[0202] input:

[0203] Aggregated data files.

[0204] output:

[0205] Data stored on a cloud server.

[0206] Step 3:

[0207] The server analyzes the received data. First, object recognition and scene analysis are used to extract features from the video data. Next, important keywords are extracted from the audio data using voice recognition technology, and text data is also analyzed. This provides detailed information about the user's home and lifestyle.

[0208] input:

[0209] Data stored on a cloud server.

[0210] output:

[0211] Parsed feature data (objects, keywords, etc.).

[0212] Step 4:

[0213] The server generates a user-specific profile based on the analyzed feature data. This profile includes the user's lifestyle, income, family structure, and essential conditions. The generated profile is stored in a database.

[0214] input:

[0215] Parsed feature data.

[0216] output:

[0217] A user-specific profile.

[0218] Step 5:

[0219] The server runs a multimodal AI based on the generated user profile. The AI ​​analyzes the profile and the original residential data to generate suitable relocation and lifestyle recommendations for the user. This AI model uses the aforementioned generative AI models (BERT, GPT-4, etc.).

[0220] input:

[0221] A user-specific profile.

[0222] output:

[0223] Suggestions for relocation and lifestyle.

[0224] Step 6:

[0225] The server sends the proposed results to the user's smartphone app using a secure protocol, and the proposed results are displayed on the user's smartphone screen.

[0226] input:

[0227] Suggestions for relocation and lifestyle.

[0228] output:

[0229] Display on smartphone app.

[0230] Step 7:

[0231] The user can check the proposals and enter feedback through a smartphone app, which is then sent back to the cloud server.

[0232] input:

[0233] User feedback.

[0234] output:

[0235] Feedback sent to cloud server.

[0236] Step 8:

[0237] The server re-performs the analysis based on the received feedback and generates new suggestions, which are then sent to the user again.

[0238] input:

[0239] User feedback.

[0240] output:

[0241] Regenerated relocation and lifestyle proposals.

[0242] Step 9:

[0243] When users visit a physical store, they can use smart glasses or a smartphone app to check the recommendations and select products and services in the store, enabling them to select more specific and optimal products and services.

[0244] input:

[0245] Regenerated relocation and lifestyle proposals.

[0246] output:

[0247] Providing optimal products and services in physical stores.

[0248] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0249] The present invention is a system that recognizes a user's emotional state and proposes optimal relocation and new lifestyle options taking that state into consideration. The system uses multimodal artificial intelligence and an emotion engine to perform detailed analysis based on the residential and lifestyle data provided by the user.

[0250] Program Overview

[0251] 1. Data Entry

[0252] Users input information such as videos of their home, blueprints, family composition, income, and place of work into the device. Data on facial expressions and tone of voice is also collected to recognize the user's emotional state.

[0253] 2. Data Transmission and Reception

[0254] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[0255] 3. Emotional state analysis

[0256] The server analyzes facial expressions and voice tones to recognize the user's emotional state. The emotion engine identifies the user's emotions, such as joy, sadness, anger, and surprise, and provides the data.

[0257] 4. Integrated analysis of all data

[0258] The server performs a comprehensive analysis of the received residential data, lifestyle data, and emotional data, including the following analyses:

[0259] Video analysis: Object recognition and scene analysis to extract features of the current dwelling.

[0260] Speech analysis: Speech recognition to convert family opinions and lifestyle characteristics into text.

[0261] Text analysis: Analysis to understand detailed specifications of the home and user requirements.

[0262] 5. Profile Generation

[0263] The server uses the analyzed data to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, and emotional state.

[0264] 6. Analysis and proposal generation using multimodal AI

[0265] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[0266] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0267] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0268] Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of their acceptance.

[0269] 7. Submitting the proposal results

[0270] The generated proposal results are sent from the server to the device, and include details of the optimal relocation location and specific details of the new lifestyle.

[0271] 8. Displaying the proposed results

[0272] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[0273] 9. Gather feedback and resubmit

[0274] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via their device. The server then analyzes the feedback and generates new proposals. This process is repeated as necessary.

[0275] Specific examples

[0276] For example, suppose User B lives in a city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, as well as information about their family, income, and workplace into their device. User B's facial expressions and tone of voice are also recorded. The device sends all data to a server, which analyzes the data and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests a property in the suburbs, surrounded by nature, that is ideal for teleworking. It also makes customized suggestions based on User B's emotional state, including suggestions for ride-sharing and local activities that will help relieve stress. User B can review the suggestions and provide feedback if necessary.

[0277] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] Users input information such as video and blueprints of the home they are currently living in, as well as family composition, income, and place of work into the device's interface. In addition, the device's camera and microphone are used to collect the user's facial expressions and voice.

[0281] Step 2:

[0282] The device organizes the data and emotion input from the user and transmits it to the server using a secure protocol, which also ensures data integrity and encryption.

[0283] Step 3:

[0284] The server receives all data (video data, drawing data, family composition, income, commute destination, emotion data) from the device and stores it securely in a database. Once the data is saved, it checks the integrity of the data.

[0285] Step 4:

[0286] The server begins parsing the received data. Specifically, the following parsing is performed:

[0287] Video analysis: Using object recognition and scene analysis, we extract the characteristics of the current residence (type of furniture, arrangement, size, etc.).

[0288] Speech and text analysis: Speech is converted into text using speech recognition technology and analyzed to confirm family opinions and lifestyle characteristics.

[0289] Emotion analysis: Analyzes facial expressions and vocal tone to identify the user's emotional state, such as joy, sadness, anger, or surprise.

[0290] Step 5:

[0291] Based on the analysis results, the server creates a personalized profile for the user, which includes the user's lifestyle, income, family composition, commute location, and even emotional state.

[0292] Step 6:

[0293] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​comprehensively analyzes the user profile and the vacant house database to propose the optimal place to move and a new lifestyle. Specifically, it does the following:

[0294] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0295] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0296] Reflecting emotional state: The suggestions reflect the user's emotional state, increasing the acceptability of the new residence and lifestyle.

[0297] Step 7:

[0298] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[0299] Step 8:

[0300] The device receives the proposed results and displays them to the user, who can then review the proposal and check the details to assess whether they meet their needs.

[0301] Step 9:

[0302] The user has an interface to enter feedback on the proposal, which may include requests to add specific conditions or modify the proposal.

[0303] Step 10:

[0304] The server receives the feedback sent by the user and analyzes it again, taking into account the feedback and the user's emotional state, and generates new suggestions. The newly generated suggestions are then sent back to the device and displayed to the user.

[0305] This allows users to find the optimal place to move to and lifestyle that appropriately reflects their desires and emotional state.

[0306] Example 2

[0307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] Modern users often consider relocating to optimize their living environment and lifestyle. However, choosing a new residence and lifestyle requires considering many factors, which can be stressful. It is particularly difficult to obtain recommendations that take into account the user's emotional state. To address these issues, the present invention aims to provide a system that proposes optimal residences and new lifestyles based on the user's emotional state, housing-related data, and lifestyle data.

[0309] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for inputting detailed information such as a video of the residence, blueprint data, family composition, income, workplace, and facial expression and tone of voice data, means for transmitting the received data to the server using a secure protocol, means for operating a facial expression analysis and tone of voice analysis engine to recognize the user's emotional state, means for performing video analysis, voice recognition, and text analysis using multiple analysis engines to understand the detailed specifications of the residence and the user's requirements, means for integrating the received data and generating a user-specific profile, means for analyzing the profile and received data using multimodal artificial intelligence to propose an optimal new residence and lifestyle, means for transmitting the proposal results to the user, means for displaying the proposal results to the user, and means for the user to input feedback on the displayed information. This makes it possible to propose an optimal new residence and new lifestyle taking the user's emotional state into consideration.

[0310] "User" refers to a person who uses this system and is an entity that provides residence-related data and lifestyle data.

[0311] "Residence-related data" refers to data provided by users that includes information such as videos of the residence, blueprint data, family composition, income, and place of work.

[0312] "Lifestyle data" refers to data that includes information about a user's lifestyle and behavioral patterns, and reflects the user's preferences and needs regarding their lifestyle.

[0313] "Device" means an electronic device used by a user to input and transmit data, including a smartphone, tablet, or PC.

[0314] "Server" means a computer system that provides the computational resources to analyze data received from users and generate and transmit proposals.

[0315] A "secure protocol" refers to a communication protocol for securely sending and receiving data, such as the SSL / TLS protocol.

[0316] A "facial expression analysis engine" is a software system that analyzes a user's facial expression data to recognize their emotional state.

[0317] The "voice tone analysis engine" is a software system that analyzes a user's voice data and recognizes their emotional state from their tone of voice.

[0318] "Video analysis" refers to the technology of analyzing video data from a residence and extracting the characteristics and environment of the living space.

[0319] A "voice recognition engine" is a software system for converting voice data into text data.

[0320] A "text analysis engine" is a software system that analyzes text data, extracts information, and understands meaning.

[0321] A "user profile" is a data set that centrally manages detailed information about a user, including their lifestyle, income, family structure, commute, emotional state, and so on.

[0322] "Multimodal AI" is an AI system that integrates and analyzes multiple types of data (e.g., text, images, audio, etc.) to make comprehensive judgments and suggestions.

[0323] "Optimal relocation destination" refers to new residence candidates recommended by the system based on the user's requirements, conditions, and emotional state.

[0324] "New lifestyles" refer to improved lifestyles that users can achieve in their new living environments, including the promotion of teleworking and the use of ride-sharing.

[0325] "Feedback" refers to opinions and requests that users input regarding the proposed content, and is data used to make re-proposals for the system.

[0326] The present invention is a system that recognizes the emotional state of a user and proposes an optimal new residence and a new lifestyle taking the emotional state into consideration. Specific embodiments of the system are described below.

[0327] First, the user uses the device to input home-related and lifestyle data. This data includes home video, blueprints, family composition, income, and commute destination. During this process, the device uses a camera and microphone to record the user's facial expressions and voice tone. The device then uses a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) to capture the user's emotional state.

[0328] The device then transmits the collected data to the server using a secure protocol (e.g., SSL / TLS). The server receives the data and stores it in a database. The server then analyzes the user's emotional state using a facial expression analysis engine and a voice tone analysis engine. At the same time, it also analyzes the home-related data and lifestyle data, applying the following techniques:

[0329] 1. Video analysis: Extracting the characteristics and environment of the living space from videos of the home. In this stage, machine learning models (e.g., YOLO [You Only Look Once] and Faster R-CNN) are used to perform object recognition and scene analysis.

[0330] 2. Speech recognition: Converting voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text).

[0331] 3. Text analysis: Analyze text data to understand user requirements and detailed housing specifications. Use natural language processing (NLP) techniques (e.g., GPT-4).

[0332] The server combines these analysis results to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, emotional state, and more.

[0333] The server then uses multimodal artificial intelligence (for example, a combined model of GPT-4 and image recognition technology) to analyze the user profile and the received data. As a result of the analysis, the following proposals are made:

[0334] 1. Proposing the best place to move: Refer to a database of vacant houses and extract properties that meet the user's requirements in a ranked format.

[0335] 2. Proposing new lifestyles: For example, making suggestions to improve users' quality of life, such as promoting teleworking and using ride-sharing.

[0336] 3. Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of the suggestions being accepted.

[0337] The generated suggestions are sent from the server to the device, which displays them to the user. The user can review the suggestions and provide feedback if necessary. This feedback is sent back to the server, which then performs a new analysis based on the feedback and generates new suggestions.

[0338] Specific examples

[0339] For example, consider the case where User B lives in the city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device, and also records their facial expressions and voice tone. The device sends this data to the server, which analyzes it and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests properties in the suburbs, rich in nature, that are ideal for teleworking, as well as suggestions for ride-sharing options and local activities that will help relieve stress. User B can review the suggestions and provide feedback as needed.

[0340] Prompt Sentence Examples

[0341] "Design a system that suggests to users who are stressed by city life that they should move to the suburbs. Analyze the user's home video, blueprint data, and emotional state (facial expressions and voice tone) to suggest the optimal place to move to and lifestyle."

[0342] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[0343] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0344] Step 1:

[0345] The user inputs information such as a video of the residence, blueprint data, family composition, income, and workplace into the device. In addition, the device's camera and microphone are used to record facial expressions and voice tones. These input data are important information for understanding the user's lifestyle and emotional state. Operations include the user taking videos and inputting various information in text format. The output is a set of data collected on the device.

[0346] Step 2:

[0347] The terminal sends the collected data to the server using the SSL / TLS protocol. During this process, the collected data is encrypted to ensure its integrity and confidentiality. In operation, a data encryption and transmission process is performed. The input is the data collected in the previous step, and the output is the data securely transmitted to the server.

[0348] Step 3:

[0349] The server stores the received data in a database. Before storing, the data is checked for completeness and consistency. Here, a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) are used to analyze the user's emotional state. Operations include storing the data in the database and processing the emotion analysis. The input is the data sent to the server, and the output is the analyzed emotion data.

[0350] Step 4:

[0351] The server runs a video analysis engine to extract the characteristics and environment of the living space from the video data of the home. For example, it uses YOLO or Faster R-CNN to perform object recognition and scene analysis. It then uses a speech recognition engine to convert the audio data into text. This allows the family's opinions and lifestyle characteristics to be obtained as text data. The operation includes the analysis of video data and the process of converting audio data into text. The input is the received video and audio data, and the output is the analyzed text data.

[0352] Step 5:

[0353] The server uses a text analysis engine to perform a detailed analysis of the housing specifications and user requirements. For example, it uses natural language processing (NLP) to extract important information. This results in the creation of a profile that reflects the user's needs. The operation involves analyzing text data and extracting information. The input is the text data obtained in the previous step, and the output is the extracted information.

[0354] Step 6:

[0355] The server generates a user-specific profile based on the analysis results. This profile includes detailed information such as lifestyle patterns, income, family structure, commute destination, and emotional state. After the profile is generated, it is stored in a database and used for subsequent analysis and proposal generation. Operations include data integration and profile generation. The input is various analyzed data, and the output is a detailed user profile.

[0356] Step 7:

[0357] The server integrates the generated user profile with the received data and runs a multimodal artificial intelligence (e.g., a combination of GPT-4 and image recognition technology). This AI comprehensively analyzes the data and generates optimal relocation and new lifestyle recommendations. The operations include the AI's operation and the recommendation generation process. The input is the user profile and received data, and the output is the optimized recommendation results.

[0358] Step 8:

[0359] The server sends the generated proposal in JSON format to the device. The proposal includes details of the optimal relocation destination and a new lifestyle suggestion. The operation includes the process of composing and sending the proposal. The input is the generated proposal, and the output is the result sent to the device.

[0360] Step 9:

[0361] The device displays the received proposals to the user through a web interface or mobile application. The user can review the proposals in detail and provide feedback, which may include new conditions or requests for modification of the proposals. The operations include displaying the proposals and providing feedback. The input is the received proposals, and the output is the user's feedback.

[0362] Step 10:

[0363] The user inputs feedback and sends it to the server via their device. The server analyzes the feedback and generates new suggestions based on new conditions. This process is repeated as necessary. The operation includes receiving and analyzing feedback and generating new suggestions. The input is the user's feedback, and the output is the revised suggestions.

[0364] (Application example 2)

[0365] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0366] A problem in modern shopping experiences is the lack of personalized suggestions that take into account the user's emotional state. As a result, users are prone to feeling stressed and fatigued, and it is difficult to find the perfect product. Furthermore, the lack of adaptive suggestions based on the user's emotional state can reduce the user's motivation to purchase and compromise the quality of the shopping experience. To solve these issues, a system is needed that can recognize the user's emotional state in real time and use that information to suggest the best products and lifestyles.

[0367] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from the user, means for analyzing the received data and generating a profile dedicated to the user, means for analyzing the received data and profile using multimodal artificial intelligence and proposing an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, and means for recognizing the user's emotional state and adapting the proposal based on the emotional state. This makes it possible to propose optimal products and lifestyles according to the user's emotional state.

[0368] A "user" is an individual human being who uses the system and is the entity that provides housing-related data and lifestyle data.

[0369] "Residence-related data" refers to information about a residence entered by a user, specifically data such as video, drawing data, family composition, income, and place of work.

[0370] "Lifestyle data" is information related to a user's lifestyle habits and preferences, past purchase history, and future aspirations.

[0371] A "profile" is a data set that describes a user's individual characteristics, generated by analyzing and integrating data collected from the user.

[0372] "Multimodal AI" is an AI system that performs integrated analysis of different types of data (e.g., text, images, audio, etc.).

[0373] "Emotional state" refers to the emotions (happiness, excitement, fatigue, stress, etc.) that a user expresses through facial expressions and voice.

[0374] "Suggestion results" are information resulting from the system proposing optimal relocation destinations, lifestyles, and products based on the user's profile and emotional state.

[0375] "Analysis means" refers to various algorithms and functions for performing data analysis within the system, specifically video analysis, audio analysis, text analysis, etc.

[0376] A "display device" is an electronic device used by a user to visually confirm the results of suggestions from the system, examples of which include smart glasses and displays.

[0377] This invention is a system that recognizes a user's emotional state and suggests optimal products and lifestyles based on that state. This system performs detailed analysis based on the user's home-related data and lifestyle data, enabling an improved shopping experience in virtual stores.

[0378] The user wears smart glasses and navigates around the virtual store. The system collects the user's facial expressions and voice in real time and recognizes their emotional state. Specifically, the system uses the following hardware and software:

[0379] Hardware and software used

[0380] Hardware: Smart glasses (with AR function), high-performance server (with CPU and GPU)

[0381] Software: Facial expression analysis API (e.g., facial expression recognition API), voice tone analysis API (e.g., voice analysis API), database (e.g., data store)

[0382] Data processing and calculation

[0383] 1. Data collection: The smart glasses collect the user's facial expression and voice data in real time.

[0384] 2. Data transmission: The collected data is transmitted to the server using a secure protocol.

[0385] 3. Emotion analysis: The server uses facial expression recognition API and voice analysis API to analyze the user's emotional state.

[0386] 4. Integrated analysis: Comprehensive analysis of emotional data, lifestyle data, and purchase history.

[0387] 5. Profile generation: Generate a detailed profile based on the user's data.

[0388] 6. Recommendation Generation: Multimodal AI suggests products and lifestyles that fit the user's profile and emotional state.

[0389] 7. Sending proposal results: The proposal results are sent from the server to the smart glasses.

[0390] 8. Display: Suggested products and lifestyles are displayed on the smart glasses display.

[0391] Specific examples

[0392] For example, consider the case where User C visits a virtual store and the smart glasses recognize "fatigue" from User C's facial expressions and voice. Based on this result, the server generates relaxation-related products (e.g., aroma diffusers, massage equipment) and lifestyle suggestions to help relieve stress (e.g., online yoga classes), and displays them on the smart glasses in real time. User C can review these suggestions and provide feedback. Based on the feedback, the suggestions are further adjusted to provide an optimal shopping experience.

[0393] Example of input prompt for generative AI model

[0394] prompt:

[0395] If the user's emotional state is "fatigue," we will suggest relaxation-related products based on the following information:

[0396] Past purchase history

[0397] User preferences (e.g., relaxation, hobbies, etc.)

[0398] Lifestyle Data

[0399] Suggestions include aroma diffusers, massage equipment, and online yoga classes.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] Data collection

[0403] A user wears smart glasses and moves around the virtual store. The smart glasses collect the user's facial expression and voice data in real time. The input data are the user's facial expression data (image sequence) and voice data, which are collected using the smart glasses' sensors.

[0404] Step 2:

[0405] Data transmission

[0406] The device (smart glasses) sends the collected facial expression data and voice data to the server using a secure protocol. The input is the collected data, and the output is the data to be sent to the server. The data is encrypted and sent over a secure channel.

[0407] Step 3:

[0408] Emotion analysis

[0409] The server uses the received facial expression data and voice data to analyze the user's emotional state using a facial expression analysis API and a voice tone analysis API. The input is facial expression data and voice data, and the output is the user's emotional state data (e.g., joy, surprise, fatigue). The analyzed emotional state is stored in a database.

[0410] Step 4:

[0411] Integrated analysis

[0412] The server performs an integrated analysis of emotional data, lifestyle data, and purchase history. The input data is the user's emotional state data, lifestyle data, and past purchase history, and the output data is an integrated user profile. The profile is generated using multimodal AI, resulting in a detailed profile that reflects the user's individual characteristics.

[0413] Step 5:

[0414] Proposal generation

[0415] The server uses multimodal artificial intelligence to suggest optimal products and lifestyles to users based on the generated user profile and emotional state data. The input is the user profile and emotional state data, and the output is the proposal results. The proposal results include relaxation products and lifestyle suggestions (e.g., aroma diffusers, massage equipment, online yoga classes, etc.).

[0416] Step 6:

[0417] Sending proposal results

[0418] The server sends the proposal results to the smart glasses. The input is the proposal results, and the output is the data sent to the smart glasses. The sent proposal results are displayed on the display of the smart glasses.

[0419] Step 7:

[0420] display

[0421] The terminal (smart glasses) visually displays the received recommendation results to the user. The input is the recommendation results from the server, and the output is a visual presentation of information to the user. The user can check the suggested products and lifestyles on the smart glasses display.

[0422] Step 8:

[0423] Feedback collection

[0424] The user provides feedback on the proposed products and lifestyles. This feedback is sent from the smart glasses to the server and reflected in the next proposal. The input is the user's feedback, and the output is the transmission of the feedback data to the server.

[0425] Step 9:

[0426] Reproposal generation

[0427] The server generates new suggestions based on user feedback. The input is the feedback data and the user profile, and the output is the new suggestions. The new suggestions are made, if necessary, taking into account the user's emotional state and new feedback.

[0428] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0429] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0434] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0436] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0438] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0439] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0440] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0441] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0443] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0444] This invention is a system for proposing optimal relocation destinations and new lifestyles that takes into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze the housing-related data and lifestyle data entered by the user and generate optimal proposals.

[0445] Program Overview

[0446] 1. Data Entry

[0447] Users enter information such as home videos, blueprints, family composition, income, and commute destination through the device interface, which allows detailed data about the user's current residence and lifestyle to be collected.

[0448] 2. Data Transmission and Reception

[0449] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[0450] 3. Data Analysis

[0451] The server analyzes the received data. Specifically, object recognition and scene analysis are used to extract features of the current home from the video data, and keywords related to the family's opinions and lifestyle are extracted from the audio data through voice recognition. Text data and drawing data are also analyzed in the same way.

[0452] 4. Profile Generation

[0453] The server then uses the analyzed data to create a personalized profile for the user, which includes information about the user's lifestyle, income, family structure, and other essential needs.

[0454] 5. Analysis and Proposal Generation Using Multimodal AI

[0455] The server operates a multimodal artificial intelligence based on the generated user profile. The AI ​​considers current housing data, the user's requests, and future needs to suggest the optimal place to move to. It also references a database of vacant homes to extract properties that meet the user's criteria in a ranked format. It also takes into account changes in social conditions to suggest new lifestyles.

[0456] 6. Submitting the proposal results

[0457] The generated proposal results are sent from the server to the terminal, which displays the proposal results to the user and provides detailed information.

[0458] 7. Gather feedback and resubmit

[0459] The user enters feedback on the displayed suggestions, which is then sent to the server via the device. The server receives the user's feedback, reanalyzes it based on the feedback, and generates new suggestions. This process can be repeated as needed.

[0460] Specific examples

[0461] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device. The device sends this information to the server, which analyzes it and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also suggests new lifestyles by recommending ride-sharing and local community activities. User A can review the suggestions and add further specific conditions and provide feedback again.

[0462] In this way, the present invention can suggest the optimum relocation destination and lifestyle suited to the individual needs of the user, thereby improving the user's quality of life.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user uses the terminal interface to input residence-related data (videos, drawings, furniture layout, etc.) and lifestyle data (family composition, income, commute destination, etc.). After the user has completed the data input, they click the submit button.

[0466] Step 2:

[0467] The device processes the data and sends it to the server using a secure protocol, including encrypting the data and verifying that the transfer was successful.

[0468] Step 3:

[0469] The server receives the data from the device, stores it securely in a database, and verifies that all data has been stored correctly.

[0470] Step 4:

[0471] The server analyzes the received data, including:

[0472] Video analysis: Using object recognition and scene analysis, the characteristics of the current residence (type of furniture, arrangement, room size, etc.) are extracted.

[0473] Voice analysis: Uses voice recognition technology to convert family members' opinions and lifestyle characteristics into text.

[0474] Text analysis: Analyzes drawing data and other text information to understand detailed specifications of the home and user requirements.

[0475] Step 5:

[0476] The server uses the analyzed data to create a personalized profile for each user, which includes detailed information about the user's lifestyle, income, family structure, and commute.

[0477] Step 6:

[0478] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[0479] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0480] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0481] Step 7:

[0482] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[0483] Step 8:

[0484] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[0485] Step 9:

[0486] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via the terminal.

[0487] Step 10:

[0488] The server receives the feedback sent by the user, analyzes it again, and generates new suggestions, which are then sent back to the device and displayed to the user.

[0489] Through this process, users can find a new home and lifestyle that best suits their needs and home environment.

[0490] Example 1

[0491] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] Conventional housing selection systems have difficulty in making proposals that fully reflect the lifestyles and needs of individual users, resulting in problems in which users are unable to find the optimal place to move to that satisfies them. In addition, the display of proposal results is mainly text, and is not presented in a format that is easy for users to understand, resulting in a poor user experience.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0494] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for transmitting the received data to the server using a secure protocol, means for the server to analyze the received data and generate a profile dedicated to the user, means for analyzing the generated profile and an external database using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, and means for transmitting the proposal results to the user and displaying them in a graphical interface. This makes it possible to propose an optimal relocation destination and lifestyle that is more tailored to the individual needs of the user than ever before, thereby improving the user experience.

[0495] "User" refers to an individual or organization that uses the system and is the entity that provides housing-related data and lifestyle data.

[0496] "Residence-related data" refers to information such as videos of the residence, blueprint data, room features, and furniture layout.

[0497] "Lifestyle data" refers to data about a user's daily life, such as family composition, income, commute, hobbies, and transportation methods.

[0498] A "terminal" is a device that allows a user to input data and communicate with a server, and includes smartphones, tablets, PCs, etc.

[0499] A "secure protocol" is a communication protocol used to ensure security when transmitting data, and generally corresponds to HTTPS.

[0500] A "server" is a computer system that analyzes data received from terminals and plays a central role in storing and analyzing data and generating proposals.

[0501] A "profile" is a collection of information specific to a user that is generated based on analyzed data, and includes lifestyle patterns, income, family structure, requirements, and the like.

[0502] "Multimodal AI" refers to an AI technology that integrates and analyzes multiple data formats (video, audio, text, etc.) to assist in solving complex problems.

[0503] "External databases" refer to public or private data sources that exist separately from the user's data, such as real estate information or environmental data.

[0504] "Suggestions" refer to the optimal relocation and lifestyle options derived through analysis, and are advice provided to users.

[0505] A "graphical interface" is a user interface that displays the proposal results to the user in a visually easy-to-understand format, and includes maps, charts, card-style information displays, and the like.

[0506] "Feedback" refers to information that users input about their opinions and requests regarding the proposed results, which the system uses to reanalyze and make improvement suggestions.

[0507] These definitions will provide a clearer understanding of the invention.

[0508] The present invention is a system that proposes optimal relocation destinations and lifestyles to users, taking into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze data entered by users related to their residence and lifestyle, and generates optimal proposals.

[0509] Hardware and software used

[0510] Users access the system using a device such as a smartphone, tablet, or PC, and enter the following data through a web form or mobile app on their device:

[0511] Housing video

[0512] Drawing data

[0513] Family composition

[0514] income

[0515] Commuter destination

[0516] Video data is taken with a smartphone or tablet camera and uploaded directly to the application, while text data such as income and family composition is collected by entering it into the interface.

[0517] Sending and Receiving Data

[0518] The terminal sends all data entered by the user to the server using a secure protocol (e.g. HTTPS). During this process, the data is encrypted and transmitted securely. The server stores the received data in a database (e.g. MySQL or PostgreSQL).

[0519] Data analysis

[0520] The server analyzes the received data. For video data, computer vision technology (e.g., OpenCV) is used to perform object recognition and scene analysis. For audio data, a speech recognition API (e.g., Google Speech-to-Text) is used to convert it to text, and natural language processing (NLP) tools (e.g., NLTK or SpaCy) are used to extract keywords. Furthermore, blueprint data is analyzed using CAD software or image analysis tools to extract floor plan and area information.

[0521] Profile Generation

[0522] Based on the analysis results, the server creates a personalized profile for the user, which includes information such as the user's lifestyle, income, family structure, and essential conditions.

[0523] Proposal Generation

[0524] The server runs multimodal artificial intelligence (e.g., TensorFlow, PyTorch) based on the generated user profile. The AI ​​refers to existing external databases (e.g., real estate information, environmental data) and proposes the optimal relocation destination, taking into account the user's requests and future needs. It also lists the proposed properties in a ranking format and recommends ride-sharing options and local community activities.

[0525] Displaying the proposed results

[0526] The server sends the generated proposal results to the terminal, which provides an interface for displaying the proposal results graphically to the user. This interface includes a map display using the Google Maps API and a card-style list display of property information.

[0527] Gathering feedback and resubmitting

[0528] The user then enters feedback on the displayed suggestions into the device. For example, they enter specific requests, such as "I'd like my commute to be shorter" or "I'd prefer a property that allows pets." The device then securely transmits this feedback back to the server. The server receives the feedback, reanalyzes it based on the newly collected information, and generates improved suggestions. This process can be repeated as many times as necessary until the user is satisfied.

[0529] Specific examples

[0530] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of the home, family composition, income, and commute destination into the device. The device then sends this information to the server using a secure protocol. The server analyzes the information and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also recommends ride-sharing and local community activities.

[0531] Prompt Sentence Examples

[0532] "We want to move to the suburbs to work remotely. We are a married couple with a combined annual income of 8 million yen. Here are the videos and blueprints of our current home. Can you suggest the best place to move to?"

[0533] By entering this prompt, the system will generate a proposal for the best possible relocation location that meets User A's needs.

[0534] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0535] Step 1:

[0536] Users enter data such as home video, blueprint data, family composition, income, and work destination through the device interface. This operation is performed using a dedicated web form or mobile app. The entered data includes video files (e.g., .mp4), blueprint image files (e.g., .jpg, .png), and text data (e.g., income, work destination).

[0537] Input: Video of residence, drawing data, family composition, income, commute destination

[0538] Output: User data temporarily stored in the device

[0539] Step 2:

[0540] The device sends the data entered by the user to the server using a secure protocol, with the data being sent securely using techniques such as AES encryption.

[0541] Input: User data

[0542] Output: Data sent using a secure protocol

[0543] Step 3:

[0544] The server receives the data sent from the device and stores it securely in a database using a database management system such as MySQL or PostgreSQL.

[0545] Input: Securely transmitted data

[0546] Output: User data stored in the database

[0547] Step 4:

[0548] The server analyzes the received and stored data. For video data, OpenCV and TensorFlow are used for object recognition and scene analysis to extract room features and furniture layout. If audio data is included, it is converted to text using the Google Speech-to-Text API, and keywords are extracted using natural language processing tools (NLTK and SpaCy). For blueprint data, image analysis software is used to calculate the room layout and area.

[0549] Input: Database user data

[0550] Output: Analyzed feature data (room features, furniture layout, keywords, floor plan, etc.)

[0551] Step 5:

[0552] Based on the analysis results, the server creates a user-specific profile, which includes information on lifestyle, income, family structure, and other essential conditions, providing a foundation for a detailed reflection of the user's needs and conditions.

[0553] Input: Parsed feature data

[0554] Output: User-specific profile

[0555] Step 6:

[0556] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​compares the data with real estate information and environmental databases to create a list of optimal relocation destinations that meet the user's requirements. The AI ​​also presents properties in a ranking format, taking into account social conditions and future needs.

[0557] Input: User-specific profile, external database

[0558] Output: Ranked list of optimal relocation destinations

[0559] Step 7:

[0560] The server sends the generated proposal results to the device, which uses the Google Maps API to display the candidate sites on a map and display detailed information about each property in a card format to make it easier for the user to understand.

[0561] Input: Ranked list of optimal relocation destinations

[0562] Output: Graphical proposal results displayed on the terminal

[0563] Step 8:

[0564] The user can then input their feedback on the displayed proposals into the terminal, for example, by inputting specific conditions or requests such as "I want to shorten my commute time" or "I would prefer a property where I can keep pets."

[0565] Input: User feedback

[0566] Output: Feedback data stored on the device

[0567] Step 9:

[0568] The device sends the user's feedback data to the server using a secure protocol, and the server receives the feedback and begins analyzing it again to generate suggestions based on the new conditions.

[0569] Input: Securely transmitted feedback data

[0570] Output: Feedback data stored on the server

[0571] Step 10:

[0572] The server re-analyzes the newly collected information and generates improved suggestions, which are again ranked and sent to the user.

[0573] Input: Feedback data, analysis system

[0574] Output: Regenerated improvement suggestions

[0575] By repeating this process, we can propose the optimal relocation destination and lifestyle that will satisfy the user.

[0576] (Application example 1)

[0577] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] In response to current changes in lifestyles and the spread of teleworking, systems that suggest optimal relocation and lifestyle options for users are needed. However, in addition to such systems, there is also a need for methods to improve the in-store experience based on users' profiles. In particular, systems that can eliminate the hassle and lack of information users face when selecting products in physical stores are important.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0580] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for analyzing the received data and generating a user-specific profile, means for analyzing the received data and profile using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, means for generating product and service proposals based on the proposal results and supporting purchases and experiences at physical stores, and means for the user to check the proposal results using a smart device at the physical store and receive further detailed information and advice. This not only makes it possible to propose optimal relocation destinations and lifestyles to users, but also to improve the purchasing experience at physical stores.

[0581] "Residence-related data" is data related to the place where the user currently resides, and includes information on the address, floor plan, videos, drawings, and surrounding environment.

[0582] "Lifestyle data" refers to information about a user's lifestyle habits and preferences, family composition, income, commute, hobbies, health status, etc.

[0583] A "profile" refers to a set of information that reflects a user's individual living patterns and needs, generated based on their home-related and lifestyle data.

[0584] "Multimodal artificial intelligence" refers to an artificial intelligence system that integrates and analyzes data in various formats, such as text, audio, images, and video, and makes comprehensive judgments like a human.

[0585] "Suggestion results" refers to the information proposed regarding the optimal relocation destination and lifestyle that the multimodal artificial intelligence derives from analyzing the user's profile.

[0586] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit to select, purchase, and experience products and services in person.

[0587] "Smart device" refers to a mobile terminal or wearable device (e.g., smartphone, smart glasses, head-mounted display) that is connected to the Internet and can run applications.

[0588] "Feedback" refers to evaluations and opinions on the proposal results provided by users, and is important information for making re-proposals based on this.

[0589] "Product and Service Suggestions" refers to a list of specific products and services suggested for purchase or experience based on the user's profile.

[0590] "Analysis means" refers to methods and technologies for processing and analyzing input data, specifically techniques including video analysis, audio analysis, and text analysis.

[0591] A "cloud server" is a server that can be accessed remotely via the Internet and is a system that provides computing resources for storing data, analyzing data, running AI models, and more.

[0592] This invention is a system that proposes optimal relocation destinations and new lifestyles based on the user's housing-related data and lifestyle data. Furthermore, this proposal is applied to in-store experiences to support the user's purchasing behavior.

[0593] First, the user enters information such as video of the home, blueprint data, family composition, income, and workplace via a smartphone app. This data is then sent to a cloud server via the internet and securely stored. The cloud server is equipped with a high-performance GPU server, which is used to analyze the data. Software used includes "BERT" and "GPT-4" for text analysis, "TensorFlow" for video analysis, and "PyTorch" for audio analysis.

[0594] The server then uses the received data to create a personalized profile for the user, including information about the user's lifestyle, income, family structure, and other essential needs. During the profile creation process, video data is analyzed using object recognition, audio data is analyzed using voice recognition, and text data is also analyzed.

[0595] Next, based on the generated profile, multimodal AI proposes optimal relocation and lifestyle options. This AI uses a generative AI model to analyze the user's requests and future needs. It also takes into account changes in social conditions to propose new lifestyles.

[0596] The results of the proposals are sent from the cloud server to smart devices such as smartphone apps and smart glasses. Users can use these devices to check the proposals and receive detailed information and advice. For example, in a physical store, by wearing smart glasses, product descriptions and layout proposals for the store can be displayed in real time, and store staff can use tablet devices to provide optimal customer service.

[0597] Users can also provide feedback on the suggestions through the app. The cloud server receives this feedback, analyzes the data again based on the feedback, and generates new suggestions. This process is repeated as needed to provide more accurate suggestions.

[0598] As a concrete example, let's consider the case where User B wants to choose new furniture. User B uses a smartphone app to input data in advance, such as family composition, income, videos and blueprints of the house they currently live in, and their lifestyle. The cloud server analyzes this data and suggests furniture and layouts that are recommended for User B. When visiting the store, User B can wear smart glasses and explore the store while receiving explanations of the products inside and advice on layout. At this time, store staff use a tablet to suggest the best products for User B.

[0599] An example prompt is:

[0600] Input data: I'm planning to go to a furniture store. I have a family of four, my income is 7 million yen, and I need office space at home to work remotely. I'll attach a video and blueprint of my home. Please suggest what kind of furniture would be suitable.

[0601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0602] Step 1:

[0603] Users use a smartphone app to input home-related and lifestyle data, including home videos, blueprints, family composition, income, and commute information. Once users enter this data, the device compiles it into a single file or dataset. The input data can be in a variety of formats, including text, video, images, and audio.

[0604] input:

[0605] Information such as videos of the residence, blueprint data, family composition, income, and commute destination.

[0606] output:

[0607] A collection of data files or datasets.

[0608] Step 2:

[0609] The device sends the compiled data file over the internet to a cloud server using the secure HTTPS protocol, which then stores the data securely in a database.

[0610] input:

[0611] Aggregated data files.

[0612] output:

[0613] Data stored on a cloud server.

[0614] Step 3:

[0615] The server analyzes the received data. First, object recognition and scene analysis are used to extract features from the video data. Next, important keywords are extracted from the audio data using voice recognition technology, and text data is also analyzed. This provides detailed information about the user's home and lifestyle.

[0616] input:

[0617] Data stored on a cloud server.

[0618] output:

[0619] Parsed feature data (objects, keywords, etc.).

[0620] Step 4:

[0621] The server generates a user-specific profile based on the analyzed feature data. This profile includes the user's lifestyle, income, family structure, and essential conditions. The generated profile is stored in a database.

[0622] input:

[0623] Parsed feature data.

[0624] output:

[0625] A user-specific profile.

[0626] Step 5:

[0627] The server runs a multimodal AI based on the generated user profile. The AI ​​analyzes the profile and the original residential data to generate suitable relocation and lifestyle recommendations for the user. This AI model uses the aforementioned generative AI models (BERT, GPT-4, etc.).

[0628] input:

[0629] A user-specific profile.

[0630] output:

[0631] Suggestions for relocation and lifestyle.

[0632] Step 6:

[0633] The server sends the proposed results to the user's smartphone app using a secure protocol, and the proposed results are displayed on the user's smartphone screen.

[0634] input:

[0635] Suggestions for relocation and lifestyle.

[0636] output:

[0637] Display on smartphone app.

[0638] Step 7:

[0639] The user can check the proposals and enter feedback through a smartphone app, which is then sent back to the cloud server.

[0640] input:

[0641] User feedback.

[0642] output:

[0643] Feedback sent to cloud server.

[0644] Step 8:

[0645] The server re-performs the analysis based on the received feedback and generates new suggestions, which are then sent to the user again.

[0646] input:

[0647] User feedback.

[0648] output:

[0649] Regenerated relocation and lifestyle proposals.

[0650] Step 9:

[0651] When users visit a physical store, they can use smart glasses or a smartphone app to check the recommendations and select products and services in the store, enabling them to select more specific and optimal products and services.

[0652] input:

[0653] Regenerated relocation and lifestyle proposals.

[0654] output:

[0655] Providing optimal products and services in physical stores.

[0656] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0657] The present invention is a system that recognizes a user's emotional state and proposes optimal relocation and new lifestyle options taking that state into consideration. The system uses multimodal artificial intelligence and an emotion engine to perform detailed analysis based on the residential and lifestyle data provided by the user.

[0658] Program Overview

[0659] 1. Data Entry

[0660] Users input information such as videos of their home, blueprints, family composition, income, and place of work into the device. Data on facial expressions and tone of voice is also collected to recognize the user's emotional state.

[0661] 2. Data Transmission and Reception

[0662] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[0663] 3. Emotional state analysis

[0664] The server analyzes facial expressions and voice tones to recognize the user's emotional state. The emotion engine identifies the user's emotions, such as joy, sadness, anger, and surprise, and provides the data.

[0665] 4. Integrated analysis of all data

[0666] The server performs a comprehensive analysis of the received residential data, lifestyle data, and emotional data, including the following analyses:

[0667] Video analysis: Object recognition and scene analysis to extract features of the current dwelling.

[0668] Speech analysis: Speech recognition to convert family opinions and lifestyle characteristics into text.

[0669] Text analysis: Analysis to understand detailed specifications of the home and user requirements.

[0670] 5. Profile Generation

[0671] The server uses the analyzed data to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, and emotional state.

[0672] 6. Analysis and proposal generation using multimodal AI

[0673] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[0674] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0675] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0676] Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of their acceptance.

[0677] 7. Submitting the proposal results

[0678] The generated proposal results are sent from the server to the device, and include details of the optimal relocation location and specific details of the new lifestyle.

[0679] 8. Displaying the proposed results

[0680] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[0681] 9. Gather feedback and resubmit

[0682] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via their device. The server then analyzes the feedback and generates new proposals. This process is repeated as necessary.

[0683] Specific examples

[0684] For example, suppose User B lives in a city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, as well as information about their family, income, and workplace into their device. User B's facial expressions and tone of voice are also recorded. The device sends all data to a server, which analyzes the data and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests a property in the suburbs, surrounded by nature, that is ideal for teleworking. It also makes customized suggestions based on User B's emotional state, including suggestions for ride-sharing and local activities that will help relieve stress. User B can review the suggestions and provide feedback if necessary.

[0685] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] Users input information such as video and blueprints of the home they are currently living in, as well as family composition, income, and place of work into the device's interface. In addition, the device's camera and microphone are used to collect the user's facial expressions and voice.

[0689] Step 2:

[0690] The device organizes the data and emotion input from the user and transmits it to the server using a secure protocol, which also ensures data integrity and encryption.

[0691] Step 3:

[0692] The server receives all data (video data, drawing data, family composition, income, commute destination, emotion data) from the device and stores it securely in a database. Once the data is saved, it checks the integrity of the data.

[0693] Step 4:

[0694] The server begins parsing the received data. Specifically, the following parsing is performed:

[0695] Video analysis: Using object recognition and scene analysis, we extract the characteristics of the current residence (type of furniture, arrangement, size, etc.).

[0696] Speech and text analysis: Speech is converted into text using speech recognition technology and analyzed to confirm family opinions and lifestyle characteristics.

[0697] Emotion analysis: Analyzes facial expressions and vocal tone to identify the user's emotional state, such as joy, sadness, anger, or surprise.

[0698] Step 5:

[0699] Based on the analysis results, the server creates a personalized profile for the user, which includes the user's lifestyle, income, family composition, commute location, and even emotional state.

[0700] Step 6:

[0701] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​comprehensively analyzes the user profile and the vacant house database to propose the optimal place to move and a new lifestyle. Specifically, it does the following:

[0702] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0703] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0704] Reflecting emotional state: The suggestions reflect the user's emotional state, increasing the acceptability of the new residence and lifestyle.

[0705] Step 7:

[0706] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[0707] Step 8:

[0708] The device receives the proposed results and displays them to the user, who can then review the proposal and check the details to assess whether they meet their needs.

[0709] Step 9:

[0710] The user has an interface to enter feedback on the proposal, which may include requests to add specific conditions or modify the proposal.

[0711] Step 10:

[0712] The server receives the feedback sent by the user and analyzes it again, taking into account the feedback and the user's emotional state, and generates new suggestions. The newly generated suggestions are then sent back to the device and displayed to the user.

[0713] This allows users to find the optimal place to move to and lifestyle that appropriately reflects their desires and emotional state.

[0714] Example 2

[0715] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] Modern users often consider relocating to optimize their living environment and lifestyle. However, choosing a new residence and lifestyle requires considering many factors, which can be stressful. It is particularly difficult to obtain recommendations that take into account the user's emotional state. To address these issues, the present invention aims to provide a system that proposes optimal residences and new lifestyles based on the user's emotional state, housing-related data, and lifestyle data.

[0717] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for inputting detailed information such as a video of the residence, blueprint data, family composition, income, workplace, and facial expression and tone of voice data, means for transmitting the received data to the server using a secure protocol, means for operating a facial expression analysis and tone of voice analysis engine to recognize the user's emotional state, means for performing video analysis, voice recognition, and text analysis using multiple analysis engines to understand the detailed specifications of the residence and the user's requirements, means for integrating the received data and generating a user-specific profile, means for analyzing the profile and received data using multimodal artificial intelligence to propose an optimal new residence and lifestyle, means for transmitting the proposal results to the user, means for displaying the proposal results to the user, and means for the user to input feedback on the displayed information. This makes it possible to propose an optimal new residence and new lifestyle taking the user's emotional state into consideration.

[0718] "User" refers to a person who uses this system and is an entity that provides residence-related data and lifestyle data.

[0719] "Residence-related data" refers to data provided by users that includes information such as videos of the residence, blueprint data, family composition, income, and place of work.

[0720] "Lifestyle data" refers to data that includes information about a user's lifestyle and behavioral patterns, and reflects the user's preferences and needs regarding their lifestyle.

[0721] "Device" means an electronic device used by a user to input and transmit data, including a smartphone, tablet, or PC.

[0722] "Server" means a computer system that provides the computational resources to analyze data received from users and generate and transmit proposals.

[0723] A "secure protocol" refers to a communication protocol for securely sending and receiving data, such as the SSL / TLS protocol.

[0724] A "facial expression analysis engine" is a software system that analyzes a user's facial expression data to recognize their emotional state.

[0725] The "voice tone analysis engine" is a software system that analyzes a user's voice data and recognizes their emotional state from their tone of voice.

[0726] "Video analysis" refers to the technology of analyzing video data from a residence and extracting the characteristics and environment of the living space.

[0727] A "voice recognition engine" is a software system for converting voice data into text data.

[0728] A "text analysis engine" is a software system that analyzes text data, extracts information, and understands meaning.

[0729] A "user profile" is a data set that centrally manages detailed information about a user, including their lifestyle, income, family structure, commute, emotional state, and so on.

[0730] "Multimodal AI" is an AI system that integrates and analyzes multiple types of data (e.g., text, images, audio, etc.) to make comprehensive judgments and suggestions.

[0731] "Optimal relocation destination" refers to new residence candidates recommended by the system based on the user's requirements, conditions, and emotional state.

[0732] "New lifestyles" refer to improved lifestyles that users can achieve in their new living environments, including the promotion of teleworking and the use of ride-sharing.

[0733] "Feedback" refers to opinions and requests that users input regarding the proposed content, and is data used to make re-proposals for the system.

[0734] The present invention is a system that recognizes the emotional state of a user and proposes an optimal new residence and a new lifestyle taking the emotional state into consideration. Specific embodiments of the system are described below.

[0735] First, the user uses the device to input home-related and lifestyle data. This data includes home video, blueprints, family composition, income, and commute destination. During this process, the device uses a camera and microphone to record the user's facial expressions and voice tone. The device then uses a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) to capture the user's emotional state.

[0736] The device then transmits the collected data to the server using a secure protocol (e.g., SSL / TLS). The server receives the data and stores it in a database. The server then analyzes the user's emotional state using a facial expression analysis engine and a voice tone analysis engine. At the same time, it also analyzes the home-related data and lifestyle data, applying the following techniques:

[0737] 1. Video analysis: Extracting the characteristics and environment of the living space from videos of the home. In this stage, machine learning models (e.g., YOLO [You Only Look Once] and Faster R-CNN) are used to perform object recognition and scene analysis.

[0738] 2. Speech recognition: Converting voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text).

[0739] 3. Text analysis: Analyze text data to understand user requirements and detailed housing specifications. Use natural language processing (NLP) techniques (e.g., GPT-4).

[0740] The server combines these analysis results to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, emotional state, and more.

[0741] The server then uses multimodal artificial intelligence (for example, a combined model of GPT-4 and image recognition technology) to analyze the user profile and the received data. As a result of the analysis, the following proposals are made:

[0742] 1. Proposing the best place to move: Refer to a database of vacant houses and extract properties that meet the user's requirements in a ranked format.

[0743] 2. Proposing new lifestyles: For example, making suggestions to improve users' quality of life, such as promoting teleworking and using ride-sharing.

[0744] 3. Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of the suggestions being accepted.

[0745] The generated suggestions are sent from the server to the device, which displays them to the user. The user can review the suggestions and provide feedback if necessary. This feedback is sent back to the server, which then performs a new analysis based on the feedback and generates new suggestions.

[0746] Specific examples

[0747] For example, consider the case where User B lives in the city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device, and also records their facial expressions and voice tone. The device sends this data to the server, which analyzes it and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests properties in the suburbs, rich in nature, that are ideal for teleworking, as well as suggestions for ride-sharing options and local activities that will help relieve stress. User B can review the suggestions and provide feedback as needed.

[0748] Prompt Sentence Examples

[0749] "Design a system that suggests to users who are stressed by city life that they should move to the suburbs. Analyze the user's home video, blueprint data, and emotional state (facial expressions and voice tone) to suggest the optimal place to move to and lifestyle."

[0750] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[0751] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0752] Step 1:

[0753] The user inputs information such as a video of the residence, blueprint data, family composition, income, and workplace into the device. In addition, the device's camera and microphone are used to record facial expressions and voice tones. These input data are important information for understanding the user's lifestyle and emotional state. Operations include the user taking videos and inputting various information in text format. The output is a set of data collected on the device.

[0754] Step 2:

[0755] The terminal sends the collected data to the server using the SSL / TLS protocol. During this process, the collected data is encrypted to ensure its integrity and confidentiality. In operation, a data encryption and transmission process is performed. The input is the data collected in the previous step, and the output is the data securely transmitted to the server.

[0756] Step 3:

[0757] The server stores the received data in a database. Before storing, the data is checked for completeness and consistency. Here, a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) are used to analyze the user's emotional state. Operations include storing the data in the database and processing the emotion analysis. The input is the data sent to the server, and the output is the analyzed emotion data.

[0758] Step 4:

[0759] The server runs a video analysis engine to extract the characteristics and environment of the living space from the video data of the home. For example, it uses YOLO or Faster R-CNN to perform object recognition and scene analysis. It then uses a speech recognition engine to convert the audio data into text. This allows the family's opinions and lifestyle characteristics to be obtained as text data. The operation includes the analysis of video data and the process of converting audio data into text. The input is the received video and audio data, and the output is the analyzed text data.

[0760] Step 5:

[0761] The server uses a text analysis engine to perform a detailed analysis of the housing specifications and user requirements. For example, it uses natural language processing (NLP) to extract important information. This results in the creation of a profile that reflects the user's needs. The operation involves analyzing text data and extracting information. The input is the text data obtained in the previous step, and the output is the extracted information.

[0762] Step 6:

[0763] The server generates a user-specific profile based on the analysis results. This profile includes detailed information such as lifestyle patterns, income, family structure, commute destination, and emotional state. After the profile is generated, it is stored in a database and used for subsequent analysis and proposal generation. Operations include data integration and profile generation. The input is various analyzed data, and the output is a detailed user profile.

[0764] Step 7:

[0765] The server integrates the generated user profile with the received data and runs a multimodal artificial intelligence (e.g., a combination of GPT-4 and image recognition technology). This AI comprehensively analyzes the data and generates optimal relocation and new lifestyle recommendations. The operations include the AI's operation and the recommendation generation process. The input is the user profile and received data, and the output is the optimized recommendation results.

[0766] Step 8:

[0767] The server sends the generated proposal in JSON format to the device. The proposal includes details of the optimal relocation destination and a new lifestyle suggestion. The operation includes the process of composing and sending the proposal. The input is the generated proposal, and the output is the result sent to the device.

[0768] Step 9:

[0769] The device displays the received proposals to the user through a web interface or mobile application. The user can review the proposals in detail and provide feedback, which may include new conditions or requests for modification of the proposals. The operations include displaying the proposals and providing feedback. The input is the received proposals, and the output is the user's feedback.

[0770] Step 10:

[0771] The user inputs feedback and sends it to the server via their device. The server analyzes the feedback and generates new suggestions based on new conditions. This process is repeated as necessary. The operation includes receiving and analyzing feedback and generating new suggestions. The input is the user's feedback, and the output is the revised suggestions.

[0772] (Application example 2)

[0773] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0774] A problem in modern shopping experiences is the lack of personalized suggestions that take into account the user's emotional state. As a result, users are prone to feeling stressed and fatigued, and it is difficult to find the perfect product. Furthermore, the lack of adaptive suggestions based on the user's emotional state can reduce the user's motivation to purchase and compromise the quality of the shopping experience. To solve these issues, a system is needed that can recognize the user's emotional state in real time and use that information to suggest the best products and lifestyles.

[0775] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from the user, means for analyzing the received data and generating a profile dedicated to the user, means for analyzing the received data and profile using multimodal artificial intelligence and proposing an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, and means for recognizing the user's emotional state and adapting the proposal based on the emotional state. This makes it possible to propose optimal products and lifestyles according to the user's emotional state.

[0776] A "user" is an individual human being who uses the system and is the entity that provides housing-related data and lifestyle data.

[0777] "Residence-related data" refers to information about a residence entered by a user, specifically data such as video, drawing data, family composition, income, and place of work.

[0778] "Lifestyle data" is information related to a user's lifestyle habits and preferences, past purchase history, and future aspirations.

[0779] A "profile" is a data set that describes a user's individual characteristics, generated by analyzing and integrating data collected from the user.

[0780] "Multimodal AI" is an AI system that performs integrated analysis of different types of data (e.g., text, images, audio, etc.).

[0781] "Emotional state" refers to the emotions (happiness, excitement, fatigue, stress, etc.) that a user expresses through facial expressions and voice.

[0782] "Suggestion results" are information resulting from the system proposing optimal relocation destinations, lifestyles, and products based on the user's profile and emotional state.

[0783] "Analysis means" refers to various algorithms and functions for performing data analysis within the system, specifically video analysis, audio analysis, text analysis, etc.

[0784] A "display device" is an electronic device used by a user to visually confirm the results of suggestions from the system, examples of which include smart glasses and displays.

[0785] This invention is a system that recognizes a user's emotional state and suggests optimal products and lifestyles based on that state. This system performs detailed analysis based on the user's home-related data and lifestyle data, enabling an improved shopping experience in virtual stores.

[0786] The user wears smart glasses and navigates around the virtual store. The system collects the user's facial expressions and voice in real time and recognizes their emotional state. Specifically, the system uses the following hardware and software:

[0787] Hardware and software used

[0788] Hardware: Smart glasses (with AR function), high-performance server (with CPU and GPU)

[0789] Software: Facial expression analysis API (e.g., facial expression recognition API), voice tone analysis API (e.g., voice analysis API), database (e.g., data store)

[0790] Data processing and calculation

[0791] 1. Data collection: The smart glasses collect the user's facial expression and voice data in real time.

[0792] 2. Data transmission: The collected data is transmitted to the server using a secure protocol.

[0793] 3. Emotion analysis: The server uses facial expression recognition API and voice analysis API to analyze the user's emotional state.

[0794] 4. Integrated analysis: Comprehensive analysis of emotional data, lifestyle data, and purchase history.

[0795] 5. Profile generation: Generate a detailed profile based on the user's data.

[0796] 6. Recommendation Generation: Multimodal AI suggests products and lifestyles that fit the user's profile and emotional state.

[0797] 7. Sending proposal results: The proposal results are sent from the server to the smart glasses.

[0798] 8. Display: Suggested products and lifestyles are displayed on the smart glasses display.

[0799] Specific examples

[0800] For example, consider the case where User C visits a virtual store and the smart glasses recognize "fatigue" from User C's facial expressions and voice. Based on this result, the server generates relaxation-related products (e.g., aroma diffusers, massage equipment) and lifestyle suggestions to help relieve stress (e.g., online yoga classes), and displays them on the smart glasses in real time. User C can review these suggestions and provide feedback. Based on the feedback, the suggestions are further adjusted to provide an optimal shopping experience.

[0801] Example of input prompt for generative AI model

[0802] prompt:

[0803] If the user's emotional state is "fatigue," we will suggest relaxation-related products based on the following information:

[0804] Past purchase history

[0805] User preferences (e.g., relaxation, hobbies, etc.)

[0806] Lifestyle Data

[0807] Suggestions include aroma diffusers, massage equipment, and online yoga classes.

[0808] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0809] Step 1:

[0810] Data collection

[0811] A user wears smart glasses and moves around the virtual store. The smart glasses collect the user's facial expression and voice data in real time. The input data are the user's facial expression data (image sequence) and voice data, which are collected using the smart glasses' sensors.

[0812] Step 2:

[0813] Data transmission

[0814] The device (smart glasses) sends the collected facial expression data and voice data to the server using a secure protocol. The input is the collected data, and the output is the data to be sent to the server. The data is encrypted and sent over a secure channel.

[0815] Step 3:

[0816] Emotion analysis

[0817] The server uses the received facial expression data and voice data to analyze the user's emotional state using a facial expression analysis API and a voice tone analysis API. The input is facial expression data and voice data, and the output is the user's emotional state data (e.g., joy, surprise, fatigue). The analyzed emotional state is stored in a database.

[0818] Step 4:

[0819] Integrated analysis

[0820] The server performs an integrated analysis of emotional data, lifestyle data, and purchase history. The input data is the user's emotional state data, lifestyle data, and past purchase history, and the output data is an integrated user profile. The profile is generated using multimodal AI, resulting in a detailed profile that reflects the user's individual characteristics.

[0821] Step 5:

[0822] Proposal generation

[0823] The server uses multimodal artificial intelligence to suggest optimal products and lifestyles to users based on the generated user profile and emotional state data. The input is the user profile and emotional state data, and the output is the proposal results. The proposal results include relaxation products and lifestyle suggestions (e.g., aroma diffusers, massage equipment, online yoga classes, etc.).

[0824] Step 6:

[0825] Sending proposal results

[0826] The server sends the proposal results to the smart glasses. The input is the proposal results, and the output is the data sent to the smart glasses. The sent proposal results are displayed on the display of the smart glasses.

[0827] Step 7:

[0828] display

[0829] The terminal (smart glasses) visually displays the received recommendation results to the user. The input is the recommendation results from the server, and the output is a visual presentation of information to the user. The user can check the suggested products and lifestyles on the smart glasses display.

[0830] Step 8:

[0831] Feedback collection

[0832] The user provides feedback on the proposed products and lifestyles. This feedback is sent from the smart glasses to the server and reflected in the next proposal. The input is the user's feedback, and the output is the transmission of the feedback data to the server.

[0833] Step 9:

[0834] Reproposal generation

[0835] The server generates new suggestions based on user feedback. The input is the feedback data and the user profile, and the output is the new suggestions. The new suggestions are made, if necessary, taking into account the user's emotional state and new feedback.

[0836] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0837] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0839] [Third embodiment]

[0840] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0841] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0842] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0843] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0844] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0845] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0846] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0847] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0848] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0849] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0850] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0851] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0852] This invention is a system for proposing optimal relocation destinations and new lifestyles that takes into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze the housing-related data and lifestyle data entered by the user and generate optimal proposals.

[0853] Program Overview

[0854] 1. Data Entry

[0855] Users enter information such as home videos, blueprints, family composition, income, and commute destination through the device interface, which allows detailed data about the user's current residence and lifestyle to be collected.

[0856] 2. Data Transmission and Reception

[0857] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[0858] 3. Data Analysis

[0859] The server analyzes the received data. Specifically, object recognition and scene analysis are used to extract features of the current home from the video data, and keywords related to the family's opinions and lifestyle are extracted from the audio data through voice recognition. Text data and drawing data are also analyzed in the same way.

[0860] 4. Profile Generation

[0861] The server then uses the analyzed data to create a personalized profile for the user, which includes information about the user's lifestyle, income, family structure, and other essential needs.

[0862] 5. Analysis and Proposal Generation Using Multimodal AI

[0863] The server operates a multimodal artificial intelligence based on the generated user profile. The AI ​​considers current housing data, the user's requests, and future needs to suggest the optimal place to move to. It also references a database of vacant homes to extract properties that meet the user's criteria in a ranked format. It also takes into account changes in social conditions to suggest new lifestyles.

[0864] 6. Submitting the proposal results

[0865] The generated proposal results are sent from the server to the terminal, which displays the proposal results to the user and provides detailed information.

[0866] 7. Gather feedback and resubmit

[0867] The user enters feedback on the displayed suggestions, which is then sent to the server via the device. The server receives the user's feedback, reanalyzes it based on the feedback, and generates new suggestions. This process can be repeated as needed.

[0868] Specific examples

[0869] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device. The device sends this information to the server, which analyzes it and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also suggests new lifestyles by recommending ride-sharing and local community activities. User A can review the suggestions and add further specific conditions and provide feedback again.

[0870] In this way, the present invention can suggest the optimum relocation destination and lifestyle suited to the individual needs of the user, thereby improving the user's quality of life.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] The user uses the terminal interface to input residence-related data (videos, drawings, furniture layout, etc.) and lifestyle data (family composition, income, commute destination, etc.). After the user has completed the data input, they click the submit button.

[0874] Step 2:

[0875] The device processes the data and sends it to the server using a secure protocol, including encrypting the data and verifying that the transfer was successful.

[0876] Step 3:

[0877] The server receives the data from the device, stores it securely in a database, and verifies that all data has been stored correctly.

[0878] Step 4:

[0879] The server analyzes the received data, including:

[0880] Video analysis: Using object recognition and scene analysis, the characteristics of the current residence (type of furniture, arrangement, room size, etc.) are extracted.

[0881] Voice analysis: Uses voice recognition technology to convert family members' opinions and lifestyle characteristics into text.

[0882] Text analysis: Analyzes drawing data and other text information to understand detailed specifications of the home and user requirements.

[0883] Step 5:

[0884] The server uses the analyzed data to create a personalized profile for each user, which includes detailed information about the user's lifestyle, income, family structure, and commute.

[0885] Step 6:

[0886] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[0887] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[0888] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[0889] Step 7:

[0890] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[0891] Step 8:

[0892] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[0893] Step 9:

[0894] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via the terminal.

[0895] Step 10:

[0896] The server receives the feedback sent by the user, analyzes it again, and generates new suggestions, which are then sent back to the device and displayed to the user.

[0897] Through this process, users can find a new home and lifestyle that best suits their needs and home environment.

[0898] Example 1

[0899] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0900] Conventional housing selection systems have difficulty in making proposals that fully reflect the lifestyles and needs of individual users, resulting in problems in which users are unable to find the optimal place to move to that satisfies them. In addition, the display of proposal results is mainly text, and is not presented in a format that is easy for users to understand, resulting in a poor user experience.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0902] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for transmitting the received data to the server using a secure protocol, means for the server to analyze the received data and generate a profile dedicated to the user, means for analyzing the generated profile and an external database using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, and means for transmitting the proposal results to the user and displaying them in a graphical interface. This makes it possible to propose an optimal relocation destination and lifestyle that is more tailored to the individual needs of the user than ever before, thereby improving the user experience.

[0903] "User" refers to an individual or organization that uses the system and is the entity that provides housing-related data and lifestyle data.

[0904] "Residence-related data" refers to information such as videos of the residence, blueprint data, room features, and furniture layout.

[0905] "Lifestyle data" refers to data about a user's daily life, such as family composition, income, commute, hobbies, and transportation methods.

[0906] A "terminal" is a device that allows a user to input data and communicate with a server, and includes smartphones, tablets, PCs, etc.

[0907] A "secure protocol" is a communication protocol used to ensure security when transmitting data, and generally corresponds to HTTPS.

[0908] A "server" is a computer system that analyzes data received from terminals and plays a central role in storing and analyzing data and generating proposals.

[0909] A "profile" is a collection of information specific to a user that is generated based on analyzed data, and includes lifestyle patterns, income, family structure, requirements, and the like.

[0910] "Multimodal AI" refers to an AI technology that integrates and analyzes multiple data formats (video, audio, text, etc.) to assist in solving complex problems.

[0911] "External databases" refer to public or private data sources that exist separately from the user's data, such as real estate information or environmental data.

[0912] "Suggestions" refer to the optimal relocation and lifestyle options derived through analysis, and are advice provided to users.

[0913] A "graphical interface" is a user interface that displays the proposal results to the user in a visually easy-to-understand format, and includes maps, charts, card-style information displays, and the like.

[0914] "Feedback" refers to information that users input about their opinions and requests regarding the proposed results, which the system uses to reanalyze and make improvement suggestions.

[0915] These definitions will provide a clearer understanding of the invention.

[0916] The present invention is a system that proposes optimal relocation destinations and lifestyles to users, taking into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze data entered by users related to their residence and lifestyle, and generates optimal proposals.

[0917] Hardware and software used

[0918] Users access the system using a device such as a smartphone, tablet, or PC, and enter the following data through a web form or mobile app on their device:

[0919] Housing video

[0920] Drawing data

[0921] Family composition

[0922] income

[0923] Commuter destination

[0924] Video data is taken with a smartphone or tablet camera and uploaded directly to the application, while text data such as income and family composition is collected by entering it into the interface.

[0925] Sending and Receiving Data

[0926] The terminal sends all data entered by the user to the server using a secure protocol (e.g. HTTPS). During this process, the data is encrypted and transmitted securely. The server stores the received data in a database (e.g. MySQL or PostgreSQL).

[0927] Data analysis

[0928] The server analyzes the received data. For video data, computer vision technology (e.g., OpenCV) is used to perform object recognition and scene analysis. For audio data, a speech recognition API (e.g., Google Speech-to-Text) is used to convert it to text, and natural language processing (NLP) tools (e.g., NLTK or SpaCy) are used to extract keywords. Furthermore, blueprint data is analyzed using CAD software or image analysis tools to extract floor plan and area information.

[0929] Profile Generation

[0930] Based on the analysis results, the server creates a personalized profile for the user, which includes information such as the user's lifestyle, income, family structure, and essential conditions.

[0931] Proposal Generation

[0932] The server runs multimodal artificial intelligence (e.g., TensorFlow, PyTorch) based on the generated user profile. The AI ​​refers to existing external databases (e.g., real estate information, environmental data) and proposes the optimal relocation destination, taking into account the user's requests and future needs. It also lists the proposed properties in a ranking format and recommends ride-sharing options and local community activities.

[0933] Displaying the proposed results

[0934] The server sends the generated proposal results to the terminal, which provides an interface for displaying the proposal results graphically to the user. This interface includes a map display using the Google Maps API and a card-style list display of property information.

[0935] Gathering feedback and resubmitting

[0936] The user then enters feedback on the displayed suggestions into the device. For example, they enter specific requests, such as "I'd like my commute to be shorter" or "I'd prefer a property that allows pets." The device then securely transmits this feedback back to the server. The server receives the feedback, reanalyzes it based on the newly collected information, and generates improved suggestions. This process can be repeated as many times as necessary until the user is satisfied.

[0937] Specific examples

[0938] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of the home, family composition, income, and commute destination into the device. The device then sends this information to the server using a secure protocol. The server analyzes the information and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also recommends ride-sharing and local community activities.

[0939] Prompt Sentence Examples

[0940] "We want to move to the suburbs to work remotely. We are a married couple with a combined annual income of 8 million yen. Here are the videos and blueprints of our current home. Can you suggest the best place to move to?"

[0941] By entering this prompt, the system will generate a proposal for the best possible relocation location that meets User A's needs.

[0942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0943] Step 1:

[0944] Users enter data such as home video, blueprint data, family composition, income, and work destination through the device interface. This operation is performed using a dedicated web form or mobile app. The entered data includes video files (e.g., .mp4), blueprint image files (e.g., .jpg, .png), and text data (e.g., income, work destination).

[0945] Input: Video of residence, drawing data, family composition, income, commute destination

[0946] Output: User data temporarily stored in the device

[0947] Step 2:

[0948] The device sends the data entered by the user to the server using a secure protocol, with the data being sent securely using techniques such as AES encryption.

[0949] Input: User data

[0950] Output: Data sent using a secure protocol

[0951] Step 3:

[0952] The server receives the data sent from the device and stores it securely in a database using a database management system such as MySQL or PostgreSQL.

[0953] Input: Securely transmitted data

[0954] Output: User data stored in the database

[0955] Step 4:

[0956] The server analyzes the received and stored data. For video data, OpenCV and TensorFlow are used for object recognition and scene analysis to extract room features and furniture layout. If audio data is included, it is converted to text using the Google Speech-to-Text API, and keywords are extracted using natural language processing tools (NLTK and SpaCy). For blueprint data, image analysis software is used to calculate the room layout and area.

[0957] Input: Database user data

[0958] Output: Analyzed feature data (room features, furniture layout, keywords, floor plan, etc.)

[0959] Step 5:

[0960] Based on the analysis results, the server creates a user-specific profile, which includes information on lifestyle, income, family structure, and other essential conditions, providing a foundation for a detailed reflection of the user's needs and conditions.

[0961] Input: Parsed feature data

[0962] Output: User-specific profile

[0963] Step 6:

[0964] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​compares the data with real estate information and environmental databases to create a list of optimal relocation destinations that meet the user's requirements. The AI ​​also presents properties in a ranking format, taking into account social conditions and future needs.

[0965] Input: User-specific profile, external database

[0966] Output: Ranked list of optimal relocation destinations

[0967] Step 7:

[0968] The server sends the generated proposal results to the device, which uses the Google Maps API to display the candidate sites on a map and display detailed information about each property in a card format to make it easier for the user to understand.

[0969] Input: Ranked list of optimal relocation destinations

[0970] Output: Graphical proposal results displayed on the terminal

[0971] Step 8:

[0972] The user can then input their feedback on the displayed proposals into the terminal, for example, by inputting specific conditions or requests such as "I want to shorten my commute time" or "I would prefer a property where I can keep pets."

[0973] Input: User feedback

[0974] Output: Feedback data stored on the device

[0975] Step 9:

[0976] The device sends the user's feedback data to the server using a secure protocol, and the server receives the feedback and begins analyzing it again to generate suggestions based on the new conditions.

[0977] Input: Securely transmitted feedback data

[0978] Output: Feedback data stored on the server

[0979] Step 10:

[0980] The server re-analyzes the newly collected information and generates improved suggestions, which are again ranked and sent to the user.

[0981] Input: Feedback data, analysis system

[0982] Output: Regenerated improvement suggestions

[0983] By repeating this process, we can propose the optimal relocation destination and lifestyle that will satisfy the user.

[0984] (Application example 1)

[0985] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0986] In response to current changes in lifestyles and the spread of teleworking, systems that suggest optimal relocation and lifestyle options for users are needed. However, in addition to such systems, there is also a need for methods to improve the in-store experience based on users' profiles. In particular, systems that can eliminate the hassle and lack of information users face when selecting products in physical stores are important.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0988] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for analyzing the received data and generating a user-specific profile, means for analyzing the received data and profile using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, means for generating product and service proposals based on the proposal results and supporting purchases and experiences at physical stores, and means for the user to check the proposal results using a smart device at the physical store and receive further detailed information and advice. This not only makes it possible to propose optimal relocation destinations and lifestyles to users, but also to improve the purchasing experience at physical stores.

[0989] "Residence-related data" is data related to the place where the user currently resides, and includes information on the address, floor plan, videos, drawings, and surrounding environment.

[0990] "Lifestyle data" refers to information about a user's lifestyle habits and preferences, family composition, income, commute, hobbies, health status, etc.

[0991] A "profile" refers to a set of information that reflects a user's individual living patterns and needs, generated based on their home-related and lifestyle data.

[0992] "Multimodal artificial intelligence" refers to an artificial intelligence system that integrates and analyzes data in various formats, such as text, audio, images, and video, and makes comprehensive judgments like a human.

[0993] "Suggestion results" refers to the information proposed regarding the optimal relocation destination and lifestyle that the multimodal artificial intelligence derives from analyzing the user's profile.

[0994] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit to select, purchase, and experience products and services in person.

[0995] "Smart device" refers to a mobile terminal or wearable device (e.g., smartphone, smart glasses, head-mounted display) that is connected to the Internet and can run applications.

[0996] "Feedback" refers to evaluations and opinions on the proposal results provided by users, and is important information for making re-proposals based on this.

[0997] "Product and Service Suggestions" refers to a list of specific products and services suggested for purchase or experience based on the user's profile.

[0998] "Analysis means" refers to methods and technologies for processing and analyzing input data, specifically techniques including video analysis, audio analysis, and text analysis.

[0999] A "cloud server" is a server that can be accessed remotely via the Internet and is a system that provides computing resources for storing data, analyzing data, running AI models, and more.

[1000] This invention is a system that proposes optimal relocation destinations and new lifestyles based on the user's housing-related data and lifestyle data. Furthermore, this proposal is applied to in-store experiences to support the user's purchasing behavior.

[1001] First, the user enters information such as video of the home, blueprint data, family composition, income, and workplace via a smartphone app. This data is then sent to a cloud server via the internet and securely stored. The cloud server is equipped with a high-performance GPU server, which is used to analyze the data. Software used includes "BERT" and "GPT-4" for text analysis, "TensorFlow" for video analysis, and "PyTorch" for audio analysis.

[1002] The server then uses the received data to create a personalized profile for the user, including information about the user's lifestyle, income, family structure, and other essential needs. During the profile creation process, video data is analyzed using object recognition, audio data is analyzed using voice recognition, and text data is also analyzed.

[1003] Next, based on the generated profile, multimodal AI proposes optimal relocation and lifestyle options. This AI uses a generative AI model to analyze the user's requests and future needs. It also takes into account changes in social conditions to propose new lifestyles.

[1004] The results of the proposals are sent from the cloud server to smart devices such as smartphone apps and smart glasses. Users can use these devices to check the proposals and receive detailed information and advice. For example, in a physical store, by wearing smart glasses, product descriptions and layout proposals for the store can be displayed in real time, and store staff can use tablet devices to provide optimal customer service.

[1005] Users can also provide feedback on the suggestions through the app. The cloud server receives this feedback, analyzes the data again based on the feedback, and generates new suggestions. This process is repeated as needed to provide more accurate suggestions.

[1006] As a concrete example, let's consider the case where User B wants to choose new furniture. User B uses a smartphone app to input data in advance, such as family composition, income, videos and blueprints of the house they currently live in, and their lifestyle. The cloud server analyzes this data and suggests furniture and layouts that are recommended for User B. When visiting the store, User B can wear smart glasses and explore the store while receiving explanations of the products inside and advice on layout. At this time, store staff use a tablet to suggest the best products for User B.

[1007] An example prompt is:

[1008] Input data: I'm planning to go to a furniture store. I have a family of four, my income is 7 million yen, and I need office space at home to work remotely. I'll attach a video and blueprint of my home. Please suggest what kind of furniture would be suitable.

[1009] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1010] Step 1:

[1011] Users use a smartphone app to input home-related and lifestyle data, including home videos, blueprints, family composition, income, and commute information. Once users enter this data, the device compiles it into a single file or dataset. The input data can be in a variety of formats, including text, video, images, and audio.

[1012] input:

[1013] Information such as videos of the residence, blueprint data, family composition, income, and commute destination.

[1014] output:

[1015] A collection of data files or datasets.

[1016] Step 2:

[1017] The device sends the compiled data file over the internet to a cloud server using the secure HTTPS protocol, which then stores the data securely in a database.

[1018] input:

[1019] Aggregated data files.

[1020] output:

[1021] Data stored on a cloud server.

[1022] Step 3:

[1023] The server analyzes the received data. First, object recognition and scene analysis are used to extract features from the video data. Next, important keywords are extracted from the audio data using voice recognition technology, and text data is also analyzed. This provides detailed information about the user's home and lifestyle.

[1024] input:

[1025] Data stored on a cloud server.

[1026] output:

[1027] Parsed feature data (objects, keywords, etc.).

[1028] Step 4:

[1029] The server generates a user-specific profile based on the analyzed feature data. This profile includes the user's lifestyle, income, family structure, and essential conditions. The generated profile is stored in a database.

[1030] input:

[1031] Parsed feature data.

[1032] output:

[1033] A user-specific profile.

[1034] Step 5:

[1035] The server runs a multimodal AI based on the generated user profile. The AI ​​analyzes the profile and the original residential data to generate suitable relocation and lifestyle recommendations for the user. This AI model uses the aforementioned generative AI models (BERT, GPT-4, etc.).

[1036] input:

[1037] A user-specific profile.

[1038] output:

[1039] Suggestions for relocation and lifestyle.

[1040] Step 6:

[1041] The server sends the proposed results to the user's smartphone app using a secure protocol, and the proposed results are displayed on the user's smartphone screen.

[1042] input:

[1043] Suggestions for relocation and lifestyle.

[1044] output:

[1045] Display on smartphone app.

[1046] Step 7:

[1047] The user can check the proposals and enter feedback through a smartphone app, which is then sent back to the cloud server.

[1048] input:

[1049] User feedback.

[1050] output:

[1051] Feedback sent to cloud server.

[1052] Step 8:

[1053] The server re-performs the analysis based on the received feedback and generates new suggestions, which are then sent to the user again.

[1054] input:

[1055] User feedback.

[1056] output:

[1057] Regenerated relocation and lifestyle proposals.

[1058] Step 9:

[1059] When users visit a physical store, they can use smart glasses or a smartphone app to check the recommendations and select products and services in the store, enabling them to select more specific and optimal products and services.

[1060] input:

[1061] Regenerated relocation and lifestyle proposals.

[1062] output:

[1063] Providing optimal products and services in physical stores.

[1064] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1065] The present invention is a system that recognizes a user's emotional state and proposes optimal relocation and new lifestyle options taking that state into consideration. The system uses multimodal artificial intelligence and an emotion engine to perform detailed analysis based on the residential and lifestyle data provided by the user.

[1066] Program Overview

[1067] 1. Data Entry

[1068] Users input information such as videos of their home, blueprints, family composition, income, and place of work into the device. Data on facial expressions and tone of voice is also collected to recognize the user's emotional state.

[1069] 2. Data Transmission and Reception

[1070] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[1071] 3. Emotional state analysis

[1072] The server analyzes facial expressions and voice tones to recognize the user's emotional state. The emotion engine identifies the user's emotions, such as joy, sadness, anger, and surprise, and provides the data.

[1073] 4. Integrated analysis of all data

[1074] The server performs a comprehensive analysis of the received residential data, lifestyle data, and emotional data, including the following analyses:

[1075] Video analysis: Object recognition and scene analysis to extract features of the current dwelling.

[1076] Speech analysis: Speech recognition to convert family opinions and lifestyle characteristics into text.

[1077] Text analysis: Analysis to understand detailed specifications of the home and user requirements.

[1078] 5. Profile Generation

[1079] The server uses the analyzed data to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, and emotional state.

[1080] 6. Analysis and proposal generation using multimodal AI

[1081] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[1082] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[1083] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[1084] Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of their acceptance.

[1085] 7. Submitting the proposal results

[1086] The generated proposal results are sent from the server to the device, and include details of the optimal relocation location and specific details of the new lifestyle.

[1087] 8. Displaying the proposed results

[1088] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[1089] 9. Gather feedback and resubmit

[1090] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via their device. The server then analyzes the feedback and generates new proposals. This process is repeated as necessary.

[1091] Specific examples

[1092] For example, suppose User B lives in a city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, as well as information about their family, income, and workplace into their device. User B's facial expressions and tone of voice are also recorded. The device sends all data to a server, which analyzes the data and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests a property in the suburbs, surrounded by nature, that is ideal for teleworking. It also makes customized suggestions based on User B's emotional state, including suggestions for ride-sharing and local activities that will help relieve stress. User B can review the suggestions and provide feedback if necessary.

[1093] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[1094] The processing flow will be explained below.

[1095] Step 1:

[1096] Users input information such as video and blueprints of the home they are currently living in, as well as family composition, income, and place of work into the device's interface. In addition, the device's camera and microphone are used to collect the user's facial expressions and voice.

[1097] Step 2:

[1098] The device organizes the data and emotion input from the user and transmits it to the server using a secure protocol, which also ensures data integrity and encryption.

[1099] Step 3:

[1100] The server receives all data (video data, drawing data, family composition, income, commute destination, emotion data) from the device and stores it securely in a database. Once the data is saved, it checks the integrity of the data.

[1101] Step 4:

[1102] The server begins parsing the received data. Specifically, the following parsing is performed:

[1103] Video analysis: Using object recognition and scene analysis, we extract the characteristics of the current residence (type of furniture, arrangement, size, etc.).

[1104] Speech and text analysis: Speech is converted into text using speech recognition technology and analyzed to confirm family opinions and lifestyle characteristics.

[1105] Emotion analysis: Analyzes facial expressions and vocal tone to identify the user's emotional state, such as joy, sadness, anger, or surprise.

[1106] Step 5:

[1107] Based on the analysis results, the server creates a personalized profile for the user, which includes the user's lifestyle, income, family composition, commute location, and even emotional state.

[1108] Step 6:

[1109] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​comprehensively analyzes the user profile and the vacant house database to propose the optimal place to move and a new lifestyle. Specifically, it does the following:

[1110] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[1111] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[1112] Reflecting emotional state: The suggestions reflect the user's emotional state, increasing the acceptability of the new residence and lifestyle.

[1113] Step 7:

[1114] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[1115] Step 8:

[1116] The device receives the proposed results and displays them to the user, who can then review the proposal and check the details to assess whether they meet their needs.

[1117] Step 9:

[1118] The user has an interface to enter feedback on the proposal, which may include requests to add specific conditions or modify the proposal.

[1119] Step 10:

[1120] The server receives the feedback sent by the user and analyzes it again, taking into account the feedback and the user's emotional state, and generates new suggestions. The newly generated suggestions are then sent back to the device and displayed to the user.

[1121] This allows users to find the optimal place to move to and lifestyle that appropriately reflects their desires and emotional state.

[1122] Example 2

[1123] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1124] Modern users often consider relocating to optimize their living environment and lifestyle. However, choosing a new residence and lifestyle requires considering many factors, which can be stressful. It is particularly difficult to obtain recommendations that take into account the user's emotional state. To address these issues, the present invention aims to provide a system that proposes optimal residences and new lifestyles based on the user's emotional state, housing-related data, and lifestyle data.

[1125] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for inputting detailed information such as a video of the residence, blueprint data, family composition, income, workplace, and facial expression and tone of voice data, means for transmitting the received data to the server using a secure protocol, means for operating a facial expression analysis and tone of voice analysis engine to recognize the user's emotional state, means for performing video analysis, voice recognition, and text analysis using multiple analysis engines to understand the detailed specifications of the residence and the user's requirements, means for integrating the received data and generating a user-specific profile, means for analyzing the profile and received data using multimodal artificial intelligence to propose an optimal new residence and lifestyle, means for transmitting the proposal results to the user, means for displaying the proposal results to the user, and means for the user to input feedback on the displayed information. This makes it possible to propose an optimal new residence and new lifestyle taking the user's emotional state into consideration.

[1126] "User" refers to a person who uses this system and is an entity that provides residence-related data and lifestyle data.

[1127] "Residence-related data" refers to data provided by users that includes information such as videos of the residence, blueprint data, family composition, income, and place of work.

[1128] "Lifestyle data" refers to data that includes information about a user's lifestyle and behavioral patterns, and reflects the user's preferences and needs regarding their lifestyle.

[1129] "Device" means an electronic device used by a user to input and transmit data, including a smartphone, tablet, or PC.

[1130] "Server" means a computer system that provides the computational resources to analyze data received from users and generate and transmit proposals.

[1131] A "secure protocol" refers to a communication protocol for securely sending and receiving data, such as the SSL / TLS protocol.

[1132] A "facial expression analysis engine" is a software system that analyzes a user's facial expression data to recognize their emotional state.

[1133] The "voice tone analysis engine" is a software system that analyzes a user's voice data and recognizes their emotional state from their tone of voice.

[1134] "Video analysis" refers to the technology of analyzing video data from a residence and extracting the characteristics and environment of the living space.

[1135] A "voice recognition engine" is a software system for converting voice data into text data.

[1136] A "text analysis engine" is a software system that analyzes text data, extracts information, and understands meaning.

[1137] A "user profile" is a data set that centrally manages detailed information about a user, including their lifestyle, income, family structure, commute, emotional state, and so on.

[1138] "Multimodal AI" is an AI system that integrates and analyzes multiple types of data (e.g., text, images, audio, etc.) to make comprehensive judgments and suggestions.

[1139] "Optimal relocation destination" refers to new residence candidates recommended by the system based on the user's requirements, conditions, and emotional state.

[1140] "New lifestyles" refer to improved lifestyles that users can achieve in their new living environments, including the promotion of teleworking and the use of ride-sharing.

[1141] "Feedback" refers to opinions and requests that users input regarding the proposed content, and is data used to make re-proposals for the system.

[1142] The present invention is a system that recognizes the emotional state of a user and proposes an optimal new residence and a new lifestyle taking the emotional state into consideration. Specific embodiments of the system are described below.

[1143] First, the user uses the device to input home-related and lifestyle data. This data includes home video, blueprints, family composition, income, and commute destination. During this process, the device uses a camera and microphone to record the user's facial expressions and voice tone. The device then uses a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) to capture the user's emotional state.

[1144] The device then transmits the collected data to the server using a secure protocol (e.g., SSL / TLS). The server receives the data and stores it in a database. The server then analyzes the user's emotional state using a facial expression analysis engine and a voice tone analysis engine. At the same time, it also analyzes the home-related data and lifestyle data, applying the following techniques:

[1145] 1. Video analysis: Extracting the characteristics and environment of the living space from videos of the home. In this stage, machine learning models (e.g., YOLO [You Only Look Once] and Faster R-CNN) are used to perform object recognition and scene analysis.

[1146] 2. Speech recognition: Converting voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text).

[1147] 3. Text analysis: Analyze text data to understand user requirements and detailed housing specifications. Use natural language processing (NLP) techniques (e.g., GPT-4).

[1148] The server combines these analysis results to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, emotional state, and more.

[1149] The server then uses multimodal artificial intelligence (for example, a combined model of GPT-4 and image recognition technology) to analyze the user profile and the received data. As a result of the analysis, the following proposals are made:

[1150] 1. Proposing the best place to move: Refer to a database of vacant houses and extract properties that meet the user's requirements in a ranked format.

[1151] 2. Proposing new lifestyles: For example, making suggestions to improve users' quality of life, such as promoting teleworking and using ride-sharing.

[1152] 3. Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of the suggestions being accepted.

[1153] The generated suggestions are sent from the server to the device, which displays them to the user. The user can review the suggestions and provide feedback if necessary. This feedback is sent back to the server, which then performs a new analysis based on the feedback and generates new suggestions.

[1154] Specific examples

[1155] For example, consider the case where User B lives in the city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device, and also records their facial expressions and voice tone. The device sends this data to the server, which analyzes it and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests properties in the suburbs, rich in nature, that are ideal for teleworking, as well as suggestions for ride-sharing options and local activities that will help relieve stress. User B can review the suggestions and provide feedback as needed.

[1156] Prompt Sentence Examples

[1157] "Design a system that suggests to users who are stressed by city life that they should move to the suburbs. Analyze the user's home video, blueprint data, and emotional state (facial expressions and voice tone) to suggest the optimal place to move to and lifestyle."

[1158] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[1159] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1160] Step 1:

[1161] The user inputs information such as a video of the residence, blueprint data, family composition, income, and workplace into the device. In addition, the device's camera and microphone are used to record facial expressions and voice tones. These input data are important information for understanding the user's lifestyle and emotional state. Operations include the user taking videos and inputting various information in text format. The output is a set of data collected on the device.

[1162] Step 2:

[1163] The terminal sends the collected data to the server using the SSL / TLS protocol. During this process, the collected data is encrypted to ensure its integrity and confidentiality. In operation, a data encryption and transmission process is performed. The input is the data collected in the previous step, and the output is the data securely transmitted to the server.

[1164] Step 3:

[1165] The server stores the received data in a database. Before storing, the data is checked for completeness and consistency. Here, a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) are used to analyze the user's emotional state. Operations include storing the data in the database and processing the emotion analysis. The input is the data sent to the server, and the output is the analyzed emotion data.

[1166] Step 4:

[1167] The server runs a video analysis engine to extract the characteristics and environment of the living space from the video data of the home. For example, it uses YOLO or Faster R-CNN to perform object recognition and scene analysis. It then uses a speech recognition engine to convert the audio data into text. This allows the family's opinions and lifestyle characteristics to be obtained as text data. The operation includes the analysis of video data and the process of converting audio data into text. The input is the received video and audio data, and the output is the analyzed text data.

[1168] Step 5:

[1169] The server uses a text analysis engine to perform a detailed analysis of the housing specifications and user requirements. For example, it uses natural language processing (NLP) to extract important information. This results in the creation of a profile that reflects the user's needs. The operation involves analyzing text data and extracting information. The input is the text data obtained in the previous step, and the output is the extracted information.

[1170] Step 6:

[1171] The server generates a user-specific profile based on the analysis results. This profile includes detailed information such as lifestyle patterns, income, family structure, commute destination, and emotional state. After the profile is generated, it is stored in a database and used for subsequent analysis and proposal generation. Operations include data integration and profile generation. The input is various analyzed data, and the output is a detailed user profile.

[1172] Step 7:

[1173] The server integrates the generated user profile with the received data and runs a multimodal artificial intelligence (e.g., a combination of GPT-4 and image recognition technology). This AI comprehensively analyzes the data and generates optimal relocation and new lifestyle recommendations. The operations include the AI's operation and the recommendation generation process. The input is the user profile and received data, and the output is the optimized recommendation results.

[1174] Step 8:

[1175] The server sends the generated proposal in JSON format to the device. The proposal includes details of the optimal relocation destination and a new lifestyle suggestion. The operation includes the process of composing and sending the proposal. The input is the generated proposal, and the output is the result sent to the device.

[1176] Step 9:

[1177] The device displays the received proposals to the user through a web interface or mobile application. The user can review the proposals in detail and provide feedback, which may include new conditions or requests for modification of the proposals. The operations include displaying the proposals and providing feedback. The input is the received proposals, and the output is the user's feedback.

[1178] Step 10:

[1179] The user inputs feedback and sends it to the server via their device. The server analyzes the feedback and generates new suggestions based on new conditions. This process is repeated as necessary. The operation includes receiving and analyzing feedback and generating new suggestions. The input is the user's feedback, and the output is the revised suggestions.

[1180] (Application example 2)

[1181] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1182] A problem in modern shopping experiences is the lack of personalized suggestions that take into account the user's emotional state. As a result, users are prone to feeling stressed and fatigued, and it is difficult to find the perfect product. Furthermore, the lack of adaptive suggestions based on the user's emotional state can reduce the user's motivation to purchase and compromise the quality of the shopping experience. To solve these issues, a system is needed that can recognize the user's emotional state in real time and use that information to suggest the best products and lifestyles.

[1183] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from the user, means for analyzing the received data and generating a profile dedicated to the user, means for analyzing the received data and profile using multimodal artificial intelligence and proposing an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, and means for recognizing the user's emotional state and adapting the proposal based on the emotional state. This makes it possible to propose optimal products and lifestyles according to the user's emotional state.

[1184] A "user" is an individual human being who uses the system and is the entity that provides housing-related data and lifestyle data.

[1185] "Residence-related data" refers to information about a residence entered by a user, specifically data such as video, drawing data, family composition, income, and place of work.

[1186] "Lifestyle data" is information related to a user's lifestyle habits and preferences, past purchase history, and future aspirations.

[1187] A "profile" is a data set that describes a user's individual characteristics, generated by analyzing and integrating data collected from the user.

[1188] "Multimodal AI" is an AI system that performs integrated analysis of different types of data (e.g., text, images, audio, etc.).

[1189] "Emotional state" refers to the emotions (happiness, excitement, fatigue, stress, etc.) that a user expresses through facial expressions and voice.

[1190] "Suggestion results" are information resulting from the system proposing optimal relocation destinations, lifestyles, and products based on the user's profile and emotional state.

[1191] "Analysis means" refers to various algorithms and functions for performing data analysis within the system, specifically video analysis, audio analysis, text analysis, etc.

[1192] A "display device" is an electronic device used by a user to visually confirm the results of suggestions from the system, examples of which include smart glasses and displays.

[1193] This invention is a system that recognizes a user's emotional state and suggests optimal products and lifestyles based on that state. This system performs detailed analysis based on the user's home-related data and lifestyle data, enabling an improved shopping experience in virtual stores.

[1194] The user wears smart glasses and navigates around the virtual store. The system collects the user's facial expressions and voice in real time and recognizes their emotional state. Specifically, the system uses the following hardware and software:

[1195] Hardware and software used

[1196] Hardware: Smart glasses (with AR function), high-performance server (with CPU and GPU)

[1197] Software: Facial expression analysis API (e.g., facial expression recognition API), voice tone analysis API (e.g., voice analysis API), database (e.g., data store)

[1198] Data processing and calculation

[1199] 1. Data collection: The smart glasses collect the user's facial expression and voice data in real time.

[1200] 2. Data transmission: The collected data is transmitted to the server using a secure protocol.

[1201] 3. Emotion analysis: The server uses facial expression recognition API and voice analysis API to analyze the user's emotional state.

[1202] 4. Integrated analysis: Comprehensive analysis of emotional data, lifestyle data, and purchase history.

[1203] 5. Profile generation: Generate a detailed profile based on the user's data.

[1204] 6. Recommendation Generation: Multimodal AI suggests products and lifestyles that fit the user's profile and emotional state.

[1205] 7. Sending proposal results: The proposal results are sent from the server to the smart glasses.

[1206] 8. Display: Suggested products and lifestyles are displayed on the smart glasses display.

[1207] Specific examples

[1208] For example, consider the case where User C visits a virtual store and the smart glasses recognize "fatigue" from User C's facial expressions and voice. Based on this result, the server generates relaxation-related products (e.g., aroma diffusers, massage equipment) and lifestyle suggestions to help relieve stress (e.g., online yoga classes), and displays them on the smart glasses in real time. User C can review these suggestions and provide feedback. Based on the feedback, the suggestions are further adjusted to provide an optimal shopping experience.

[1209] Example of input prompt for generative AI model

[1210] prompt:

[1211] If the user's emotional state is "fatigue," we will suggest relaxation-related products based on the following information:

[1212] Past purchase history

[1213] User preferences (e.g., relaxation, hobbies, etc.)

[1214] Lifestyle Data

[1215] Suggestions include aroma diffusers, massage equipment, and online yoga classes.

[1216] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1217] Step 1:

[1218] Data collection

[1219] A user wears smart glasses and moves around the virtual store. The smart glasses collect the user's facial expression and voice data in real time. The input data are the user's facial expression data (image sequence) and voice data, which are collected using the smart glasses' sensors.

[1220] Step 2:

[1221] Data transmission

[1222] The device (smart glasses) sends the collected facial expression data and voice data to the server using a secure protocol. The input is the collected data, and the output is the data to be sent to the server. The data is encrypted and sent over a secure channel.

[1223] Step 3:

[1224] Emotion analysis

[1225] The server uses the received facial expression data and voice data to analyze the user's emotional state using a facial expression analysis API and a voice tone analysis API. The input is facial expression data and voice data, and the output is the user's emotional state data (e.g., joy, surprise, fatigue). The analyzed emotional state is stored in a database.

[1226] Step 4:

[1227] Integrated analysis

[1228] The server performs an integrated analysis of emotional data, lifestyle data, and purchase history. The input data is the user's emotional state data, lifestyle data, and past purchase history, and the output data is an integrated user profile. The profile is generated using multimodal AI, resulting in a detailed profile that reflects the user's individual characteristics.

[1229] Step 5:

[1230] Proposal generation

[1231] The server uses multimodal artificial intelligence to suggest optimal products and lifestyles to users based on the generated user profile and emotional state data. The input is the user profile and emotional state data, and the output is the proposal results. The proposal results include relaxation products and lifestyle suggestions (e.g., aroma diffusers, massage equipment, online yoga classes, etc.).

[1232] Step 6:

[1233] Sending proposal results

[1234] The server sends the proposal results to the smart glasses. The input is the proposal results, and the output is the data sent to the smart glasses. The sent proposal results are displayed on the display of the smart glasses.

[1235] Step 7:

[1236] display

[1237] The terminal (smart glasses) visually displays the received recommendation results to the user. The input is the recommendation results from the server, and the output is a visual presentation of information to the user. The user can check the suggested products and lifestyles on the smart glasses display.

[1238] Step 8:

[1239] Feedback collection

[1240] The user provides feedback on the proposed products and lifestyles. This feedback is sent from the smart glasses to the server and reflected in the next proposal. The input is the user's feedback, and the output is the transmission of the feedback data to the server.

[1241] Step 9:

[1242] Reproposal generation

[1243] The server generates new suggestions based on user feedback. The input is the feedback data and the user profile, and the output is the new suggestions. The new suggestions are made, if necessary, taking into account the user's emotional state and new feedback.

[1244] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1245] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1246] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1247] [Fourth embodiment]

[1248] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1249] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1250] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1251] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1252] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1253] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1254] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1255] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1256] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1257] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1258] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1259] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1260] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] This invention is a system for proposing optimal relocation destinations and new lifestyles that takes into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze the housing-related data and lifestyle data entered by the user and generate optimal proposals.

[1262] Program Overview

[1263] 1. Data Entry

[1264] Users enter information such as videos of their home, blueprints, family composition, income, and commute destination through the device interface, which allows detailed data about the user's current residence and lifestyle to be collected.

[1265] 2. Data Transmission and Reception

[1266] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[1267] 3. Data Analysis

[1268] The server analyzes the received data. Specifically, object recognition and scene analysis are used to extract features of the current home from the video data, and keywords related to the family's opinions and lifestyle are extracted from the audio data through voice recognition. Text data and drawing data are also analyzed in the same way.

[1269] 4. Profile Generation

[1270] The server uses the analyzed data to create a personalized profile for each user, including their lifestyle, income, family structure, and essential needs.

[1271] 5. Analysis and Proposal Generation Using Multimodal AI

[1272] The server operates a multimodal artificial intelligence based on the generated user profile. The AI ​​considers current housing data, the user's requests, and future needs to suggest the optimal place to move to. It also references a database of vacant homes to extract properties that meet the user's criteria in a ranked format. It also takes into account changes in social conditions to suggest new lifestyles.

[1273] 6. Submitting the proposal results

[1274] The generated proposal results are sent from the server to the terminal, which displays the proposal results to the user and provides detailed information.

[1275] 7. Gather feedback and resubmit

[1276] The user enters feedback on the displayed suggestions, which is then sent to the server via the device. The server receives the user's feedback, reanalyzes it based on the feedback, and generates new suggestions. This process can be repeated as needed.

[1277] Specific examples

[1278] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device. The device sends this information to the server, which analyzes it and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also suggests new lifestyles by recommending ride-sharing and local community activities. User A can review the suggestions and add further specific conditions and provide feedback again.

[1279] In this way, the present invention can suggest the optimum relocation destination and lifestyle suited to the individual needs of the user, thereby improving the user's quality of life.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] The user uses the terminal interface to input residence-related data (videos, drawings, furniture layout, etc.) and lifestyle data (family composition, income, commute destination, etc.). After the user has completed the data input, they click the submit button.

[1283] Step 2:

[1284] The device processes the data entered and sends it to the server using a secure protocol, including encrypting the data and verifying that the transfer was successful.

[1285] Step 3:

[1286] The server receives the data from the device, stores it securely in a database, and verifies that all data has been stored correctly.

[1287] Step 4:

[1288] The server analyzes the received data, including:

[1289] Video analysis: Using object recognition and scene analysis, the characteristics of the current residence (type of furniture, arrangement, room size, etc.) are extracted.

[1290] Voice analysis: Uses voice recognition technology to convert family members' opinions and lifestyle characteristics into text.

[1291] Text analysis: Analyzes drawing data and other text information to understand detailed specifications of the home and user requirements.

[1292] Step 5:

[1293] The server uses the analyzed data to create a personalized profile for each user, which includes detailed information about the user's lifestyle, income, family structure, and commute.

[1294] Step 6:

[1295] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[1296] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[1297] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[1298] Step 7:

[1299] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[1300] Step 8:

[1301] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[1302] Step 9:

[1303] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via the terminal.

[1304] Step 10:

[1305] The server receives the feedback sent by the user, analyzes it again, and generates new suggestions, which are then sent back to the device and displayed to the user.

[1306] Through this process, users can find a new home and lifestyle that best suits their needs and home environment.

[1307] Example 1

[1308] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1309] Conventional housing selection systems have difficulty in making proposals that fully reflect the lifestyles and needs of individual users, resulting in problems in which users are unable to find the optimal place to move to that satisfies them. In addition, the display of proposal results is mainly text, and is not presented in a format that is easy for users to understand, resulting in a poor user experience.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1311] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for transmitting the received data to the server using a secure protocol, means for the server to analyze the received data and generate a profile dedicated to the user, means for analyzing the generated profile and an external database using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, and means for transmitting the proposal results to the user and displaying them in a graphical interface. This makes it possible to propose an optimal relocation destination and lifestyle that is more tailored to the individual needs of the user than ever before, thereby improving the user experience.

[1312] "User" refers to an individual or organization that uses the system and is the entity that provides housing-related data and lifestyle data.

[1313] "Residence-related data" refers to information such as videos of the residence, blueprint data, room features, and furniture layout.

[1314] "Lifestyle data" refers to data about a user's daily life, such as family composition, income, commute, hobbies, and transportation methods.

[1315] A "terminal" is a device that allows a user to input data and communicate with a server, and includes smartphones, tablets, PCs, etc.

[1316] A "secure protocol" is a communication protocol used to ensure security when transmitting data, and generally corresponds to HTTPS.

[1317] A "server" is a computer system that analyzes data received from terminals and plays a central role in storing and analyzing data and generating proposals.

[1318] A "profile" is a collection of information specific to a user that is generated based on analyzed data, and includes lifestyle patterns, income, family structure, requirements, and the like.

[1319] "Multimodal AI" refers to an AI technology that integrates and analyzes multiple data formats (video, audio, text, etc.) to assist in solving complex problems.

[1320] "External databases" refer to public or private data sources that exist separately from the user's data, such as real estate information or environmental data.

[1321] "Suggestions" refer to the optimal relocation and lifestyle options derived through analysis, and are advice provided to users.

[1322] A "graphical interface" is a user interface that displays the proposal results to the user in a visually easy-to-understand format, and includes maps, charts, card-style information displays, and the like.

[1323] "Feedback" refers to information that users enter as opinions or requests regarding the proposed results, which the system uses to reanalyze and make improvement suggestions.

[1324] These definitions will provide a clearer understanding of the invention.

[1325] The present invention is a system that proposes optimal relocation destinations and lifestyles to users, taking into consideration the spread of teleworking, the lifting of restrictions on ride-sharing, changes in family structure, etc. This system uses multimodal artificial intelligence to analyze data entered by users related to their residence and lifestyle, and generates optimal proposals.

[1326] Hardware and software used

[1327] Users access the system using a device such as a smartphone, tablet, or PC, and enter the following data through a web form or mobile app on their device:

[1328] Housing video

[1329] Drawing data

[1330] Family composition

[1331] income

[1332] Commuter destination

[1333] Video data is taken with a smartphone or tablet camera and uploaded directly to the application, while text data such as income and family composition is collected by entering it into the interface.

[1334] Sending and Receiving Data

[1335] The terminal sends all data entered by the user to the server using a secure protocol (e.g. HTTPS). During this process, the data is encrypted and transmitted securely. The server stores the received data in a database (e.g. MySQL or PostgreSQL).

[1336] Data analysis

[1337] The server analyzes the received data. For video data, computer vision technology (e.g., OpenCV) is used to perform object recognition and scene analysis. For audio data, a speech recognition API (e.g., Google Speech-to-Text) is used to convert it to text, and natural language processing (NLP) tools (e.g., NLTK or SpaCy) are used to extract keywords. Furthermore, blueprint data is analyzed using CAD software or image analysis tools to extract floor plan and area information.

[1338] Profile Generation

[1339] Based on the analysis results, the server creates a personalized profile for each user, which includes the user's lifestyle, income, family structure, and essential conditions.

[1340] Proposal Generation

[1341] The server runs multimodal artificial intelligence (e.g., TensorFlow, PyTorch) based on the generated user profile. The AI ​​refers to existing external databases (e.g., real estate information, environmental data) and proposes the optimal relocation destination, taking into account the user's requests and future needs. It also lists the proposed properties in a ranking format and recommends ride-sharing options and local community activities.

[1342] Displaying the proposed results

[1343] The server sends the generated proposal results to the terminal, which provides an interface for displaying the proposal results graphically to the user. This interface includes a map display using the Google Maps API and a card-style list display of property information.

[1344] Gathering feedback and resubmitting

[1345] The user then enters feedback on the displayed suggestions into the device. For example, they enter specific requests, such as "I'd like my commute to be shorter" or "I'd prefer a property that allows pets." The device then securely transmits this feedback back to the server. The server receives the feedback, reanalyzes it based on the newly collected information, and generates improved suggestions. This process can be repeated as many times as necessary until the user is satisfied.

[1346] Specific examples

[1347] For example, suppose User A lives in a city and wants to move to the suburbs to work remotely. User A enters information such as videos and blueprints of the home, family composition, income, and commute destination into the device. The device then sends this information to the server using a secure protocol. The server analyzes the information and generates a profile specifically for User A. Based on this profile, the multimodal AI suggests properties in suburban areas rich in nature that are ideal for teleworking. It also recommends ride-sharing and local community activities.

[1348] Prompt Sentence Examples

[1349] "We want to move to the suburbs to work remotely. We are a married couple with a combined annual income of 8 million yen. Here are the videos and blueprints of our current home. Can you suggest the best place to move to?"

[1350] By entering this prompt, the system will generate a proposal for the best possible relocation location that meets User A's needs.

[1351] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1352] Step 1:

[1353] Users enter data such as home video, blueprint data, family composition, income, and work destination through the device interface. This operation is performed using a dedicated web form or mobile app. The entered data includes video files (e.g., .mp4), blueprint image files (e.g., .jpg, .png), and text data (e.g., income, work destination).

[1354] Input: Video of residence, drawing data, family composition, income, commute destination

[1355] Output: User data temporarily stored in the device

[1356] Step 2:

[1357] The device sends the data entered by the user to the server using a secure protocol, with the data being sent securely using techniques such as AES encryption.

[1358] Input: User data

[1359] Output: Data sent using a secure protocol

[1360] Step 3:

[1361] The server receives the data sent from the device and stores it securely in a database using a database management system such as MySQL or PostgreSQL.

[1362] Input: Securely transmitted data

[1363] Output: User data stored in the database

[1364] Step 4:

[1365] The server analyzes the received and stored data. For video data, OpenCV and TensorFlow are used for object recognition and scene analysis to extract room features and furniture layout. If audio data is included, it is converted to text using the Google Speech-to-Text API, and keywords are extracted using natural language processing tools (NLTK and SpaCy). For blueprint data, image analysis software is used to calculate the room layout and area.

[1366] Input: Database user data

[1367] Output: Analyzed feature data (room features, furniture layout, keywords, floor plan, etc.)

[1368] Step 5:

[1369] Based on the analysis results, the server creates a user-specific profile, which includes information on lifestyle, income, family structure, and other essential conditions, providing a foundation for a detailed reflection of the user's needs and conditions.

[1370] Input: Parsed feature data

[1371] Output: User-specific profile

[1372] Step 6:

[1373] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​compares the data with real estate information and environmental databases to create a list of optimal relocation destinations that meet the user's requirements. The AI ​​also presents properties in a ranking format, taking into account social conditions and future needs.

[1374] Input: User-specific profile, external database

[1375] Output: Ranked list of optimal relocation destinations

[1376] Step 7:

[1377] The server sends the generated proposal results to the device, which uses the Google Maps API to display the candidate sites on a map and display detailed information about each property in a card format to make it easier for the user to understand.

[1378] Input: Ranked list of optimal relocation destinations

[1379] Output: Graphical proposal results displayed on the terminal

[1380] Step 8:

[1381] The user can then input their feedback on the displayed proposals into the terminal, for example, entering specific conditions or requests such as "I want to shorten my commute time" or "I would prefer a property where I can keep pets."

[1382] Input: User feedback

[1383] Output: Feedback data stored on the device

[1384] Step 9:

[1385] The device sends the user's feedback data to the server using a secure protocol, and the server receives the feedback and begins analyzing it again to generate suggestions based on the new conditions.

[1386] Input: Securely transmitted feedback data

[1387] Output: Feedback data stored on the server

[1388] Step 10:

[1389] The server re-analyzes the newly collected information and generates improved suggestions, which are again ranked and sent to the user.

[1390] Input: Feedback data, analysis system

[1391] Output: Regenerated improvement suggestions

[1392] By repeating this process, we can propose the optimal relocation destination and lifestyle that will satisfy the user.

[1393] (Application example 1)

[1394] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1395] In response to current changes in lifestyles and the spread of teleworking, systems that suggest optimal relocation and lifestyle options for users are needed. However, in addition to such systems, there is also a need for methods to improve the in-store experience based on users' profiles. In particular, systems that can eliminate the hassle and lack of information users face when selecting products in physical stores are important.

[1396] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1397] In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for analyzing the received data and generating a user-specific profile, means for analyzing the received data and profile using multimodal artificial intelligence to propose an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, means for generating product and service proposals based on the proposal results and supporting purchases and experiences at physical stores, and means for the user to check the proposal results using a smart device at the physical store and receive further detailed information and advice. This not only makes it possible to propose optimal relocation destinations and lifestyles to users, but also to improve the purchasing experience at physical stores.

[1398] "Residence-related data" is data related to the place where the user currently resides, and includes information on the address, floor plan, videos, drawings, and surrounding environment.

[1399] "Lifestyle data" refers to information about a user's lifestyle habits and preferences, family composition, income, commute, hobbies, health status, etc.

[1400] A "profile" refers to a set of information that reflects a user's individual living patterns and needs, generated based on their home-related and lifestyle data.

[1401] "Multimodal artificial intelligence" refers to an artificial intelligence system that integrates and analyzes data in various formats, such as text, audio, images, and video, and makes comprehensive judgments like a human.

[1402] "Suggestion results" refers to the information proposed regarding the optimal relocation destination and lifestyle that the multimodal artificial intelligence derives from analyzing the user's profile.

[1403] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit to select, purchase, and experience products and services in person.

[1404] "Smart device" refers to a mobile terminal or wearable device (e.g., smartphone, smart glasses, head-mounted display) that is connected to the Internet and can run applications.

[1405] "Feedback" refers to evaluations and opinions on the proposal results provided by users, and is important information for making re-proposals based on this.

[1406] "Product and Service Suggestions" refers to a list of specific products and services suggested for purchase or experience based on the user's profile.

[1407] "Analysis means" refers to methods and technologies for processing and analyzing input data, specifically techniques including video analysis, audio analysis, and text analysis.

[1408] A "cloud server" is a server that can be accessed remotely via the Internet and is a system that provides computing resources for storing data, analyzing data, running AI models, and more.

[1409] This invention is a system that proposes optimal relocation destinations and new lifestyles based on the user's housing-related data and lifestyle data. Furthermore, this proposal is applied to in-store experiences to support the user's purchasing behavior.

[1410] First, the user enters information such as video of the home, blueprint data, family composition, income, and workplace via a smartphone app. This data is then sent to a cloud server via the internet and securely stored. The cloud server is equipped with a high-performance GPU server, which is used to analyze the data. Software used includes "BERT" and "GPT-4" for text analysis, "TensorFlow" for video analysis, and "PyTorch" for audio analysis.

[1411] The server then uses the received data to create a personalized profile for the user, including information about the user's lifestyle, income, family structure, and other essential needs. During the profile creation process, video data is analyzed using object recognition, audio data is analyzed using voice recognition, and text data is also analyzed.

[1412] Next, based on the generated profile, multimodal AI proposes optimal relocation and lifestyle options. This AI uses a generative AI model to analyze the user's requests and future needs. It also takes into account changes in social conditions to propose new lifestyles.

[1413] The results of the proposals are sent from the cloud server to smart devices such as smartphone apps and smart glasses. Users can use these devices to check the proposals and receive detailed information and advice. For example, in a physical store, by wearing smart glasses, product descriptions and layout proposals for the store can be displayed in real time, and store staff can use tablet devices to provide optimal customer service.

[1414] Users can also provide feedback on the suggestions through the app. The cloud server receives this feedback, analyzes the data again based on the feedback, and generates new suggestions. This process is repeated as needed to provide more accurate suggestions.

[1415] As a concrete example, let's consider the case where User B wants to choose new furniture. User B uses a smartphone app to input data in advance, such as family composition, income, videos and blueprints of the house they currently live in, and their lifestyle. The cloud server analyzes this data and suggests furniture and layouts that are recommended for User B. When visiting the store, User B can wear smart glasses and explore the store while receiving explanations of the products inside and advice on layout. At this time, store staff use a tablet to suggest the best products for User B.

[1416] An example prompt is:

[1417] Input data: I'm planning to go to a furniture store. I have a family of four, my income is 7 million yen, and I need office space at home to work remotely. I'll attach a video and blueprint of my home. Please suggest what kind of furniture would be suitable.

[1418] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1419] Step 1:

[1420] Users use a smartphone app to input home-related and lifestyle data, including home videos, blueprints, family composition, income, and commute information. Once users enter this data, the device compiles it into a single file or dataset. The input data can be in a variety of formats, including text, video, images, and audio.

[1421] input:

[1422] Information such as videos of the residence, blueprint data, family composition, income, and commute destination.

[1423] output:

[1424] A collection of data files or datasets.

[1425] Step 2:

[1426] The device sends the compiled data file over the internet to a cloud server using the secure HTTPS protocol, which then stores the data securely in a database.

[1427] input:

[1428] Aggregated data files.

[1429] output:

[1430] Data stored on a cloud server.

[1431] Step 3:

[1432] The server analyzes the received data. First, object recognition and scene analysis are used to extract features from the video data. Next, important keywords are extracted from the audio data using voice recognition technology, and text data is also analyzed. This provides detailed information about the user's home and lifestyle.

[1433] input:

[1434] Data stored on a cloud server.

[1435] output:

[1436] Parsed feature data (objects, keywords, etc.).

[1437] Step 4:

[1438] The server generates a user-specific profile based on the analyzed feature data. This profile includes the user's lifestyle, income, family structure, and essential conditions. The generated profile is stored in a database.

[1439] input:

[1440] Parsed feature data.

[1441] output:

[1442] A user-specific profile.

[1443] Step 5:

[1444] The server runs a multimodal AI based on the generated user profile. The AI ​​analyzes the profile and the original residential data to generate suitable relocation and lifestyle recommendations for the user. This AI model uses the aforementioned generative AI models (BERT, GPT-4, etc.).

[1445] input:

[1446] A user-specific profile.

[1447] output:

[1448] Suggestions for relocation and lifestyle.

[1449] Step 6:

[1450] The server sends the proposed results to the user's smartphone app using a secure protocol, and the proposed results are displayed on the user's smartphone screen.

[1451] input:

[1452] Suggestions for relocation and lifestyle.

[1453] output:

[1454] Display on smartphone app.

[1455] Step 7:

[1456] The user can check the proposals and enter feedback through a smartphone app, which is then sent back to the cloud server.

[1457] input:

[1458] User feedback.

[1459] output:

[1460] Feedback sent to cloud server.

[1461] Step 8:

[1462] The server re-performs the analysis based on the received feedback and generates new suggestions, which are then sent to the user again.

[1463] input:

[1464] User feedback.

[1465] output:

[1466] Regenerated relocation and lifestyle proposals.

[1467] Step 9:

[1468] When users visit a physical store, they can use smart glasses or a smartphone app to check the recommendations and select products and services in the store, allowing them to select more specific and optimal products and services.

[1469] input:

[1470] Regenerated relocation and lifestyle proposals.

[1471] output:

[1472] Providing optimal products and services in physical stores.

[1473] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1474] The present invention is a system that recognizes a user's emotional state and proposes optimal relocation and new lifestyle options taking that state into consideration. The system uses multimodal artificial intelligence and an emotion engine to perform detailed analysis based on the residential and lifestyle data provided by the user.

[1475] Program Overview

[1476] 1. Data Entry

[1477] Users input information such as videos of their home, blueprints, family composition, income, and place of work into the device. Data on facial expressions and tone of voice is also collected to recognize the user's emotional state.

[1478] 2. Data Transmission and Reception

[1479] The terminal transmits the entered data using a secure protocol to a server, which receives it and stores it securely in a database.

[1480] 3. Emotional state analysis

[1481] The server analyzes facial expressions and voice tones to recognize the user's emotional state. The emotion engine identifies the user's emotions, such as joy, sadness, anger, and surprise, and provides the data.

[1482] 4. Integrated analysis of all data

[1483] The server performs a comprehensive analysis of the received residential data, lifestyle data, and emotional data, including the following analyses:

[1484] Video analysis: Object recognition and scene analysis to extract features of the current dwelling.

[1485] Speech analysis: Speech recognition to convert family opinions and lifestyle characteristics into text.

[1486] Text analysis: Analysis to understand detailed specifications of the home and user requirements.

[1487] 5. Profile Generation

[1488] The server uses the analyzed data to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, and emotional state.

[1489] 6. Analysis and proposal generation using multimodal AI

[1490] The server runs a multimodal AI based on the generated user profile. The AI ​​comprehensively analyzes the profile and the received data and performs the following:

[1491] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[1492] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[1493] Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of their acceptance.

[1494] 7. Submitting the proposal results

[1495] The generated proposal results are sent from the server to the device, and include details of the optimal relocation location and specific details of the new lifestyle.

[1496] 8. Displaying the proposed results

[1497] The terminal displays the proposal results received from the server to the user, who can then use the interface to check the displayed information and input feedback on the proposal.

[1498] 9. Gather feedback and resubmit

[1499] The user inputs feedback on the proposal (e.g., requests to add specific conditions or modify the proposal) and sends it to the server via their device. The server then analyzes the feedback and generates new proposals. This process is repeated as necessary.

[1500] Specific examples

[1501] For example, suppose User B lives in a city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, as well as information about their family, income, and workplace into their device. User B's facial expressions and tone of voice are also recorded. The device sends all data to a server, which analyzes the data and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests a property in the suburbs, surrounded by nature, that is ideal for teleworking. It also makes customized suggestions based on User B's emotional state, including suggestions for ride-sharing and local activities that will help relieve stress. User B can review the suggestions and provide feedback if necessary.

[1502] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[1503] The processing flow will be explained below.

[1504] Step 1:

[1505] Users input information such as video and blueprints of the home they are currently living in, as well as family composition, income, and place of work into the device's interface. In addition, the device's camera and microphone are used to collect the user's facial expressions and voice.

[1506] Step 2:

[1507] The device organizes the data and emotion input from the user and transmits it to the server using a secure protocol, which also ensures data integrity and encryption.

[1508] Step 3:

[1509] The server receives all data (video data, drawing data, family composition, income, commute destination, emotion data) from the device and stores it securely in a database. Once the data is saved, it checks the integrity of the data.

[1510] Step 4:

[1511] The server begins parsing the received data. Specifically, the following parsing is performed:

[1512] Video analysis: Using object recognition and scene analysis, we extract the characteristics of the current residence (type of furniture, arrangement, size, etc.).

[1513] Speech and text analysis: Speech is converted into text using speech recognition technology and analyzed to confirm family opinions and lifestyle characteristics.

[1514] Emotion analysis: Analyzes facial expressions and vocal tone to identify the user's emotional state, such as joy, sadness, anger, or surprise.

[1515] Step 5:

[1516] Based on the analysis results, the server creates a personalized profile for the user, which includes the user's lifestyle, income, family composition, commute location, and even emotional state.

[1517] Step 6:

[1518] The server runs a multimodal artificial intelligence based on the generated user profile. The AI ​​comprehensively analyzes the user profile and the vacant house database to propose the optimal place to move and a new lifestyle. Specifically, it does the following:

[1519] Suggesting the best place to move: Refer to a database of vacant houses and extract properties that meet the user's criteria in a ranked format.

[1520] Proposing new lifestyles: Proposing new lifestyles taking into account social conditions (e.g., promotion of teleworking, use of ride-sharing).

[1521] Reflecting emotional state: The suggestions reflect the user's emotional state, increasing the acceptability of the new residence and lifestyle.

[1522] Step 7:

[1523] The server then sends the generated proposal to the device, which includes details of the optimal relocation location and specific details of the new lifestyle.

[1524] Step 8:

[1525] The device receives the proposed results and displays them to the user, who can then review the proposal and check the details to assess whether they meet their needs.

[1526] Step 9:

[1527] The user has an interface to enter feedback on the proposal, which may include requests to add specific conditions or modify the proposal.

[1528] Step 10:

[1529] The server receives the feedback sent by the user and analyzes it again, taking into account the feedback and the user's emotional state, and generates new suggestions. The newly generated suggestions are then sent back to the device and displayed to the user.

[1530] This allows users to find the optimal place to move to and lifestyle that appropriately reflects their desires and emotional state.

[1531] Example 2

[1532] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] Modern users often consider relocating to optimize their living environment and lifestyle. However, choosing a new residence and lifestyle requires considering many factors, which can be stressful. It is particularly difficult to obtain recommendations that take into account the user's emotional state. To address these issues, the present invention aims to provide a system that proposes optimal residences and new lifestyles based on the user's emotional state, housing-related data, and lifestyle data.

[1534] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from a user, means for inputting detailed information such as a video of the residence, blueprint data, family composition, income, workplace, and facial expression and tone of voice data, means for transmitting the received data to the server using a secure protocol, means for operating a facial expression analysis and tone of voice analysis engine to recognize the user's emotional state, means for performing video analysis, voice recognition, and text analysis using multiple analysis engines to understand the detailed specifications of the residence and the user's requirements, means for integrating the received data and generating a user-specific profile, means for analyzing the profile and received data using multimodal artificial intelligence to propose an optimal new residence and lifestyle, means for transmitting the proposal results to the user, means for displaying the proposal results to the user, and means for the user to input feedback on the displayed information. This makes it possible to propose an optimal new residence and new lifestyle taking the user's emotional state into consideration.

[1535] "User" refers to a person who uses this system and is an entity that provides residence-related data and lifestyle data.

[1536] "Residence-related data" refers to data provided by users that includes information such as videos of the residence, blueprint data, family composition, income, and place of work.

[1537] "Lifestyle data" refers to data that includes information about a user's lifestyle and behavioral patterns, and reflects the user's preferences and needs regarding their lifestyle.

[1538] "Device" means an electronic device used by a user to input and transmit data, including a smartphone, tablet, or PC.

[1539] "Server" means a computer system that provides the computational resources to analyze data received from users and generate and transmit proposals.

[1540] A "secure protocol" refers to a communication protocol for securely sending and receiving data, such as the SSL / TLS protocol.

[1541] A "facial expression analysis engine" is a software system that analyzes a user's facial expression data to recognize their emotional state.

[1542] The "voice tone analysis engine" is a software system that analyzes a user's voice data and recognizes their emotional state from their tone of voice.

[1543] "Video analysis" refers to the technology of analyzing video data from a residence and extracting the characteristics and environment of the living space.

[1544] A "voice recognition engine" is a software system for converting voice data into text data.

[1545] A "text analysis engine" is a software system that analyzes text data, extracts information, and understands meaning.

[1546] A "user profile" is a data set that centrally manages detailed information about a user, including their lifestyle, income, family structure, commute, emotional state, and so on.

[1547] "Multimodal AI" is an AI system that integrates and analyzes multiple types of data (e.g., text, images, audio, etc.) to make comprehensive judgments and suggestions.

[1548] "Optimal relocation destination" refers to new residence candidates recommended by the system based on the user's requirements, conditions, and emotional state.

[1549] "New lifestyles" refer to improved lifestyles that users can achieve in their new living environments, including the promotion of teleworking and the use of ride-sharing.

[1550] "Feedback" refers to opinions and requests that users input regarding the proposed content, and is data used to make re-proposals for the system.

[1551] The present invention is a system that recognizes the emotional state of a user and proposes an optimal new residence and a new lifestyle taking the emotional state into consideration. Specific embodiments of the system are described below.

[1552] First, the user uses the device to input home-related and lifestyle data. This data includes home video, blueprints, family composition, income, and commute destination. During this process, the device uses a camera and microphone to record the user's facial expressions and voice tone. The device then uses a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) to capture the user's emotional state.

[1553] The device then transmits the collected data to the server using a secure protocol (e.g., SSL / TLS). The server receives the data and stores it in a database. The server then analyzes the user's emotional state using a facial expression analysis engine and a voice tone analysis engine. At the same time, it also analyzes the home-related data and lifestyle data, applying the following techniques:

[1554] 1. Video analysis: Extracting the characteristics and environment of the living space from videos of the home. In this stage, machine learning models (e.g., YOLO [You Only Look Once] and Faster R-CNN) are used to perform object recognition and scene analysis.

[1555] 2. Speech recognition: Converting voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text).

[1556] 3. Text analysis: Analyze text data to understand user requirements and detailed housing specifications. Use natural language processing (NLP) techniques (e.g., GPT-4).

[1557] The server combines these analysis results to create a personalized profile for each user, detailing their lifestyle, income, family structure, commute, emotional state, and more.

[1558] The server then uses multimodal artificial intelligence (for example, a combined model of GPT-4 and image recognition technology) to analyze the user profile and the received data. As a result of the analysis, the following proposals are made:

[1559] 1. Proposing the best place to move: Refer to a database of vacant houses and extract properties that meet the user's requirements in a ranked format.

[1560] 2. Proposing new lifestyles: For example, making suggestions to improve users' quality of life, such as promoting teleworking and using ride-sharing.

[1561] 3. Reflecting emotional state: Reflecting the user's emotional state in the content of suggestions increases the likelihood of the suggestions being accepted.

[1562] The generated suggestions are sent from the server to the device, which displays them to the user. The user can review the suggestions and provide feedback if necessary. This feedback is sent back to the server, which then performs a new analysis based on the feedback and generates new suggestions.

[1563] Specific examples

[1564] For example, consider the case where User B lives in the city and wants to move to a less stressful environment. User B enters information such as videos and blueprints of their home, family composition, income, and commute destination into their device, and also records their facial expressions and voice tone. The device sends this data to the server, which analyzes it and creates a profile specifically for User B. Based on this profile, the multimodal AI suggests properties in the suburbs, rich in nature, that are ideal for teleworking, as well as suggestions for ride-sharing options and local activities that will help relieve stress. User B can review the suggestions and provide feedback as needed.

[1565] Prompt Sentence Examples

[1566] "Design a system that suggests to users who are stressed by city life that they should move to the suburbs. Analyze the user's home video, blueprint data, and emotional state (facial expressions and voice tone) to suggest the optimal place to move to and lifestyle."

[1567] In this way, the present invention can suggest optimal relocation destinations and lifestyles that take into account the user's emotional state, thereby improving the user's quality of life.

[1568] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1569] Step 1:

[1570] The user inputs information such as a video of the residence, blueprint data, family composition, income, and workplace into the device. In addition, the device's camera and microphone are used to record facial expressions and voice tones. These input data are important information for understanding the user's lifestyle and emotional state. Operations include the user taking videos and inputting various information in text format. The output is a set of data collected on the device.

[1571] Step 2:

[1572] The terminal sends the collected data to the server using the SSL / TLS protocol. During this process, the collected data is encrypted to ensure its integrity and confidentiality. In operation, a data encryption and transmission process is performed. The input is the data collected in the previous step, and the output is the data securely transmitted to the server.

[1573] Step 3:

[1574] The server stores the received data in a database. Before storing, the data is checked for completeness and consistency. Here, a facial expression analysis engine (e.g., OpenCV or Emotion API) and a voice tone analysis engine (e.g., Google Cloud Speech-to-Text API) are used to analyze the user's emotional state. Operations include storing the data in the database and processing the emotion analysis. The input is the data sent to the server, and the output is the analyzed emotion data.

[1575] Step 4:

[1576] The server runs a video analysis engine to extract the characteristics and environment of the living space from the video data of the home. For example, it uses YOLO or Faster R-CNN to perform object recognition and scene analysis. It then uses a speech recognition engine to convert the audio data into text. This allows the family's opinions and lifestyle characteristics to be obtained as text data. The operation includes the analysis of video data and the process of converting audio data into text. The input is the received video and audio data, and the output is the analyzed text data.

[1577] Step 5:

[1578] The server uses a text analysis engine to perform a detailed analysis of the housing specifications and user requirements. For example, it uses natural language processing (NLP) to extract important information. This results in the creation of a profile that reflects the user's needs. The operation involves analyzing text data and extracting information. The input is the text data obtained in the previous step, and the output is the extracted information.

[1579] Step 6:

[1580] The server generates a user-specific profile based on the analysis results. This profile includes detailed information such as lifestyle patterns, income, family structure, commute destination, and emotional state. After the profile is generated, it is stored in a database and used for subsequent analysis and proposal generation. Operations include data integration and profile generation. The input is various analyzed data, and the output is a detailed user profile.

[1581] Step 7:

[1582] The server integrates the generated user profile with the received data and runs a multimodal artificial intelligence (e.g., a combination of GPT-4 and image recognition technology). This AI comprehensively analyzes the data and generates optimal relocation and new lifestyle recommendations. The operations include the AI's operation and the recommendation generation process. The input is the user profile and received data, and the output is the optimized recommendation results.

[1583] Step 8:

[1584] The server sends the generated proposal in JSON format to the device. The proposal includes details of the optimal relocation destination and a new lifestyle suggestion. The operation includes the process of composing and sending the proposal. The input is the generated proposal, and the output is the result sent to the device.

[1585] Step 9:

[1586] The device displays the received proposals to the user through a web interface or mobile application. The user can review the proposals in detail and provide feedback, which may include new conditions or requests for modification of the proposals. The operations include displaying the proposals and providing feedback. The input is the received proposals, and the output is the user's feedback.

[1587] Step 10:

[1588] The user inputs feedback and sends it to the server via their device. The server analyzes the feedback and generates new suggestions based on new conditions. This process is repeated as necessary. The operation includes receiving and analyzing feedback and generating new suggestions. The input is the user's feedback, and the output is the revised suggestions.

[1589] (Application example 2)

[1590] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1591] A problem in modern shopping experiences is the lack of personalized suggestions that take into account the user's emotional state. As a result, users are prone to feeling stressed and fatigued, and it is difficult to find the perfect product. Furthermore, the lack of adaptive suggestions based on the user's emotional state can reduce the user's motivation to purchase and compromise the quality of the shopping experience. To solve these issues, a system is needed that can recognize the user's emotional state in real time and use that information to suggest the best products and lifestyles.

[1592] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving residence-related data and lifestyle data from the user, means for analyzing the received data and generating a profile dedicated to the user, means for analyzing the received data and profile using multimodal artificial intelligence and proposing an optimal relocation destination and lifestyle, means for transmitting the proposal results to the user, and means for recognizing the user's emotional state and adapting the proposal based on the emotional state. This makes it possible to propose optimal products and lifestyles according to the user's emotional state.

[1593] A "user" is an individual human being who uses the system and is the entity that provides housing-related data and lifestyle data.

[1594] "Residence-related data" refers to information about a residence entered by a user, specifically data such as video, drawing data, family composition, income, and place of work.

[1595] "Lifestyle data" is information related to a user's lifestyle habits and preferences, past purchase history, and future aspirations.

[1596] A "profile" is a data set that describes a user's individual characteristics, generated by analyzing and integrating data collected from the user.

[1597] "Multimodal AI" is an AI system that performs integrated analysis of different types of data (e.g., text, images, audio, etc.).

[1598] "Emotional state" refers to the emotions (happiness, excitement, fatigue, stress, etc.) that a user expresses through facial expressions and voice.

[1599] "Suggestion results" are information resulting from the system proposing optimal relocation destinations, lifestyles, and products based on the user's profile and emotional state.

[1600] "Analysis means" refers to various algorithms and functions for performing data analysis within the system, specifically video analysis, audio analysis, text analysis, etc.

[1601] A "display device" is an electronic device used by a user to visually confirm the results of suggestions from the system, examples of which include smart glasses and displays.

[1602] This invention is a system that recognizes a user's emotional state and suggests optimal products and lifestyles based on that state. This system performs detailed analysis based on the user's home-related data and lifestyle data, enabling an improved shopping experience in virtual stores.

[1603] The user wears smart glasses and navigates around the virtual store. The system collects the user's facial expressions and voice in real time and recognizes their emotional state. Specifically, the system uses the following hardware and software:

[1604] Hardware and software used

[1605] Hardware: Smart glasses (with AR function), high-performance server (with CPU and GPU)

[1606] Software: Facial expression analysis API (e.g., facial expression recognition API), voice tone analysis API (e.g., voice analysis API), database (e.g., data store)

[1607] Data processing and calculation

[1608] 1. Data collection: The smart glasses collect the user's facial expression and voice data in real time.

[1609] 2. Data transmission: The collected data is transmitted to the server using a secure protocol.

[1610] 3. Emotion analysis: The server uses facial expression recognition API and voice analysis API to analyze the user's emotional state.

[1611] 4. Integrated analysis: Comprehensive analysis of emotional data, lifestyle data, and purchase history.

[1612] 5. Profile generation: Generate a detailed profile based on the user's data.

[1613] 6. Recommendation Generation: Multimodal AI suggests products and lifestyles that fit the user's profile and emotional state.

[1614] 7. Sending proposal results: The proposal results are sent from the server to the smart glasses.

[1615] 8. Display: Suggested products and lifestyles are displayed on the smart glasses display.

[1616] Specific examples

[1617] For example, consider the case where User C visits a virtual store and the smart glasses recognize "fatigue" from User C's facial expressions and voice. Based on this result, the server generates relaxation-related products (e.g., aroma diffusers, massage equipment) and lifestyle suggestions to help relieve stress (e.g., online yoga classes), and displays them on the smart glasses in real time. User C can review these suggestions and provide feedback. Based on the feedback, the suggestions are further adjusted to provide an optimal shopping experience.

[1618] Example of input prompt for generative AI model

[1619] prompt:

[1620] If the user's emotional state is "fatigue," we will suggest relaxation-related products based on the following information:

[1621] Past purchase history

[1622] User preferences (e.g., relaxation, hobbies, etc.)

[1623] Lifestyle Data

[1624] Suggestions include aroma diffusers, massage equipment, and online yoga classes.

[1625] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1626] Step 1:

[1627] Data collection

[1628] A user wears smart glasses and moves around the virtual store. The smart glasses collect the user's facial expression and voice data in real time. The input data are the user's facial expression data (image sequence) and voice data, which are collected using the smart glasses' sensors.

[1629] Step 2:

[1630] Data transmission

[1631] The device (smart glasses) sends the collected facial expression data and voice data to the server using a secure protocol. The input is the collected data, and the output is the data to be sent to the server. The data is encrypted and sent over a secure channel.

[1632] Step 3:

[1633] Emotion analysis

[1634] The server uses the received facial expression data and voice data to analyze the user's emotional state using a facial expression analysis API and a voice tone analysis API. The input is facial expression data and voice data, and the output is the user's emotional state data (e.g., joy, surprise, fatigue). The analyzed emotional state is stored in a database.

[1635] Step 4:

[1636] Integrated analysis

[1637] The server performs an integrated analysis of emotional data, lifestyle data, and purchase history. The input data is the user's emotional state data, lifestyle data, and past purchase history, and the output data is an integrated user profile. The profile is generated using multimodal AI, resulting in a detailed profile that reflects the user's individual characteristics.

[1638] Step 5:

[1639] Proposal generation

[1640] The server uses multimodal artificial intelligence to suggest optimal products and lifestyles to users based on the generated user profile and emotional state data. The input is the user profile and emotional state data, and the output is the proposal results. The proposal results include relaxation products and lifestyle suggestions (e.g., aroma diffusers, massage equipment, online yoga classes, etc.).

[1641] Step 6:

[1642] Sending proposal results

[1643] The server sends the proposal results to the smart glasses. The input is the proposal results, and the output is the data sent to the smart glasses. The sent proposal results are displayed on the display of the smart glasses.

[1644] Step 7:

[1645] display

[1646] The terminal (smart glasses) visually displays the received recommendation results to the user. The input is the recommendation results from the server, and the output is a visual presentation of information to the user. The user can check the suggested products and lifestyles on the smart glasses display.

[1647] Step 8:

[1648] Feedback collection

[1649] The user provides feedback on the proposed products and lifestyles. This feedback is sent from the smart glasses to the server and reflected in the next proposal. The input is the user's feedback, and the output is the transmission of the feedback data to the server.

[1650] Step 9:

[1651] Reproposal generation

[1652] The server generates new suggestions based on user feedback. The input is the feedback data and the user profile, and the output is the new suggestions. The new suggestions are made, if necessary, taking into account the user's emotional state and new feedback.

[1653] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1654] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1655] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1656] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1657] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1658] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1659] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1660] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1661] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1662] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1663] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1664] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1665] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1666] 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.

[1667] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1668] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1669] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1670] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1671] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1672] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1673] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1674] The following is further disclosed regarding the above embodiment.

[1675] (Claim 1)

[1676] means for receiving residence-related and lifestyle data from a user;

[1677] means for analyzing the received data and generating a user-specific profile;

[1678] A means for analyzing received data and profiles using multimodal artificial intelligence to suggest optimal relocation destinations and lifestyles;

[1679] means for transmitting the proposal results to the user;

[1680] A system including:

[1681] (Claim 2)

[1682] means for receiving and transmitting user-provided feedback to a server;

[1683] means for generating suggestions again based on the feedback sent to the server;

[1684] The system of claim 1 further comprising:

[1685] (Claim 3)

[1686] A method for using multiple analytical methods such as video analysis, audio analysis, and text analysis based on data entered by a user;

[1687] means for integrating the analysis results to generate a user profile;

[1688] 10. The system of claim 1, comprising:

[1689] (Claim 4)

[1690] A means of analyzing data such as the user's lifestyle, income, family structure, and commute destination to make lifestyle suggestions;

[1691] A method to rank the best relocation destinations based on a comparison with a database of vacant homes,

[1692] 10. The system of claim 1, comprising:

[1693] "Example 1"

[1694] (Claim 1)

[1695] means for receiving residence-related and lifestyle data from a user;

[1696] means for transmitting the received data to a server using a secure protocol;

[1697] means for the server to analyze the received data and generate a profile specific to the user;

[1698] A means for analyzing the profile generated using multimodal artificial intelligence and external databases to suggest optimal relocation destinations and lifestyles;

[1699] means for transmitting the proposal results to the user and displaying them in a graphical interface;

[1700] A system including:

[1701] (Claim 2)

[1702] means for receiving and transmitting user-provided feedback to a server;

[1703] means for generating suggestions again based on the feedback sent to the server;

[1704] a means for sending the proposal results back to the user;

[1705] The system of claim 1 further comprising:

[1706] (Claim 3)

[1707] A method for using multiple analytical methods such as video analysis, audio analysis, text analysis, and drawing analysis based on data entered by a user;

[1708] means for integrating the analysis results to generate a user profile;

[1709] 10. The system of claim 1, comprising:

[1710] "Application Example 1"

[1711] (Claim 1)

[1712] means for receiving residence-related and lifestyle data from a user;

[1713] means for analyzing the received data and generating a user-specific profile;

[1714] A means for analyzing received data and profiles using multimodal artificial intelligence to suggest optimal relocation destinations and lifestyles;

[1715] means for transmitting the proposal results to the user;

[1716] A means to generate product and service proposals based on the proposal results and support purchases and experiences in physical stores;

[1717] A means for users to check the proposal results using a smart device in a physical store and receive more detailed information and advice.

[1718] A system including:

[1719] (Claim 2)

[1720] means for receiving and transmitting user-provided feedback to a server;

[1721] means for generating suggestions again based on the feedback sent to the server;

[1722] A means to reflect the regenerated recommendations in the physical store experience,

[1723] The system of claim 1 further comprising:

[1724] (Claim 3)

[1725] A method for using multiple analytical methods such as video analysis, audio analysis, and text analysis based on data entered by a user;

[1726] means for integrating the analysis results to generate a user profile;

[1727] A means to provide product and service suggestions based on user profiles in physical stores,

[1728] 10. The system of claim 1, comprising:

[1729] "Example 2: Combining Emotion Engines"

[1730] (Claim 1)

[1731] means for receiving residence-related and lifestyle data from a user;

[1732] A means for inputting detailed information such as video of the residence, drawing data, family composition, income, place of work, and facial expression and tone of voice information;

[1733] means for transmitting the received data to a server using a secure protocol;

[1734] means for operating a facial expression analysis and voice tone analysis engine to recognize the emotional state of a user;

[1735] Using multiple analytical engines to perform video analysis, voice recognition, and text analysis, we can understand the detailed specifications of the home and the user's requirements.

[1736] means for aggregating the received data to generate a user-specific profile;

[1737] A means for analyzing the profile and received data using multimodal artificial intelligence to suggest the most suitable relocation destination and lifestyle;

[1738] means for transmitting the proposal results to the user;

[1739] means for displaying the recommendation results to the user;

[1740] a means for the user to input feedback on the displayed information;

[1741] A system including:

[1742] (Claim 2)

[1743] means for receiving and transmitting user-provided feedback to a server;

[1744] means for generating suggestions again based on the feedback sent to the server;

[1745] means for optimizing recommendations based on the emotional state of the user;

[1746] The system of claim 1 further comprising:

[1747] (Claim 3)

[1748] A method for using multiple analytical methods such as video analysis, audio analysis, and text analysis based on data entered by a user;

[1749] means for integrating the analysis results to generate a user profile;

[1750] 10. The system of claim 1, comprising:

[1751] "Application example 2 when combining emotion engines"

[1752] (Claim 1)

[1753] means for receiving residence-related and lifestyle data from a user;

[1754] means for analyzing the received data and generating a user-specific profile;

[1755] A means for analyzing received data and profiles using multimodal artificial intelligence to suggest optimal relocation destinations and lifestyles;

[1756] means for transmitting the proposal results to the user;

[1757] means for recognizing the emotional state of the user and adapting suggestions based on the emotional state;

[1758] A system including:

[1759] (Claim 2)

[1760] means for receiving and transmitting user-provided feedback to a server;

[1761] means for generating suggestions again based on the feedback sent to the server;

[1762] A means for analyzing the user's emotional state in real time and reflecting it in feedback and re-suggestions;

[1763] The system of claim 1 further comprising:

[1764] (Claim 3)

[1765] A method for using multiple analytical methods such as video analysis, audio analysis, and text analysis based on data entered by a user;

[1766] means for integrating the analysis results to generate a user profile;

[1767] A means of predicting user purchasing behavior based on their profile and suggesting optimal products;

[1768] 10. The system of claim 1, further comprising means for visually displaying the suggestion results on a user-worn display device. [Explanation of symbols]

[1769] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving residence-related and lifestyle data from a user; means for analyzing the received data and generating a user-specific profile; A means for analyzing received data and profiles using multimodal artificial intelligence to suggest optimal relocation destinations and lifestyles; means for transmitting the proposal results to the user; A system including:

2. means for receiving and transmitting user-provided feedback to a server; means for generating suggestions again based on the feedback sent to the server; The system of claim 1 further comprising:

3. A method for using multiple analytical methods such as video analysis, audio analysis, and text analysis based on data entered by a user; means for integrating the analysis results to generate a user profile; The system of claim 1 , comprising:

4. A means of analyzing data such as the user's lifestyle, income, family structure, and commute destination to make lifestyle suggestions; A method to rank the best relocation destinations based on a comparison with a database of vacant homes, The system of claim 1 , comprising:

Images