System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting information from a user; means for using a generation system to analyze the collected information; means for generating a profile based on the analyzed information; means for populating the generated profile into a plurality of tailored templates; means for storing the generated profile; means for managing profiles that can be published and shared; means for collecting feedback from others on published profiles; and means for analyzing the collected feedback and providing recommendations for modifying or adding profiles.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, it is important for users to create detailed and accurate profiles in many situations. However, creating a profile requires a lot of effort, and users often find the process of gathering the necessary information and compiling it tedious. As a result, profile creation and updates are delayed, putting users at a disadvantage when using matchmaking services for job hunting and marriage hunting. Having a detailed and accurate profile is also important for increasing credibility in business situations. The purpose of this invention is to provide a system that allows users to efficiently and easily create and manage detailed profiles. [Means for solving the problem]

[0005] The present invention solves the problems by providing the following means. First, a means for collecting information from users, such as survey responses, online platform data, image data, and evaluations by others is provided. Then, a means for using a generation system to analyze the collected information and generate a profile based on the analysis results is provided. Furthermore, a means for incorporating the generated profile into templates for multiple purposes and assembling a profile tailored to the purpose is provided. Users can select generated profile items and modify or add to the profile while receiving recommendations from the generation system. In addition, a management means for saving the generated profile and enabling publication and sharing settings is provided. Feedback from others is collected for the published profile, and the collected feedback is analyzed to provide further recommendations for modifying or adding to the profile. This series of means enables users to efficiently and easily create, manage, publish, and update detailed profiles.

[0006] "User" refers to any individual or company that uses the System to create, manage, publish, and share their profile.

[0007] "Means of collecting information" refers to the technologies and processes for collecting user information, such as questionnaire forms, data collection functions on online platforms, image data collection functions, and functions for collecting evaluations by others.

[0008] "Generation system" refers to a system that analyzes collected information and automatically generates each item of a profile.

[0009] "Template" refers to a profile format that has a predetermined format or structure for assembling generated profiles for a specific purpose.

[0010] "Feedback" refers to information such as ratings, comments, and suggestions provided by others in response to a publicly available profile.

[0011] "Recommendation" refers to the act of providing advice or suggestions for profile revisions or additions based on collected feedback.

[0012] "Public" refers to the act or state of making the created profile viewable by other users or third parties.

[0013] "Sharing" refers to the intentional act of sharing a generated profile with a specific person or group.

[0014] A "profile" refers to a document that lists a user's personal information, career history, preferences, strengths and weaknesses, etc.

[0015] A "category" refers to a collection of themes or items used to classify collected information. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system for streamlining profile creation, automating and supporting the series of processes of information collection, information analysis, profile creation, profile publication and sharing, and updating based on feedback. To implement this system, the following specific elements are required:

[0038] 1. Collection of information

[0039] 1.1 Survey form

[0040] The user accesses the questionnaire form and inputs information about themselves (such as likes, dislikes, past work history, strengths, weaknesses, etc.).

[0041] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0042] 1.2 Collection of social media data

[0043] Users give permission to link their social media accounts to the system.

[0044] The server uses the API to retrieve data such as follow information and posts from social media and store it in a database.

[0045] 1.3 Collection of smartphone photo data

[0046] The user allows access to the photo data in the smartphone.

[0047] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[0048] 1.4 Collecting peer reviews

[0049] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0050] The server stores the collected evaluations of others in a database.

[0051] 2. Analysis of Information

[0052] The server sends all collected data to the production system.

[0053] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.).

[0054] 3. Create a profile

[0055] The server presents the user with profile items generated based on the analysis results.

[0056] The user selects from the presented items and chooses a template according to the purpose (for example, for business, hobby, etc.).

[0057] The server combines the selected items with the template to generate a complete profile.

[0058] The user names and saves the created profile.

[0059] The server registers the saved profile in a database.

[0060] 4. Profile Publishing and Sharing

[0061] Users can select the profiles they want to make public and share and set them up.

[0062] The server publishes the selected profile to other users based on the user's settings.

[0063] Viewing users can view public profiles and provide feedback.

[0064] 5. Feedback-driven updates

[0065] The server analyzes the collected feedback and sends it to the production system.

[0066] The generator analyzes the feedback and provides recommendations for profile modifications and additions.

[0067] The user reviews the recommendations from the generation system and makes modifications or additions to the profile.

[0068] The server stores the modified or added profile in the database again and updates the public information as necessary.

[0069] Specific examples

[0070] For example, when a user creates a business profile, the following steps are taken:

[0071] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[0072] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[0073] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[0074] 4. The server sends the collected data to the generation system, which analyzes the user's work history and areas of expertise.

[0075] 5. The generation system generates items such as "5 years of experience in the IT industry," "Good at software development," and "Has project management skills."

[0076] 6. The user selects these items and puts them into a business template.

[0077] 7. The server saves the generated profile and publishes it with the user's settings.

[0078] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[0079] 9. The server sends feedback to the generation system and makes a recommendation to "Add details of successful projects" as a result of the analysis.

[0080] 10. The user can then modify their profile based on the recommendations and republish it.

[0081] As described above, the present invention provides consistent support from profile collection to publication and improvement based on feedback, thereby realizing efficient profile creation.

[0082] The processing flow will be explained below.

[0083] Step 1:

[0084] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0085] Step 2:

[0086] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0087] Step 3:

[0088] Users give permission to link their social media accounts to the system.

[0089] Step 4:

[0090] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[0091] Step 5:

[0092] The user allows access to the photo data in the smartphone.

[0093] Step 6:

[0094] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[0095] Step 7:

[0096] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0097] Step 8:

[0098] The server stores the collected evaluations and feedback from others in a database.

[0099] Step 9:

[0100] The server sends all collected data to the production system.

[0101] Step 10:

[0102] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0103] Step 11:

[0104] The server generates profile items based on the analysis results and presents them to the user.

[0105] Step 12:

[0106] The user selects the profile items they wish to adopt from the generated profile items.

[0107] Step 13:

[0108] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0109] Step 14:

[0110] The server combines the selected items with the template to generate a complete profile.

[0111] Step 15:

[0112] The user names and saves the created profile (e.g., "Business Profile").

[0113] Step 16:

[0114] The server registers the saved profile in a database.

[0115] Step 17:

[0116] Users select the profiles they wish to make public and share.

[0117] Step 18:

[0118] The server publishes the selected profile based on the settings.

[0119] Step 19:

[0120] Viewers can review public profiles and provide feedback.

[0121] Step 20:

[0122] The server stores the provided feedback in a database and sends it to the generation system.

[0123] Step 21:

[0124] The generation system analyzes the collected feedback and provides recommendations for profile modifications and additions.

[0125] Step 22:

[0126] Users can review the recommendations from the generation system and modify or add to their profile as needed.

[0127] Step 23:

[0128] The server saves the modified or added profile information back into the database and updates the public information.

[0129] Example 1

[0130] 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."

[0131] The purpose of this invention is to provide a system that allows users to easily and efficiently create, manage, and share their own profiles. In particular, the system aims to realize a system that collects information from various sources, analyzes it, automatically generates a profile, and enables users to update their profile based on feedback, thereby significantly reducing the time and effort required by the user.

[0132] 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.

[0133] In this invention, the server includes means for collecting information from users, means for saving the collected information in real time, means for acquiring data from social media, means for acquiring and analyzing photo metadata from terminals, means for collecting feedback from others in the network, means for using a generation system to analyze the collected information, means for classifying the analyzed information into different categories, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for a user to select generated profile items, means for saving the generated profile, means for managing profiles that can be set to public and shareable, means for collecting feedback from others on published profiles, and means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, thereby enabling users to efficiently create, manage, and share their own profiles without hassle.

[0134] "User" refers to any individual or business that uses the System to create, manage, and share their profile.

[0135] "Means for collecting information" refers to methods and devices for obtaining information from users, such as survey responses, social media data, photo metadata, and ratings from others.

[0136] "Real-time storage means" refers to methods and technologies for instantly recording collected information in a storage device such as a database.

[0137] "Means of obtaining data from social media" refers to methods and technologies that use social media APIs to obtain user following information, posting data, etc.

[0138] "Means for obtaining and analyzing photo metadata" refers to methods and technologies for obtaining and analyzing photo meta information (location information, tags, dates, etc.) from a user's device.

[0139] "Means for collecting feedback" refers to methods and techniques for collecting opinions and ratings from third parties, such as the user's friends and colleagues.

[0140] "Generation system" refers to the artificial intelligence model or platform that analyzes collected information and generates profiles.

[0141] "Means of categorizing information into different categories" refers to methods and techniques for separating collected data into appropriate categories (likes, dislikes, history, etc.).

[0142] "Profile generating means" refers to methods or techniques for creating a user profile based on the analyzed information.

[0143] "Template filling" refers to the method or technique for adapting the generated profile to a format appropriate for the application.

[0144] "Means for selecting profile items" refers to the interface or technology that allows a user to review and select each item in the generated profile.

[0145] "Means for storing a profile" refers to a method or technology for recording and storing the generated profile in a database or the like.

[0146] "Profile Management Means" refers to methods or technologies that allow users to manage the public or sharing settings of their stored profile.

[0147] "Feedback collection means" refers to methods and technologies for collecting opinions and ratings from other users regarding a public profile.

[0148] "Means for providing recommendations for profile modifications or additions" refers to methods or technologies for suggesting modifications or additions to a profile based on collected feedback.

[0149] The present invention relates to a system that allows users to efficiently create, manage, and share personal profiles. The system based on the present invention includes steps of collecting information, analyzing, creating profiles, publishing and sharing, and collecting and updating feedback.

[0150] Hardware and software used

[0151] To implement this system, the following hardware and software are required.

[0152] Survey form: A web form built using HTML and JavaScript.

[0153] Database: A relational database such as MySQL or PostgreSQL.

[0154] Social Media API: Uses Twitter API and Facebook Graph API to obtain social media data.

[0155] Smartphone data analysis: Uses Android SDK and iOS SDK.

[0156] Generative Systems: Generative AI models such as OpenAI GPT-4.

[0157] Collection of information

[0158] First, the user accesses the questionnaire form and enters their information, such as "likes," "dislikes," and "past work history." The entered information is sent to the server in real time and stored in a database.

[0159] Next, the user gives permission to link their social media account to the system, and the server uses an API to retrieve follow information and post data from the social media and store it in a database.

[0160] Furthermore, the user allows the system to access the photo data stored on the smartphone. The system analyzes the metadata of the photos (location, tags, date, etc.) to identify their contents. The analyzed data is sent to a server and stored in a database.

[0161] Finally, the server sends feedback requests to friends and colleagues in the user's network and stores the collected ratings of others in a database.

[0162] Analysis of information

[0163] The server sends all collected data to a generation system (e.g., OpenAI GPT-4), which analyzes survey responses, social media data, photo data, and peer ratings and categorizes each piece of information into appropriate categories (likes, dislikes, career history, etc.).

[0164] Generate a profile

[0165] The profile items generated based on the analysis results are presented to the user. The user can choose from these items and select a template, such as for business or leisure. The server combines the selected items with the template to generate a complete profile. The generated profile is stored in a database, and the user can update and manage it as needed.

[0166] Profile Publishing / Sharing

[0167] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the user's settings. Third parties can access the public profile and provide feedback.

[0168] Updates based on feedback

[0169] The server analyzes the collected feedback and sends it to a generation system (e.g., OpenAI GPT-4). The generation system generates recommendations for profile modifications and additions based on the feedback. The user can review these recommendations and modify or add to their profile. This ensures that the user's profile is always up-to-date and optimal.

[0170] Specific examples

[0171] For example, if a user is creating a profile for business use, they might use the following prompt:

[0172] "Please provide us with information about your work history in the IT industry and your area of ​​expertise in software development. We will generate a profile based on this information."

[0173] The above is an embodiment of the system of the present invention.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The user accesses the questionnaire form and enters information such as likes, dislikes, past work history, strengths, and weaknesses. After entering the information, they press the submit button. This input information is sent to the server in JSON format. The server parses the received JSON data and saves it in a MySQL or PostgreSQL database. Items such as "Favorite food: Sushi" and "Strengths: Programming" are saved in the database.

[0177] Step 2:

[0178] Users grant permission to link their social media accounts to the system. The server obtains a token through OAuth authentication and uses this token to call the Twitter API or Facebook Graph API. The follow list and post data obtained from the API are then stored in a database after business-related keywords are extracted. For example, information such as "there are many programmers on the follow list" or "there are many programming-related articles in the posts" is stored in the database.

[0179] Step 3:

[0180] The user allows access to the photo data stored on their smartphone. The device uses the Android SDK or iOS SDK to analyze the photo metadata (location, tags, date, etc.). The analysis results are sent to the server and stored in a database. For example, the data might be saved as "Travel Photos, Hawaii, March 2022."

[0181] Step 4:

[0182] The server then sends feedback requests to friends and colleagues within the user's network. These people then respond with their ratings via a web form. The collected feedback is then stored in a database. For example, a rating such as "Mr. / Ms. X has excellent communication skills" may be recorded.

[0183] Step 5:

[0184] The server sends all collected data to a generation system (e.g., OpenAI GPT-4). The generation system analyzes the data and classifies the information into different categories (likes, dislikes, work history, etc.). The results of this analysis are passed to the server and stored in a database. For example, an item such as "sushi in the likes category and high places in the dislikes category" may be registered.

[0185] Step 6:

[0186] The server generates profile items based on the analysis results and presents them to the user via a web interface. The user selects from the displayed items those that best suit their purpose (business, hobby, etc.). For example, items such as "5 years of experience in the IT industry" and "good at software development" can be selected.

[0187] Step 7:

[0188] The server combines the selected items with the template to generate a complete profile, which is then stored in a database. The user can then name the profile (e.g., "Business Profile") and save it.

[0189] Step 8:

[0190] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the settings. Viewers can access the published profile and provide feedback.

[0191] Step 9:

[0192] The server analyzes the collected feedback and sends it to a generation system, which generates recommendations for profile modifications and additions based on the feedback, such as "add more detailed project information."

[0193] Step 10:

[0194] Users can check the recommendations from the generation system and modify or add to their profile. The server saves the modified or added profile back into the database and updates the public information. This allows profiles to be kept up to date with the latest information.

[0195] (Application example 1)

[0196] 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."

[0197] Conventional customer profile generation systems have the problem of being unable to provide customers with a fully personalized shopping experience. In particular, in brick-and-mortar stores, there is no way to effectively utilize information based on online data in real time, making it difficult to improve customer satisfaction. Additionally, the process of collecting feedback and updating profiles based on it is often done manually, which is time-consuming and labor-intensive, making it inefficient.

[0198] 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.

[0199] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, and means for generating a profile based on the analyzed information. This allows for personalized product recommendations using customer data. Furthermore, adding means for presenting information based on the recommendations on a visual display can enhance the customer's shopping experience.

[0200] "User" means any person or entity that uses the System.

[0201] "Information collection means" refers to a method or device for obtaining necessary data from users.

[0202] A "generation system" is a combination of software or hardware that analyzes collected information and generates a profile.

[0203] "Template" refers to a predefined form or format into which a generated profile may be inserted.

[0204] The "publication and sharing setting means" refers to a setting function for enabling the created profile to be shared with others.

[0205] "Public Profile" refers to profile information that is made available for viewing by others based on a user's settings.

[0206] "Feedback collection means" refers to a method or device for collecting opinions and ratings from others regarding a published profile.

[0207] "Individualized product proposal means" refers to a function that proposes the most suitable products and services to individual customers based on collected and analyzed customer data.

[0208] "Visual display means" refers to a device or function for visually presenting information using a smart device or the like.

[0209] System Configuration

[0210] This invention provides a system that collects and analyzes customer information, creates profiles, and makes personalized product recommendations in physical stores. This system is composed of a server, terminals, and users.

[0211] 1. Collection of information

[0212] The server collects information from users. Specifically, users access a survey form and enter and link their preferences, past purchase history, social media accounts, etc. The front end of the survey form is built using React Native, and data is stored in Firebase. Social media data is obtained using the Twitter API and Instagram API.

[0213] 2. Analysis of Information

[0214] The server sends the collected information to a generation system, which uses Python and TensorFlow to analyze the user data and identify patterns of user preferences.

[0215] 3. Create a profile

[0216] The server generates profile items based on the analysis results and presents them to the user. These profiles are generated through a system built with Django and the database is PostgreSQL. The user selects from the presented items and inputs them into a template appropriate for the purpose.

[0217] 4. Profile Publishing and Sharing

[0218] The generated profile is published to smart devices (smartphones and smart glasses). The server implements the public API with Flask, and ARKit or ARCore is used for the smart glasses as a visual display.

[0219] 5. Feedback-driven updates

[0220] Feedback from others is collected on your public profile and sent to a server, where it is stored in Firebase and fed back to the analytics engine for new analysis, ensuring constantly updated recommendations.

[0221] Typical use cases

[0222] When a user visits a supermarket, new product suggestions and special sale information will be displayed on the smart glasses based on the user's pre-registered profile data, and this information will be updated in real time to improve the user's shopping experience.

[0223] Prompt Sentence Examples

[0224] "Build an application that will make optimal product recommendations to customers in a supermarket based on their profile data. This application must include the following elements: data analysis using Python and TensorFlow, a front-end built with React Native, an API for collecting social media data (Twitter, Instagram), information displayed on smart glasses, and a system configuration that can update recommendations based on feedback."

[0225] This invention makes it possible to provide customers with a personalized shopping experience and improve customer satisfaction in physical stores.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The server collects information from users. Specifically, users access a survey form and input and link their preferences, past purchase history, social media accounts, etc. The survey form is built using React Native, and the collected data is saved in Firebase in real time. The input is the survey content and social media link information, and the output is a structured dataset.

[0229] Step 2:

[0230] The server sends the collected information to the generation system. Specifically, it acquires social media data using the Twitter API and Instagram API, and combines this with data from the survey and image data (metadata from photos stored on smartphones). The input is a structured dataset and the acquired social media data, and the output is an integrated dataset.

[0231] Step 3:

[0232] The server uses a generative system (Python and TensorFlow) to analyze the collected information and identify user preference patterns. Natural language processing (NLP) and clustering algorithms are used to classify the data into categories such as "likes," "dislikes," "past work history," and "strengths." The input is the integrated dataset, and the output is a data object containing the analyzed results.

[0233] Step 4:

[0234] The server generates a profile based on the analysis results and feeds it into templates for multiple purposes. Using a generation system (Django), it generates and presents profile items that the user can select. The input is the analysis data object, and the output is the profile items presented to the user.

[0235] Step 5:

[0236] The user selects the generated profile items and then chooses the most suitable template. The server generates a complete profile based on this selection and stores it in PostgreSQL. The input is the user's selections and the output is the stored profile data.

[0237] Step 6:

[0238] The server manages profiles that can be publicly and shared, and displays them on smart devices as needed. The public API is implemented in Flask, and information is displayed on smart glasses or smartphones through a visual display (ARKit or ARCore). The input is the saved profile data and the user's public settings, and the output is the information displayed on the visual display.

[0239] Step 7:

[0240] Others provide feedback on public profiles, which is stored by the server in Firebase. The input is feedback information from others, and the output is the stored feedback data.

[0241] Step 8:

[0242] The server analyzes the collected feedback and provides recommendations for profile modifications and additions. It uses a generation system (Python) to perform the analysis and generate suggestions based on the feedback. The input is the stored feedback data, and the output is the analysis results and recommendations.

[0243] Step 9:

[0244] The user confirms the recommended modifications and additions and updates their profile. The server saves the modified / added profile back to PostgreSQL and updates the public content as necessary. The input is the recommended items and the user's modifications, and the output is the updated profile data.

[0245] 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.

[0246] The present invention is a system for streamlining profile creation, supporting a series of processes including information collection, information analysis, profile creation, profile publication and sharing, updating based on feedback, and emotion analysis using an emotion engine. Specifically, to implement the present invention, the following elements are required:

[0247] 1. Collection of information

[0248] 1.1 Survey form

[0249] The user accesses the questionnaire form and inputs their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0250] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0251] 1.2 Collection of social media data

[0252] Users give permission to link their social media accounts to the system.

[0253] The server uses the API to obtain follow information and post data from social media and store it in a database.

[0254] 1.3 Collection of smartphone photo data

[0255] The user allows access to the photo data in the smartphone.

[0256] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[0257] 1.4 Collecting peer reviews

[0258] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0259] The server stores the collected evaluations and feedback from others in a database.

[0260] 2. Analysis of Information

[0261] The server sends all collected data to the production system.

[0262] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0263] 3. Adding an Emotion Engine

[0264] The server sends the collected data to the emotion engine.

[0265] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[0266] The emotion engine sends the analysis results to the generation system.

[0267] 4. Create a profile

[0268] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0269] The user selects the profile items they wish to adopt from the generated profile items.

[0270] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0271] The server combines the selected items with the template to generate a complete profile.

[0272] The user names and saves the created profile (e.g., "Business Profile").

[0273] The server registers the saved profile in a database.

[0274] 5. Profile Publishing and Sharing

[0275] Users can select the profiles they want to make public and share and set them up.

[0276] The server publishes the selected profile to other users based on the user's settings.

[0277] Viewing users can view public profiles and provide feedback.

[0278] 6. Feedback-driven updates

[0279] The server sends the collected feedback to the generation system and the emotion engine.

[0280] The generative system and sentiment engine analyze the feedback and provide recommendations for profile modifications and additions.

[0281] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[0282] The server saves the modified or added profile information back into the database and updates the public information.

[0283] Specific examples

[0284] For example, when a user creates a business profile, the following steps are taken:

[0285] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[0286] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[0287] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[0288] 4. The server sends the collected data to the generation system and emotion engine to analyze the user's work history, areas of expertise, and emotional state.

[0289] 5. The generation system and emotion engine generate items that reflect “5 years of experience in the IT industry,” “good at software development,” “project management skills,” and “positive emotions upon project success.”

[0290] 6. The user selects these items and puts them into a business template.

[0291] 7. The server saves the generated profile and publishes it with the user's settings.

[0292] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[0293] 9. The server sends the feedback to the generation system and emotion engine, and the analysis results in a recommendation to "Add details of successful projects."

[0294] 10. The user can then modify their profile based on the recommendations and republish it.

[0295] As described above, the present invention provides integrated support for all processes of information collection, analysis, generation, publication, feedback, and sentiment analysis, thereby realizing efficient profile creation.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0299] Step 2:

[0300] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0301] Step 3:

[0302] Users give permission to link their social media accounts to the system.

[0303] Step 4:

[0304] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[0305] Step 5:

[0306] The user allows access to the photo data in the smartphone.

[0307] Step 6:

[0308] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[0309] Step 7:

[0310] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0311] Step 8:

[0312] The server stores the collected evaluations and feedback from others in a database.

[0313] Step 9:

[0314] The server sends all collected data to the production system.

[0315] Step 10:

[0316] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0317] Step 11:

[0318] The server sends the collected data to the emotion engine.

[0319] Step 12:

[0320] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[0321] Step 13:

[0322] The emotion engine sends the results of the emotion analysis to the generation system.

[0323] Step 14:

[0324] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0325] Step 15:

[0326] The user selects the profile items they wish to adopt from the generated profile items.

[0327] Step 16:

[0328] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0329] Step 17:

[0330] The server combines the selected items with the template to generate a complete profile.

[0331] Step 18:

[0332] The user names and saves the created profile (e.g., "Business Profile").

[0333] Step 19:

[0334] The server registers the saved profile in a database.

[0335] Step 20:

[0336] Users select the profiles they wish to make public and share.

[0337] Step 21:

[0338] The server publishes the selected profile based on the settings.

[0339] Step 22:

[0340] Viewers can review public profiles and provide feedback.

[0341] Step 23:

[0342] The server stores the provided feedback in a database and sends it to the generation system and emotion engine.

[0343] Step 24:

[0344] The generative system and sentiment engine analyze the collected feedback and provide recommendations for profile modifications and additions.

[0345] Step 25:

[0346] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[0347] Step 26:

[0348] The server saves the modified or added profile information back into the database and updates the public information.

[0349] Example 2

[0350] 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."

[0351] In modern society, users increasingly need to efficiently create and manage their own profiles from a wide variety of information sources. However, existing systems require complex processes for information collection, analysis, and profile creation, and do not analyze users' emotions. This means that creating personalized profiles requires a great deal of time and effort. Furthermore, profile updates based on user feedback are not automated, resulting in a poor user experience. Therefore, there is a need for a more efficient profile creation system that includes emotion analysis.

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

[0353] In this invention, the server includes means for collecting information from users, means for using a generation system that analyzes the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, means for performing emotion analysis, and an emotion engine that analyzes emotional states based on the collected information. This allows the process from information collection to profile generation, publication, feedback, and emotion analysis to be effectively executed as a series of processes, enabling users to efficiently create and manage personalized and highly accurate profiles.

[0354] "User" means an individual or organization that uses the system to create and manage a profile.

[0355] "Information" refers to data collected from users, including survey responses, online platform data, image data, and evaluations by others.

[0356] A "generation system" is a combination of software or hardware that analyzes collected information, classifies it into different categories, and generates a profile.

[0357] A "template" is a predetermined format for applying the generated profile depending on the purpose, and includes types such as for business and hobby.

[0358] "Feedback" refers to opinions and ratings collected from others regarding a publicly available profile.

[0359] An "emotion engine" is a software or hardware system that analyzes a user's emotional state based on collected information.

[0360] A "profile" is a collection of data that shows the overview, history, and characteristics of an individual or organization, generated based on the user's information.

[0361] "Publication and sharing settings" refers to a setting and management function for making the created profile viewable by others.

[0362] "Modifying or adding" refers to updating an existing profile or adding new items based on collected feedback and sentiment analysis results.

[0363] MODE FOR CARRYING OUT THE INVENTION

[0364] This invention is a system that allows users to efficiently create and manage profiles. The embodiment of the invention includes the following series of processes: information collection, information analysis, profile creation, profile publication and sharing, update based on feedback, and emotion analysis using an emotion engine.

[0365] First, the user accesses the questionnaire form and enters their own information (likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The database typically uses an SQL or NoSQL database management system.

[0366] Next, the user gives permission to link their social media account. The server uses the API to retrieve follow information and post data from the social media and stores it in a database. For example, by using the Twitter API, it is possible to retrieve a user's tweets and follower list.

[0367] Furthermore, the user allows the system to access the photo data stored on the smartphone. The device analyzes the metadata of the photos (location information, tags, dates, etc.) and sends the contents to the server for storage. For example, the location information and dates contained in the photos stored on the device can be analyzed and saved as a record of a specific event or trip.

[0368] The server also sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected feedback and ratings from others in a database. This feedback includes the user's evaluation of the project and the characteristics of the project as seen by others.

[0369] The server sends all collected data to a generation system, which analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.). For example, the generation system uses natural language processing (NLP) techniques to analyze text data and extract keywords.

[0370] The server sends the collected data to the emotion engine, which analyzes the user's emotions from survey responses, social media data, photo data, and other people's ratings. For example, if there are many positive posts, it is analyzed as "positive emotion." The emotion engine then sends the analysis results to the generation system.

[0371] Next, the server generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user can select the profile items they want to use from the generated profile items and also select a profile template that suits their purpose. The server combines the selected items and template to generate a complete profile. The user can name and save the generated profile (e.g., "Business Profile"), and the server registers the saved profile in its database.

[0372] Users select the profile they want to make public and share and configure it accordingly. The server then publishes the selected profile to other users based on the user's settings. Other users can view the published profile and provide feedback.

[0373] The server sends the collected feedback to the generation system and emotion engine for re-analysis. This generates recommendations for profile modifications and additions. The user checks the recommendations and modifies or adds to their profile as necessary. The server then saves the modified or added profile back to the database and updates the public information.

[0374] Additionally, a generative AI model is used to generate profiles, so specific profile items can be generated by entering prompts such as:

[0375] "Generate a profile for a professional with 5 years of experience in the IT industry."

[0376] "What are the characteristics of an engineer who is good at software development?"

[0377] This allows for the creation of personalized and highly accurate profiles of users based on the information and feedback collected.

[0378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0379] Step 1:

[0380] The user accesses a questionnaire form provided by the system and enters their own information (likes, dislikes, past work history, strengths, weaknesses).

[0381] Input: Survey response data entered by the user.

[0382] Data processing: The server receives the data entered by the user in the form in real time and stores it in a database.

[0383] Output: User information stored in the database.

[0384] Specific behavior: When the submit button on the form is pressed, the server receives a POST request and stores the data in the backend database.

[0385] Step 2:

[0386] Users give permission to link their social media accounts to the system.

[0387] Input: User's social media account link information.

[0388] Data processing: The server uses the API to obtain follow information and post data from social media.

[0389] Output: Social media data stored in a database.

[0390] Specific operation: The server-side backend calls the API endpoints of each social media platform, analyzes the responses, and stores them in a database.

[0391] Step 3:

[0392] The user allows access to the photo data in the smartphone.

[0393] Input: Permission to access photo data on the user's smartphone.

[0394] Data processing: The device analyzes the photo's metadata (location, tags, date, etc.).

[0395] Output: Photo metadata sent and stored on the server.

[0396] Specific operation: The system crawls the photo data stored on the smartphone, extracts metadata, and centralizes it. The device then sends the data to the server.

[0397] Step 4:

[0398] The server sends feedback requests to others in the user's network.

[0399] Input: The user's contact list.

[0400] Data processing: Feedback requests are sent.

[0401] Output: The collected feedback data is stored in a database.

[0402] What happens: The server looks up the user's contact list and sends them an email or notification requesting feedback. Friends and colleagues provide feedback, and the data is stored on the server.

[0403] Step 5:

[0404] The server sends all collected data to the production system.

[0405] Input: Survey responses stored in a database, social media data, photo metadata, and feedback from others.

[0406] Data processing: Send various data to the generation system in bulk.

[0407] Output: Data sent to the generating system.

[0408] Specific operation: The server extracts the necessary data from the database and sends it to the generation system via an API call.

[0409] Step 6:

[0410] The generation system analyzes the data and classifies it into different categories.

[0411] Input: Data sent to the generating system (survey responses, social media data, photo metadata, feedback from others).

[0412] Data processing: Analyze text data using natural language processing (NLP) techniques and extract keywords.

[0413] Output: Categorized data.

[0414] Specific operation: The generation system analyzes the data it receives and classifies it into categories such as "likes," "dislikes," and "career."

[0415] Step 7:

[0416] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0417] Input: Parsed data and sentiment analysis results.

[0418] Data processing: The generated profile items are presented to the user.

[0419] Output: The profile items presented to the user.

[0420] Specific operation: The server sends the data received from the generation system to the front end and presents it to the user.

[0421] Step 8:

[0422] The user selects the profile items he or she wishes to adopt from the generated profile items.

[0423] Input: The profile items presented to the user.

[0424] Data processing: The user selects the profile items to adopt.

[0425] Output: The selected profile items.

[0426] Specific operation: The user selects the profile items they wish to adopt from the displayed profile items.

[0427] Step 9:

[0428] The user selects a profile template according to the purpose.

[0429] Input: The purpose of the profile (for business, hobby, etc.).

[0430] Data processing: The appropriate template is applied.

[0431] Output: Profile items with applied template.

[0432] Specific operation: The user selects a predetermined template (for example, a business template), and the server aggregates the data based on it.

[0433] Step 10:

[0434] The server combines the selected items with the template to generate a complete profile.

[0435] Input: Selected profile items and template.

[0436] Data processing: merging items and templates.

[0437] Output: The complete profile generated.

[0438] Specific Actions: The server applies the selected items to the template and synthesizes the final profile.

[0439] Step 11:

[0440] The user names and saves the created profile.

[0441] Input: The generated profile.

[0442] Data processing: Name your profile.

[0443] Output: Named profiles.

[0444] Specific behavior: The user names the profile "Business Profile" and clicks the save button.

[0445] Step 12:

[0446] The server registers the saved profile in a database.

[0447] Input: A named profile.

[0448] Data processing: Registration of profile data in database.

[0449] Output: The profile registered in the database.

[0450] Specific operation: The server triggers a save action to save the profile data to the database.

[0451] Step 13:

[0452] Users can select the profiles they want to make public and share and set them up.

[0453] Input: A profile registered in the database.

[0454] Data processing: Applying public / sharing settings.

[0455] Output: Profile with public / shared settings applied.

[0456] Specific operation: The user selects "Business Profile" and sets the visibility.

[0457] Step 14:

[0458] The server publishes the selected profile to other users based on the user's settings.

[0459] Input: Profile with visibility settings applied.

[0460] Data processing: Generation and distribution of public URLs.

[0461] Output: Your profile as it appears to other users.

[0462] Specific behavior: The server generates a viewable URL based on the publishing settings and provides access to other users.

[0463] Step 15:

[0464] The server collects feedback from others on the published profile.

[0465] Input: Feedback from others.

[0466] Data processing: collection and storage of feedback data.

[0467] Output: Feedback stored in a database.

[0468] Specific Actions: Other users view your public profile and provide feedback, such as "I'd like to know more about your specific projects."

[0469] Step 16:

[0470] The server sends the collected feedback to the generation system and emotion engine for re-analysis.

[0471] Input: Feedback stored in the database.

[0472] Data processing: Analysis of feedback with generative systems and emotion engines.

[0473] Output: Recommendations for corrections and additions based on the analysis results.

[0474] Specific operation: The server extracts the feedback data and sends it to the generation system and emotion engine.

[0475] Step 17:

[0476] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[0477] Input: Suggested corrections or additions.

[0478] Data processing: Modifying your profile or applying additional details.

[0479] Output: The updated profile.

[0480] Specific operation: The user checks the recommendations and makes corrections on the profile editing screen.

[0481] Step 18:

[0482] The server saves the modified or added profile back into the database and updates the public information.

[0483] Input: Updated profile.

[0484] Data processing: Resave your profile and update public information.

[0485] Output: Updated public profile.

[0486] Specific operation: The server saves the updated profile data to the database and regenerates the public URL.

[0487] (Application example 2)

[0488] 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."

[0489] Currently, many users use online shopping, but there is a lack of efficient methods for recommending products that match their tastes and preferences. As a result, users often spend a lot of time trying to find products that suit them. Furthermore, online shopping sites lack the means to accurately grasp users' tastes and preferences, making it difficult to make effective product recommendations. To address this issue, a system is needed that can efficiently collect and analyze user information and provide personalized product recommendations.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0491] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, and means for recommending a variety of products to users in a personalized manner, thereby enabling efficient recommendation of optimal products based on the user's detailed preferences and past behavioral data.

[0492] "User" means an individual who uses the System to provide information and utilizes the generated profile.

[0493] "Means of collection" refers to means for collecting questionnaire forms, data from online platforms, image data, and evaluations by others.

[0494] "Generation system" refers to a system that has the ability to analyze collected information and generate a profile.

[0495] "Analyzed information" refers to data that has been analyzed and categorized from collected raw data.

[0496] "Profile" refers to data including descriptive text and recommendation information generated based on user information.

[0497] "Template" refers to a format for formatting the generated profile as text.

[0498] "Means for generating" refers to means for automatically generating profile items based on the analyzed information.

[0499] "Means for storing" refers to means for storing the generated profile in a database.

[0500] "Means for management" refers to means for setting public and sharing settings for the created profile.

[0501] "Means of collecting feedback from others" refers to means of collecting opinions and ratings from third parties regarding a public profile.

[0502] "Means for Analyzing Feedback" means means for analyzing collected feedback and providing recommendations regarding profile modifications and additions.

[0503] "Personalized recommendation methods" refer to methods that recommend the most suitable products based on the user's information and preferences.

[0504] To implement the present invention, the elements of the server, the terminal, and the user must work in cooperation with each other.

[0505] First, the user accesses a questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The user also gives permission to link their online platform account to the system. This link allows the server to use an API to obtain follow information and post data from the online platform and store it in the database. The user also allows the server to access the photo data on their smartphone, and the device analyzes the photo metadata (location information, tags, date, etc.) and sends the content to the server. The server then sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected ratings and feedback from others in the database.

[0506] Next, the server sends all collected data to the generation system. The generation system analyzes the survey responses, online platform data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career, etc.). The server then sends the collected data to the emotion engine. The emotion engine analyzes the user's emotions from the survey responses, online platform data, photo data, other people's ratings, etc. and sends the analysis results to the generation system.

[0507] The server then generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user selects the profile items they want to use from the generated profile items and selects a profile template appropriate for their purpose (e.g., business, hobby, etc.). The server combines the selected items and template to generate a complete profile. The user names and saves the generated profile (e.g., "Business Profile"), and the server registers the saved profile in a database.

[0508] Publication and sharing are also important elements. Users select the profiles they want to publish and share and configure them. The server publishes the selected profiles to other users based on the user's settings. Viewing users view the published profiles and provide feedback. The server sends the collected feedback to the generation system and emotion engine for analysis. As a result, it provides recommendations for modifying or adding items to the profile. Users check the recommendations from the generation system and emotion engine and modify or add to their profile as necessary. The server saves the modified or added profiles back into the database and updates the published content.

[0509] This system realizes the function of recommending a variety of products to users in a personalized manner. As a specific example, it can efficiently recommend optimal products based on the user's detailed preferences and past behavioral data. For example, when a user logs in to a shopping site and creates their own profile, products related to the user's tastes and hobbies are automatically recommended based on that profile.

[0510] As a concrete example, a detailed profile can be generated by inputting the following prompt sentence into the generative AI model:

[0511] User Information:

[0512] Name: User A

[0513] Email: user@example.com

[0514] Date of Birth: 1980-05-15

[0515] Social Media Data:

[0516] Topics: Technology, Gadgets, Healthcare

[0517] Photo data:

[0518] Date: 2023-09-10

[0519] Location: City, Country

[0520] Tags: Travel, Tourism

[0521] Others' ratings:

[0522] Email: friend@example.com Feedback: I love to travel and have an inquisitive personality.

[0523] Fixes based on feedback:

[0524] "Based on interest data from users, we recommend products, especially those related to technology and gadgets, as well as travel accessories. We provide a personalized shopping experience with constructive feedback sharing the latest information on travel and gadgets."

[0525] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0526] Step 1:

[0527] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The entered information is sent to the server and stored in a database. This is to collect the basic data needed to generate a profile.

[0528] Step 2:

[0529] The user gives permission to link their online platform account (e.g., social media) to the system. The server uses the API to obtain follow information and post data from the online platform and stores it in a database. This allows data reflecting the user's interests to be collected.

[0530] Step 3:

[0531] The user allows access to the photo data stored on their smartphone. The device analyzes the photo's metadata (location, tags, date, etc.) and sends the information to the server. The server stores the information in a database. The location, date, tags, etc. indicate the characteristics of the photo and are used to identify the user's area of ​​activity and areas of interest.

[0532] Step 4:

[0533] The server sends feedback requests to others in the user's network (friends, colleagues, etc.) via email or other means. When others provide feedback, it is collected by the server and stored in a database. The objective evaluations of others complement the user's profile from multiple angles.

[0534] Step 5:

[0535] The server sends all collected data to the generation system, which analyzes survey responses, online platform data, photo data, and other users' ratings and categorizes the information into different categories (e.g., likes, dislikes, career history, etc.). The categorized data is used to generate profile items.

[0536] Step 6:

[0537] The server sends the collected data to the emotion engine, which analyzes the user's emotions based on survey responses, online platform data, photo data, and other users' ratings. The analysis results are sent to the generation system, which generates profile items that reflect the user's emotional state.

[0538] Step 7:

[0539] The server generates profile items based on the analysis results and sentiment analysis results and presents them to the user. The user selects the profile items they want to adopt from the generated profile items. The selected information is sent to the server.

[0540] Step 8:

[0541] The user selects a profile template based on their intended use (e.g., business, hobby, etc.), and the server combines the selected items with the template to generate a complete profile, which is then stored and managed in a database.

[0542] Step 9:

[0543] Users select the profiles they want to make public and share, and configure them accordingly. The server then publishes the selected profiles to other users based on the user's settings, allowing browsing users to view the published profiles.

[0544] Step 10:

[0545] The server collects feedback provided by browsing users and stores it in a database. The collected feedback is sent to the generation system and emotion engine for analysis. Based on the analysis results, recommendations for profile modifications and additions are provided to the user.

[0546] Step 11:

[0547] Users can review the recommendations from the generation system and emotion engine, and modify or add to their profile as needed. The modified or added profile is saved back in the database, and the public information is updated.

[0548] Step 12:

[0549] Finally, the generated profile data is used to recommend various products to users in a personalized manner. The server efficiently recommends optimal products based on the user's detailed preferences and past behavioral data. As a result, users can enjoy an individually customized shopping experience.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] [Second embodiment]

[0554] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0565] 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."

[0566] This invention is a system for streamlining profile creation, automating and supporting the series of processes of information collection, information analysis, profile creation, profile publication and sharing, and updating based on feedback. To implement this system, the following specific elements are required:

[0567] 1. Collection of information

[0568] 1.1 Survey form

[0569] The user accesses the questionnaire form and inputs information about themselves (such as likes, dislikes, past work history, strengths, weaknesses, etc.).

[0570] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0571] 1.2 Collection of social media data

[0572] Users give permission to link their social media accounts to the system.

[0573] The server uses the API to retrieve data such as follow information and posts from social media and store it in a database.

[0574] 1.3 Collection of smartphone photo data

[0575] The user allows access to the photo data in the smartphone.

[0576] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[0577] 1.4 Collecting peer reviews

[0578] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0579] The server stores the collected evaluations of others in a database.

[0580] 2. Analysis of Information

[0581] The server sends all collected data to the production system.

[0582] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.).

[0583] 3. Create a profile

[0584] The server presents the user with profile items generated based on the analysis results.

[0585] The user selects from the presented items and chooses a template according to the purpose (for example, for business, hobby, etc.).

[0586] The server combines the selected items with the template to generate a complete profile.

[0587] The user names and saves the created profile.

[0588] The server registers the saved profile in a database.

[0589] 4. Profile Publishing and Sharing

[0590] Users can select the profiles they want to make public and share and set them up.

[0591] The server publishes the selected profile to other users based on the user's settings.

[0592] Viewing users can view public profiles and provide feedback.

[0593] 5. Feedback-driven updates

[0594] The server analyzes the collected feedback and sends it to the production system.

[0595] The generator analyzes the feedback and provides recommendations for profile modifications and additions.

[0596] The user reviews the recommendations from the generation system and makes modifications or additions to the profile.

[0597] The server stores the modified or added profile in the database again and updates the public information as necessary.

[0598] Specific examples

[0599] For example, when a user creates a business profile, the following steps are taken:

[0600] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[0601] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[0602] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[0603] 4. The server sends the collected data to the generation system, which analyzes the user's work history and areas of expertise.

[0604] 5. The generation system generates items such as "5 years of experience in the IT industry," "Good at software development," and "Has project management skills."

[0605] 6. The user selects these items and puts them into a business template.

[0606] 7. The server saves the generated profile and publishes it with the user's settings.

[0607] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[0608] 9. The server sends feedback to the generation system and makes a recommendation to "Add details of successful projects" as a result of the analysis.

[0609] 10. The user can then modify their profile based on the recommendations and republish it.

[0610] As described above, the present invention provides consistent support from profile collection to publication and improvement based on feedback, thereby realizing efficient profile creation.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0614] Step 2:

[0615] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0616] Step 3:

[0617] Users give permission to link their social media accounts to the system.

[0618] Step 4:

[0619] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[0620] Step 5:

[0621] The user allows access to the photo data in the smartphone.

[0622] Step 6:

[0623] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[0624] Step 7:

[0625] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0626] Step 8:

[0627] The server stores the collected evaluations and feedback from others in a database.

[0628] Step 9:

[0629] The server sends all collected data to the production system.

[0630] Step 10:

[0631] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0632] Step 11:

[0633] The server generates profile items based on the analysis results and presents them to the user.

[0634] Step 12:

[0635] The user selects the profile items they wish to adopt from the generated profile items.

[0636] Step 13:

[0637] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0638] Step 14:

[0639] The server combines the selected items with the template to generate a complete profile.

[0640] Step 15:

[0641] The user names and saves the created profile (e.g., "Business Profile").

[0642] Step 16:

[0643] The server registers the saved profile in a database.

[0644] Step 17:

[0645] Users select the profiles they wish to make public and share.

[0646] Step 18:

[0647] The server publishes the selected profile based on the settings.

[0648] Step 19:

[0649] Viewers can review public profiles and provide feedback.

[0650] Step 20:

[0651] The server stores the provided feedback in a database and sends it to the generation system.

[0652] Step 21:

[0653] The generation system analyzes the collected feedback and provides recommendations for profile modifications and additions.

[0654] Step 22:

[0655] Users can review the recommendations from the generation system and modify or add to their profile as needed.

[0656] Step 23:

[0657] The server saves the modified or added profile information back into the database and updates the public information.

[0658] Example 1

[0659] 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."

[0660] The purpose of this invention is to provide a system that allows users to easily and efficiently create, manage, and share their own profiles. In particular, the system aims to realize a system that collects information from various sources, analyzes it, automatically generates a profile, and enables users to update their profile based on feedback, thereby significantly reducing the time and effort required by the user.

[0661] 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.

[0662] In this invention, the server includes means for collecting information from users, means for saving the collected information in real time, means for acquiring data from social media, means for acquiring and analyzing photo metadata from terminals, means for collecting feedback from others in the network, means for using a generation system to analyze the collected information, means for classifying the analyzed information into different categories, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for a user to select generated profile items, means for saving the generated profile, means for managing profiles that can be set to public and shareable, means for collecting feedback from others on published profiles, and means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, thereby enabling users to efficiently create, manage, and share their own profiles without hassle.

[0663] "User" refers to any individual or business that uses the System to create, manage, and share their profile.

[0664] "Means for collecting information" refers to methods and devices for obtaining information from users, such as survey responses, social media data, photo metadata, and ratings from others.

[0665] "Real-time storage means" refers to methods and technologies for instantly recording collected information in a storage device such as a database.

[0666] "Means of obtaining data from social media" refers to methods and technologies that use social media APIs to obtain user following information, posting data, etc.

[0667] "Means for obtaining and analyzing photo metadata" refers to methods and technologies for obtaining and analyzing photo meta information (location information, tags, dates, etc.) from a user's device.

[0668] "Means for collecting feedback" refers to methods and techniques for collecting opinions and ratings from third parties, such as the user's friends and colleagues.

[0669] "Generation system" refers to the artificial intelligence model or platform that analyzes collected information and generates profiles.

[0670] "Means of categorizing information into different categories" refers to methods and techniques for separating collected data into appropriate categories (likes, dislikes, history, etc.).

[0671] "Profile generating means" refers to methods or techniques for creating a user profile based on the analyzed information.

[0672] "Template filling" refers to the method or technique for adapting the generated profile to a format appropriate for the application.

[0673] "Means for selecting profile items" refers to the interface or technology that allows a user to review and select each item in the generated profile.

[0674] "Means for storing a profile" refers to a method or technology for recording and storing the generated profile in a database or the like.

[0675] "Profile Management Means" refers to methods or technologies that allow users to manage the public or sharing settings of their stored profile.

[0676] "Feedback collection means" refers to methods and technologies for collecting opinions and ratings from other users regarding a public profile.

[0677] "Means for providing recommendations for profile modifications or additions" refers to methods or technologies for suggesting modifications or additions to a profile based on collected feedback.

[0678] The present invention relates to a system that allows users to efficiently create, manage, and share personal profiles. The system based on the present invention includes steps of collecting information, analyzing, creating profiles, publishing and sharing, and collecting and updating feedback.

[0679] Hardware and software used

[0680] To implement this system, the following hardware and software are required.

[0681] Survey form: A web form built using HTML and JavaScript.

[0682] Database: A relational database such as MySQL or PostgreSQL.

[0683] Social Media API: Uses Twitter API and Facebook Graph API to obtain social media data.

[0684] Smartphone data analysis: Uses Android SDK and iOS SDK.

[0685] Generative Systems: Generative AI models such as OpenAI GPT-4.

[0686] Collection of information

[0687] First, the user accesses the questionnaire form and enters their information, such as "likes," "dislikes," and "past work history." The entered information is sent to the server in real time and stored in a database.

[0688] Next, the user gives permission to link their social media account to the system, and the server uses an API to retrieve follow information and post data from the social media and store it in a database.

[0689] Furthermore, the user allows the system to access the photo data stored on the smartphone. The system analyzes the metadata of the photos (location, tags, date, etc.) to identify their contents. The analyzed data is sent to a server and stored in a database.

[0690] Finally, the server sends feedback requests to friends and colleagues in the user's network and stores the collected ratings of others in a database.

[0691] Analysis of information

[0692] The server sends all collected data to a generation system (e.g., OpenAI GPT-4), which analyzes survey responses, social media data, photo data, and peer ratings and categorizes each piece of information into appropriate categories (likes, dislikes, career history, etc.).

[0693] Generate a profile

[0694] The profile items generated based on the analysis results are presented to the user. The user can choose from these items and select a template, such as for business or leisure. The server combines the selected items with the template to generate a complete profile. The generated profile is stored in a database, and the user can update and manage it as needed.

[0695] Profile Publishing / Sharing

[0696] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the user's settings. Third parties can access the public profile and provide feedback.

[0697] Updates based on feedback

[0698] The server analyzes the collected feedback and sends it to a generation system (e.g., OpenAI GPT-4). The generation system generates recommendations for profile modifications and additions based on the feedback. The user can review these recommendations and modify or add to their profile. This ensures that the user's profile is always up-to-date and optimal.

[0699] Specific examples

[0700] For example, if a user is creating a profile for business use, they might use the following prompt:

[0701] "Please provide us with information about your work history in the IT industry and your area of ​​expertise in software development. We will generate a profile based on this information."

[0702] The above is an embodiment of the system of the present invention.

[0703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0704] Step 1:

[0705] The user accesses the questionnaire form and enters information such as likes, dislikes, past work history, strengths, and weaknesses. After entering the information, they press the submit button. This input information is sent to the server in JSON format. The server parses the received JSON data and saves it in a MySQL or PostgreSQL database. Items such as "Favorite food: Sushi" and "Strengths: Programming" are saved in the database.

[0706] Step 2:

[0707] Users grant permission to link their social media accounts to the system. The server obtains a token through OAuth authentication and uses this token to call the Twitter API or Facebook Graph API. The follow list and post data obtained from the API are then stored in a database after business-related keywords are extracted. For example, information such as "there are many programmers on the follow list" or "there are many programming-related articles in the posts" is stored in the database.

[0708] Step 3:

[0709] The user allows access to the photo data stored on their smartphone. The device uses the Android SDK or iOS SDK to analyze the photo metadata (location, tags, date, etc.). The analysis results are sent to the server and stored in a database. For example, the data might be saved as "Travel Photos, Hawaii, March 2022."

[0710] Step 4:

[0711] The server then sends feedback requests to friends and colleagues within the user's network. These people then respond with their ratings via a web form. The collected feedback is then stored in a database. For example, a rating such as "Mr. / Ms. X has excellent communication skills" may be recorded.

[0712] Step 5:

[0713] The server sends all collected data to a generation system (e.g., OpenAI GPT-4). The generation system analyzes the data and classifies the information into different categories (likes, dislikes, work history, etc.). The results of this analysis are passed to the server and stored in a database. For example, an item such as "sushi in the likes category and high places in the dislikes category" may be registered.

[0714] Step 6:

[0715] The server generates profile items based on the analysis results and presents them to the user via a web interface. The user selects from the displayed items those that best suit their purpose (business, hobby, etc.). For example, items such as "5 years of experience in the IT industry" and "good at software development" can be selected.

[0716] Step 7:

[0717] The server combines the selected items with the template to generate a complete profile, which is then stored in a database. The user can then name the profile (e.g., "Business Profile") and save it.

[0718] Step 8:

[0719] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the settings. Viewers can access the published profile and provide feedback.

[0720] Step 9:

[0721] The server analyzes the collected feedback and sends it to a generation system, which generates recommendations for profile modifications and additions based on the feedback, such as "add more detailed project information."

[0722] Step 10:

[0723] Users can check the recommendations from the generation system and modify or add to their profile. The server saves the modified or added profile back into the database and updates the public information. This allows profiles to be kept up to date with the latest information.

[0724] (Application example 1)

[0725] 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."

[0726] Conventional customer profile generation systems have the problem of being unable to provide customers with a fully personalized shopping experience. In particular, in brick-and-mortar stores, there is no way to effectively utilize information based on online data in real time, making it difficult to improve customer satisfaction. Additionally, the process of collecting feedback and updating profiles based on it is often done manually, which is time-consuming and labor-intensive, making it inefficient.

[0727] 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.

[0728] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, and means for generating a profile based on the analyzed information. This allows for personalized product recommendations using customer data. Furthermore, adding means for presenting information based on the recommendations on a visual display can enhance the customer's shopping experience.

[0729] "User" means any person or entity that uses the System.

[0730] "Information collection means" refers to a method or device for obtaining necessary data from users.

[0731] A "generation system" is a combination of software or hardware that analyzes collected information and generates a profile.

[0732] "Template" refers to a predefined form or format into which a generated profile may be inserted.

[0733] The "publication and sharing setting means" refers to a setting function for enabling the created profile to be shared with others.

[0734] "Public Profile" refers to profile information that is made available for viewing by others based on a user's settings.

[0735] "Feedback collection means" refers to a method or device for collecting opinions and ratings from others regarding a published profile.

[0736] "Individualized product proposal means" refers to a function that proposes the most suitable products and services to individual customers based on collected and analyzed customer data.

[0737] "Visual display means" refers to a device or function for visually presenting information using a smart device or the like.

[0738] System Configuration

[0739] This invention provides a system that collects and analyzes customer information, creates profiles, and makes personalized product recommendations in physical stores. This system is composed of a server, terminals, and users.

[0740] 1. Collection of information

[0741] The server collects information from users. Specifically, users access a survey form and enter and link their preferences, past purchase history, social media accounts, etc. The front end of the survey form is built using React Native, and data is stored in Firebase. Social media data is obtained using the Twitter API and Instagram API.

[0742] 2. Analysis of Information

[0743] The server sends the collected information to a generation system, which uses Python and TensorFlow to analyze the user data and identify patterns of user preferences.

[0744] 3. Create a profile

[0745] The server generates profile items based on the analysis results and presents them to the user. These profiles are generated through a system built with Django and the database is PostgreSQL. The user selects from the presented items and inputs them into a template appropriate for the purpose.

[0746] 4. Profile Publishing and Sharing

[0747] The generated profile is published to smart devices (smartphones and smart glasses). The server implements the public API with Flask, and ARKit or ARCore is used for the smart glasses as a visual display.

[0748] 5. Feedback-driven updates

[0749] Feedback from others is collected on your public profile and sent to a server, where it is stored in Firebase and fed back to the analytics engine for new analysis, ensuring constantly updated recommendations.

[0750] Typical use cases

[0751] When a user visits a supermarket, new product suggestions and special sale information will be displayed on the smart glasses based on the user's pre-registered profile data, and this information will be updated in real time to improve the user's shopping experience.

[0752] Prompt Sentence Examples

[0753] "Build an application that will make optimal product recommendations to customers in a supermarket based on their profile data. This application must include the following elements: data analysis using Python and TensorFlow, a front-end built with React Native, an API for collecting social media data (Twitter, Instagram), information displayed on smart glasses, and a system configuration that can update recommendations based on feedback."

[0754] This invention makes it possible to provide customers with a personalized shopping experience and improve customer satisfaction in physical stores.

[0755] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0756] Step 1:

[0757] The server collects information from users. Specifically, users access a survey form and input and link their preferences, past purchase history, social media accounts, etc. The survey form is built using React Native, and the collected data is saved in Firebase in real time. The input is the survey content and social media link information, and the output is a structured dataset.

[0758] Step 2:

[0759] The server sends the collected information to the generation system. Specifically, it acquires social media data using the Twitter API and Instagram API, and combines this with data from the survey and image data (metadata from photos stored on smartphones). The input is a structured dataset and the acquired social media data, and the output is an integrated dataset.

[0760] Step 3:

[0761] The server uses a generative system (Python and TensorFlow) to analyze the collected information and identify user preference patterns. Natural language processing (NLP) and clustering algorithms are used to classify the data into categories such as "likes," "dislikes," "past work history," and "strengths." The input is the integrated dataset, and the output is a data object containing the analyzed results.

[0762] Step 4:

[0763] The server generates a profile based on the analysis results and feeds it into templates for multiple purposes. Using a generation system (Django), it generates and presents profile items that the user can select. The input is the analysis data object, and the output is the profile items presented to the user.

[0764] Step 5:

[0765] The user selects the generated profile items and then chooses the most suitable template. The server generates a complete profile based on this selection and stores it in PostgreSQL. The input is the user's selections and the output is the stored profile data.

[0766] Step 6:

[0767] The server manages profiles that can be publicly and shared, and displays them on smart devices as needed. The public API is implemented in Flask, and information is displayed on smart glasses or smartphones through a visual display (ARKit or ARCore). The input is the saved profile data and the user's public settings, and the output is the information displayed on the visual display.

[0768] Step 7:

[0769] Others provide feedback on public profiles, which is stored by the server in Firebase. The input is feedback information from others, and the output is the stored feedback data.

[0770] Step 8:

[0771] The server analyzes the collected feedback and provides recommendations for profile modifications and additions. It uses a generation system (Python) to perform the analysis and generate suggestions based on the feedback. The input is the stored feedback data, and the output is the analysis results and recommendations.

[0772] Step 9:

[0773] The user confirms the recommended modifications and additions and updates their profile. The server saves the modified / added profile back to PostgreSQL and updates the public content as necessary. The input is the recommended items and the user's modifications, and the output is the updated profile data.

[0774] 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.

[0775] The present invention is a system for streamlining profile creation, supporting a series of processes including information collection, information analysis, profile creation, profile publication and sharing, updating based on feedback, and emotion analysis using an emotion engine. Specifically, to implement the present invention, the following elements are required:

[0776] 1. Collection of information

[0777] 1.1 Survey form

[0778] The user accesses the questionnaire form and inputs their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0779] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0780] 1.2 Collection of social media data

[0781] Users give permission to link their social media accounts to the system.

[0782] The server uses the API to obtain follow information and post data from social media and store it in a database.

[0783] 1.3 Collection of smartphone photo data

[0784] The user allows access to the photo data in the smartphone.

[0785] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[0786] 1.4 Collecting peer reviews

[0787] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0788] The server stores the collected evaluations and feedback from others in a database.

[0789] 2. Analysis of Information

[0790] The server sends all collected data to the production system.

[0791] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0792] 3. Adding an Emotion Engine

[0793] The server sends the collected data to the emotion engine.

[0794] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[0795] The emotion engine sends the analysis results to the generation system.

[0796] 4. Create a profile

[0797] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0798] The user selects the profile items they wish to adopt from the generated profile items.

[0799] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0800] The server combines the selected items with the template to generate a complete profile.

[0801] The user names and saves the created profile (e.g., "Business Profile").

[0802] The server registers the saved profile in a database.

[0803] 5. Profile Publishing and Sharing

[0804] Users can select the profiles they want to make public and share and set them up.

[0805] The server publishes the selected profile to other users based on the user's settings.

[0806] Viewing users can view public profiles and provide feedback.

[0807] 6. Feedback-driven updates

[0808] The server sends the collected feedback to the generation system and the emotion engine.

[0809] The generative system and sentiment engine analyze the feedback and provide recommendations for profile modifications and additions.

[0810] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[0811] The server saves the modified or added profile information back into the database and updates the public information.

[0812] Specific examples

[0813] For example, when a user creates a business profile, the following steps are taken:

[0814] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[0815] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[0816] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[0817] 4. The server sends the collected data to the generation system and emotion engine to analyze the user's work history, areas of expertise, and emotional state.

[0818] 5. The generation system and emotion engine generate items that reflect “5 years of experience in the IT industry,” “good at software development,” “project management skills,” and “positive emotions upon project success.”

[0819] 6. The user selects these items and puts them into a business template.

[0820] 7. The server saves the generated profile and publishes it with the user's settings.

[0821] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[0822] 9. The server sends the feedback to the generation system and emotion engine, and the analysis results in a recommendation to "Add details of successful projects."

[0823] 10. The user can then modify their profile based on the recommendations and republish it.

[0824] As described above, the present invention provides integrated support for all processes of information collection, analysis, generation, publication, feedback, and sentiment analysis, thereby realizing efficient profile creation.

[0825] The processing flow will be explained below.

[0826] Step 1:

[0827] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[0828] Step 2:

[0829] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[0830] Step 3:

[0831] Users give permission to link their social media accounts to the system.

[0832] Step 4:

[0833] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[0834] Step 5:

[0835] The user allows access to the photo data in the smartphone.

[0836] Step 6:

[0837] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[0838] Step 7:

[0839] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[0840] Step 8:

[0841] The server stores the collected evaluations and feedback from others in a database.

[0842] Step 9:

[0843] The server sends all collected data to the production system.

[0844] Step 10:

[0845] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[0846] Step 11:

[0847] The server sends the collected data to the emotion engine.

[0848] Step 12:

[0849] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[0850] Step 13:

[0851] The emotion engine sends the results of the emotion analysis to the generation system.

[0852] Step 14:

[0853] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0854] Step 15:

[0855] The user selects the profile items they wish to adopt from the generated profile items.

[0856] Step 16:

[0857] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[0858] Step 17:

[0859] The server combines the selected items with the template to generate a complete profile.

[0860] Step 18:

[0861] The user names and saves the created profile (e.g., "Business Profile").

[0862] Step 19:

[0863] The server registers the saved profile in a database.

[0864] Step 20:

[0865] Users select the profiles they wish to make public and share.

[0866] Step 21:

[0867] The server publishes the selected profile based on the settings.

[0868] Step 22:

[0869] Viewers can review public profiles and provide feedback.

[0870] Step 23:

[0871] The server stores the provided feedback in a database and sends it to the generation system and emotion engine.

[0872] Step 24:

[0873] The generative system and sentiment engine analyze the collected feedback and provide recommendations for profile modifications and additions.

[0874] Step 25:

[0875] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[0876] Step 26:

[0877] The server saves the modified or added profile information back into the database and updates the public information.

[0878] Example 2

[0879] 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."

[0880] In modern society, users increasingly need to efficiently create and manage their own profiles from a wide variety of information sources. However, existing systems require complex processes for information collection, analysis, and profile creation, and do not analyze users' emotions. This means that creating personalized profiles requires a great deal of time and effort. Furthermore, profile updates based on user feedback are not automated, resulting in a poor user experience. Therefore, there is a need for a more efficient profile creation system that includes emotion analysis.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0882] In this invention, the server includes means for collecting information from users, means for using a generation system that analyzes the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, means for performing emotion analysis, and an emotion engine that analyzes emotional states based on the collected information. This allows the process from information collection to profile generation, publication, feedback, and emotion analysis to be effectively executed as a series of processes, enabling users to efficiently create and manage personalized and highly accurate profiles.

[0883] "User" means an individual or organization that uses the system to create and manage a profile.

[0884] "Information" refers to data collected from users, including survey responses, online platform data, image data, and evaluations by others.

[0885] A "generation system" is a combination of software or hardware that analyzes collected information, classifies it into different categories, and generates a profile.

[0886] A "template" is a predetermined format for applying the generated profile depending on the purpose, and includes types such as for business and hobby.

[0887] "Feedback" refers to opinions and ratings collected from others regarding a publicly available profile.

[0888] An "emotion engine" is a software or hardware system that analyzes a user's emotional state based on collected information.

[0889] A "profile" is a collection of data that shows the overview, history, and characteristics of an individual or organization, generated based on the user's information.

[0890] "Publication and sharing settings" refers to a setting and management function for making the created profile viewable by others.

[0891] "Modifying or adding" refers to updating an existing profile or adding new items based on collected feedback and sentiment analysis results.

[0892] MODE FOR CARRYING OUT THE INVENTION

[0893] This invention is a system that allows users to efficiently create and manage profiles. The embodiment of the invention includes the following series of processes: information collection, information analysis, profile creation, profile publication and sharing, update based on feedback, and emotion analysis using an emotion engine.

[0894] First, the user accesses the questionnaire form and enters their own information (likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The database typically uses an SQL or NoSQL database management system.

[0895] Next, the user gives permission to link their social media account. The server uses the API to retrieve follow information and post data from the social media and stores it in a database. For example, by using the Twitter API, it is possible to retrieve a user's tweets and follower list.

[0896] Furthermore, the user allows the system to access the photo data stored on the smartphone. The device analyzes the metadata of the photos (location information, tags, dates, etc.) and sends the contents to the server for storage. For example, the location information and dates contained in the photos stored on the device can be analyzed and saved as a record of a specific event or trip.

[0897] The server also sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected feedback and ratings from others in a database. This feedback includes the user's evaluation of the project and the characteristics of the project as seen by others.

[0898] The server sends all collected data to a generation system, which analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.). For example, the generation system uses natural language processing (NLP) techniques to analyze text data and extract keywords.

[0899] The server sends the collected data to the emotion engine, which analyzes the user's emotions from survey responses, social media data, photo data, and other people's ratings. For example, if there are many positive posts, it is analyzed as "positive emotion." The emotion engine then sends the analysis results to the generation system.

[0900] Next, the server generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user can select the profile items they want to use from the generated profile items and also select a profile template that suits their purpose. The server combines the selected items and template to generate a complete profile. The user can name and save the generated profile (e.g., "Business Profile"), and the server registers the saved profile in its database.

[0901] Users select the profile they want to make public and share and configure it accordingly. The server then publishes the selected profile to other users based on the user's settings. Other users can view the published profile and provide feedback.

[0902] The server sends the collected feedback to the generation system and emotion engine for re-analysis. This generates recommendations for profile modifications and additions. The user checks the recommendations and modifies or adds to their profile as necessary. The server then saves the modified or added profile back to the database and updates the public information.

[0903] Additionally, a generative AI model is used to generate profiles, so specific profile items can be generated by entering prompts such as:

[0904] "Generate a profile for a professional with 5 years of experience in the IT industry."

[0905] "What are the characteristics of an engineer who is good at software development?"

[0906] This allows for the creation of personalized and highly accurate profiles of users based on the information and feedback collected.

[0907] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0908] Step 1:

[0909] The user accesses a questionnaire form provided by the system and enters their own information (likes, dislikes, past work history, strengths, weaknesses).

[0910] Input: Survey response data entered by the user.

[0911] Data processing: The server receives the data entered by the user in the form in real time and stores it in a database.

[0912] Output: User information stored in the database.

[0913] Specific behavior: When the submit button on the form is pressed, the server receives a POST request and stores the data in the backend database.

[0914] Step 2:

[0915] Users give permission to link their social media accounts to the system.

[0916] Input: User's social media account link information.

[0917] Data processing: The server uses the API to obtain follow information and post data from social media.

[0918] Output: Social media data stored in a database.

[0919] Specific operation: The server-side backend calls the API endpoints of each social media platform, analyzes the responses, and stores them in a database.

[0920] Step 3:

[0921] The user allows access to the photo data in the smartphone.

[0922] Input: Permission to access photo data on the user's smartphone.

[0923] Data processing: The device analyzes the photo's metadata (location, tags, date, etc.).

[0924] Output: Photo metadata sent and stored on the server.

[0925] Specific operation: The system crawls the photo data stored on the smartphone, extracts metadata, and centralizes it. The device then sends the data to the server.

[0926] Step 4:

[0927] The server sends feedback requests to others in the user's network.

[0928] Input: The user's contact list.

[0929] Data processing: Feedback requests are sent.

[0930] Output: The collected feedback data is stored in a database.

[0931] What happens: The server looks up the user's contact list and sends them an email or notification requesting feedback. Friends and colleagues provide feedback, and the data is stored on the server.

[0932] Step 5:

[0933] The server sends all collected data to the production system.

[0934] Input: Survey responses stored in a database, social media data, photo metadata, and feedback from others.

[0935] Data processing: Send various data to the generation system in bulk.

[0936] Output: Data sent to the generating system.

[0937] Specific operation: The server extracts the necessary data from the database and sends it to the generation system via an API call.

[0938] Step 6:

[0939] The generation system analyzes the data and classifies it into different categories.

[0940] Input: Data sent to the generating system (survey responses, social media data, photo metadata, feedback from others).

[0941] Data processing: Analyze text data using natural language processing (NLP) techniques and extract keywords.

[0942] Output: Categorized data.

[0943] Specific operation: The generation system analyzes the data it receives and classifies it into categories such as "likes," "dislikes," and "career."

[0944] Step 7:

[0945] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[0946] Input: Parsed data and sentiment analysis results.

[0947] Data processing: The generated profile items are presented to the user.

[0948] Output: The profile items presented to the user.

[0949] Specific operation: The server sends the data received from the generation system to the front end and presents it to the user.

[0950] Step 8:

[0951] The user selects the profile items he or she wishes to adopt from the generated profile items.

[0952] Input: The profile items presented to the user.

[0953] Data processing: The user selects the profile items to adopt.

[0954] Output: The selected profile items.

[0955] Specific operation: The user selects the profile items they wish to adopt from the displayed profile items.

[0956] Step 9:

[0957] The user selects a profile template according to the purpose.

[0958] Input: The purpose of the profile (for business, hobby, etc.).

[0959] Data processing: The appropriate template is applied.

[0960] Output: Profile items with applied template.

[0961] Specific operation: The user selects a predetermined template (for example, a business template), and the server aggregates the data based on it.

[0962] Step 10:

[0963] The server combines the selected items with the template to generate a complete profile.

[0964] Input: Selected profile items and template.

[0965] Data processing: merging items and templates.

[0966] Output: The complete profile generated.

[0967] Specific Actions: The server applies the selected items to the template and synthesizes the final profile.

[0968] Step 11:

[0969] The user names and saves the created profile.

[0970] Input: The generated profile.

[0971] Data processing: Name your profile.

[0972] Output: Named profiles.

[0973] Specific behavior: The user names the profile "Business Profile" and clicks the save button.

[0974] Step 12:

[0975] The server registers the saved profile in a database.

[0976] Input: A named profile.

[0977] Data processing: Registration of profile data in database.

[0978] Output: The profile registered in the database.

[0979] Specific operation: The server triggers a save action to save the profile data to the database.

[0980] Step 13:

[0981] Users can select the profiles they want to make public and share and set them up.

[0982] Input: A profile registered in the database.

[0983] Data processing: Applying public / sharing settings.

[0984] Output: Profile with public / shared settings applied.

[0985] Specific operation: The user selects "Business Profile" and sets the visibility.

[0986] Step 14:

[0987] The server publishes the selected profile to other users based on the user's settings.

[0988] Input: Profile with visibility settings applied.

[0989] Data processing: Generation and distribution of public URLs.

[0990] Output: Your profile as it appears to other users.

[0991] Specific behavior: The server generates a viewable URL based on the publishing settings and provides access to other users.

[0992] Step 15:

[0993] The server collects feedback from others on the published profile.

[0994] Input: Feedback from others.

[0995] Data processing: collection and storage of feedback data.

[0996] Output: Feedback stored in a database.

[0997] Specific Actions: Other users view your public profile and provide feedback, such as "I'd like to know more about your specific projects."

[0998] Step 16:

[0999] The server sends the collected feedback to the generation system and emotion engine for re-analysis.

[1000] Input: Feedback stored in the database.

[1001] Data processing: Analysis of feedback with generative systems and emotion engines.

[1002] Output: Recommendations for corrections and additions based on the analysis results.

[1003] Specific operation: The server extracts the feedback data and sends it to the generation system and emotion engine.

[1004] Step 17:

[1005] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1006] Input: Suggested corrections or additions.

[1007] Data processing: Modifying your profile or applying additional details.

[1008] Output: The updated profile.

[1009] Specific operation: The user checks the recommendations and makes corrections on the profile editing screen.

[1010] Step 18:

[1011] The server saves the modified or added profile back into the database and updates the public information.

[1012] Input: Updated profile.

[1013] Data processing: Resave your profile and update public information.

[1014] Output: Updated public profile.

[1015] Specific operation: The server saves the updated profile data to the database and regenerates the public URL.

[1016] (Application example 2)

[1017] 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."

[1018] Currently, many users use online shopping, but there is a lack of efficient methods for recommending products that match their tastes and preferences. As a result, users often spend a lot of time trying to find products that suit them. Furthermore, online shopping sites lack the means to accurately grasp users' tastes and preferences, making it difficult to make effective product recommendations. To address this issue, a system is needed that can efficiently collect and analyze user information and provide personalized product recommendations.

[1019] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1020] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, and means for recommending a variety of products to users in a personalized manner, thereby enabling efficient recommendation of optimal products based on the user's detailed preferences and past behavioral data.

[1021] "User" means an individual who uses the System to provide information and utilizes the generated profile.

[1022] "Means of collection" refers to means for collecting questionnaire forms, data from online platforms, image data, and evaluations by others.

[1023] "Generation system" refers to a system that has the ability to analyze collected information and generate a profile.

[1024] "Analyzed information" refers to data that has been analyzed and categorized from collected raw data.

[1025] "Profile" refers to data including descriptive text and recommendation information generated based on user information.

[1026] "Template" refers to a format for formatting the generated profile as text.

[1027] "Means for generating" refers to means for automatically generating profile items based on the analyzed information.

[1028] "Means for storing" refers to means for storing the generated profile in a database.

[1029] "Means for management" refers to means for setting public and sharing settings for the created profile.

[1030] "Means of collecting feedback from others" refers to means of collecting opinions and ratings from third parties regarding a public profile.

[1031] "Means for Analyzing Feedback" means means for analyzing collected feedback and providing recommendations regarding profile modifications and additions.

[1032] "Personalized recommendation methods" refer to methods that recommend the most suitable products based on the user's information and preferences.

[1033] To implement the present invention, the elements of the server, the terminal, and the user must work in cooperation with each other.

[1034] First, the user accesses a questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The user also gives permission to link their online platform account to the system. This link allows the server to use an API to obtain follow information and post data from the online platform and store it in the database. The user also allows the server to access the photo data on their smartphone, and the device analyzes the photo metadata (location information, tags, date, etc.) and sends the content to the server. The server then sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected ratings and feedback from others in the database.

[1035] Next, the server sends all collected data to the generation system. The generation system analyzes the survey responses, online platform data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career, etc.). The server then sends the collected data to the emotion engine. The emotion engine analyzes the user's emotions from the survey responses, online platform data, photo data, other people's ratings, etc. and sends the analysis results to the generation system.

[1036] The server then generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user selects the profile items they want to use from the generated profile items and selects a profile template appropriate for their purpose (e.g., business, hobby, etc.). The server combines the selected items and template to generate a complete profile. The user names and saves the generated profile (e.g., "Business Profile"), and the server registers the saved profile in a database.

[1037] Publication and sharing are also important elements. Users select the profiles they want to publish and share and configure them. The server publishes the selected profiles to other users based on the user's settings. Viewing users view the published profiles and provide feedback. The server sends the collected feedback to the generation system and emotion engine for analysis. As a result, it provides recommendations for modifying or adding items to the profile. Users check the recommendations from the generation system and emotion engine and modify or add to their profile as necessary. The server saves the modified or added profiles back into the database and updates the published content.

[1038] This system realizes the function of recommending a variety of products to users in a personalized manner. As a specific example, it can efficiently recommend optimal products based on the user's detailed preferences and past behavioral data. For example, when a user logs in to a shopping site and creates their own profile, products related to the user's tastes and hobbies are automatically recommended based on that profile.

[1039] As a concrete example, a detailed profile can be generated by inputting the following prompt sentence into the generative AI model:

[1040] User Information:

[1041] Name: User A

[1042] Email: user@example.com

[1043] Date of Birth: 1980-05-15

[1044] Social Media Data:

[1045] Topics: Technology, Gadgets, Healthcare

[1046] Photo data:

[1047] Date: 2023-09-10

[1048] Location: City, Country

[1049] Tags: Travel, Tourism

[1050] Others' ratings:

[1051] Email: friend@example.com Feedback: I love to travel and have an inquisitive personality.

[1052] Fixes based on feedback:

[1053] "Based on interest data from users, we recommend products, especially those related to technology and gadgets, as well as travel accessories. We provide a personalized shopping experience with constructive feedback sharing the latest information on travel and gadgets."

[1054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1055] Step 1:

[1056] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The entered information is sent to the server and stored in a database. This is to collect the basic data needed to generate a profile.

[1057] Step 2:

[1058] The user gives permission to link their online platform account (e.g., social media) to the system. The server uses the API to obtain follow information and post data from the online platform and stores it in a database. This allows data reflecting the user's interests to be collected.

[1059] Step 3:

[1060] The user allows access to the photo data stored on their smartphone. The device analyzes the photo's metadata (location, tags, date, etc.) and sends the information to the server. The server stores the information in a database. The location, date, tags, etc. indicate the characteristics of the photo and are used to identify the user's area of ​​activity and areas of interest.

[1061] Step 4:

[1062] The server sends feedback requests to others in the user's network (friends, colleagues, etc.) via email or other means. When others provide feedback, it is collected by the server and stored in a database. The objective evaluations of others complement the user's profile from multiple angles.

[1063] Step 5:

[1064] The server sends all collected data to the generation system, which analyzes survey responses, online platform data, photo data, and other users' ratings and categorizes the information into different categories (e.g., likes, dislikes, career history, etc.). The categorized data is used to generate profile items.

[1065] Step 6:

[1066] The server sends the collected data to the emotion engine, which analyzes the user's emotions based on survey responses, online platform data, photo data, and other users' ratings. The analysis results are sent to the generation system, which generates profile items that reflect the user's emotional state.

[1067] Step 7:

[1068] The server generates profile items based on the analysis results and sentiment analysis results and presents them to the user. The user selects the profile items they want to adopt from the generated profile items. The selected information is sent to the server.

[1069] Step 8:

[1070] The user selects a profile template based on their intended use (e.g., business, hobby, etc.), and the server combines the selected items with the template to generate a complete profile, which is then stored and managed in a database.

[1071] Step 9:

[1072] Users select the profiles they want to make public and share, and configure them accordingly. The server then publishes the selected profiles to other users based on the user's settings, allowing browsing users to view the published profiles.

[1073] Step 10:

[1074] The server collects feedback provided by browsing users and stores it in a database. The collected feedback is sent to the generation system and emotion engine for analysis. Based on the analysis results, recommendations for profile modifications and additions are provided to the user.

[1075] Step 11:

[1076] Users can review the recommendations from the generation system and emotion engine, and modify or add to their profile as needed. The modified or added profile is saved back in the database, and the public information is updated.

[1077] Step 12:

[1078] Finally, the generated profile data is used to recommend various products to users in a personalized manner. The server efficiently recommends optimal products based on the user's detailed preferences and past behavioral data. As a result, users can enjoy an individually customized shopping experience.

[1079] 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.

[1080] 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.

[1081] 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.

[1082] [Third embodiment]

[1083] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1084] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1085] 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).

[1086] 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.

[1087] 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.

[1088] 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).

[1089] 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.

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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.

[1094] 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."

[1095] This invention is a system for streamlining profile creation, automating and supporting the series of processes of information collection, information analysis, profile creation, profile publication and sharing, and updating based on feedback. To implement this system, the following specific elements are required:

[1096] 1. Collection of information

[1097] 1.1 Survey form

[1098] The user accesses the questionnaire form and inputs information about themselves (such as likes, dislikes, past work history, strengths, weaknesses, etc.).

[1099] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1100] 1.2 Collection of social media data

[1101] Users give permission to link their social media accounts to the system.

[1102] The server uses the API to retrieve data such as follow information and posts from social media and store it in a database.

[1103] 1.3 Collection of smartphone photo data

[1104] The user allows access to the photo data in the smartphone.

[1105] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[1106] 1.4 Collecting peer reviews

[1107] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1108] The server stores the collected evaluations of others in a database.

[1109] 2. Analysis of Information

[1110] The server sends all collected data to the production system.

[1111] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.).

[1112] 3. Create a profile

[1113] The server presents the user with profile items generated based on the analysis results.

[1114] The user selects from the presented items and chooses a template according to the purpose (for example, for business, hobby, etc.).

[1115] The server combines the selected items with the template to generate a complete profile.

[1116] The user names and saves the created profile.

[1117] The server registers the saved profile in a database.

[1118] 4. Profile Publishing and Sharing

[1119] Users can select the profiles they want to make public and share and set them up.

[1120] The server publishes the selected profile to other users based on the user's settings.

[1121] Viewing users can view public profiles and provide feedback.

[1122] 5. Feedback-driven updates

[1123] The server analyzes the collected feedback and sends it to the production system.

[1124] The generator analyzes the feedback and provides recommendations for profile modifications and additions.

[1125] The user reviews the recommendations from the generation system and makes modifications or additions to the profile.

[1126] The server stores the modified or added profile in the database again and updates the public information as necessary.

[1127] Specific examples

[1128] For example, when a user creates a business profile, the following steps are taken:

[1129] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[1130] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[1131] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[1132] 4. The server sends the collected data to the generation system, which analyzes the user's work history and areas of expertise.

[1133] 5. The generation system generates items such as "5 years of experience in the IT industry," "Good at software development," and "Has project management skills."

[1134] 6. The user selects these items and puts them into a business template.

[1135] 7. The server saves the generated profile and publishes it with the user's settings.

[1136] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[1137] 9. The server sends feedback to the generation system and makes a recommendation to "Add details of successful projects" as a result of the analysis.

[1138] 10. The user can then modify their profile based on the recommendations and republish it.

[1139] As described above, the present invention provides consistent support from profile collection to publication and improvement based on feedback, thereby realizing efficient profile creation.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1143] Step 2:

[1144] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1145] Step 3:

[1146] Users give permission to link their social media accounts to the system.

[1147] Step 4:

[1148] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[1149] Step 5:

[1150] The user allows access to the photo data in the smartphone.

[1151] Step 6:

[1152] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[1153] Step 7:

[1154] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1155] Step 8:

[1156] The server stores the collected evaluations and feedback from others in a database.

[1157] Step 9:

[1158] The server sends all collected data to the production system.

[1159] Step 10:

[1160] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1161] Step 11:

[1162] The server generates profile items based on the analysis results and presents them to the user.

[1163] Step 12:

[1164] The user selects the profile items they wish to adopt from the generated profile items.

[1165] Step 13:

[1166] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1167] Step 14:

[1168] The server combines the selected items with the template to generate a complete profile.

[1169] Step 15:

[1170] The user names and saves the created profile (e.g., "Business Profile").

[1171] Step 16:

[1172] The server registers the saved profile in a database.

[1173] Step 17:

[1174] Users select the profiles they wish to make public and share.

[1175] Step 18:

[1176] The server publishes the selected profile based on the settings.

[1177] Step 19:

[1178] Viewers can review public profiles and provide feedback.

[1179] Step 20:

[1180] The server stores the provided feedback in a database and sends it to the generation system.

[1181] Step 21:

[1182] The generation system analyzes the collected feedback and provides recommendations for profile modifications and additions.

[1183] Step 22:

[1184] Users can review the recommendations from the generation system and modify or add to their profile as needed.

[1185] Step 23:

[1186] The server saves the modified or added profile information back into the database and updates the public information.

[1187] Example 1

[1188] 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."

[1189] The purpose of this invention is to provide a system that allows users to easily and efficiently create, manage, and share their own profiles. In particular, the system aims to realize a system that collects information from various sources, analyzes it, automatically generates a profile, and enables users to update their profile based on feedback, thereby significantly reducing the time and effort required by the user.

[1190] 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.

[1191] In this invention, the server includes means for collecting information from users, means for saving the collected information in real time, means for acquiring data from social media, means for acquiring and analyzing photo metadata from terminals, means for collecting feedback from others in the network, means for using a generation system to analyze the collected information, means for classifying the analyzed information into different categories, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for a user to select generated profile items, means for saving the generated profile, means for managing profiles that can be set to public and shareable, means for collecting feedback from others on published profiles, and means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, thereby enabling users to efficiently create, manage, and share their own profiles without hassle.

[1192] "User" refers to any individual or business that uses the System to create, manage, and share their profile.

[1193] "Means for collecting information" refers to methods and devices for obtaining information from users, such as survey responses, social media data, photo metadata, and ratings from others.

[1194] "Real-time storage means" refers to methods and technologies for instantly recording collected information in a storage device such as a database.

[1195] "Means of obtaining data from social media" refers to methods and technologies that use social media APIs to obtain user following information, posting data, etc.

[1196] "Means for obtaining and analyzing photo metadata" refers to methods and technologies for obtaining and analyzing photo meta information (location information, tags, dates, etc.) from a user's device.

[1197] "Means for collecting feedback" refers to methods and techniques for collecting opinions and ratings from third parties, such as the user's friends and colleagues.

[1198] "Generation system" refers to the artificial intelligence model or platform that analyzes collected information and generates profiles.

[1199] "Means of categorizing information into different categories" refers to methods and techniques for separating collected data into appropriate categories (likes, dislikes, history, etc.).

[1200] "Profile generating means" refers to methods or techniques for creating a user profile based on the analyzed information.

[1201] "Template filling" refers to the method or technique for adapting the generated profile to a format appropriate for the application.

[1202] "Means for selecting profile items" refers to the interface or technology that allows a user to review and select each item in the generated profile.

[1203] "Means for storing a profile" refers to a method or technology for recording and storing the generated profile in a database or the like.

[1204] "Profile Management Means" refers to methods or technologies that allow users to manage the public or sharing settings of their stored profile.

[1205] "Feedback collection means" refers to methods and technologies for collecting opinions and ratings from other users regarding a public profile.

[1206] "Means for providing recommendations for profile modifications or additions" refers to methods or technologies for suggesting modifications or additions to a profile based on collected feedback.

[1207] The present invention relates to a system that allows users to efficiently create, manage, and share personal profiles. The system based on the present invention includes steps of collecting information, analyzing, creating profiles, publishing and sharing, and collecting and updating feedback.

[1208] Hardware and software used

[1209] To implement this system, the following hardware and software are required.

[1210] Survey form: A web form built using HTML and JavaScript.

[1211] Database: A relational database such as MySQL or PostgreSQL.

[1212] Social Media API: Uses Twitter API and Facebook Graph API to obtain social media data.

[1213] Smartphone data analysis: Uses Android SDK and iOS SDK.

[1214] Generative Systems: Generative AI models such as OpenAI GPT-4.

[1215] Collection of information

[1216] First, the user accesses the questionnaire form and enters their information, such as "likes," "dislikes," and "past work history." The entered information is sent to the server in real time and stored in a database.

[1217] Next, the user gives permission to link their social media account to the system, and the server uses an API to retrieve follow information and post data from the social media and store it in a database.

[1218] Furthermore, the user allows the system to access the photo data stored on the smartphone. The system analyzes the metadata of the photos (location, tags, date, etc.) to identify their contents. The analyzed data is sent to a server and stored in a database.

[1219] Finally, the server sends feedback requests to friends and colleagues in the user's network and stores the collected ratings of others in a database.

[1220] Analysis of information

[1221] The server sends all collected data to a generation system (e.g., OpenAI GPT-4), which analyzes survey responses, social media data, photo data, and peer ratings and categorizes each piece of information into appropriate categories (likes, dislikes, career history, etc.).

[1222] Generate a profile

[1223] The profile items generated based on the analysis results are presented to the user. The user can choose from these items and select a template, such as for business or leisure. The server combines the selected items with the template to generate a complete profile. The generated profile is stored in a database, and the user can update and manage it as needed.

[1224] Profile Publishing / Sharing

[1225] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the user's settings. Third parties can access the public profile and provide feedback.

[1226] Updates based on feedback

[1227] The server analyzes the collected feedback and sends it to a generation system (e.g., OpenAI GPT-4). The generation system generates recommendations for profile modifications and additions based on the feedback. The user can review these recommendations and modify or add to their profile. This ensures that the user's profile is always up-to-date and optimal.

[1228] Specific examples

[1229] For example, if a user is creating a profile for business use, they might use the following prompt:

[1230] "Please provide us with information about your work history in the IT industry and your area of ​​expertise in software development. We will generate a profile based on this information."

[1231] The above is an embodiment of the system of the present invention.

[1232] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1233] Step 1:

[1234] The user accesses the questionnaire form and enters information such as likes, dislikes, past work history, strengths, and weaknesses. After entering the information, they press the submit button. This input information is sent to the server in JSON format. The server parses the received JSON data and saves it in a MySQL or PostgreSQL database. Items such as "Favorite food: Sushi" and "Strengths: Programming" are saved in the database.

[1235] Step 2:

[1236] Users grant permission to link their social media accounts to the system. The server obtains a token through OAuth authentication and uses this token to call the Twitter API or Facebook Graph API. The follow list and post data obtained from the API are then stored in a database after business-related keywords are extracted. For example, information such as "there are many programmers on the follow list" or "there are many programming-related articles in the posts" is stored in the database.

[1237] Step 3:

[1238] The user allows access to the photo data stored on their smartphone. The device uses the Android SDK or iOS SDK to analyze the photo metadata (location, tags, date, etc.). The analysis results are sent to the server and stored in a database. For example, the data might be saved as "Travel Photos, Hawaii, March 2022."

[1239] Step 4:

[1240] The server then sends feedback requests to friends and colleagues within the user's network. These people then respond with their ratings via a web form. The collected feedback is then stored in a database. For example, a rating such as "Mr. / Ms. X has excellent communication skills" may be recorded.

[1241] Step 5:

[1242] The server sends all collected data to a generation system (e.g., OpenAI GPT-4). The generation system analyzes the data and classifies the information into different categories (likes, dislikes, work history, etc.). The results of this analysis are passed to the server and stored in a database. For example, an item such as "sushi in the likes category and high places in the dislikes category" may be registered.

[1243] Step 6:

[1244] The server generates profile items based on the analysis results and presents them to the user via a web interface. The user selects from the displayed items those that best suit their purpose (business, hobby, etc.). For example, items such as "5 years of experience in the IT industry" and "good at software development" can be selected.

[1245] Step 7:

[1246] The server combines the selected items with the template to generate a complete profile, which is then stored in a database. The user can then name the profile (e.g., "Business Profile") and save it.

[1247] Step 8:

[1248] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the settings. Viewers can access the published profile and provide feedback.

[1249] Step 9:

[1250] The server analyzes the collected feedback and sends it to a generation system, which generates recommendations for profile modifications and additions based on the feedback, such as "add more detailed project information."

[1251] Step 10:

[1252] Users can check the recommendations from the generation system and modify or add to their profile. The server saves the modified or added profile back into the database and updates the public information. This allows profiles to be kept up to date with the latest information.

[1253] (Application example 1)

[1254] 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."

[1255] Conventional customer profile generation systems have the problem of being unable to provide customers with a fully personalized shopping experience. In particular, in brick-and-mortar stores, there is no way to effectively utilize information based on online data in real time, making it difficult to improve customer satisfaction. Additionally, the process of collecting feedback and updating profiles based on it is often done manually, which is time-consuming and labor-intensive, making it inefficient.

[1256] 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.

[1257] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, and means for generating a profile based on the analyzed information. This allows for personalized product recommendations using customer data. Furthermore, adding means for presenting information based on the recommendations on a visual display can enhance the customer's shopping experience.

[1258] "User" means any person or entity that uses the System.

[1259] "Information collection means" refers to a method or device for obtaining necessary data from users.

[1260] A "generation system" is a combination of software or hardware that analyzes collected information and generates a profile.

[1261] "Template" refers to a predefined form or format into which a generated profile may be inserted.

[1262] The "publication and sharing setting means" refers to a setting function for enabling the created profile to be shared with others.

[1263] "Public Profile" refers to profile information that is made available for viewing by others based on a user's settings.

[1264] "Feedback collection means" refers to a method or device for collecting opinions and ratings from others regarding a published profile.

[1265] "Individualized product proposal means" refers to a function that proposes the most suitable products and services to individual customers based on collected and analyzed customer data.

[1266] "Visual display means" refers to a device or function for visually presenting information using a smart device or the like.

[1267] System Configuration

[1268] This invention provides a system that collects and analyzes customer information, creates profiles, and makes personalized product recommendations in physical stores. This system is composed of a server, terminals, and users.

[1269] 1. Collection of information

[1270] The server collects information from users. Specifically, users access a survey form and enter and link their preferences, past purchase history, social media accounts, etc. The front end of the survey form is built using React Native, and data is stored in Firebase. Social media data is obtained using the Twitter API and Instagram API.

[1271] 2. Analysis of Information

[1272] The server sends the collected information to a generation system, which uses Python and TensorFlow to analyze the user data and identify patterns of user preferences.

[1273] 3. Create a profile

[1274] The server generates profile items based on the analysis results and presents them to the user. These profiles are generated through a system built with Django and the database is PostgreSQL. The user selects from the presented items and inputs them into a template appropriate for the purpose.

[1275] 4. Profile Publishing and Sharing

[1276] The generated profile is published to smart devices (smartphones and smart glasses). The server implements the public API with Flask, and ARKit or ARCore is used for the smart glasses as a visual display.

[1277] 5. Feedback-driven updates

[1278] Feedback from others is collected on your public profile and sent to a server, where it is stored in Firebase and fed back to the analytics engine for new analysis, ensuring constantly updated recommendations.

[1279] Typical use cases

[1280] When a user visits a supermarket, new product suggestions and special sale information will be displayed on the smart glasses based on the user's pre-registered profile data, and this information will be updated in real time to improve the user's shopping experience.

[1281] Prompt Sentence Examples

[1282] "Build an application that will make optimal product recommendations to customers in a supermarket based on their profile data. This application must include the following elements: data analysis using Python and TensorFlow, a front-end built with React Native, an API for collecting social media data (Twitter, Instagram), information displayed on smart glasses, and a system configuration that can update recommendations based on feedback."

[1283] This invention makes it possible to provide customers with a personalized shopping experience and improve customer satisfaction in physical stores.

[1284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1285] Step 1:

[1286] The server collects information from users. Specifically, users access a survey form and input and link their preferences, past purchase history, social media accounts, etc. The survey form is built using React Native, and the collected data is saved in Firebase in real time. The input is the survey content and social media link information, and the output is a structured dataset.

[1287] Step 2:

[1288] The server sends the collected information to the generation system. Specifically, it acquires social media data using the Twitter API and Instagram API, and combines this with data from the survey and image data (metadata from photos stored on smartphones). The input is a structured dataset and the acquired social media data, and the output is an integrated dataset.

[1289] Step 3:

[1290] The server uses a generative system (Python and TensorFlow) to analyze the collected information and identify user preference patterns. Natural language processing (NLP) and clustering algorithms are used to classify the data into categories such as "likes," "dislikes," "past work history," and "strengths." The input is the integrated dataset, and the output is a data object containing the analyzed results.

[1291] Step 4:

[1292] The server generates a profile based on the analysis results and feeds it into templates for multiple purposes. Using a generation system (Django), it generates and presents profile items that the user can select. The input is the analysis data object, and the output is the profile items presented to the user.

[1293] Step 5:

[1294] The user selects the generated profile items and then chooses the most suitable template. The server generates a complete profile based on this selection and stores it in PostgreSQL. The input is the user's selections and the output is the stored profile data.

[1295] Step 6:

[1296] The server manages profiles that can be publicly and shared, and displays them on smart devices as needed. The public API is implemented in Flask, and information is displayed on smart glasses or smartphones through a visual display (ARKit or ARCore). The input is the saved profile data and the user's public settings, and the output is the information displayed on the visual display.

[1297] Step 7:

[1298] Others provide feedback on public profiles, which is stored by the server in Firebase. The input is feedback information from others, and the output is the stored feedback data.

[1299] Step 8:

[1300] The server analyzes the collected feedback and provides recommendations for profile modifications and additions. It uses a generation system (Python) to perform the analysis and generate suggestions based on the feedback. The input is the stored feedback data, and the output is the analysis results and recommendations.

[1301] Step 9:

[1302] The user confirms the recommended modifications and additions and updates their profile. The server saves the modified / added profile back to PostgreSQL and updates the public content as necessary. The input is the recommended items and the user's modifications, and the output is the updated profile data.

[1303] 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.

[1304] The present invention is a system for streamlining profile creation, supporting a series of processes including information collection, information analysis, profile creation, profile publication and sharing, updating based on feedback, and emotion analysis using an emotion engine. Specifically, to implement the present invention, the following elements are required:

[1305] 1. Collection of information

[1306] 1.1 Survey form

[1307] The user accesses the questionnaire form and inputs their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1308] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1309] 1.2 Collection of social media data

[1310] Users give permission to link their social media accounts to the system.

[1311] The server uses the API to obtain follow information and post data from social media and store it in a database.

[1312] 1.3 Collection of smartphone photo data

[1313] The user allows access to the photo data in the smartphone.

[1314] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[1315] 1.4 Collecting peer reviews

[1316] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1317] The server stores the collected evaluations and feedback from others in a database.

[1318] 2. Analysis of Information

[1319] The server sends all collected data to the production system.

[1320] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1321] 3. Adding an Emotion Engine

[1322] The server sends the collected data to the emotion engine.

[1323] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[1324] The emotion engine sends the analysis results to the generation system.

[1325] 4. Create a profile

[1326] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[1327] The user selects the profile items they wish to adopt from the generated profile items.

[1328] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1329] The server combines the selected items with the template to generate a complete profile.

[1330] The user names and saves the created profile (e.g., "Business Profile").

[1331] The server registers the saved profile in a database.

[1332] 5. Profile Publishing and Sharing

[1333] Users can select the profiles they want to make public and share and set them up.

[1334] The server publishes the selected profile to other users based on the user's settings.

[1335] Viewing users can view public profiles and provide feedback.

[1336] 6. Feedback-driven updates

[1337] The server sends the collected feedback to the generation system and the emotion engine.

[1338] The generative system and sentiment engine analyze the feedback and provide recommendations for profile modifications and additions.

[1339] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1340] The server saves the modified or added profile information back into the database and updates the public information.

[1341] Specific examples

[1342] For example, when a user creates a business profile, the following steps are taken:

[1343] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[1344] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[1345] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[1346] 4. The server sends the collected data to the generation system and emotion engine to analyze the user's work history, areas of expertise, and emotional state.

[1347] 5. The generation system and emotion engine generate items that reflect “5 years of experience in the IT industry,” “good at software development,” “project management skills,” and “positive emotions upon project success.”

[1348] 6. The user selects these items and puts them into a business template.

[1349] 7. The server saves the generated profile and publishes it with the user's settings.

[1350] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[1351] 9. The server sends the feedback to the generation system and emotion engine, and the analysis results in a recommendation to "Add details of successful projects."

[1352] 10. The user can then modify their profile based on the recommendations and republish it.

[1353] As described above, the present invention provides integrated support for all processes of information collection, analysis, generation, publication, feedback, and sentiment analysis, thereby realizing efficient profile creation.

[1354] The processing flow will be explained below.

[1355] Step 1:

[1356] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1357] Step 2:

[1358] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1359] Step 3:

[1360] Users give permission to link their social media accounts to the system.

[1361] Step 4:

[1362] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[1363] Step 5:

[1364] The user allows access to the photo data in the smartphone.

[1365] Step 6:

[1366] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[1367] Step 7:

[1368] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1369] Step 8:

[1370] The server stores the collected evaluations and feedback from others in a database.

[1371] Step 9:

[1372] The server sends all collected data to the production system.

[1373] Step 10:

[1374] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1375] Step 11:

[1376] The server sends the collected data to the emotion engine.

[1377] Step 12:

[1378] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[1379] Step 13:

[1380] The emotion engine sends the results of the emotion analysis to the generation system.

[1381] Step 14:

[1382] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[1383] Step 15:

[1384] The user selects the profile items they wish to adopt from the generated profile items.

[1385] Step 16:

[1386] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1387] Step 17:

[1388] The server combines the selected items with the template to generate a complete profile.

[1389] Step 18:

[1390] The user names and saves the created profile (e.g., "Business Profile").

[1391] Step 19:

[1392] The server registers the saved profile in a database.

[1393] Step 20:

[1394] Users select the profiles they wish to make public and share.

[1395] Step 21:

[1396] The server publishes the selected profile based on the settings.

[1397] Step 22:

[1398] Viewers can review public profiles and provide feedback.

[1399] Step 23:

[1400] The server stores the provided feedback in a database and sends it to the generation system and emotion engine.

[1401] Step 24:

[1402] The generative system and sentiment engine analyze the collected feedback and provide recommendations for profile modifications and additions.

[1403] Step 25:

[1404] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1405] Step 26:

[1406] The server saves the modified or added profile information back into the database and updates the public information.

[1407] Example 2

[1408] 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."

[1409] In modern society, users increasingly need to efficiently create and manage their own profiles from a wide variety of information sources. However, existing systems require complex processes for information collection, analysis, and profile creation, and do not analyze users' emotions. This means that creating personalized profiles requires a great deal of time and effort. Furthermore, profile updates based on user feedback are not automated, resulting in a poor user experience. Therefore, there is a need for a more efficient profile creation system that includes emotion analysis.

[1410] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1411] In this invention, the server includes means for collecting information from users, means for using a generation system that analyzes the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, means for performing emotion analysis, and an emotion engine that analyzes emotional states based on the collected information. This allows the process from information collection to profile generation, publication, feedback, and emotion analysis to be effectively executed as a series of processes, enabling users to efficiently create and manage personalized and highly accurate profiles.

[1412] "User" means an individual or organization that uses the system to create and manage a profile.

[1413] "Information" refers to data collected from users, including survey responses, online platform data, image data, and evaluations by others.

[1414] A "generation system" is a combination of software or hardware that analyzes collected information, classifies it into different categories, and generates a profile.

[1415] A "template" is a predetermined format for applying the generated profile depending on the purpose, and includes types such as for business and hobby.

[1416] "Feedback" refers to opinions and ratings collected from others regarding a publicly available profile.

[1417] An "emotion engine" is a software or hardware system that analyzes a user's emotional state based on collected information.

[1418] A "profile" is a collection of data that shows the overview, history, and characteristics of an individual or organization, generated based on the user's information.

[1419] "Publication and sharing settings" refers to a setting and management function for making the created profile viewable by others.

[1420] "Modifying or adding" refers to updating an existing profile or adding new items based on collected feedback and sentiment analysis results.

[1421] MODE FOR CARRYING OUT THE INVENTION

[1422] This invention is a system that allows users to efficiently create and manage profiles. The embodiment of the invention includes the following series of processes: information collection, information analysis, profile creation, profile publication and sharing, update based on feedback, and emotion analysis using an emotion engine.

[1423] First, the user accesses the questionnaire form and enters their own information (likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The database typically uses an SQL or NoSQL database management system.

[1424] Next, the user gives permission to link their social media account. The server uses the API to retrieve follow information and post data from the social media and stores it in a database. For example, by using the Twitter API, it is possible to retrieve a user's tweets and follower list.

[1425] Furthermore, the user allows the system to access the photo data stored on the smartphone. The device analyzes the metadata of the photos (location information, tags, dates, etc.) and sends the contents to the server for storage. For example, the location information and dates contained in the photos stored on the device can be analyzed and saved as a record of a specific event or trip.

[1426] The server also sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected feedback and ratings from others in a database. This feedback includes the user's evaluation of the project and the characteristics of the project as seen by others.

[1427] The server sends all collected data to a generation system, which analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.). For example, the generation system uses natural language processing (NLP) techniques to analyze text data and extract keywords.

[1428] The server sends the collected data to the emotion engine, which analyzes the user's emotions from survey responses, social media data, photo data, and other people's ratings. For example, if there are many positive posts, it is analyzed as "positive emotion." The emotion engine then sends the analysis results to the generation system.

[1429] Next, the server generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user can select the profile items they want to use from the generated profile items and also select a profile template that suits their purpose. The server combines the selected items and template to generate a complete profile. The user can name and save the generated profile (e.g., "Business Profile"), and the server registers the saved profile in its database.

[1430] Users select the profile they want to make public and share and configure it accordingly. The server then publishes the selected profile to other users based on the user's settings. Other users can view the published profile and provide feedback.

[1431] The server sends the collected feedback to the generation system and emotion engine for re-analysis. This generates recommendations for profile modifications and additions. The user checks the recommendations and modifies or adds to their profile as necessary. The server then saves the modified or added profile back to the database and updates the public information.

[1432] Additionally, a generative AI model is used to generate profiles, so specific profile items can be generated by entering prompts such as:

[1433] "Generate a profile for a professional with 5 years of experience in the IT industry."

[1434] "What are the characteristics of an engineer who is good at software development?"

[1435] This allows for the creation of personalized and highly accurate profiles of users based on the information and feedback collected.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Step 1:

[1438] The user accesses a questionnaire form provided by the system and enters their own information (likes, dislikes, past work history, strengths, weaknesses).

[1439] Input: Survey response data entered by the user.

[1440] Data processing: The server receives the data entered by the user in the form in real time and stores it in a database.

[1441] Output: User information stored in the database.

[1442] Specific behavior: When the submit button on the form is pressed, the server receives a POST request and stores the data in the backend database.

[1443] Step 2:

[1444] Users give permission to link their social media accounts to the system.

[1445] Input: User's social media account link information.

[1446] Data processing: The server uses the API to obtain follow information and post data from social media.

[1447] Output: Social media data stored in a database.

[1448] Specific operation: The server-side backend calls the API endpoints of each social media platform, analyzes the responses, and stores them in a database.

[1449] Step 3:

[1450] The user allows access to the photo data in the smartphone.

[1451] Input: Permission to access photo data on the user's smartphone.

[1452] Data processing: The device analyzes the photo's metadata (location, tags, date, etc.).

[1453] Output: Photo metadata sent and stored on the server.

[1454] Specific operation: The system crawls the photo data stored on the smartphone, extracts metadata, and centralizes it. The device then sends the data to the server.

[1455] Step 4:

[1456] The server sends feedback requests to others in the user's network.

[1457] Input: The user's contact list.

[1458] Data processing: Feedback requests are sent.

[1459] Output: The collected feedback data is stored in a database.

[1460] What happens: The server looks up the user's contact list and sends them an email or notification requesting feedback. Friends and colleagues provide feedback, and the data is stored on the server.

[1461] Step 5:

[1462] The server sends all collected data to the production system.

[1463] Input: Survey responses stored in a database, social media data, photo metadata, and feedback from others.

[1464] Data processing: Send various data to the generation system in bulk.

[1465] Output: Data sent to the generating system.

[1466] Specific operation: The server extracts the necessary data from the database and sends it to the generation system via an API call.

[1467] Step 6:

[1468] The generation system analyzes the data and classifies it into different categories.

[1469] Input: Data sent to the generating system (survey responses, social media data, photo metadata, feedback from others).

[1470] Data processing: Analyze text data using natural language processing (NLP) techniques and extract keywords.

[1471] Output: Categorized data.

[1472] Specific operation: The generation system analyzes the data it receives and classifies it into categories such as "likes," "dislikes," and "career."

[1473] Step 7:

[1474] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[1475] Input: Parsed data and sentiment analysis results.

[1476] Data processing: The generated profile items are presented to the user.

[1477] Output: The profile items presented to the user.

[1478] Specific operation: The server sends the data received from the generation system to the front end and presents it to the user.

[1479] Step 8:

[1480] The user selects the profile items he or she wishes to adopt from the generated profile items.

[1481] Input: The profile items presented to the user.

[1482] Data processing: The user selects the profile items to adopt.

[1483] Output: The selected profile items.

[1484] Specific operation: The user selects the profile items they wish to adopt from the displayed profile items.

[1485] Step 9:

[1486] The user selects a profile template according to the purpose.

[1487] Input: The purpose of the profile (for business, hobby, etc.).

[1488] Data processing: The appropriate template is applied.

[1489] Output: Profile items with applied template.

[1490] Specific operation: The user selects a predetermined template (for example, a business template), and the server aggregates the data based on it.

[1491] Step 10:

[1492] The server combines the selected items with the template to generate a complete profile.

[1493] Input: Selected profile items and template.

[1494] Data processing: merging items and templates.

[1495] Output: The complete profile generated.

[1496] Specific Actions: The server applies the selected items to the template and synthesizes the final profile.

[1497] Step 11:

[1498] The user names and saves the created profile.

[1499] Input: The generated profile.

[1500] Data processing: Name your profile.

[1501] Output: Named profiles.

[1502] Specific behavior: The user names the profile "Business Profile" and clicks the save button.

[1503] Step 12:

[1504] The server registers the saved profile in a database.

[1505] Input: A named profile.

[1506] Data processing: Registration of profile data in database.

[1507] Output: The profile registered in the database.

[1508] Specific operation: The server triggers a save action to save the profile data to the database.

[1509] Step 13:

[1510] Users can select the profiles they want to make public and share and set them up.

[1511] Input: A profile registered in the database.

[1512] Data processing: Applying public / sharing settings.

[1513] Output: Profile with public / shared settings applied.

[1514] Specific operation: The user selects "Business Profile" and sets the visibility.

[1515] Step 14:

[1516] The server publishes the selected profile to other users based on the user's settings.

[1517] Input: Profile with visibility settings applied.

[1518] Data processing: Generation and distribution of public URLs.

[1519] Output: Your profile as it appears to other users.

[1520] Specific behavior: The server generates a viewable URL based on the publishing settings and provides access to other users.

[1521] Step 15:

[1522] The server collects feedback from others on the published profile.

[1523] Input: Feedback from others.

[1524] Data processing: collection and storage of feedback data.

[1525] Output: Feedback stored in a database.

[1526] Specific Actions: Other users view your public profile and provide feedback, such as "I'd like to know more about your specific projects."

[1527] Step 16:

[1528] The server sends the collected feedback to the generation system and emotion engine for re-analysis.

[1529] Input: Feedback stored in the database.

[1530] Data processing: Analysis of feedback with generative systems and emotion engines.

[1531] Output: Recommendations for corrections and additions based on the analysis results.

[1532] Specific operation: The server extracts the feedback data and sends it to the generation system and emotion engine.

[1533] Step 17:

[1534] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1535] Input: Suggested corrections or additions.

[1536] Data processing: Modifying your profile or applying additional details.

[1537] Output: The updated profile.

[1538] Specific operation: The user checks the recommendations and makes corrections on the profile editing screen.

[1539] Step 18:

[1540] The server saves the modified or added profile back into the database and updates the public information.

[1541] Input: Updated profile.

[1542] Data processing: Resave your profile and update public information.

[1543] Output: Updated public profile.

[1544] Specific operation: The server saves the updated profile data to the database and regenerates the public URL.

[1545] (Application example 2)

[1546] 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."

[1547] Currently, many users use online shopping, but there is a lack of efficient methods for recommending products that match their tastes and preferences. As a result, users often spend a lot of time trying to find products that suit them. Furthermore, online shopping sites lack the means to accurately grasp users' tastes and preferences, making it difficult to make effective product recommendations. To address this issue, a system is needed that can efficiently collect and analyze user information and provide personalized product recommendations.

[1548] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1549] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, and means for recommending a variety of products to users in a personalized manner, thereby enabling efficient recommendation of optimal products based on the user's detailed preferences and past behavioral data.

[1550] "User" means an individual who uses the System to provide information and utilizes the generated profile.

[1551] "Means of collection" refers to means for collecting questionnaire forms, data from online platforms, image data, and evaluations by others.

[1552] "Generation system" refers to a system that has the ability to analyze collected information and generate a profile.

[1553] "Analyzed information" refers to data that has been analyzed and categorized from collected raw data.

[1554] "Profile" refers to data including descriptive text and recommendation information generated based on user information.

[1555] "Template" refers to a format for formatting the generated profile as text.

[1556] "Means for generating" refers to means for automatically generating profile items based on the analyzed information.

[1557] "Means for storing" refers to means for storing the generated profile in a database.

[1558] "Means for management" refers to means for setting public and sharing settings for the created profile.

[1559] "Means of collecting feedback from others" refers to means of collecting opinions and ratings from third parties regarding a public profile.

[1560] "Means for Analyzing Feedback" means means for analyzing collected feedback and providing recommendations regarding profile modifications and additions.

[1561] "Personalized recommendation methods" refer to methods that recommend the most suitable products based on the user's information and preferences.

[1562] To implement the present invention, the elements of the server, the terminal, and the user must work in cooperation with each other.

[1563] First, the user accesses a questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The user also gives permission to link their online platform account to the system. This link allows the server to use an API to obtain follow information and post data from the online platform and store it in the database. The user also allows the server to access the photo data on their smartphone, and the device analyzes the photo metadata (location information, tags, date, etc.) and sends the content to the server. The server then sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected ratings and feedback from others in the database.

[1564] Next, the server sends all collected data to the generation system. The generation system analyzes the survey responses, online platform data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career, etc.). The server then sends the collected data to the emotion engine. The emotion engine analyzes the user's emotions from the survey responses, online platform data, photo data, other people's ratings, etc. and sends the analysis results to the generation system.

[1565] The server then generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user selects the profile items they want to use from the generated profile items and selects a profile template appropriate for their purpose (e.g., business, hobby, etc.). The server combines the selected items and template to generate a complete profile. The user names and saves the generated profile (e.g., "Business Profile"), and the server registers the saved profile in a database.

[1566] Publication and sharing are also important elements. Users select the profiles they want to publish and share and configure them. The server publishes the selected profiles to other users based on the user's settings. Viewing users view the published profiles and provide feedback. The server sends the collected feedback to the generation system and emotion engine for analysis. As a result, it provides recommendations for modifying or adding items to the profile. Users check the recommendations from the generation system and emotion engine and modify or add to their profile as necessary. The server saves the modified or added profiles back into the database and updates the published content.

[1567] This system realizes the function of recommending a variety of products to users in a personalized manner. As a specific example, it can efficiently recommend optimal products based on the user's detailed preferences and past behavioral data. For example, when a user logs in to a shopping site and creates their own profile, products related to the user's tastes and hobbies are automatically recommended based on that profile.

[1568] As a concrete example, a detailed profile can be generated by inputting the following prompt sentence into the generative AI model:

[1569] User Information:

[1570] Name: User A

[1571] Email: user@example.com

[1572] Date of Birth: 1980-05-15

[1573] Social Media Data:

[1574] Topics: Technology, Gadgets, Healthcare

[1575] Photo data:

[1576] Date: 2023-09-10

[1577] Location: City, Country

[1578] Tags: Travel, Tourism

[1579] Others' ratings:

[1580] Email: friend@example.com Feedback: I love to travel and have an inquisitive personality.

[1581] Fixes based on feedback:

[1582] "Based on interest data from users, we recommend products, especially those related to technology and gadgets, as well as travel accessories. We provide a personalized shopping experience with constructive feedback sharing the latest information on travel and gadgets."

[1583] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1584] Step 1:

[1585] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The entered information is sent to the server and stored in a database. This is to collect the basic data needed to generate a profile.

[1586] Step 2:

[1587] The user gives permission to link their online platform account (e.g., social media) to the system. The server uses the API to obtain follow information and post data from the online platform and stores it in a database. This allows data reflecting the user's interests to be collected.

[1588] Step 3:

[1589] The user allows access to the photo data stored on their smartphone. The device analyzes the photo's metadata (location, tags, date, etc.) and sends the information to the server. The server stores the information in a database. The location, date, tags, etc. indicate the characteristics of the photo and are used to identify the user's area of ​​activity and areas of interest.

[1590] Step 4:

[1591] The server sends feedback requests to others in the user's network (friends, colleagues, etc.) via email or other means. When others provide feedback, it is collected by the server and stored in a database. The objective evaluations of others complement the user's profile from multiple angles.

[1592] Step 5:

[1593] The server sends all collected data to the generation system, which analyzes survey responses, online platform data, photo data, and other users' ratings and categorizes the information into different categories (e.g., likes, dislikes, career history, etc.). The categorized data is used to generate profile items.

[1594] Step 6:

[1595] The server sends the collected data to the emotion engine, which analyzes the user's emotions based on survey responses, online platform data, photo data, and other users' ratings. The analysis results are sent to the generation system, which generates profile items that reflect the user's emotional state.

[1596] Step 7:

[1597] The server generates profile items based on the analysis results and sentiment analysis results and presents them to the user. The user selects the profile items they want to adopt from the generated profile items. The selected information is sent to the server.

[1598] Step 8:

[1599] The user selects a profile template based on their intended use (e.g., business, hobby, etc.), and the server combines the selected items with the template to generate a complete profile, which is then stored and managed in a database.

[1600] Step 9:

[1601] Users select the profiles they want to make public and share, and configure them accordingly. The server then publishes the selected profiles to other users based on the user's settings, allowing browsing users to view the published profiles.

[1602] Step 10:

[1603] The server collects feedback provided by browsing users and stores it in a database. The collected feedback is sent to the generation system and emotion engine for analysis. Based on the analysis results, recommendations for profile modifications and additions are provided to the user.

[1604] Step 11:

[1605] Users can review the recommendations from the generation system and emotion engine, and modify or add to their profile as needed. The modified or added profile is saved back in the database, and the public information is updated.

[1606] Step 12:

[1607] Finally, the generated profile data is used to recommend various products to users in a personalized manner. The server efficiently recommends optimal products based on the user's detailed preferences and past behavioral data. As a result, users can enjoy an individually customized shopping experience.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] [Fourth embodiment]

[1612] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1613] 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.

[1614] 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).

[1615] 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.

[1616] 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.

[1617] 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).

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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."

[1625] This invention is a system for streamlining profile creation, automating and supporting the series of processes of information collection, information analysis, profile creation, profile publication and sharing, and updating based on feedback. To implement this system, the following specific elements are required:

[1626] 1. Collection of information

[1627] 1.1 Survey form

[1628] The user accesses the questionnaire form and inputs information about themselves (such as likes, dislikes, past work history, strengths, weaknesses, etc.).

[1629] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1630] 1.2 Collection of social media data

[1631] Users give permission to link their social media accounts to the system.

[1632] The server uses the API to retrieve data such as follow information and posts from social media and store it in a database.

[1633] 1.3 Collection of smartphone photo data

[1634] The user allows access to the photo data in the smartphone.

[1635] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[1636] 1.4 Collecting peer reviews

[1637] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1638] The server stores the collected evaluations of others in a database.

[1639] 2. Analysis of Information

[1640] The server sends all collected data to the production system.

[1641] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.).

[1642] 3. Create a profile

[1643] The server presents the user with profile items generated based on the analysis results.

[1644] The user selects from the presented items and chooses a template according to the purpose (for example, for business, hobby, etc.).

[1645] The server combines the selected items with the template to generate a complete profile.

[1646] The user names and saves the created profile.

[1647] The server registers the saved profile in a database.

[1648] 4. Profile Publishing and Sharing

[1649] Users can select the profiles they want to make public and share and set them up.

[1650] The server publishes the selected profile to other users based on the user's settings.

[1651] Viewing users can view public profiles and provide feedback.

[1652] 5. Feedback-driven updates

[1653] The server analyzes the collected feedback and sends it to the production system.

[1654] The generator analyzes the feedback and provides recommendations for profile modifications and additions.

[1655] The user reviews the recommendations from the generation system and makes modifications or additions to the profile.

[1656] The server stores the modified or added profile in the database again and updates the public information as necessary.

[1657] Specific examples

[1658] For example, when a user creates a business profile, the following steps are taken:

[1659] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[1660] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[1661] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[1662] 4. The server sends the collected data to the generation system, which analyzes the user's work history and areas of expertise.

[1663] 5. The generation system generates items such as "5 years of experience in the IT industry," "Good at software development," and "Has project management skills."

[1664] 6. The user selects these items and puts them into a business template.

[1665] 7. The server saves the generated profile and publishes it with the user's settings.

[1666] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[1667] 9. The server sends feedback to the generation system and makes a recommendation to "Add details of successful projects" as a result of the analysis.

[1668] 10. The user can then modify their profile based on the recommendations and republish it.

[1669] As described above, the present invention provides consistent support from profile collection to publication and improvement based on feedback, thereby realizing efficient profile creation.

[1670] The processing flow will be explained below.

[1671] Step 1:

[1672] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1673] Step 2:

[1674] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1675] Step 3:

[1676] Users give permission to link their social media accounts to the system.

[1677] Step 4:

[1678] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[1679] Step 5:

[1680] The user allows access to the photo data in the smartphone.

[1681] Step 6:

[1682] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[1683] Step 7:

[1684] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1685] Step 8:

[1686] The server stores the collected evaluations and feedback from others in a database.

[1687] Step 9:

[1688] The server sends all collected data to the production system.

[1689] Step 10:

[1690] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1691] Step 11:

[1692] The server generates profile items based on the analysis results and presents them to the user.

[1693] Step 12:

[1694] The user selects the profile items they wish to adopt from the generated profile items.

[1695] Step 13:

[1696] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1697] Step 14:

[1698] The server combines the selected items with the template to generate a complete profile.

[1699] Step 15:

[1700] The user names and saves the created profile (e.g., "Business Profile").

[1701] Step 16:

[1702] The server registers the saved profile in a database.

[1703] Step 17:

[1704] Users select the profiles they wish to make public and share.

[1705] Step 18:

[1706] The server publishes the selected profile based on the settings.

[1707] Step 19:

[1708] Viewers can review public profiles and provide feedback.

[1709] Step 20:

[1710] The server stores the provided feedback in a database and sends it to the generation system.

[1711] Step 21:

[1712] The generation system analyzes the collected feedback and provides recommendations for profile modifications and additions.

[1713] Step 22:

[1714] Users can review the recommendations from the generation system and modify or add to their profile as needed.

[1715] Step 23:

[1716] The server saves the modified or added profile information back into the database and updates the public information.

[1717] Example 1

[1718] 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."

[1719] The purpose of this invention is to provide a system that allows users to easily and efficiently create, manage, and share their own profiles. In particular, the system aims to realize a system that collects information from various sources, analyzes it, automatically generates a profile, and enables users to update their profile based on feedback, thereby significantly reducing the time and effort required by the user.

[1720] 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.

[1721] In this invention, the server includes means for collecting information from users, means for saving the collected information in real time, means for acquiring data from social media, means for acquiring and analyzing photo metadata from terminals, means for collecting feedback from others in the network, means for using a generation system to analyze the collected information, means for classifying the analyzed information into different categories, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for a user to select generated profile items, means for saving the generated profile, means for managing profiles that can be set to public and shareable, means for collecting feedback from others on published profiles, and means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, thereby enabling users to efficiently create, manage, and share their own profiles without hassle.

[1722] "User" refers to any individual or business that uses the System to create, manage, and share their profile.

[1723] "Means for collecting information" refers to methods and devices for obtaining information from users, such as survey responses, social media data, photo metadata, and ratings from others.

[1724] "Real-time storage means" refers to methods and technologies for instantly recording collected information in a storage device such as a database.

[1725] "Means of obtaining data from social media" refers to methods and technologies that use social media APIs to obtain user following information, posting data, etc.

[1726] "Means for obtaining and analyzing photo metadata" refers to methods and technologies for obtaining and analyzing photo meta information (location information, tags, dates, etc.) from a user's device.

[1727] "Means for collecting feedback" refers to methods and techniques for collecting opinions and ratings from third parties, such as the user's friends and colleagues.

[1728] "Generation system" refers to the artificial intelligence model or platform that analyzes collected information and generates profiles.

[1729] "Means of categorizing information into different categories" refers to methods and techniques for separating collected data into appropriate categories (likes, dislikes, history, etc.).

[1730] "Profile generating means" refers to methods or techniques for creating a user profile based on the analyzed information.

[1731] "Template filling" refers to the method or technique for adapting the generated profile to a format appropriate for the application.

[1732] "Means for selecting profile items" refers to the interface or technology that allows a user to review and select each item in the generated profile.

[1733] "Means for storing a profile" refers to a method or technology for recording and storing the generated profile in a database or the like.

[1734] "Profile Management Means" refers to methods or technologies that allow users to manage the public or sharing settings of their stored profile.

[1735] "Feedback collection means" refers to methods and technologies for collecting opinions and ratings from other users regarding a public profile.

[1736] "Means for providing recommendations for profile modifications or additions" refers to methods or technologies for suggesting modifications or additions to a profile based on collected feedback.

[1737] The present invention relates to a system that allows users to efficiently create, manage, and share personal profiles. The system based on the present invention includes steps of collecting information, analyzing, creating profiles, publishing and sharing, and collecting and updating feedback.

[1738] Hardware and software used

[1739] To implement this system, the following hardware and software are required.

[1740] Survey form: A web form built using HTML and JavaScript.

[1741] Database: A relational database such as MySQL or PostgreSQL.

[1742] Social Media API: Uses Twitter API and Facebook Graph API to obtain social media data.

[1743] Smartphone data analysis: Uses Android SDK and iOS SDK.

[1744] Generative Systems: Generative AI models such as OpenAI GPT-4.

[1745] Collection of information

[1746] First, the user accesses the questionnaire form and enters their information, such as "likes," "dislikes," and "past work history." The entered information is sent to the server in real time and stored in a database.

[1747] Next, the user gives permission to link their social media account to the system, and the server uses an API to retrieve follow information and post data from the social media and store it in a database.

[1748] Furthermore, the user allows the system to access the photo data stored on the smartphone. The system analyzes the metadata of the photos (location, tags, date, etc.) to identify their contents. The analyzed data is sent to a server and stored in a database.

[1749] Finally, the server sends feedback requests to friends and colleagues in the user's network and stores the collected ratings of others in a database.

[1750] Analysis of information

[1751] The server sends all collected data to a generation system (e.g., OpenAI GPT-4), which analyzes survey responses, social media data, photo data, and peer ratings and categorizes each piece of information into appropriate categories (likes, dislikes, career history, etc.).

[1752] Generate a profile

[1753] The profile items generated based on the analysis results are presented to the user. The user can choose from these items and select a template, such as for business or leisure. The server combines the selected items with the template to generate a complete profile. The generated profile is stored in a database, and the user can update and manage it as needed.

[1754] Profile Publishing / Sharing

[1755] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the user's settings. Third parties can access the public profile and provide feedback.

[1756] Updates based on feedback

[1757] The server analyzes the collected feedback and sends it to a generation system (e.g., OpenAI GPT-4). The generation system generates recommendations for profile modifications and additions based on the feedback. The user can review these recommendations and modify or add to their profile. This ensures that the user's profile is always up-to-date and optimal.

[1758] Specific examples

[1759] For example, if a user is creating a profile for business use, they might use the following prompt:

[1760] "Please provide us with information about your work history in the IT industry and your area of ​​expertise in software development. We will generate a profile based on this information."

[1761] The above is an embodiment of the system of the present invention.

[1762] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1763] Step 1:

[1764] The user accesses the questionnaire form and enters information such as likes, dislikes, past work history, strengths, and weaknesses. After entering the information, they press the submit button. This input information is sent to the server in JSON format. The server parses the received JSON data and saves it in a MySQL or PostgreSQL database. Items such as "Favorite food: Sushi" and "Strengths: Programming" are saved in the database.

[1765] Step 2:

[1766] Users grant permission to link their social media accounts to the system. The server obtains a token through OAuth authentication and uses this token to call the Twitter API or Facebook Graph API. The follow list and post data obtained from the API are then stored in a database after business-related keywords are extracted. For example, information such as "there are many programmers on the follow list" or "there are many programming-related articles in the posts" is stored in the database.

[1767] Step 3:

[1768] The user allows access to the photo data stored on their smartphone. The device uses the Android SDK or iOS SDK to analyze the photo metadata (location, tags, date, etc.). The analysis results are sent to the server and stored in a database. For example, the data might be saved as "Travel Photos, Hawaii, March 2022."

[1769] Step 4:

[1770] The server then sends feedback requests to friends and colleagues within the user's network. These people then respond with their ratings via a web form. The collected feedback is then stored in a database. For example, a rating such as "Mr. / Ms. X has excellent communication skills" may be recorded.

[1771] Step 5:

[1772] The server sends all collected data to a generation system (e.g., OpenAI GPT-4). The generation system analyzes the data and classifies the information into different categories (likes, dislikes, work history, etc.). The results of this analysis are passed to the server and stored in a database. For example, an item such as "sushi in the likes category and high places in the dislikes category" may be registered.

[1773] Step 6:

[1774] The server generates profile items based on the analysis results and presents them to the user via a web interface. The user selects from the displayed items those that best suit their purpose (business, hobby, etc.). For example, items such as "5 years of experience in the IT industry" and "good at software development" can be selected.

[1775] Step 7:

[1776] The server combines the selected items with the template to generate a complete profile, which is then stored in a database. The user can then name the profile (e.g., "Business Profile") and save it.

[1777] Step 8:

[1778] Users select the profile they want to make public and share, and configure it accordingly. The server then makes the profile public to other users based on the settings. Viewers can access the published profile and provide feedback.

[1779] Step 9:

[1780] The server analyzes the collected feedback and sends it to a generation system, which generates recommendations for profile modifications and additions based on the feedback, such as "add more detailed project information."

[1781] Step 10:

[1782] Users can check the recommendations from the generation system and modify or add to their profile. The server saves the modified or added profile back into the database and updates the public information. This allows profiles to be kept up to date with the latest information.

[1783] (Application example 1)

[1784] 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."

[1785] Conventional customer profile generation systems have the problem of being unable to provide customers with a fully personalized shopping experience. In particular, in brick-and-mortar stores, there is no way to effectively utilize information based on online data in real time, making it difficult to improve customer satisfaction. Additionally, the process of collecting feedback and updating profiles based on it is often done manually, which is time-consuming and labor-intensive, making it inefficient.

[1786] 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.

[1787] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, and means for generating a profile based on the analyzed information. This allows for personalized product recommendations using customer data. Furthermore, adding means for presenting information based on the recommendations on a visual display can enhance the customer's shopping experience.

[1788] "User" means any person or entity that uses the System.

[1789] "Information collection means" refers to a method or device for obtaining necessary data from users.

[1790] A "generation system" is a combination of software or hardware that analyzes collected information and generates a profile.

[1791] "Template" refers to a predefined form or format into which a generated profile may be inserted.

[1792] The "publication and sharing setting means" refers to a setting function for enabling the created profile to be shared with others.

[1793] "Public Profile" refers to profile information that is made available for viewing by others based on a user's settings.

[1794] "Feedback collection means" refers to a method or device for collecting opinions and ratings from others regarding a published profile.

[1795] "Individualized product proposal means" refers to a function that proposes the most suitable products and services to individual customers based on collected and analyzed customer data.

[1796] "Visual display means" refers to a device or function for visually presenting information using a smart device or the like.

[1797] System Configuration

[1798] This invention provides a system that collects and analyzes customer information, creates profiles, and makes personalized product recommendations in physical stores. This system is composed of a server, terminals, and users.

[1799] 1. Collection of information

[1800] The server collects information from users. Specifically, users access a survey form and enter and link their preferences, past purchase history, social media accounts, etc. The front end of the survey form is built using React Native, and data is stored in Firebase. Social media data is obtained using the Twitter API and Instagram API.

[1801] 2. Analysis of Information

[1802] The server sends the collected information to a generation system, which uses Python and TensorFlow to analyze the user data and identify patterns of user preferences.

[1803] 3. Create a profile

[1804] The server generates profile items based on the analysis results and presents them to the user. These profiles are generated through a system built with Django and the database is PostgreSQL. The user selects from the presented items and inputs them into a template appropriate for the purpose.

[1805] 4. Profile Publishing and Sharing

[1806] The generated profile is published to smart devices (smartphones and smart glasses). The server implements the public API with Flask, and ARKit or ARCore is used for the smart glasses as a visual display.

[1807] 5. Feedback-driven updates

[1808] Feedback from others is collected on your public profile and sent to a server, where it is stored in Firebase and fed back to the analytics engine for new analysis, ensuring constantly updated recommendations.

[1809] Typical use cases

[1810] When a user visits a supermarket, new product suggestions and special sale information will be displayed on the smart glasses based on the user's pre-registered profile data, and this information will be updated in real time to improve the user's shopping experience.

[1811] Prompt Sentence Examples

[1812] "Build an application that will make optimal product recommendations to customers in a supermarket based on their profile data. This application must include the following elements: data analysis using Python and TensorFlow, a front-end built with React Native, an API for collecting social media data (Twitter, Instagram), information displayed on smart glasses, and a system configuration that can update recommendations based on feedback."

[1813] This invention makes it possible to provide customers with a personalized shopping experience and improve customer satisfaction in physical stores.

[1814] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1815] Step 1:

[1816] The server collects information from users. Specifically, users access a survey form and input and link their preferences, past purchase history, social media accounts, etc. The survey form is built using React Native, and the collected data is saved in Firebase in real time. The input is the survey content and social media link information, and the output is a structured dataset.

[1817] Step 2:

[1818] The server sends the collected information to the generation system. Specifically, it acquires social media data using the Twitter API and Instagram API, and combines this with data from the survey and image data (metadata from photos stored on smartphones). The input is a structured dataset and the acquired social media data, and the output is an integrated dataset.

[1819] Step 3:

[1820] The server uses a generative system (Python and TensorFlow) to analyze the collected information and identify user preference patterns. Natural language processing (NLP) and clustering algorithms are used to classify the data into categories such as "likes," "dislikes," "past work history," and "strengths." The input is the integrated dataset, and the output is a data object containing the analyzed results.

[1821] Step 4:

[1822] The server generates a profile based on the analysis results and feeds it into templates for multiple purposes. Using a generation system (Django), it generates and presents profile items that the user can select. The input is the analysis data object, and the output is the profile items presented to the user.

[1823] Step 5:

[1824] The user selects the generated profile items and then chooses the most suitable template. The server generates a complete profile based on this selection and stores it in PostgreSQL. The input is the user's selections and the output is the stored profile data.

[1825] Step 6:

[1826] The server manages profiles that can be publicly and shared, and displays them on smart devices as needed. The public API is implemented in Flask, and information is displayed on smart glasses or smartphones through a visual display (ARKit or ARCore). The input is the saved profile data and the user's public settings, and the output is the information displayed on the visual display.

[1827] Step 7:

[1828] Others provide feedback on public profiles, which is stored by the server in Firebase. The input is feedback information from others, and the output is the stored feedback data.

[1829] Step 8:

[1830] The server analyzes the collected feedback and provides recommendations for profile modifications and additions. It uses a generation system (Python) to perform the analysis and generate suggestions based on the feedback. The input is the stored feedback data, and the output is the analysis results and recommendations.

[1831] Step 9:

[1832] The user confirms the recommended modifications and additions and updates their profile. The server saves the modified / added profile back to PostgreSQL and updates the public content as necessary. The input is the recommended items and the user's modifications, and the output is the updated profile data.

[1833] 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.

[1834] The present invention is a system for streamlining profile creation, supporting a series of processes including information collection, information analysis, profile creation, profile publication and sharing, updating based on feedback, and emotion analysis using an emotion engine. Specifically, to implement the present invention, the following elements are required:

[1835] 1. Collection of information

[1836] 1.1 Survey form

[1837] The user accesses the questionnaire form and inputs their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1838] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1839] 1.2 Collection of social media data

[1840] Users give permission to link their social media accounts to the system.

[1841] The server uses the API to obtain follow information and post data from social media and store it in a database.

[1842] 1.3 Collection of smartphone photo data

[1843] The user allows access to the photo data in the smartphone.

[1844] The device analyzes the metadata of the photo (location information, tags, date, etc.) to identify its contents, which are then sent to the server and stored.

[1845] 1.4 Collecting peer reviews

[1846] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1847] The server stores the collected evaluations and feedback from others in a database.

[1848] 2. Analysis of Information

[1849] The server sends all collected data to the production system.

[1850] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1851] 3. Adding an Emotion Engine

[1852] The server sends the collected data to the emotion engine.

[1853] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[1854] The emotion engine sends the analysis results to the generation system.

[1855] 4. Create a profile

[1856] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[1857] The user selects the profile items they wish to adopt from the generated profile items.

[1858] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1859] The server combines the selected items with the template to generate a complete profile.

[1860] The user names and saves the created profile (e.g., "Business Profile").

[1861] The server registers the saved profile in a database.

[1862] 5. Profile Publishing and Sharing

[1863] Users can select the profiles they want to make public and share and set them up.

[1864] The server publishes the selected profile to other users based on the user's settings.

[1865] Viewing users can view public profiles and provide feedback.

[1866] 6. Feedback-driven updates

[1867] The server sends the collected feedback to the generation system and the emotion engine.

[1868] The generative system and sentiment engine analyze the feedback and provide recommendations for profile modifications and additions.

[1869] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1870] The server saves the modified or added profile information back into the database and updates the public information.

[1871] Specific examples

[1872] For example, when a user creates a business profile, the following steps are taken:

[1873] 1. Users fill out a questionnaire form and provide information about their work history in the IT industry and their area of ​​expertise, software development.

[1874] 2. The server retrieves relevant posts and follow lists from the user's social media and extracts business-related keywords.

[1875] 3. The device analyzes photo data from the user's smartphone showing the progress of the project.

[1876] 4. The server sends the collected data to the generation system and emotion engine to analyze the user's work history, areas of expertise, and emotional state.

[1877] 5. The generation system and emotion engine generate items that reflect “5 years of experience in the IT industry,” “good at software development,” “project management skills,” and “positive emotions upon project success.”

[1878] 6. The user selects these items and puts them into a business template.

[1879] 7. The server saves the generated profile and publishes it with the user's settings.

[1880] 8. Other users will view your public profile and provide feedback, such as "I'd like to know more about your specific project."

[1881] 9. The server sends the feedback to the generation system and emotion engine, and the analysis results in a recommendation to "Add details of successful projects."

[1882] 10. The user can then modify their profile based on the recommendations and republish it.

[1883] As described above, the present invention provides integrated support for all processes of information collection, analysis, generation, publication, feedback, and sentiment analysis, thereby realizing efficient profile creation.

[1884] The processing flow will be explained below.

[1885] Step 1:

[1886] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses).

[1887] Step 2:

[1888] The server collects the information entered into the questionnaire form in real time and stores it in a database.

[1889] Step 3:

[1890] Users give permission to link their social media accounts to the system.

[1891] Step 4:

[1892] The server uses the API to obtain follow information and post data from social media and stores it in a database.

[1893] Step 5:

[1894] The user allows access to the photo data in the smartphone.

[1895] Step 6:

[1896] The device analyzes the metadata of the photo (location, tags, date, etc.) to identify its contents, which are then sent to the server and stored in a database.

[1897] Step 7:

[1898] The server sends feedback requests to others in the user's network (friends, colleagues, etc.).

[1899] Step 8:

[1900] The server stores the collected evaluations and feedback from others in a database.

[1901] Step 9:

[1902] The server sends all collected data to the production system.

[1903] Step 10:

[1904] The generation system analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, history, etc.).

[1905] Step 11:

[1906] The server sends the collected data to the emotion engine.

[1907] Step 12:

[1908] The emotion engine analyzes user emotions based on survey responses, social media data, photo data, and other users' ratings.

[1909] Step 13:

[1910] The emotion engine sends the results of the emotion analysis to the generation system.

[1911] Step 14:

[1912] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[1913] Step 15:

[1914] The user selects the profile items they wish to adopt from the generated profile items.

[1915] Step 16:

[1916] The user selects a profile template according to the purpose (for example, for business, hobby, etc.).

[1917] Step 17:

[1918] The server combines the selected items with the template to generate a complete profile.

[1919] Step 18:

[1920] The user names and saves the created profile (e.g., "Business Profile").

[1921] Step 19:

[1922] The server registers the saved profile in a database.

[1923] Step 20:

[1924] Users select the profiles they wish to make public and share.

[1925] Step 21:

[1926] The server publishes the selected profile based on the settings.

[1927] Step 22:

[1928] Viewers can review public profiles and provide feedback.

[1929] Step 23:

[1930] The server stores the provided feedback in a database and sends it to the generation system and emotion engine.

[1931] Step 24:

[1932] The generative system and sentiment engine analyze the collected feedback and provide recommendations for profile modifications and additions.

[1933] Step 25:

[1934] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[1935] Step 26:

[1936] The server saves the modified or added profile information back into the database and updates the public information.

[1937] Example 2

[1938] 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."

[1939] In modern society, users increasingly need to efficiently create and manage their own profiles from a wide variety of information sources. However, existing systems require complex processes for information collection, analysis, and profile creation, and do not analyze users' emotions. This means that creating personalized profiles requires a great deal of time and effort. Furthermore, profile updates based on user feedback are not automated, resulting in a poor user experience. Therefore, there is a need for a more efficient profile creation system that includes emotion analysis.

[1940] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1941] In this invention, the server includes means for collecting information from users, means for using a generation system that analyzes the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, means for performing emotion analysis, and an emotion engine that analyzes emotional states based on the collected information. This allows the process from information collection to profile generation, publication, feedback, and emotion analysis to be effectively executed as a series of processes, enabling users to efficiently create and manage personalized and highly accurate profiles.

[1942] "User" means an individual or organization that uses the system to create and manage a profile.

[1943] "Information" refers to data collected from users, including survey responses, online platform data, image data, and evaluations by others.

[1944] A "generation system" is a combination of software or hardware that analyzes collected information, classifies it into different categories, and generates a profile.

[1945] A "template" is a predetermined format for applying the generated profile depending on the purpose, and includes types such as for business and hobby.

[1946] "Feedback" refers to opinions and ratings collected from others regarding a publicly available profile.

[1947] An "emotion engine" is a software or hardware system that analyzes a user's emotional state based on collected information.

[1948] A "profile" is a collection of data that shows the overview, history, and characteristics of an individual or organization, generated based on the user's information.

[1949] "Publication and sharing settings" refers to a setting and management function for making the created profile viewable by others.

[1950] "Modifying or adding" refers to updating an existing profile or adding new items based on collected feedback and sentiment analysis results.

[1951] MODE FOR CARRYING OUT THE INVENTION

[1952] This invention is a system that allows users to efficiently create and manage profiles. The embodiment of the invention includes the following series of processes: information collection, information analysis, profile creation, profile publication and sharing, update based on feedback, and emotion analysis using an emotion engine.

[1953] First, the user accesses the questionnaire form and enters their own information (likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The database typically uses an SQL or NoSQL database management system.

[1954] Next, the user gives permission to link their social media account. The server uses the API to retrieve follow information and post data from the social media and stores it in a database. For example, by using the Twitter API, it is possible to retrieve a user's tweets and follower list.

[1955] Furthermore, the user allows the system to access the photo data stored on the smartphone. The device analyzes the metadata of the photos (location information, tags, dates, etc.) and sends the contents to the server for storage. For example, the location information and dates contained in the photos stored on the device can be analyzed and saved as a record of a specific event or trip.

[1956] The server also sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected feedback and ratings from others in a database. This feedback includes the user's evaluation of the project and the characteristics of the project as seen by others.

[1957] The server sends all collected data to a generation system, which analyzes survey responses, social media data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career history, etc.). For example, the generation system uses natural language processing (NLP) techniques to analyze text data and extract keywords.

[1958] The server sends the collected data to the emotion engine, which analyzes the user's emotions from survey responses, social media data, photo data, and other people's ratings. For example, if there are many positive posts, it is analyzed as "positive emotion." The emotion engine then sends the analysis results to the generation system.

[1959] Next, the server generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user can select the profile items they want to use from the generated profile items and also select a profile template that suits their purpose. The server combines the selected items and template to generate a complete profile. The user can name and save the generated profile (e.g., "Business Profile"), and the server registers the saved profile in its database.

[1960] Users select the profile they want to make public and share and configure it accordingly. The server then publishes the selected profile to other users based on the user's settings. Other users can view the published profile and provide feedback.

[1961] The server sends the collected feedback to the generation system and emotion engine for re-analysis. This generates recommendations for profile modifications and additions. The user checks the recommendations and modifies or adds to their profile as necessary. The server then saves the modified or added profile back to the database and updates the public information.

[1962] Additionally, a generative AI model is used to generate profiles, so specific profile items can be generated by entering prompts such as:

[1963] "Generate a profile for a professional with 5 years of experience in the IT industry."

[1964] "What are the characteristics of an engineer who is good at software development?"

[1965] This allows for the creation of personalized and highly accurate profiles of users based on the information and feedback collected.

[1966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1967] Step 1:

[1968] The user accesses a questionnaire form provided by the system and enters their own information (likes, dislikes, past work history, strengths, weaknesses).

[1969] Input: Survey response data entered by the user.

[1970] Data processing: The server receives the data entered by the user in the form in real time and stores it in a database.

[1971] Output: User information stored in the database.

[1972] Specific behavior: When the submit button on the form is pressed, the server receives a POST request and stores the data in the backend database.

[1973] Step 2:

[1974] Users give permission to link their social media accounts to the system.

[1975] Input: User's social media account link information.

[1976] Data processing: The server uses the API to obtain follow information and post data from social media.

[1977] Output: Social media data stored in a database.

[1978] Specific operation: The server-side backend calls the API endpoints of each social media platform, analyzes the responses, and stores them in a database.

[1979] Step 3:

[1980] The user allows access to the photo data in the smartphone.

[1981] Input: Permission to access photo data on the user's smartphone.

[1982] Data processing: The device analyzes the photo's metadata (location, tags, date, etc.).

[1983] Output: Photo metadata sent and stored on the server.

[1984] Specific operation: The system crawls the photo data stored on the smartphone, extracts metadata, and centralizes it. The device then sends the data to the server.

[1985] Step 4:

[1986] The server sends feedback requests to others in the user's network.

[1987] Input: The user's contact list.

[1988] Data processing: Feedback requests are sent.

[1989] Output: The collected feedback data is stored in a database.

[1990] What happens: The server looks up the user's contact list and sends them an email or notification requesting feedback. Friends and colleagues provide feedback, and the data is stored on the server.

[1991] Step 5:

[1992] The server sends all collected data to the production system.

[1993] Input: Survey responses stored in a database, social media data, photo metadata, and feedback from others.

[1994] Data processing: Send various data to the generation system in bulk.

[1995] Output: Data sent to the generating system.

[1996] Specific operation: The server extracts the necessary data from the database and sends it to the generation system via an API call.

[1997] Step 6:

[1998] The generation system analyzes the data and classifies it into different categories.

[1999] Input: Data sent to the generating system (survey responses, social media data, photo metadata, feedback from others).

[2000] Data processing: Analyze text data using natural language processing (NLP) techniques and extract keywords.

[2001] Output: Categorized data.

[2002] Specific operation: The generation system analyzes the data it receives and classifies it into categories such as "likes," "dislikes," and "career."

[2003] Step 7:

[2004] The server generates profile items based on the analysis results and emotion analysis results and presents them to the user.

[2005] Input: Parsed data and sentiment analysis results.

[2006] Data processing: The generated profile items are presented to the user.

[2007] Output: The profile items presented to the user.

[2008] Specific operation: The server sends the data received from the generation system to the front end and presents it to the user.

[2009] Step 8:

[2010] The user selects the profile items he or she wishes to adopt from the generated profile items.

[2011] Input: The profile items presented to the user.

[2012] Data processing: The user selects the profile items to adopt.

[2013] Output: The selected profile items.

[2014] Specific operation: The user selects the profile items they wish to adopt from the displayed profile items.

[2015] Step 9:

[2016] The user selects a profile template according to the purpose.

[2017] Input: The purpose of the profile (for business, hobby, etc.).

[2018] Data processing: The appropriate template is applied.

[2019] Output: Profile items with applied template.

[2020] Specific operation: The user selects a predetermined template (for example, a business template), and the server aggregates the data based on it.

[2021] Step 10:

[2022] The server combines the selected items with the template to generate a complete profile.

[2023] Input: Selected profile items and template.

[2024] Data processing: merging items and templates.

[2025] Output: The complete profile generated.

[2026] Specific Actions: The server applies the selected items to the template and synthesizes the final profile.

[2027] Step 11:

[2028] The user names and saves the created profile.

[2029] Input: The generated profile.

[2030] Data processing: Name your profile.

[2031] Output: Named profiles.

[2032] Specific behavior: The user names the profile "Business Profile" and clicks the save button.

[2033] Step 12:

[2034] The server registers the saved profile in a database.

[2035] Input: A named profile.

[2036] Data processing: Registration of profile data in database.

[2037] Output: The profile registered in the database.

[2038] Specific operation: The server triggers a save action to save the profile data to the database.

[2039] Step 13:

[2040] Users can select the profiles they want to make public and share and set them up.

[2041] Input: A profile registered in the database.

[2042] Data processing: Applying public / sharing settings.

[2043] Output: Profile with public / shared settings applied.

[2044] Specific operation: The user selects "Business Profile" and sets the visibility.

[2045] Step 14:

[2046] The server publishes the selected profile to other users based on the user's settings.

[2047] Input: Profile with visibility settings applied.

[2048] Data processing: Generation and distribution of public URLs.

[2049] Output: Your profile as it appears to other users.

[2050] Specific behavior: The server generates a viewable URL based on the publishing settings and provides access to other users.

[2051] Step 15:

[2052] The server collects feedback from others on the published profile.

[2053] Input: Feedback from others.

[2054] Data processing: collection and storage of feedback data.

[2055] Output: Feedback stored in a database.

[2056] Specific Actions: Other users view your public profile and provide feedback, such as "I'd like to know more about your specific projects."

[2057] Step 16:

[2058] The server sends the collected feedback to the generation system and emotion engine for re-analysis.

[2059] Input: Feedback stored in the database.

[2060] Data processing: Analysis of feedback with generative systems and emotion engines.

[2061] Output: Recommendations for corrections and additions based on the analysis results.

[2062] Specific operation: The server extracts the feedback data and sends it to the generation system and emotion engine.

[2063] Step 17:

[2064] Users review recommendations from the generation system and emotion engine and modify or add to their profile as needed.

[2065] Input: Suggested corrections or additions.

[2066] Data processing: Modifying your profile or applying additional details.

[2067] Output: The updated profile.

[2068] Specific operation: The user checks the recommendations and makes corrections on the profile editing screen.

[2069] Step 18:

[2070] The server saves the modified or added profile back into the database and updates the public information.

[2071] Input: Updated profile.

[2072] Data processing: Resave your profile and update public information.

[2073] Output: Updated public profile.

[2074] Specific operation: The server saves the updated profile data to the database and regenerates the public URL.

[2075] (Application example 2)

[2076] 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."

[2077] Currently, many users use online shopping, but there is a lack of efficient methods for recommending products that match their tastes and preferences. As a result, users often spend a lot of time trying to find products that suit them. Furthermore, online shopping sites lack the means to accurately grasp users' tastes and preferences, making it difficult to make effective product recommendations. To address this issue, a system is needed that can efficiently collect and analyze user information and provide personalized product recommendations.

[2078] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2079] In this invention, the server includes means for collecting information from users, means for using a generation system to analyze the collected information, means for generating a profile based on the analyzed information, means for incorporating the generated profile into templates for multiple uses, means for saving the generated profile, means for managing profiles that can be set to be public or shared, means for collecting feedback from others on the published profile, means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile, and means for recommending a variety of products to users in a personalized manner, thereby enabling efficient recommendation of optimal products based on the user's detailed preferences and past behavioral data.

[2080] "User" means an individual who uses the System to provide information and utilizes the generated profile.

[2081] "Means of collection" refers to means for collecting questionnaire forms, data from online platforms, image data, and evaluations by others.

[2082] "Generation system" refers to a system that has the ability to analyze collected information and generate a profile.

[2083] "Analyzed information" refers to data that has been analyzed and categorized from collected raw data.

[2084] "Profile" refers to data including descriptive text and recommendation information generated based on user information.

[2085] "Template" refers to a format for formatting the generated profile as text.

[2086] "Means for generating" refers to means for automatically generating profile items based on the analyzed information.

[2087] "Means for storing" refers to means for storing the generated profile in a database.

[2088] "Means for management" refers to means for setting public and sharing settings for the created profile.

[2089] "Means of collecting feedback from others" refers to means of collecting opinions and ratings from third parties regarding a public profile.

[2090] "Means for Analyzing Feedback" means means for analyzing collected feedback and providing recommendations regarding profile modifications and additions.

[2091] "Personalized recommendation methods" refer to methods that recommend the most suitable products based on the user's information and preferences.

[2092] To implement the present invention, the elements of the server, the terminal, and the user must work in cooperation with each other.

[2093] First, the user accesses a questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The server collects the information entered in the questionnaire form in real time and stores it in a database. The user also gives permission to link their online platform account to the system. This link allows the server to use an API to obtain follow information and post data from the online platform and store it in the database. The user also allows the server to access the photo data on their smartphone, and the device analyzes the photo metadata (location information, tags, date, etc.) and sends the content to the server. The server then sends feedback requests to others in the user's network (friends, colleagues, etc.) and stores the collected ratings and feedback from others in the database.

[2094] Next, the server sends all collected data to the generation system. The generation system analyzes the survey responses, online platform data, photo data, and other people's ratings and classifies the information into different categories (likes, dislikes, career, etc.). The server then sends the collected data to the emotion engine. The emotion engine analyzes the user's emotions from the survey responses, online platform data, photo data, other people's ratings, etc. and sends the analysis results to the generation system.

[2095] The server then generates profile items based on the analysis and sentiment analysis results and presents them to the user. The user selects the profile items they want to use from the generated profile items and selects a profile template appropriate for their purpose (e.g., business, hobby, etc.). The server combines the selected items and template to generate a complete profile. The user names and saves the generated profile (e.g., "Business Profile"), and the server registers the saved profile in a database.

[2096] Publication and sharing are also important elements. Users select the profiles they want to publish and share and configure them. The server publishes the selected profiles to other users based on the user's settings. Viewing users view the published profiles and provide feedback. The server sends the collected feedback to the generation system and emotion engine for analysis. As a result, it provides recommendations for modifying or adding items to the profile. Users check the recommendations from the generation system and emotion engine and modify or add to their profile as necessary. The server saves the modified or added profiles back into the database and updates the published content.

[2097] This system realizes the function of recommending a variety of products to users in a personalized manner. As a specific example, it can efficiently recommend optimal products based on the user's detailed preferences and past behavioral data. For example, when a user logs in to a shopping site and creates their own profile, products related to the user's tastes and hobbies are automatically recommended based on that profile.

[2098] As a concrete example, a detailed profile can be generated by inputting the following prompt sentence into the generative AI model:

[2099] User Information:

[2100] Name: User A

[2101] Email: user@example.com

[2102] Date of Birth: 1980-05-15

[2103] Social Media Data:

[2104] Topics: Technology, Gadgets, Healthcare

[2105] Photo data:

[2106] Date: 2023-09-10

[2107] Location: City, Country

[2108] Tags: Travel, Tourism

[2109] Others' ratings:

[2110] Email: friend@example.com Feedback: I love to travel and have an inquisitive personality.

[2111] Fixes based on feedback:

[2112] "Based on interest data from users, we recommend products, especially those related to technology and gadgets, as well as travel accessories. We provide a personalized shopping experience with constructive feedback sharing the latest information on travel and gadgets."

[2113] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2114] Step 1:

[2115] The user accesses the questionnaire form and enters their own information (e.g., likes, dislikes, past work history, strengths, weaknesses). The entered information is sent to the server and stored in a database. This is to collect the basic data needed to generate a profile.

[2116] Step 2:

[2117] The user gives permission to link their online platform account (e.g., social media) to the system. The server uses the API to obtain follow information and post data from the online platform and stores it in a database. This allows data reflecting the user's interests to be collected.

[2118] Step 3:

[2119] The user allows access to the photo data stored on their smartphone. The device analyzes the photo's metadata (location, tags, date, etc.) and sends the information to the server. The server stores the information in a database. The location, date, tags, etc. indicate the characteristics of the photo and are used to identify the user's area of ​​activity and areas of interest.

[2120] Step 4:

[2121] The server sends feedback requests to others in the user's network (friends, colleagues, etc.) via email or other means. When others provide feedback, it is collected by the server and stored in a database. The objective evaluations of others complement the user's profile from multiple angles.

[2122] Step 5:

[2123] The server sends all collected data to the generation system, which analyzes survey responses, online platform data, photo data, and other users' ratings and categorizes the information into different categories (e.g., likes, dislikes, career history, etc.). The categorized data is used to generate profile items.

[2124] Step 6:

[2125] The server sends the collected data to the emotion engine, which analyzes the user's emotions based on survey responses, online platform data, photo data, and other users' ratings. The analysis results are sent to the generation system, which generates profile items that reflect the user's emotional state.

[2126] Step 7:

[2127] The server generates profile items based on the analysis results and sentiment analysis results and presents them to the user. The user selects the profile items they want to adopt from the generated profile items. The selected information is sent to the server.

[2128] Step 8:

[2129] The user selects a profile template based on their intended use (e.g., business, hobby, etc.), and the server combines the selected items with the template to generate a complete profile, which is then stored and managed in a database.

[2130] Step 9:

[2131] Users select the profiles they want to make public and share, and configure them accordingly. The server then publishes the selected profiles to other users based on the user's settings, allowing browsing users to view the published profiles.

[2132] Step 10:

[2133] The server collects feedback provided by browsing users and stores it in a database. The collected feedback is sent to the generation system and emotion engine for analysis. Based on the analysis results, recommendations for profile modifications and additions are provided to the user.

[2134] Step 11:

[2135] Users can review the recommendations from the generation system and emotion engine, and modify or add to their profile as needed. The modified or added profile is saved back in the database, and the public information is updated.

[2136] Step 12:

[2137] Finally, the generated profile data is used to recommend various products to users in a personalized manner. The server efficiently recommends optimal products based on the user's detailed preferences and past behavioral data. As a result, users can enjoy an individually customized shopping experience.

[2138] 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.

[2139] 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.

[2140] 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.

[2141] 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.

[2142] 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.

[2143] 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.

[2144] 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).

[2145] 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.

[2146] 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 neve...

Claims

1. The means by which information is collected from users; a means for using the generating system to analyze the collected information; means for generating a profile based on the analyzed information; A means for feeding the generated profile into templates for multiple uses; means for storing the generated profile; A way to manage your profile, which can be public and shared; a means for collecting feedback from others on your public profile; a means for analyzing the collected feedback and providing recommendations for modifying or adding to the profile; A system including:

2. The system of claim 1 , wherein the collected information includes survey responses, online platform data, image data, and evaluations by others.

3. means for classifying the analyzed information into different categories; 2. The system of claim 1, further comprising means for a user to select the profile items generated.

Images