System

Abstract

A system is provided.SOLUTION: The system includes a means for periodically inputting information of a person who is newly acquainted with or heard from another person, a means for automatically generating a visual correlation diagram based on the inputted information, a means for making the generated correlation diagram searchable, and a means for obtaining permission for searching the correlation diagram of another user and making the correlation diagram searchable.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 everyday life and business environments, information about human relationships is increasing daily, making it difficult to organize and effectively utilize it. As a result, important information about human relationships becomes unclear, making it difficult to efficiently search for and introduce talent. Conventional methods require a great deal of effort to manually organize and understand each individual's information, and it is difficult to visually grasp correlations. Therefore, efficient information management and utilization are desired. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means. Specifically, the present invention provides a system including means for periodically inputting information about individuals newly acquainted or heard from others, means for automatically generating a visual correlation diagram based on the input information, means for making the generated correlation diagram searchable, and means for obtaining permission to search other users' correlation diagrams and making them searchable. This system is characterized by storing personal information input by the user in a database and providing a corresponding correlation diagram based on search criteria. Furthermore, the information input means allows input of items including occupation, contact information, age, and relationship tags, and when generating the correlation diagram, it is possible to represent relationships with nodes and edges based on graph theory. This enables efficient and visually intuitive management and utilization of information about human relationships.

[0006] The "information input means" is a means for the user to periodically input personal information about individuals that the user has newly met or heard from other people.

[0007] The "means for automatically generating a correlation diagram" is a means for automatically generating a visual correlation diagram based on input personal information.

[0008] The "correlation diagram search means" is a means for making the generated correlation diagram searchable.

[0009] The "permission acquisition means" is a means for acquiring permission to search for correlation diagrams of other users.

[0010] A "database" is a system for storing and managing input personal information.

[0011] "Search conditions" are conditions for extracting information based on specific attributes or relationships.

[0012] A "node" is a point in the relationship diagram that represents an individual person or entity.

[0013] An "edge" is a line that represents the relationship between nodes in a correlation diagram.

[0014] "Graph theory" is a mathematical theory that represents relationships using nodes and edges. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0036] The system of the present invention includes a user's input of human relationship information, automatic generation of a correlation diagram by a server, and a search function, etc. The present invention can be implemented as follows.

[0037] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0038] The server then automatically generates a correlation diagram based on the received information. The server analyzes the information in the database and generates a correlation diagram that visually shows the relationships between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges show their relationships.

[0039] Users can view and search correlation diagrams generated based on the information they input on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0040] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0041] Specific examples

[0042] Example 1: Entering information about a new acquaintance

[0043] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0044] Name: Ichiro Yamada

[0045] Age: 40

[0046] Occupation: Project Manager

[0047] Contact: yamada.ichiro@example.com

[0048] Relationship: Meeting Participant

[0049] This information is sent to a server and stored in a database.

[0050] Example 2: Automatic generation and viewing of correlation diagrams

[0051] The server receives Yamada Ichiro's information and compares it with existing information in the database to generate a correlation diagram, which visually shows, for example, Yamada Ichiro's relationships with all the project members he is involved in.

[0052] The user can view this relationship diagram on their device and further search for the occupation "project manager." In this case, the server will extract people with similar occupations from the database and provide an updated relationship diagram.

[0053] Example 3: Allowing other users to view correlation diagrams

[0054] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to easily find new talent and business partners, enabling efficient networking.

[0055] In this way, the present invention can greatly improve the efficiency of managing and utilizing relationships in everyday life and in business environments.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The user opens the application on their device. The user selects the form to add a new contact.

[0059] Step 2:

[0060] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0061] Step 3:

[0062] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0063] Step 4:

[0064] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0065] Step 5:

[0066] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0067] Step 6:

[0068] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0069] Step 7:

[0070] The server starts the process of generating a correlation diagram based on the newly saved information: the server runs an SQL query to retrieve all relevant contact information from the database.

[0071] Step 8:

[0072] The server analyzes the acquired data and converts the relationships between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0073] Step 9:

[0074] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0075] Step 10:

[0076] The server receives the request, executes an SQL query to extract the corresponding information from the database, and generates a correlation diagram based on the extracted information and returns it to the device.

[0077] Step 11:

[0078] The correlation diagram received by the terminal is visually displayed.

[0079] Step 12:

[0080] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0081] Step 13:

[0082] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0083] Step 14:

[0084] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0085] Example 1

[0086] 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."

[0087] Currently, systems for visually managing interpersonal relationships require a large number of manual operations and are not particularly efficient. It is particularly difficult to centrally manage information about new acquaintances and information obtained from other users, and to automatically generate and search visual correlation diagrams. Furthermore, viewing other users' correlation diagrams requires a time-consuming permission process, which often hinders rapid information sharing and networking. Therefore, there is a need for a system that can manage and visualize relationships, as well as enable efficient information sharing with other users.

[0088] 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.

[0089] In this invention, the server includes means for inputting information on newly acquainted individuals or individuals heard from other users at a terminal, means for transmitting the input information to the server, means for the server to store the received information in a database, means for the server to automatically generate a visual correlation diagram based on the stored information, means for making the generated correlation diagram searchable, means for extracting information from the database based on specific conditions and providing the relevant correlation diagram, and means for obtaining permission to search other users' correlation diagrams and making the correlation diagrams searchable. This makes it possible to efficiently manage and visualize relationships between individuals and quickly share information with other users.

[0090] A "terminal" is an electronic device that allows a user to input and transmit information.

[0091] "Input means" refers to software functionality, including interfaces and forms, through which a user can input information.

[0092] "Means for transmitting information to the server" refers to protocols and communication means for securely transmitting data from the terminal to the server.

[0093] A "server" is a computer system that receives, stores, processes data, and responds to requests from clients.

[0094] A "database" is a system or software for storing data in a structured format.

[0095] The "means for storing information" is a software process for storing the received data in a database.

[0096] A "means for automatically generating correlation diagrams" is an algorithm or software that uses stored data to form nodes and edges based on graph theory, and visually represents relationships.

[0097] A "searchability means" is a software function that allows a user to extract data based on specific criteria and display it visually.

[0098] The "specific conditions" are keywords and filtering conditions used by the user when conducting a search.

[0099] The "means of information extraction" is the query or process used to retrieve data from a database that matches the search criteria.

[0100] The "visual display means" is a software function for displaying the generated correlation diagram on a terminal in a format that is easy for the user to understand.

[0101] "Means for obtaining permission" refers to the process for requesting and managing the permission required to view other users' relationship diagrams.

[0102] The system of this invention allows users to input interpersonal relationship information using a terminal, and the server automatically generates, searches, and displays a correlation diagram based on that information. The operation of the entire system is described in detail below.

[0103] System Configuration

[0104] Hardware and software use cases:

[0105] Terminal: A device used by the user to input information, such as a smartphone, tablet, or laptop. A dedicated application is installed on the device.

[0106] Server: A computer system that receives, stores, and processes data, and is typically hosted on a cloud service (e.g., AWS (registered trademark)).

[0107] Database: A system for storing saved information, typically a NoSQL database such as MongoDB.

[0108] Software Libraries: Python NetworkX and D3.js are used to generate and visualize the correlation diagram.

[0109] Enter and submit information

[0110] Steps for users to enter information:

[0111] The user launches the dedicated application and enters information such as name, age, occupation, contact information, relationship tags, etc. into the input form. The user interface of this application is designed to be easy and intuitive to use.

[0112] Examples:

[0113] The user enters information about the new person he or she has met as follows:

[0114] Name: Example: Yamada Taro

[0115] Age: 40

[0116] Occupation: Project Manager

[0117] Contact: taro.yamada@example.com

[0118] Relationship: Meeting Participant

[0119] The entered information is sent from the terminal to the server, and the data is encrypted and protected using the HTTPS protocol.

[0120] Retention of Information

[0121] The server receives the information and stores it in the database:

[0122] The server analyzes the received information and stores it in a database (e.g., MongoDB), which manages data in document format and allows for easy searching and updating.

[0123] Automatic generation of correlation diagrams

[0124] Steps for the server to generate the correlation diagram:

[0125] The server analyzes the information in the database and automatically generates a correlation diagram based on graph theory. This process uses the Python NetworkX library, where each person is represented as a node and relationships are represented as edges. The generated correlation diagram is then rendered using a visualization library (e.g., D3.js) and later sent to the user's device.

[0126] Examples:

[0127] Based on the information that "Yamada Taro" is participating in a new project, a correlation diagram is generated showing his relationships with all existing project members.

[0128] View and search correlation diagrams

[0129] Steps for users to view and search the generated correlation diagram:

[0130] The user can view the correlation diagram generated on their device and then perform a search based on specific criteria, using tags such as occupations and relationships. The server then extracts the relevant information from the database and provides the user with an updated correlation diagram.

[0131] Examples:

[0132] When a user searches based on the occupation tag "project manager," the server extracts information about people with similar occupations and displays an updated correlation diagram.

[0133] Permission to view other users' correlation diagrams

[0134] Steps for users to request and approve permission to view the relationship diagram:

[0135] User A requests permission to view User B's correlation diagram, and User B approves. The server grants viewing authority to User A, after which User A can search and view User B's correlation diagram. This enables efficient information sharing.

[0136] Examples:

[0137] User A requests to view User B's correlation diagram in order to find a new business partner, and if User B approves, User A can view the correlation diagram.

[0138] Through the above process, the system of the present invention efficiently manages and visualizes interpersonal relationships, enabling rapid information sharing with other users.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] User input of information

[0142] The user launches the dedicated application and enters information about the new acquaintance or the person they have received from other users into the input form. Specifically, the input items include name, age, occupation, contact information, and relationship tags.

[0143] Input: Personal information (e.g., name, age, occupation, contact information, relationship tags)

[0144] Output: Personal information data entered

[0145] Specific operation: A user enters "Yamada Taro, age 40, project manager, taro.yamada@example.com, meeting participant" into an input form on a smartphone app.

[0146] Step 2:

[0147] Sending data from the device to the server

[0148] The device sends the entered personal information to the server using the HTTPS protocol, and the data is encrypted.

[0149] Input: Personal information data entered

[0150] Output: Personal information data sent to the server

[0151] Specific operation: Information about "Yamada Taro" is sent from the device to the server in encrypted form.

[0152] Step 3:

[0153] Server storage of information

[0154] The server analyzes the received personal information and stores it in a database using MongoDB, which manages data in document format.

[0155] Input: Personal information data sent to the server

[0156] Output: Personal information data stored in a database

[0157] Specific operation: The server receives the information about "Yamada Taro" and saves it as a document in the "contacts" collection in MongoDB.

[0158] Step 4:

[0159] Automatic generation of correlation diagrams by the server

[0160] The server analyzes the information in the database and automatically generates a correlation diagram that visually represents the relationships between individuals, using the Python NetworkX library.

[0161] Input: All personal information stored in the database

[0162] Output: Automatically generated correlation diagram data

[0163] Specific operation: The server analyzes the relationship between "Yamada Taro" and existing data, generates nodes and edges, and constructs a correlation diagram.

[0164] Step 5:

[0165] Sending correlation diagram from server to terminal

[0166] The server sends the generated correlation diagram to the terminal. The D3.js library is used for visualization, providing a dynamic and interactive correlation diagram.

[0167] Input: Auto-generated correlation diagram data

[0168] Output: Visualized correlation diagram data sent to the device

[0169] Specific operation: The server draws the correlation diagram using D3.js and sends it to the terminal via the Internet.

[0170] Step 6:

[0171] User-friendly correlation diagram browsing and search

[0172] Users view the correlation diagram on their devices and search for information based on specific criteria (e.g., occupation, age, relationship). The server extracts the relevant information from the database and provides an updated correlation diagram.

[0173] Input: User search criteria (e.g., occupation tag "Project Manager")

[0174] Output: Updated correlation diagram data

[0175] Specific operation: The user performs a search narrowed down to "Project Manager," and the server extracts the relevant person information and displays an updated correlation diagram.

[0176] Step 7:

[0177] Request and approve permission to view other users' relationship diagrams

[0178] When User A requests permission to view User B's correlation diagram and User B approves this, the server grants User A permission to view the diagram.

[0179] Input: Permission request from User A, Permission approval from User B

[0180] Output: Viewing permissions granted to User A

[0181] Specific operation: When User A sends a request on the app and User B approves it, the server grants User A permission to view User B's relationship diagram.

[0182] (Application example 1)

[0183] 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."

[0184] Conventional relationship information management systems make it difficult to manually organize and manage information about newly acquainted individuals or individuals heard from others. Furthermore, the visualization and search functions for relationships are limited, making it difficult to update information in real time or share information efficiently. Furthermore, while there is a demand for intuitive operation using smart devices and visual display devices, there has been a lack of systems that can achieve this.

[0185] 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.

[0186] In this invention, the server includes means for periodically inputting information about newly acquainted individuals or individuals heard from other people, means for automatically generating a visual correlation diagram based on the input information, means for making the generated correlation diagram searchable, means for obtaining permission to search other users' correlation diagrams and making them searchable, means for saving the input personal information in a database and updating the correlation diagram in real time, and means for visually displaying the generated correlation diagram. This enables efficient management and updating of interpersonal relationship information and the generation and viewing of visual correlation diagrams in real time. Furthermore, intuitive operation using a smart device can significantly improve user convenience.

[0187] "Information about a person that the user has newly met or has heard from other people" refers to information about a person that the user has newly met or that the user has received through other people.

[0188] "Means for periodic input" refers to a method or device that allows a user to continuously input information at regular intervals.

[0189] A "visual correlation diagram" refers to a diagram that visually shows the relationships between people based on information recorded by the user.

[0190] "Means for automatically generating a correlation diagram" refers to a method or device in which a system automatically visualizes the relationships between people based on input information and creates a correlation diagram.

[0191] The "means for making the diagram searchable" refers to a method or device that allows the generated correlation diagram to be searched for specific conditions or keywords and information to be obtained.

[0192] The "means for obtaining permission and making the correlation diagram searchable" refers to a method or device for obtaining permission to view other users' correlation diagrams and making the correlation diagrams searchable.

[0193] The "means for saving information in a database and updating the correlation diagram in real time" refers to a method or device for saving input information in a database and instantly updating the correlation diagram when the information is added or updated.

[0194] The term "means for visually displaying" refers to a method or device for visually displaying the generated correlation diagram on a terminal used by a user.

[0195] "Tags for occupation, contact information, age, and relationship" refer to attribute information used by the user when entering personal information.

[0196] "Means for representing relationships using nodes and edges based on graph theory" refers to a method or device that uses graph theory to represent people as nodes (points) and depict their relationships as edges (lines).

[0197] "Visual display device" refers to a device that can display graphics or text data, such as a smartphone, tablet, or monitor.

[0198] "Means for real-time updating" refers to a method or device that instantly updates a database or display content each time new information is entered.

[0199] "Means for inputting and viewing correlation diagrams using a device" refers to a method or device that allows a user to input correlation diagram information or view the generated correlation diagram using a smart device, etc.

[0200] The system of this invention is designed to efficiently manage personal information acquired by users and to generate and update a correlation diagram of human relationships in real time. Specific embodiments of this invention will be described below.

[0201] First, a user uses a visual display device such as a smartphone or tablet to enter new personal information into a dedicated application, including name, age, occupation, contact details, and relationship tags. The information acquired through the input form is sent to the server in real time.

[0202] The server uses an SQLite3 database to store the received personal information. The stored information is automatically generated as a correlation diagram of relationships between nodes (points) and edges (lines) based on graph theory. This process uses Python libraries such as NetworkX and Matplotlib. The server immediately updates the correlation diagram each time new information is input and generates data for visual display.

[0203] Users can then view the generated relationship diagram in real time and use the search function on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding relationship diagram in real time, allowing users to easily obtain the information they need and efficiently manage their relationships.

[0204] It also includes a function to obtain permission to view other users' correlation diagrams. User A requests permission to view a correlation diagram from User B, and if User B approves, the server grants viewing authority to User A. This allows multiple users to share network information and realize efficient networking.

[0205] A specific example is when a sales assistant in a store inputs the personal information of a new customer and then displays the relationships in a correlation diagram in real time. For example, the assistant inputs the information of customer "Taro Tanaka" and records his relationship with an existing customer as a "colleague." This information is immediately saved on the server, and a correlation diagram is generated and updated in real time.

[0206] When creating a program using a generative AI model, an example prompt is as follows:

[0207] "Based on the given customer information, please create a Python program to generate a relationship diagram in real time and save it in a SQLite3 database. Visualize the relationships based on the relationship tags for each customer."

[0208] In this way, the system of the present invention allows users to efficiently manage interpersonal relationship information and visually generate and update correlation diagrams, thereby significantly improving the efficiency of managing and effectively utilizing interpersonal relationships in everyday life and business environments.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] A user launches a dedicated application on a smartphone or tablet and inputs information about a new acquaintance or a person they have heard from others. At this time, they fill in an input form with information such as name, age, occupation, contact details, and relationship tags. This input data is sent from the device to the server. The input is done through a text field, and as a result, JSON-formatted data is generated and sent to the server.

[0212] Step 2:

[0213] The server parses the received JSON format data and saves it in an SQLite3 database. During the saving process, the entered personal information is added as a record to the corresponding table in the database. Each item in the input data (name, age, occupation, contact information, relationship tags) is mapped to a column in the database to ensure it is saved.

[0214] Step 3:

[0215] The server generates a visual correlation diagram in real time based on the stored information. Specifically, it uses Python's NetworkX library to represent individuals as nodes (points) and relationships as edges (lines). It retrieves the necessary information from the database and creates and updates nodes and edges. This operation generates graph data.

[0216] Step 4:

[0217] Using the generated graph data, the server uses Python's Matplotlib library to draw a visual correlation diagram. The resulting graph image is a graph of human relationships composed of nodes and edges. This image is then converted into a format that can be viewed by users.

[0218] Step 5:

[0219] The server sends the generated correlation diagram to the user's device in real time and displays it visually via an application. The user can check the correlation diagram on the device screen. In the display process, the drawn graph image is converted into a format that can be displayed on the device screen and sent to the user's device.

[0220] Step 6:

[0221] Users use the search function to search and filter the correlation diagram based on specific criteria (e.g., occupation or relationship tags). The user enters the search criteria, and the server again queries the database to extract the relevant data. Based on the search results, a new correlation diagram is generated and displayed.

[0222] Step 7:

[0223] User A requests permission to view another user B's correlation diagram. If user B approves the request through the application, the server grants user A permission to access user B's correlation diagram data. After granting permission, user A can search and view user B's correlation diagram.

[0224] Step 8:

[0225] If the entered personal information is new, the server updates the correlation diagram in real time. During the update process, new nodes and edges are added to the existing graph, and the diagram is redrawn. As a result, a correlation diagram reflecting the latest human relationships is generated and sent to the user's device again for display.

[0226] 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.

[0227] The system of this invention combines a user's input of interpersonal relationship information, automatic generation and search functions of correlation diagrams by a server, and an emotion engine. The emotion engine identifies emotions from user input and voice and uses them to generate and adjust correlation diagrams. The present invention can be implemented as follows.

[0228] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0229] Additionally, the system incorporates an emotion engine that analyzes emotions from text and voice inputs by the user. The emotion engine determines the user's emotional state and how that emotion affects the information input. For example, if the user is emotional, the relevance of the information may be emphasized.

[0230] The server then automatically generates a correlation diagram based on the received information and the results of the sentiment analysis. The server analyzes the information in the database and generates a visual correlation diagram that takes into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges indicate their relationships and emotional strengths.

[0231] Users can view and search correlation diagrams generated based on the information they input and the results of emotion analysis on their devices. When searching based on specific criteria, such as occupation, relationship tags, or even emotional state, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0232] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0233] Specific examples

[0234] Example 1: Entering information about a new acquaintance

[0235] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0236] Name: Ichiro Yamada

[0237] Age: 40

[0238] Occupation: Project Manager

[0239] Contact: yamada.ichiro@example.com

[0240] Relationship: Meeting Participant

[0241] Furthermore, the user can input a comment expressing their feelings, such as "Ichiro Yamada was very cooperative." The emotion engine analyzes this comment and recognizes it as a positive emotion. This information and emotional data are sent to the server and stored in a database.

[0242] Example 2: Automatic generation and viewing of correlation diagrams taking emotions into account

[0243] The server receives Ichiro Yamada's information and emotion data, compares it with existing information in the database, and generates a correlation diagram. The correlation diagram visually represents Ichiro Yamada's relationships with other project members. Furthermore, if positive emotions affect the strength of relationships, it highlights that Ichiro Yamada is very cooperative.

[0244] Users can view this correlation diagram on their devices and even search for it based on criteria such as "project manager" or "people with whom they have positive feelings." In this case, the server extracts people with similar attributes and emotional ratings from the database and provides an updated correlation diagram.

[0245] Example 3: Allowing other users to view correlation diagrams

[0246] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to find new talent and business partners by taking into account emotional evaluations, enabling efficient networking.

[0247] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it is possible to achieve more intuitive and reliable evaluation and utilization of human relationships.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The user opens the application on their device. The user selects the form to add a new contact.

[0251] Step 2:

[0252] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0253] Step 3:

[0254] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0255] Step 4:

[0256] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0257] Step 5:

[0258] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0259] Step 6:

[0260] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0261] Step 7:

[0262] When a user enters an emotional comment (e.g., "It was cooperative") into the input form, the emotion engine analyzes the comment, and the device generates emotion data as the analysis result.

[0263] Step 8:

[0264] The device then sends the information, including the emotion data, back to the server, which then stores the received emotion data and personal information in a database.

[0265] Step 9:

[0266] The server starts the process of generating a correlation diagram based on the newly stored information and emotion data. The server runs an SQL query to retrieve all relevant contact information and emotion data from the database.

[0267] Step 10:

[0268] The server analyzes the acquired data and converts the relationships and emotional strengths between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0269] Step 11:

[0270] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0271] Step 12:

[0272] The server receives the request and executes an SQL query to extract the corresponding information and emotion data from the database. The server generates a correlation diagram based on the extracted information and returns it to the device.

[0273] Step 13:

[0274] The device visually displays the correlation diagram it receives, which shows the strength of the relationships between nodes based on emotion data.

[0275] Step 14:

[0276] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0277] Step 15:

[0278] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0279] Step 16:

[0280] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0281] In this way, it is possible to manage and effectively utilize interpersonal information that takes emotion data into account.

[0282] Example 2

[0283] 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."

[0284] Conventional human relationship information management systems have difficulty taking into account qualitative comments and emotions from users. Furthermore, because the generation and search of correlation diagrams relied on simple data, it was not possible to reflect individual emotions or the strength of relationships. This made it difficult for users to obtain intuitive and reliable information when evaluating and managing relationships.

[0285] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing emotions from the user's input content and voice, means for adjusting the correlation diagram based on the emotion analysis result, and means for saving the emotion analysis result in a database. This makes it possible to generate and search for a correlation diagram with higher accuracy that reflects the user's emotions.

[0286] "Personal information" is information that can be used to identify a specific individual, including name, age, occupation, contact information, relationship tags, etc.

[0287] A "correlation diagram" is a graph that visually shows individual people and their relationships, and uses nodes and edges to express the strength of relationships and emotional states.

[0288] An "emotion engine" is a software or hardware component that identifies emotions from text or voice input by a user and analyzes that emotional state.

[0289] "Emotion analysis results" are data on the user's emotional state analyzed by the emotion engine, and are information classified into emotion categories such as positive, negative, and neutral.

[0290] "Search conditions" are conditions such as specific attributes, tags, and emotional states that a user specifies when searching a correlation diagram.

[0291] A "database" is a digital storage system for systematically storing and managing data such as personal information entered by users and the results of sentiment analysis.

[0292] A "node" is a graph element that represents an individual person in the correlation diagram.

[0293] An "edge" is a line in a graph that represents the relationship between nodes in a correlation diagram, and visually indicates the strength or emotional state of that relationship.

[0294] A "visual display" is a graphical representation that presents information to a user in an easy-to-view manner on a device's display.

[0295] The system of this invention allows users to input information about new acquaintances, perform emotion analysis, and then generate, display, and search correlation diagrams. Specifically, users use a dedicated application to input personal information such as name and occupation from their terminal, and the information is managed and analyzed on the server, which then visually displays the correlation diagram. The main components of this system include a user terminal, a server, an emotion engine, and a database.

[0296] First, the user uses a device with the dedicated application installed. The user enters the name, age, occupation, contact information, relationship tags, etc. into the input form. Specifically, the user enters information such as "Name: Yamada Ichiro," "Age: 40," "Occupation: Project Manager," "Contact: yamada.ichiro@example.com," and "Relationship: Meeting Participant." At the same time, the user can also enter comments expressing their feelings, such as "Yamada Ichiro was very cooperative."

[0297] The entered information is sent from the terminal to the server. The server stores the received data in a database. The database uses a relational database such as MySQL (registered trademark) or PostgreSQL. The server then uses an emotion engine (e.g., Google (registered trademark) Cloud Natural Language API or IBM Watson (registered trademark) Natural Language Understanding) to analyze emotions from the user's comments. For example, a comment such as "Yamada Ichiro was very cooperative" is recognized as a positive emotion.

[0298] The server generates a correlation diagram based on the information in the database and the results of the sentiment analysis. This step can be performed using a library that applies graph theory (e.g., Python's NetworkX library). The correlation diagram is composed of nodes and edges, with the nodes representing individual people and the edges representing the relationships and emotional strengths between them. The generated correlation diagram can be viewed visually by the user on their device.

[0299] Users can search the generated correlation diagram based on specific criteria, such as "project managers" or "people with whom they have positive feelings." The server then extracts the relevant information from the database and provides the updated correlation diagram to the user's device.

[0300] Furthermore, users are also provided with a function to request permission to view other users' correlation diagrams. When User A requests permission to view User B's correlation diagram and User B approves, the server grants viewing authority to User A. This allows User A to search and view User B's correlation diagram.

[0301] Prompt Sentence Examples

[0302] "I entered information about Ichiro Yamada, whom I met at a new conference, and found him to be very cooperative. Based on this information, please use the emotion engine to analyze positive emotions and automatically generate a correlation diagram that reflects those emotions."

[0303] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it achieves more intuitive and reliable evaluation and utilization of human relationships.

[0304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0305] Step 1: Enter your information

[0306] The user uses their device to enter information about a new acquaintance into the application's input form. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags. They can also enter comments that express their feelings. This generates input data (e.g., "Name: Yamada Ichiro," "Age: 40 years old," "Occupation: Project manager," "Relationship: Meeting participant," "Comment: Yamada Ichiro was very cooperative").

[0307] Input: Personal information and emotional comments entered by the user into the terminal

[0308] Output: The input data is generated

[0309] Step 2: Send and store data

[0310] The terminal sends data entered by the user to the server. The server saves the received data in a database. This process uses a POST request using the HTTP protocol, and a relational database (e.g., MySQL or PostgreSQL) is used as the database. For example, the input data is sent to the server in JSON format, where it is analyzed and saved.

[0311] Input: Data entered by the user into the terminal

[0312] Output: Data is saved to the database

[0313] Step 3: Sentiment Analysis

[0314] The server extracts comments from the stored data and performs sentiment analysis using an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding). For example, it analyzes the comment "Yamada Ichiro was very cooperative" and recognizes it as a positive sentiment. The analysis results are then stored back in the database.

[0315] Input: Comment data

[0316] Output: Sentiment analysis results (positive, negative, etc.)

[0317] Step 4: Generate correlation diagram

[0318] The server generates a correlation diagram based on the information in the database and the results of sentiment analysis. The correlation diagram is created using a library that applies graph theory (e.g., Python's NetworkX library), with nodes representing people and edges representing relationships and emotional strength. For example, Ichiro Yamada is visualized as a node, with his relationships with other project members as edges.

[0319] Input: Personal information in the database and sentiment analysis results

[0320] Output: correlation diagram data

[0321] Step 5: Visualize the correlation diagram

[0322] The server sends the generated correlation diagram to the device, which receives it and displays it visually within a dedicated application. For example, the generated correlation diagram is intuitively displayed on the device screen with nodes and edges.

[0323] Input: correlation diagram data

[0324] Output: Correlation diagram displayed on the terminal

[0325] Step 6: Search for correlation diagrams

[0326] The user inputs specific criteria (e.g., "project manager" or "positive emotion") to search for correlation diagrams. At this time, the device sends the search criteria to the server. The server searches the database, generates the corresponding correlation diagram, and sends it to the device. The device receives it and displays it visually.

[0327] Input: Search criteria (tags, emotional state, etc.)

[0328] Output: The corresponding correlation diagram is displayed.

[0329] Step 7: Allow other users to view the correlation diagram

[0330] A user requests permission to view another user's correlation diagram. For example, if user A requests permission to view user B's correlation diagram and user B approves it, the request and approval are processed on the server, and user A is granted permission to view it.

[0331] Input: Viewing permission request and approval

[0332] Output: Viewing permission granted

[0333] By implementing the above steps, users can easily generate, display, and search correlation diagrams that take emotions into account, enabling more reliable evaluation and management of human relationships.

[0334] (Application example 2)

[0335] 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."

[0336] Previously, relationship management systems only collected personal information and generated visual correlation diagrams, but did not have the functionality to adjust the environment in real time based on the user's emotional state. As a result, it was not possible to effectively express personal connections based on the user's emotions, and there was a need for flexible environment adjustments in response to emotional changes.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for periodically inputting information about newly acquainted individuals or individuals heard from other people; means for automatically generating a visual correlation diagram based on the input information; means for making the generated correlation diagram searchable; means for obtaining permission to search other users' correlation diagrams and making them searchable; means for using an emotion analysis engine to identify emotions from the user's input text or voice and reflecting the emotional state in the intensity of the correlation diagram; means for updating the generated correlation diagram in real time and displaying it on the smart device; and means for automatically adjusting the device's environmental settings (music, temperature, lighting, etc.) according to the user's emotional state. This allows for visual management of human relationships based on the user's emotions and optimal environmental adjustment in real time.

[0338] "Information about individuals that you have newly met or heard from others" is information about people that you have obtained based on your own experiences or hearsay from others.

[0339] A "visual correlation diagram" is a diagram constructed with nodes and edges based on graph theory that visually represents the relationships and emotional strengths between individual people.

[0340] "Means for making things searchable" refers to a function that allows users to search for information in the database based on specific conditions, and extract and display the relevant correlation diagram.

[0341] "Means of obtaining permission and making it searchable" refers to the ability to request permission to access other users' relationship diagrams and, if permission is granted, make them searchable and viewable.

[0342] An "emotion analysis engine" is an analytical device that identifies emotions from text and voice input by the user and reflects that information in the strength of relationships and environmental settings.

[0343] "Real-time update means" refers to a function that instantly keeps correlation diagrams and environment settings up to date based on user input and sentiment analysis results.

[0344] A "smart device" is a digital terminal for inputting and displaying information, and includes smartphones, smart glasses, head-mounted displays, etc.

[0345] "Means for automatically adjusting environmental settings" refers to a function that appropriately changes the music, temperature, lighting, etc. in the car based on the user's emotional state.

[0346] The system embodying the present invention has functions of information input by a user, data processing and correlation diagram generation by a server, and display and environment adjustment by a smart device.

[0347] First, the user uses a smart device (smartphone, smart glasses, head-mounted display, etc.) to input information about a new acquaintance or someone they have heard from another person. The input form includes items such as name, age, occupation, contact information, and relationship tags, and the user enters this information through the device. In addition, the system has the ability to identify emotions from the text and voice input by the user.

[0348] An emotion analysis engine is used to analyze emotions from input text and voice. This emotion analysis engine uses libraries such as Python's "TextBlob" to identify emotional states such as positive and negative. The server reflects the acquired emotional data in a correlation diagram.

[0349] The server then analyzes the information in the database and generates a visual correlation diagram, taking into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Each node represents an individual person, and the edges indicate their relationships and emotional strengths. Libraries such as "NetworkX" and "Matplotlib" are used to generate the correlation diagram.

[0350] The server updates the generated correlation diagram in real time and displays it on the smart device. Users can search based on specific criteria (e.g., occupation, relationship tags, emotional state), and the server extracts the relevant information from the database and provides the corresponding correlation diagram.

[0351] Furthermore, it has the ability to automatically adjust the environmental settings of smart devices (music, temperature, lighting, etc.) according to the user's emotional state, allowing users to browse and search for information in a comfortable environment.

[0352] As a concrete example, consider the case where User A enters information about "Person A" whom he met at a new meeting. User A uses his smartphone to enter the following information:

[0353] Name: Individual A

[0354] Age: 40

[0355] Occupation: Project Manager

[0356] Contact: example@example.com

[0357] Relationship: Meeting Participant

[0358] Furthermore, a comment expressing an emotion, such as "Individual A was very cooperative," is entered. The emotion analysis engine analyzes this comment and identifies it as a positive emotion. The server generates a correlation diagram based on this information and emotion data and displays it on User A's smart device.

[0359] Examples of prompts generated based on specific examples include:

[0360] "We have a new passenger, Individual B, on board. He's very cooperative and friendly."

[0361] "Passengers seem a little tired at the moment."

[0362] In this way, the present invention can visually manage human relationships based on the user's emotions and provide optimal environmental adjustments in real time.

[0363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0364] Step 1:

[0365] The user uses a smart device to input information about a new acquaintance or information they have heard from other people. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags into an input form. This input information is sent from the device to the server and stored in a database. Input: Relationship information entered by the user. Output: Data sent to the server and stored in the database.

[0366] Step 2:

[0367] The text or voice input by the user is sent to the sentiment analysis engine. The sentiment analysis engine is used to analyze emotions from the text or voice. Specifically, it uses the Python "TextBlob" library to classify emotions into positive, negative, or neutral. Input: Text or voice data input by the user. Output: Analyzed sentiment data (emotional state).

[0368] Step 3:

[0369] The server generates a correlation diagram based on the collected interpersonal relationship information and emotional data. NetworkX and Matplotlib are used to generate the correlation diagram. Specifically, each node represents an individual person, and edges indicate their relationships and emotional strength. Input: Interpersonal relationship information and emotional data in the database. Output: Generated correlation diagram (graph).

[0370] Step 4:

[0371] The generated correlation diagram is updated in real time and displayed on the smart device. When a user searches based on specific conditions (e.g., occupation, relationship tags, emotional state), the server extracts the relevant information from the database and provides the corresponding correlation diagram. Input: User's search conditions. Output: Display of the updated correlation diagram.

[0372] Step 5:

[0373] The server automatically adjusts the smart device's environmental settings based on the user's emotional state. These environmental settings include music, temperature, lighting, etc. For example, if the user inputs "I'm tired," the server will play soothing music and dim the lights. Input: User's emotional data. Output: Adjusted device environmental settings.

[0374] Step 6:

[0375] A request is sent to the server to grant access to another user's correlation diagram, and if granted, the correlation diagram becomes searchable and viewable. When User A requests access to User B's correlation diagram and User B approves, the server grants viewing permission to User A. Input: Access permission request. Output: Granting viewing permission and access to the correlation diagram.

[0376] 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.

[0377] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0378] 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.

[0379] [Second embodiment]

[0380] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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."

[0392] The system of the present invention includes a user's input of human relationship information, automatic generation of a correlation diagram by a server, and a search function, etc. The present invention can be implemented as follows.

[0393] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0394] The server then automatically generates a correlation diagram based on the received information. The server analyzes the information in the database and generates a correlation diagram that visually shows the relationships between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges show their relationships.

[0395] Users can view and search correlation diagrams generated based on the information they input on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0396] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0397] Specific examples

[0398] Example 1: Entering information about a new acquaintance

[0399] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0400] Name: Ichiro Yamada

[0401] Age: 40

[0402] Occupation: Project Manager

[0403] Contact: yamada.ichiro@example.com

[0404] Relationship: Meeting Participant

[0405] This information is sent to a server and stored in a database.

[0406] Example 2: Automatic generation and viewing of correlation diagrams

[0407] The server receives Yamada Ichiro's information and compares it with existing information in the database to generate a correlation diagram, which visually shows, for example, Yamada Ichiro's relationships with all the project members he is involved in.

[0408] The user can view this relationship diagram on their device and further search for the occupation "project manager." In this case, the server will extract people with similar occupations from the database and provide an updated relationship diagram.

[0409] Example 3: Allowing other users to view correlation diagrams

[0410] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to easily find new talent and business partners, enabling efficient networking.

[0411] In this way, the present invention can greatly improve the efficiency of managing and utilizing relationships in everyday life and in business environments.

[0412] The processing flow will be explained below.

[0413] Step 1:

[0414] The user opens the application on their device. The user selects the form to add a new contact.

[0415] Step 2:

[0416] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0417] Step 3:

[0418] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0419] Step 4:

[0420] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0421] Step 5:

[0422] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0423] Step 6:

[0424] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0425] Step 7:

[0426] The server starts the process of generating a correlation diagram based on the newly saved information: the server runs an SQL query to retrieve all relevant contact information from the database.

[0427] Step 8:

[0428] The server analyzes the acquired data and converts the relationships between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0429] Step 9:

[0430] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0431] Step 10:

[0432] The server receives the request, executes an SQL query to extract the corresponding information from the database, and generates a correlation diagram based on the extracted information and returns it to the device.

[0433] Step 11:

[0434] The correlation diagram received by the terminal is visually displayed.

[0435] Step 12:

[0436] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0437] Step 13:

[0438] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0439] Step 14:

[0440] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0441] Example 1

[0442] 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."

[0443] Currently, systems for visually managing interpersonal relationships require a large number of manual operations and are not particularly efficient. It is particularly difficult to centrally manage information about new acquaintances and information obtained from other users, and to automatically generate and search visual correlation diagrams. Furthermore, viewing other users' correlation diagrams requires a time-consuming permission process, which often hinders rapid information sharing and networking. Therefore, there is a need for a system that can manage and visualize relationships, as well as enable efficient information sharing with other users.

[0444] 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.

[0445] In this invention, the server includes means for inputting information on newly acquainted individuals or individuals heard from other users at a terminal, means for transmitting the input information to the server, means for the server to store the received information in a database, means for the server to automatically generate a visual correlation diagram based on the stored information, means for making the generated correlation diagram searchable, means for extracting information from the database based on specific conditions and providing the relevant correlation diagram, and means for obtaining permission to search other users' correlation diagrams and making the correlation diagrams searchable. This makes it possible to efficiently manage and visualize relationships between individuals and quickly share information with other users.

[0446] A "terminal" is an electronic device that allows a user to input and transmit information.

[0447] "Input means" refers to software functionality, including interfaces and forms, through which a user can input information.

[0448] "Means for transmitting information to the server" refers to protocols and communication means for securely transmitting data from the terminal to the server.

[0449] A "server" is a computer system that receives, stores, processes data, and responds to requests from clients.

[0450] A "database" is a system or software for storing data in a structured format.

[0451] The "means for storing information" is a software process for storing the received data in a database.

[0452] A "means for automatically generating correlation diagrams" is an algorithm or software that uses stored data to form nodes and edges based on graph theory, and visually represents relationships.

[0453] A "searchability means" is a software function that allows a user to extract data based on specific criteria and display it visually.

[0454] The "specific conditions" are keywords and filtering conditions used by the user when conducting a search.

[0455] The "means of information extraction" is the query or process used to retrieve data from a database that matches the search criteria.

[0456] The "visual display means" is a software function for displaying the generated correlation diagram on a terminal in a format that is easy for the user to understand.

[0457] "Means for obtaining permission" refers to the process for requesting and managing the permission required to view other users' relationship diagrams.

[0458] The system of this invention allows users to input interpersonal relationship information using a terminal, and the server automatically generates, searches, and displays a correlation diagram based on that information. The operation of the entire system is described in detail below.

[0459] System Configuration

[0460] Hardware and software use cases:

[0461] Terminal: A device used by the user to input information, such as a smartphone, tablet, or laptop. A dedicated application is installed on the device.

[0462] Server: A computer system that receives, stores, and processes data, typically hosted on a cloud service (e.g., AWS).

[0463] Database: A system for storing saved information, typically a NoSQL database such as MongoDB.

[0464] Software Libraries: Python NetworkX and D3.js are used to generate and visualize the correlation diagram.

[0465] Enter and submit information

[0466] Steps for users to enter information:

[0467] The user launches the dedicated application and enters information such as name, age, occupation, contact information, relationship tags, etc. into the input form. The user interface of this application is designed to be easy and intuitive to use.

[0468] Examples:

[0469] The user enters information about the new person he or she has met as follows:

[0470] Name: Example: Yamada Taro

[0471] Age: 40

[0472] Occupation: Project Manager

[0473] Contact: taro.yamada@example.com

[0474] Relationship: Meeting Participant

[0475] The entered information is sent from the terminal to the server, and the data is encrypted and protected using the HTTPS protocol.

[0476] Retention of Information

[0477] The server receives the information and stores it in the database:

[0478] The server analyzes the received information and stores it in a database (e.g., MongoDB), which manages data in document format and allows for easy searching and updating.

[0479] Automatic generation of correlation diagrams

[0480] Steps for the server to generate the correlation diagram:

[0481] The server analyzes the information in the database and automatically generates a correlation diagram based on graph theory. This process uses the Python NetworkX library, where each person is represented as a node and relationships are represented as edges. The generated correlation diagram is then rendered using a visualization library (e.g., D3.js) and later sent to the user's device.

[0482] Examples:

[0483] Based on the information that "Yamada Taro" is participating in a new project, a correlation diagram is generated showing his relationships with all existing project members.

[0484] View and search correlation diagrams

[0485] Steps for users to view and search the generated correlation diagram:

[0486] The user can view the correlation diagram generated on their device and then perform a search based on specific criteria, using tags such as occupations and relationships. The server then extracts the relevant information from the database and provides the user with an updated correlation diagram.

[0487] Examples:

[0488] When a user searches based on the occupation tag "project manager," the server extracts information about people with similar occupations and displays an updated correlation diagram.

[0489] Permission to view other users' correlation diagrams

[0490] Steps for users to request and approve permission to view the relationship diagram:

[0491] User A requests permission to view User B's correlation diagram, and User B approves. The server grants viewing authority to User A, after which User A can search and view User B's correlation diagram. This enables efficient information sharing.

[0492] Examples:

[0493] User A requests to view User B's correlation diagram in order to find a new business partner, and if User B approves, User A can view the correlation diagram.

[0494] Through the above process, the system of the present invention efficiently manages and visualizes interpersonal relationships, enabling rapid information sharing with other users.

[0495] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0496] Step 1:

[0497] User input of information

[0498] The user launches the dedicated application and enters information about the new acquaintance or the person they have received from other users into the input form. Specifically, the input items include name, age, occupation, contact information, and relationship tags.

[0499] Input: Personal information (e.g., name, age, occupation, contact information, relationship tags)

[0500] Output: Personal information data entered

[0501] Specific operation: A user enters "Yamada Taro, age 40, project manager, taro.yamada@example.com, meeting participant" into an input form on a smartphone app.

[0502] Step 2:

[0503] Sending data from the device to the server

[0504] The device sends the entered personal information to the server using the HTTPS protocol, and the data is encrypted.

[0505] Input: Personal information data entered

[0506] Output: Personal information data sent to the server

[0507] Specific operation: Information about "Yamada Taro" is sent from the device to the server in encrypted form.

[0508] Step 3:

[0509] Server storage of information

[0510] The server analyzes the received personal information and stores it in a database using MongoDB, which manages data in document format.

[0511] Input: Personal information data sent to the server

[0512] Output: Personal information data stored in a database

[0513] Specific operation: The server receives the information about "Yamada Taro" and saves it as a document in the "contacts" collection in MongoDB.

[0514] Step 4:

[0515] Automatic generation of correlation diagrams by the server

[0516] The server analyzes the information in the database and automatically generates a correlation diagram that visually represents the relationships between individuals, using the Python NetworkX library.

[0517] Input: All personal information stored in the database

[0518] Output: Automatically generated correlation diagram data

[0519] Specific operation: The server analyzes the relationship between "Yamada Taro" and existing data, generates nodes and edges, and constructs a correlation diagram.

[0520] Step 5:

[0521] Sending correlation diagram from server to terminal

[0522] The server sends the generated correlation diagram to the terminal. The D3.js library is used for visualization, providing a dynamic and interactive correlation diagram.

[0523] Input: Auto-generated correlation diagram data

[0524] Output: Visualized correlation diagram data sent to the device

[0525] Specific operation: The server draws the correlation diagram using D3.js and sends it to the terminal via the Internet.

[0526] Step 6:

[0527] User-friendly correlation diagram browsing and search

[0528] Users view the correlation diagram on their devices and search for information based on specific criteria (e.g., occupation, age, relationship). The server extracts the relevant information from the database and provides an updated correlation diagram.

[0529] Input: User search criteria (e.g., occupation tag "Project Manager")

[0530] Output: Updated correlation diagram data

[0531] Specific operation: The user performs a search narrowed down to "Project Manager," and the server extracts the relevant person information and displays an updated correlation diagram.

[0532] Step 7:

[0533] Request and approve permission to view other users' relationship diagrams

[0534] When User A requests permission to view User B's correlation diagram and User B approves this, the server grants User A permission to view the diagram.

[0535] Input: Permission request from User A, Permission approval from User B

[0536] Output: Viewing permissions granted to User A

[0537] Specific operation: When User A sends a request on the app and User B approves it, the server grants User A permission to view User B's relationship diagram.

[0538] (Application example 1)

[0539] 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."

[0540] Conventional relationship information management systems make it difficult to manually organize and manage information about newly acquainted individuals or individuals heard from others. Furthermore, the visualization and search functions for relationships are limited, making it difficult to update information in real time or share information efficiently. Furthermore, while there is a demand for intuitive operation using smart devices and visual display devices, there has been a lack of systems that can achieve this.

[0541] 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.

[0542] In this invention, the server includes means for periodically inputting information about newly acquainted individuals or individuals heard from other people, means for automatically generating a visual correlation diagram based on the input information, means for making the generated correlation diagram searchable, means for obtaining permission to search other users' correlation diagrams and making them searchable, means for saving the input personal information in a database and updating the correlation diagram in real time, and means for visually displaying the generated correlation diagram. This enables efficient management and updating of interpersonal relationship information and the generation and viewing of visual correlation diagrams in real time. Furthermore, intuitive operation using a smart device can significantly improve user convenience.

[0543] "Information about a person that the user has newly met or has heard from other people" refers to information about a person that the user has newly met or that the user has received through other people.

[0544] "Means for periodic input" refers to a method or device that allows a user to continuously input information at regular intervals.

[0545] A "visual correlation diagram" refers to a diagram that visually shows the relationships between people based on information recorded by the user.

[0546] "Means for automatically generating a correlation diagram" refers to a method or device in which a system automatically visualizes the relationships between people based on input information and creates a correlation diagram.

[0547] The "means for making the diagram searchable" refers to a method or device that allows the generated correlation diagram to be searched for specific conditions or keywords and information to be obtained.

[0548] The "means for obtaining permission and making the correlation diagram searchable" refers to a method or device for obtaining permission to view other users' correlation diagrams and making the correlation diagrams searchable.

[0549] The "means for saving information in a database and updating the correlation diagram in real time" refers to a method or device for saving input information in a database and instantly updating the correlation diagram when the information is added or updated.

[0550] The term "means for visually displaying" refers to a method or device for visually displaying the generated correlation diagram on a terminal used by a user.

[0551] "Tags for occupation, contact information, age, and relationship" refer to attribute information used by the user when entering personal information.

[0552] "Means for representing relationships using nodes and edges based on graph theory" refers to a method or device that uses graph theory to represent people as nodes (points) and depict their relationships as edges (lines).

[0553] "Visual display device" refers to a device that can display graphics or text data, such as a smartphone, tablet, or monitor.

[0554] "Means for real-time updating" refers to a method or device that instantly updates a database or display content each time new information is entered.

[0555] "Means for inputting and viewing correlation diagrams using a device" refers to a method or device that allows a user to input correlation diagram information or view the generated correlation diagram using a smart device, etc.

[0556] The system of this invention is designed to efficiently manage personal information acquired by users and to generate and update a correlation diagram of human relationships in real time. Specific embodiments of this invention will be described below.

[0557] First, a user uses a visual display device such as a smartphone or tablet to enter new personal information into a dedicated application, including name, age, occupation, contact details, and relationship tags. The information acquired through the input form is sent to the server in real time.

[0558] The server uses an SQLite3 database to store the received personal information. The stored information is automatically generated as a correlation diagram of relationships between nodes (points) and edges (lines) based on graph theory. This process uses Python libraries such as NetworkX and Matplotlib. The server immediately updates the correlation diagram each time new information is input and generates data for visual display.

[0559] Users can then view the generated relationship diagram in real time and use the search function on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding relationship diagram in real time, allowing users to easily obtain the information they need and efficiently manage their relationships.

[0560] It also includes a function to obtain permission to view other users' correlation diagrams. User A requests permission to view a correlation diagram from User B, and if User B approves, the server grants viewing authority to User A. This allows multiple users to share network information and realize efficient networking.

[0561] A specific example is when a sales assistant in a store inputs the personal information of a new customer and then displays the relationships in a correlation diagram in real time. For example, the assistant inputs the information of customer "Taro Tanaka" and records his relationship with an existing customer as a "colleague." This information is immediately saved on the server, and a correlation diagram is generated and updated in real time.

[0562] When creating a program using a generative AI model, an example prompt is as follows:

[0563] "Based on the given customer information, please create a Python program to generate a relationship diagram in real time and save it in a SQLite3 database. Visualize the relationships based on the relationship tags for each customer."

[0564] In this way, the system of the present invention allows users to efficiently manage interpersonal relationship information and visually generate and update correlation diagrams, thereby significantly improving the efficiency of managing and effectively utilizing interpersonal relationships in everyday life and business environments.

[0565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0566] Step 1:

[0567] A user launches a dedicated application on a smartphone or tablet and inputs information about a new acquaintance or a person they have heard from others. At this time, they fill in an input form with information such as name, age, occupation, contact details, and relationship tags. This input data is sent from the device to the server. The input is done through a text field, and as a result, JSON-formatted data is generated and sent to the server.

[0568] Step 2:

[0569] The server parses the received JSON format data and saves it in an SQLite3 database. During the saving process, the entered personal information is added as a record to the corresponding table in the database. Each item in the input data (name, age, occupation, contact information, relationship tags) is mapped to a column in the database to ensure it is saved.

[0570] Step 3:

[0571] The server generates a visual correlation diagram in real time based on the stored information. Specifically, it uses Python's NetworkX library to represent individuals as nodes (points) and relationships as edges (lines). It retrieves the necessary information from the database and creates and updates nodes and edges. This operation generates graph data.

[0572] Step 4:

[0573] Using the generated graph data, the server uses Python's Matplotlib library to draw a visual correlation diagram. The resulting graph image is a graph of human relationships composed of nodes and edges. This image is then converted into a format that can be viewed by users.

[0574] Step 5:

[0575] The server sends the generated correlation diagram to the user's device in real time and displays it visually via an application. The user can check the correlation diagram on the device screen. In the display process, the drawn graph image is converted into a format that can be displayed on the device screen and sent to the user's device.

[0576] Step 6:

[0577] Users use the search function to search and filter the correlation diagram based on specific criteria (e.g., occupation or relationship tags). The user enters the search criteria, and the server again queries the database to extract the relevant data. Based on the search results, a new correlation diagram is generated and displayed.

[0578] Step 7:

[0579] User A requests permission to view another user B's correlation diagram. If user B approves the request through the application, the server grants user A permission to access user B's correlation diagram data. After granting permission, user A can search and view user B's correlation diagram.

[0580] Step 8:

[0581] If the entered personal information is new, the server updates the correlation diagram in real time. During the update process, new nodes and edges are added to the existing graph, and the diagram is redrawn. As a result, a correlation diagram reflecting the latest human relationships is generated and sent to the user's device again for display.

[0582] 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.

[0583] The system of this invention combines a user's input of interpersonal relationship information, automatic generation and search functions of correlation diagrams by a server, and an emotion engine. The emotion engine identifies emotions from user input and voice and uses them to generate and adjust correlation diagrams. The present invention can be implemented as follows.

[0584] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0585] Additionally, the system incorporates an emotion engine that analyzes emotions from text and voice inputs by the user. The emotion engine determines the user's emotional state and how that emotion affects the information input. For example, if the user is emotional, the relevance of the information may be emphasized.

[0586] The server then automatically generates a correlation diagram based on the received information and the results of the sentiment analysis. The server analyzes the information in the database and generates a visual correlation diagram that takes into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges indicate their relationships and emotional strengths.

[0587] Users can view and search correlation diagrams generated based on the information they input and the results of emotion analysis on their devices. When searching based on specific criteria, such as occupation, relationship tags, or even emotional state, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0588] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0589] Specific examples

[0590] Example 1: Entering information about a new acquaintance

[0591] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0592] Name: Ichiro Yamada

[0593] Age: 40

[0594] Occupation: Project Manager

[0595] Contact: yamada.ichiro@example.com

[0596] Relationship: Meeting Participant

[0597] Furthermore, the user can input a comment expressing their feelings, such as "Ichiro Yamada was very cooperative." The emotion engine analyzes this comment and recognizes it as a positive emotion. This information and emotional data are sent to the server and stored in a database.

[0598] Example 2: Automatic generation and viewing of correlation diagrams taking emotions into account

[0599] The server receives Ichiro Yamada's information and emotion data, compares it with existing information in the database, and generates a correlation diagram. The correlation diagram visually represents Ichiro Yamada's relationships with other project members. Furthermore, if positive emotions affect the strength of relationships, it highlights that Ichiro Yamada is very cooperative.

[0600] Users can view this correlation diagram on their devices and even search for it based on criteria such as "project manager" or "people with whom they have positive feelings." In this case, the server extracts people with similar attributes and emotional ratings from the database and provides an updated correlation diagram.

[0601] Example 3: Allowing other users to view correlation diagrams

[0602] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to find new talent and business partners by taking into account emotional evaluations, enabling efficient networking.

[0603] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it is possible to achieve more intuitive and reliable evaluation and utilization of human relationships.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] The user opens the application on their device. The user selects the form to add a new contact.

[0607] Step 2:

[0608] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0609] Step 3:

[0610] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0611] Step 4:

[0612] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0613] Step 5:

[0614] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0615] Step 6:

[0616] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0617] Step 7:

[0618] When a user enters an emotional comment (e.g., "It was cooperative") into the input form, the emotion engine analyzes the comment, and the device generates emotion data as the analysis result.

[0619] Step 8:

[0620] The device then sends the information, including the emotion data, back to the server, which then stores the received emotion data and personal information in a database.

[0621] Step 9:

[0622] The server starts the process of generating a correlation diagram based on the newly stored information and emotion data. The server runs an SQL query to retrieve all relevant contact information and emotion data from the database.

[0623] Step 10:

[0624] The server analyzes the acquired data and converts the relationships and emotional strengths between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0625] Step 11:

[0626] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0627] Step 12:

[0628] The server receives the request and executes an SQL query to extract the corresponding information and emotion data from the database. The server generates a correlation diagram based on the extracted information and returns it to the device.

[0629] Step 13:

[0630] The device visually displays the correlation diagram it receives, which shows the strength of the relationships between nodes based on emotion data.

[0631] Step 14:

[0632] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0633] Step 15:

[0634] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0635] Step 16:

[0636] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0637] In this way, it is possible to manage and effectively utilize interpersonal information that takes emotion data into account.

[0638] Example 2

[0639] 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."

[0640] Conventional human relationship information management systems have difficulty taking into account qualitative comments and emotions from users. Furthermore, because the generation and search of correlation diagrams relied on simple data, it was not possible to reflect individual emotions or the strength of relationships. This made it difficult for users to obtain intuitive and reliable information when evaluating and managing relationships.

[0641] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing emotions from the user's input content and voice, means for adjusting the correlation diagram based on the emotion analysis result, and means for saving the emotion analysis result in a database. This makes it possible to generate and search for a correlation diagram with higher accuracy that reflects the user's emotions.

[0642] "Personal information" is information that can be used to identify a specific individual, including name, age, occupation, contact information, relationship tags, etc.

[0643] A "correlation diagram" is a graph that visually shows individual people and their relationships, and uses nodes and edges to express the strength of relationships and emotional states.

[0644] An "emotion engine" is a software or hardware component that identifies emotions from text or voice input by a user and analyzes that emotional state.

[0645] "Emotion analysis results" are data on the user's emotional state analyzed by the emotion engine, and are information classified into emotion categories such as positive, negative, and neutral.

[0646] "Search conditions" are conditions such as specific attributes, tags, and emotional states that a user specifies when searching a correlation diagram.

[0647] A "database" is a digital storage system for systematically storing and managing data such as personal information entered by users and the results of sentiment analysis.

[0648] A "node" is a graph element that represents an individual person in the correlation diagram.

[0649] An "edge" is a line in a graph that represents the relationship between nodes in a correlation diagram, and visually indicates the strength or emotional state of that relationship.

[0650] A "visual display" is a graphical representation that presents information to a user in an easy-to-view manner on a device's display.

[0651] The system of this invention allows users to input information about new acquaintances, perform emotion analysis, and then generate, display, and search correlation diagrams. Specifically, users use a dedicated application to input personal information such as name and occupation from their terminal, and the information is managed and analyzed on the server, which then visually displays the correlation diagram. The main components of this system include a user terminal, a server, an emotion engine, and a database.

[0652] First, the user uses a device with the dedicated application installed. The user enters the name, age, occupation, contact information, relationship tags, etc. into the input form. Specifically, the user enters information such as "Name: Yamada Ichiro," "Age: 40," "Occupation: Project Manager," "Contact: yamada.ichiro@example.com," and "Relationship: Meeting Participant." At the same time, the user can also enter comments expressing their feelings, such as "Yamada Ichiro was very cooperative."

[0653] The entered information is sent from the device to the server. The server stores the received data in a database. The database uses a relational database such as MySQL or PostgreSQL. The server then uses an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze emotions from the user's comments. For example, a comment such as "Yamada Ichiro was very cooperative" is recognized as a positive emotion.

[0654] The server generates a correlation diagram based on the information in the database and the results of the sentiment analysis. This step can be performed using a library that applies graph theory (e.g., Python's NetworkX library). The correlation diagram is composed of nodes and edges, with the nodes representing individual people and the edges representing the relationships and emotional strengths between them. The generated correlation diagram can be viewed visually by the user on their device.

[0655] Users can search the generated correlation diagram based on specific criteria, such as "project managers" or "people with whom they have positive feelings." The server then extracts the relevant information from the database and provides the updated correlation diagram to the user's device.

[0656] Furthermore, users are also provided with a function to request permission to view other users' correlation diagrams. When User A requests permission to view User B's correlation diagram and User B approves, the server grants viewing authority to User A. This allows User A to search and view User B's correlation diagram.

[0657] Prompt Sentence Examples

[0658] "I entered information about Ichiro Yamada, whom I met at a new conference, and found him to be very cooperative. Based on this information, please use the emotion engine to analyze positive emotions and automatically generate a correlation diagram that reflects those emotions."

[0659] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it achieves more intuitive and reliable evaluation and utilization of human relationships.

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

[0661] Step 1: Enter your information

[0662] The user uses their device to enter information about a new acquaintance into the application's input form. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags. They can also enter comments that express their feelings. This generates input data (e.g., "Name: Yamada Ichiro," "Age: 40 years old," "Occupation: Project manager," "Relationship: Meeting participant," "Comment: Yamada Ichiro was very cooperative").

[0663] Input: Personal information and emotional comments entered by the user into the terminal

[0664] Output: The input data is generated

[0665] Step 2: Send and store data

[0666] The terminal sends data entered by the user to the server. The server saves the received data in a database. This process uses a POST request using the HTTP protocol, and a relational database (e.g., MySQL or PostgreSQL) is used as the database. For example, the input data is sent to the server in JSON format, where it is analyzed and saved.

[0667] Input: Data entered by the user into the terminal

[0668] Output: Data is saved to the database

[0669] Step 3: Sentiment Analysis

[0670] The server extracts comments from the stored data and performs sentiment analysis using an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding). For example, it analyzes the comment "Yamada Ichiro was very cooperative" and recognizes it as a positive sentiment. The analysis results are then stored back in the database.

[0671] Input: Comment data

[0672] Output: Sentiment analysis results (positive, negative, etc.)

[0673] Step 4: Generate correlation diagram

[0674] The server generates a correlation diagram based on the information in the database and the results of sentiment analysis. The correlation diagram is created using a library that applies graph theory (e.g., Python's NetworkX library), with nodes representing people and edges representing relationships and emotional strength. For example, Ichiro Yamada is visualized as a node, with his relationships with other project members as edges.

[0675] Input: Personal information in the database and sentiment analysis results

[0676] Output: correlation diagram data

[0677] Step 5: Visualize the correlation diagram

[0678] The server sends the generated correlation diagram to the device, which receives it and displays it visually within a dedicated application. For example, the generated correlation diagram is intuitively displayed on the device screen with nodes and edges.

[0679] Input: correlation diagram data

[0680] Output: Correlation diagram displayed on the terminal

[0681] Step 6: Search for correlation diagrams

[0682] The user inputs specific criteria (e.g., "project manager" or "positive emotion") to search for correlation diagrams. At this time, the device sends the search criteria to the server. The server searches the database, generates the corresponding correlation diagram, and sends it to the device. The device receives it and displays it visually.

[0683] Input: Search criteria (tags, emotional state, etc.)

[0684] Output: The corresponding correlation diagram is displayed.

[0685] Step 7: Allow other users to view the correlation diagram

[0686] A user requests permission to view another user's correlation diagram. For example, if user A requests permission to view user B's correlation diagram and user B approves it, the request and approval are processed on the server, and user A is granted permission to view it.

[0687] Input: Viewing permission request and approval

[0688] Output: Viewing permission granted

[0689] By implementing the above steps, users can easily generate, display, and search correlation diagrams that take emotions into account, enabling more reliable evaluation and management of human relationships.

[0690] (Application example 2)

[0691] 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."

[0692] Previously, relationship management systems only collected personal information and generated visual correlation diagrams, but did not have the functionality to adjust the environment in real time based on the user's emotional state. As a result, it was not possible to effectively express personal connections based on the user's emotions, and there was a need for flexible environment adjustments in response to emotional changes.

[0693] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for periodically inputting information about newly acquainted individuals or individuals heard from other people; means for automatically generating a visual correlation diagram based on the input information; means for making the generated correlation diagram searchable; means for obtaining permission to search other users' correlation diagrams and making them searchable; means for using an emotion analysis engine to identify emotions from the user's input text or voice and reflecting the emotional state in the intensity of the correlation diagram; means for updating the generated correlation diagram in real time and displaying it on the smart device; and means for automatically adjusting the device's environmental settings (music, temperature, lighting, etc.) according to the user's emotional state. This allows for visual management of human relationships based on the user's emotions and optimal environmental adjustment in real time.

[0694] "Information about individuals that you have newly met or heard from others" is information about people that you have obtained based on your own experiences or hearsay from others.

[0695] A "visual correlation diagram" is a diagram constructed with nodes and edges based on graph theory that visually represents the relationships and emotional strengths between individual people.

[0696] "Means for making things searchable" refers to a function that allows users to search for information in the database based on specific conditions, and extract and display the relevant correlation diagram.

[0697] "Means of obtaining permission and making it searchable" refers to the ability to request permission to access other users' relationship diagrams and, if permission is granted, make them searchable and viewable.

[0698] An "emotion analysis engine" is an analytical device that identifies emotions from text and voice input by the user and reflects that information in the strength of relationships and environmental settings.

[0699] "Real-time update means" refers to a function that instantly keeps correlation diagrams and environment settings up to date based on user input and sentiment analysis results.

[0700] A "smart device" is a digital terminal for inputting and displaying information, and includes smartphones, smart glasses, head-mounted displays, etc.

[0701] "Means for automatically adjusting environmental settings" refers to a function that appropriately changes the music, temperature, lighting, etc. in the car based on the user's emotional state.

[0702] The system embodying the present invention has functions of information input by a user, data processing and correlation diagram generation by a server, and display and environment adjustment by a smart device.

[0703] First, the user uses a smart device (smartphone, smart glasses, head-mounted display, etc.) to input information about a new acquaintance or someone they have heard from another person. The input form includes items such as name, age, occupation, contact information, and relationship tags, and the user enters this information through the device. In addition, the system has the ability to identify emotions from the text and voice input by the user.

[0704] An emotion analysis engine is used to analyze emotions from input text and voice. This emotion analysis engine uses libraries such as Python's "TextBlob" to identify emotional states such as positive and negative. The server reflects the acquired emotional data in a correlation diagram.

[0705] The server then analyzes the information in the database and generates a visual correlation diagram, taking into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Each node represents an individual person, and the edges indicate their relationships and emotional strengths. Libraries such as "NetworkX" and "Matplotlib" are used to generate the correlation diagram.

[0706] The server updates the generated correlation diagram in real time and displays it on the smart device. Users can search based on specific criteria (e.g., occupation, relationship tags, emotional state), and the server extracts the relevant information from the database and provides the corresponding correlation diagram.

[0707] Furthermore, it has the ability to automatically adjust the environmental settings of smart devices (music, temperature, lighting, etc.) according to the user's emotional state, allowing users to browse and search for information in a comfortable environment.

[0708] As a concrete example, consider the case where User A enters information about "Person A" whom he met at a new meeting. User A uses his smartphone to enter the following information:

[0709] Name: Individual A

[0710] Age: 40

[0711] Occupation: Project Manager

[0712] Contact: example@example.com

[0713] Relationship: Meeting Participant

[0714] Furthermore, a comment expressing an emotion, such as "Individual A was very cooperative," is entered. The emotion analysis engine analyzes this comment and identifies it as a positive emotion. The server generates a correlation diagram based on this information and emotion data and displays it on User A's smart device.

[0715] Examples of prompts generated based on specific examples include:

[0716] "We have a new passenger, Individual B, on board. He's very cooperative and friendly."

[0717] "Passengers seem a little tired at the moment."

[0718] In this way, the present invention can visually manage human relationships based on the user's emotions and provide optimal environmental adjustments in real time.

[0719] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0720] Step 1:

[0721] The user uses a smart device to input information about a new acquaintance or information they have heard from other people. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags into an input form. This input information is sent from the device to the server and stored in a database. Input: Relationship information entered by the user. Output: Data sent to the server and stored in the database.

[0722] Step 2:

[0723] The text or voice input by the user is sent to the sentiment analysis engine. The sentiment analysis engine is used to analyze emotions from the text or voice. Specifically, it uses the Python "TextBlob" library to classify emotions into positive, negative, or neutral. Input: Text or voice data input by the user. Output: Analyzed sentiment data (emotional state).

[0724] Step 3:

[0725] The server generates a correlation diagram based on the collected interpersonal relationship information and emotional data. NetworkX and Matplotlib are used to generate the correlation diagram. Specifically, each node represents an individual person, and edges indicate their relationships and emotional strength. Input: Interpersonal relationship information and emotional data in the database. Output: Generated correlation diagram (graph).

[0726] Step 4:

[0727] The generated correlation diagram is updated in real time and displayed on the smart device. When a user searches based on specific conditions (e.g., occupation, relationship tags, emotional state), the server extracts the relevant information from the database and provides the corresponding correlation diagram. Input: User's search conditions. Output: Display of the updated correlation diagram.

[0728] Step 5:

[0729] The server automatically adjusts the smart device's environmental settings based on the user's emotional state. These environmental settings include music, temperature, lighting, etc. For example, if the user inputs "I'm tired," the server will play soothing music and dim the lights. Input: User's emotional data. Output: Adjusted device environmental settings.

[0730] Step 6:

[0731] A request is sent to the server to grant access to another user's correlation diagram, and if granted, the correlation diagram becomes searchable and viewable. When User A requests access to User B's correlation diagram and User B approves, the server grants viewing permission to User A. Input: Access permission request. Output: Granting viewing permission and access to the correlation diagram.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] [Third embodiment]

[0736] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0737] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0738] 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).

[0739] 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.

[0740] 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.

[0741] 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).

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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."

[0748] The system of the present invention includes a user's input of human relationship information, automatic generation of a correlation diagram by a server, and a search function, etc. The present invention can be implemented as follows.

[0749] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0750] The server then automatically generates a correlation diagram based on the received information. The server analyzes the information in the database and generates a correlation diagram that visually shows the relationships between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges show their relationships.

[0751] Users can view and search correlation diagrams generated based on the information they input on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0752] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0753] Specific examples

[0754] Example 1: Entering information about a new acquaintance

[0755] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0756] Name: Ichiro Yamada

[0757] Age: 40

[0758] Occupation: Project Manager

[0759] Contact: yamada.ichiro@example.com

[0760] Relationship: Meeting Participant

[0761] This information is sent to a server and stored in a database.

[0762] Example 2: Automatic generation and viewing of correlation diagrams

[0763] The server receives Yamada Ichiro's information and compares it with existing information in the database to generate a correlation diagram, which visually shows, for example, Yamada Ichiro's relationships with all the project members he is involved in.

[0764] The user can view this relationship diagram on their device and further search for the occupation "project manager." In this case, the server will extract people with similar occupations from the database and provide an updated relationship diagram.

[0765] Example 3: Allowing other users to view correlation diagrams

[0766] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to easily find new talent and business partners, enabling efficient networking.

[0767] In this way, the present invention can greatly improve the efficiency of managing and utilizing relationships in everyday life and in business environments.

[0768] The processing flow will be explained below.

[0769] Step 1:

[0770] The user opens the application on their device. The user selects the form to add a new contact.

[0771] Step 2:

[0772] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0773] Step 3:

[0774] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0775] Step 4:

[0776] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0777] Step 5:

[0778] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0779] Step 6:

[0780] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0781] Step 7:

[0782] The server starts the process of generating a correlation diagram based on the newly saved information: the server runs an SQL query to retrieve all relevant contact information from the database.

[0783] Step 8:

[0784] The server analyzes the acquired data and converts the relationships between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0785] Step 9:

[0786] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0787] Step 10:

[0788] The server receives the request, executes an SQL query to extract the corresponding information from the database, and generates a correlation diagram based on the extracted information and returns it to the device.

[0789] Step 11:

[0790] The correlation diagram received by the terminal is visually displayed.

[0791] Step 12:

[0792] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0793] Step 13:

[0794] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0795] Step 14:

[0796] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0797] Example 1

[0798] 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."

[0799] Currently, systems for visually managing interpersonal relationships require a large number of manual operations and are not particularly efficient. It is particularly difficult to centrally manage information about new acquaintances and information obtained from other users, and to automatically generate and search visual correlation diagrams. Furthermore, viewing other users' correlation diagrams requires a time-consuming permission process, which often hinders rapid information sharing and networking. Therefore, there is a need for a system that can manage and visualize relationships, as well as enable efficient information sharing with other users.

[0800] 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.

[0801] In this invention, the server includes means for inputting information on newly acquainted individuals or individuals heard from other users at a terminal, means for transmitting the input information to the server, means for the server to store the received information in a database, means for the server to automatically generate a visual correlation diagram based on the stored information, means for making the generated correlation diagram searchable, means for extracting information from the database based on specific conditions and providing the relevant correlation diagram, and means for obtaining permission to search other users' correlation diagrams and making the correlation diagrams searchable. This makes it possible to efficiently manage and visualize relationships between individuals and quickly share information with other users.

[0802] A "terminal" is an electronic device that allows a user to input and transmit information.

[0803] "Input means" refers to software functionality, including interfaces and forms, through which a user can input information.

[0804] "Means for transmitting information to the server" refers to protocols and communication means for securely transmitting data from the terminal to the server.

[0805] A "server" is a computer system that receives, stores, processes data, and responds to requests from clients.

[0806] A "database" is a system or software for storing data in a structured format.

[0807] The "means for storing information" is a software process for storing the received data in a database.

[0808] A "means for automatically generating correlation diagrams" is an algorithm or software that uses stored data to form nodes and edges based on graph theory, and visually represents relationships.

[0809] A "searchability means" is a software function that allows a user to extract data based on specific criteria and display it visually.

[0810] The "specific conditions" are keywords and filtering conditions used by the user when conducting a search.

[0811] The "means of information extraction" is the query or process used to retrieve data from a database that matches the search criteria.

[0812] The "visual display means" is a software function for displaying the generated correlation diagram on a terminal in a format that is easy for the user to understand.

[0813] "Means for obtaining permission" refers to the process for requesting and managing the permission required to view other users' relationship diagrams.

[0814] The system of this invention allows users to input interpersonal relationship information using a terminal, and the server automatically generates, searches, and displays a correlation diagram based on that information. The operation of the entire system is described in detail below.

[0815] System Configuration

[0816] Hardware and software use cases:

[0817] Terminal: A device used by the user to input information, such as a smartphone, tablet, or laptop. A dedicated application is installed on the device.

[0818] Server: A computer system that receives, stores, and processes data, typically hosted on a cloud service (e.g., AWS).

[0819] Database: A system for storing saved information, typically a NoSQL database such as MongoDB.

[0820] Software Libraries: Python NetworkX and D3.js are used to generate and visualize the correlation diagram.

[0821] Enter and submit information

[0822] Steps for users to enter information:

[0823] The user launches the dedicated application and enters information such as name, age, occupation, contact information, relationship tags, etc. into the input form. The user interface of this application is designed to be easy and intuitive to use.

[0824] Examples:

[0825] The user enters information about the new person he or she has met as follows:

[0826] Name: Example: Yamada Taro

[0827] Age: 40

[0828] Occupation: Project Manager

[0829] Contact: taro.yamada@example.com

[0830] Relationship: Meeting Participant

[0831] The entered information is sent from the terminal to the server, and the data is encrypted and protected using the HTTPS protocol.

[0832] Retention of Information

[0833] The server receives the information and stores it in the database:

[0834] The server analyzes the received information and stores it in a database (e.g., MongoDB), which manages data in document format and allows for easy searching and updating.

[0835] Automatic generation of correlation diagrams

[0836] Steps for the server to generate the correlation diagram:

[0837] The server analyzes the information in the database and automatically generates a correlation diagram based on graph theory. This process uses the Python NetworkX library, where each person is represented as a node and relationships are represented as edges. The generated correlation diagram is then rendered using a visualization library (e.g., D3.js) and later sent to the user's device.

[0838] Examples:

[0839] Based on the information that "Yamada Taro" is participating in a new project, a correlation diagram is generated showing his relationships with all existing project members.

[0840] View and search correlation diagrams

[0841] Steps for users to view and search the generated correlation diagram:

[0842] The user can view the correlation diagram generated on their device and then perform a search based on specific criteria, using tags such as occupations and relationships. The server then extracts the relevant information from the database and provides the user with an updated correlation diagram.

[0843] Examples:

[0844] When a user searches based on the occupation tag "project manager," the server extracts information about people with similar occupations and displays an updated correlation diagram.

[0845] Permission to view other users' correlation diagrams

[0846] Steps for users to request and approve permission to view the relationship diagram:

[0847] User A requests permission to view User B's correlation diagram, and User B approves. The server grants viewing authority to User A, after which User A can search and view User B's correlation diagram. This enables efficient information sharing.

[0848] Examples:

[0849] User A requests to view User B's correlation diagram in order to find a new business partner, and if User B approves, User A can view the correlation diagram.

[0850] Through the above process, the system of the present invention efficiently manages and visualizes interpersonal relationships, enabling rapid information sharing with other users.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] User input of information

[0854] The user launches the dedicated application and enters information about the new acquaintance or the person they have received from other users into the input form. Specifically, the input items include name, age, occupation, contact information, and relationship tags.

[0855] Input: Personal information (e.g., name, age, occupation, contact information, relationship tags)

[0856] Output: Personal information data entered

[0857] Specific operation: A user enters "Yamada Taro, age 40, project manager, taro.yamada@example.com, meeting participant" into an input form on a smartphone app.

[0858] Step 2:

[0859] Sending data from the device to the server

[0860] The device sends the entered personal information to the server using the HTTPS protocol, and the data is encrypted.

[0861] Input: Personal information data entered

[0862] Output: Personal information data sent to the server

[0863] Specific operation: Information about "Yamada Taro" is sent from the device to the server in encrypted form.

[0864] Step 3:

[0865] Server storage of information

[0866] The server analyzes the received personal information and stores it in a database using MongoDB, which manages data in document format.

[0867] Input: Personal information data sent to the server

[0868] Output: Personal information data stored in a database

[0869] Specific operation: The server receives the information about "Yamada Taro" and saves it as a document in the "contacts" collection in MongoDB.

[0870] Step 4:

[0871] Automatic generation of correlation diagrams by the server

[0872] The server analyzes the information in the database and automatically generates a correlation diagram that visually represents the relationships between individuals, using the Python NetworkX library.

[0873] Input: All personal information stored in the database

[0874] Output: Automatically generated correlation diagram data

[0875] Specific operation: The server analyzes the relationship between "Yamada Taro" and existing data, generates nodes and edges, and constructs a correlation diagram.

[0876] Step 5:

[0877] Sending correlation diagram from server to terminal

[0878] The server sends the generated correlation diagram to the terminal. The D3.js library is used for visualization, providing a dynamic and interactive correlation diagram.

[0879] Input: Auto-generated correlation diagram data

[0880] Output: Visualized correlation diagram data sent to the device

[0881] Specific operation: The server draws the correlation diagram using D3.js and sends it to the terminal via the Internet.

[0882] Step 6:

[0883] User-friendly correlation diagram browsing and search

[0884] Users view the correlation diagram on their devices and search for information based on specific criteria (e.g., occupation, age, relationship). The server extracts the relevant information from the database and provides an updated correlation diagram.

[0885] Input: User search criteria (e.g., occupation tag "Project Manager")

[0886] Output: Updated correlation diagram data

[0887] Specific operation: The user performs a search narrowed down to "Project Manager," and the server extracts the relevant person information and displays an updated correlation diagram.

[0888] Step 7:

[0889] Request and approve permission to view other users' relationship diagrams

[0890] When User A requests permission to view User B's correlation diagram and User B approves this, the server grants User A permission to view the diagram.

[0891] Input: Permission request from User A, Permission approval from User B

[0892] Output: Viewing permissions granted to User A

[0893] Specific operation: When User A sends a request on the app and User B approves it, the server grants User A permission to view User B's relationship diagram.

[0894] (Application example 1)

[0895] 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."

[0896] Conventional relationship information management systems make it difficult to manually organize and manage information about newly acquainted individuals or individuals heard from others. Furthermore, the visualization and search functions for relationships are limited, making it difficult to update information in real time or share information efficiently. Furthermore, while there is a demand for intuitive operation using smart devices and visual display devices, there has been a lack of systems that can achieve this.

[0897] 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.

[0898] In this invention, the server includes means for periodically inputting information about newly acquainted individuals or individuals heard from other people, means for automatically generating a visual correlation diagram based on the input information, means for making the generated correlation diagram searchable, means for obtaining permission to search other users' correlation diagrams and making them searchable, means for saving the input personal information in a database and updating the correlation diagram in real time, and means for visually displaying the generated correlation diagram. This enables efficient management and updating of interpersonal relationship information and the generation and viewing of visual correlation diagrams in real time. Furthermore, intuitive operation using a smart device can significantly improve user convenience.

[0899] "Information about a person that the user has newly met or has heard from other people" refers to information about a person that the user has newly met or that the user has received through other people.

[0900] "Means for periodic input" refers to a method or device that allows a user to continuously input information at regular intervals.

[0901] A "visual correlation diagram" refers to a diagram that visually shows the relationships between people based on information recorded by the user.

[0902] "Means for automatically generating a correlation diagram" refers to a method or device in which a system automatically visualizes the relationships between people based on input information and creates a correlation diagram.

[0903] The "means for making the diagram searchable" refers to a method or device that allows the generated correlation diagram to be searched for specific conditions or keywords and information to be obtained.

[0904] The "means for obtaining permission and making the correlation diagram searchable" refers to a method or device for obtaining permission to view other users' correlation diagrams and making the correlation diagrams searchable.

[0905] The "means for saving information in a database and updating the correlation diagram in real time" refers to a method or device for saving input information in a database and instantly updating the correlation diagram when the information is added or updated.

[0906] The term "means for visually displaying" refers to a method or device for visually displaying the generated correlation diagram on a terminal used by a user.

[0907] "Tags for occupation, contact information, age, and relationship" refer to attribute information used by the user when entering personal information.

[0908] "Means for representing relationships using nodes and edges based on graph theory" refers to a method or device that uses graph theory to represent people as nodes (points) and depict their relationships as edges (lines).

[0909] "Visual display device" refers to a device that can display graphics or text data, such as a smartphone, tablet, or monitor.

[0910] "Means for real-time updating" refers to a method or device that instantly updates a database or display content each time new information is entered.

[0911] "Means for inputting and viewing correlation diagrams using a device" refers to a method or device that allows a user to input correlation diagram information or view the generated correlation diagram using a smart device, etc.

[0912] The system of this invention is designed to efficiently manage personal information acquired by users and to generate and update a correlation diagram of human relationships in real time. Specific embodiments of this invention will be described below.

[0913] First, a user uses a visual display device such as a smartphone or tablet to enter new personal information into a dedicated application, including name, age, occupation, contact details, and relationship tags. The information acquired through the input form is sent to the server in real time.

[0914] The server uses an SQLite3 database to store the received personal information. The stored information is automatically generated as a correlation diagram of relationships between nodes (points) and edges (lines) based on graph theory. This process uses Python libraries such as NetworkX and Matplotlib. The server immediately updates the correlation diagram each time new information is input and generates data for visual display.

[0915] Users can then view the generated relationship diagram in real time and use the search function on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding relationship diagram in real time, allowing users to easily obtain the information they need and efficiently manage their relationships.

[0916] It also includes a function to obtain permission to view other users' correlation diagrams. User A requests permission to view a correlation diagram from User B, and if User B approves, the server grants viewing authority to User A. This allows multiple users to share network information and realize efficient networking.

[0917] A specific example is when a sales assistant in a store inputs the personal information of a new customer and then displays the relationships in a correlation diagram in real time. For example, the assistant inputs the information of customer "Taro Tanaka" and records his relationship with an existing customer as a "colleague." This information is immediately saved on the server, and a correlation diagram is generated and updated in real time.

[0918] When creating a program using a generative AI model, an example prompt is as follows:

[0919] "Based on the given customer information, please create a Python program to generate a relationship diagram in real time and save it in a SQLite3 database. Visualize the relationships based on the relationship tags for each customer."

[0920] In this way, the system of the present invention allows users to efficiently manage interpersonal relationship information and visually generate and update correlation diagrams, thereby significantly improving the efficiency of managing and effectively utilizing interpersonal relationships in everyday life and business environments.

[0921] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0922] Step 1:

[0923] A user launches a dedicated application on a smartphone or tablet and inputs information about a new acquaintance or a person they have heard from others. At this time, they fill in an input form with information such as name, age, occupation, contact details, and relationship tags. This input data is sent from the device to the server. The input is done through a text field, and as a result, JSON-formatted data is generated and sent to the server.

[0924] Step 2:

[0925] The server parses the received JSON format data and saves it in an SQLite3 database. During the saving process, the entered personal information is added as a record to the corresponding table in the database. Each item in the input data (name, age, occupation, contact information, relationship tags) is mapped to a column in the database to ensure it is saved.

[0926] Step 3:

[0927] The server generates a visual correlation diagram in real time based on the stored information. Specifically, it uses Python's NetworkX library to represent individuals as nodes (points) and relationships as edges (lines). It retrieves the necessary information from the database and creates and updates nodes and edges. This operation generates graph data.

[0928] Step 4:

[0929] Using the generated graph data, the server uses Python's Matplotlib library to draw a visual correlation diagram. The resulting graph image is a graph of human relationships composed of nodes and edges. This image is then converted into a format that can be viewed by users.

[0930] Step 5:

[0931] The server sends the generated correlation diagram to the user's device in real time and displays it visually via an application. The user can check the correlation diagram on the device screen. In the display process, the drawn graph image is converted into a format that can be displayed on the device screen and sent to the user's device.

[0932] Step 6:

[0933] Users use the search function to search and filter the correlation diagram based on specific criteria (e.g., occupation or relationship tags). The user enters the search criteria, and the server again queries the database to extract the relevant data. Based on the search results, a new correlation diagram is generated and displayed.

[0934] Step 7:

[0935] User A requests permission to view another user B's correlation diagram. If user B approves the request through the application, the server grants user A permission to access user B's correlation diagram data. After granting permission, user A can search and view user B's correlation diagram.

[0936] Step 8:

[0937] If the entered personal information is new, the server updates the correlation diagram in real time. During the update process, new nodes and edges are added to the existing graph, and the diagram is redrawn. As a result, a correlation diagram reflecting the latest human relationships is generated and sent to the user's device again for display.

[0938] 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.

[0939] The system of this invention combines a user's input of interpersonal relationship information, automatic generation and search functions of correlation diagrams by a server, and an emotion engine. The emotion engine identifies emotions from user input and voice and uses them to generate and adjust correlation diagrams. The present invention can be implemented as follows.

[0940] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[0941] Additionally, the system incorporates an emotion engine that analyzes emotions from text and voice inputs by the user. The emotion engine determines the user's emotional state and how that emotion affects the information input. For example, if the user is emotional, the relevance of the information may be emphasized.

[0942] The server then automatically generates a correlation diagram based on the received information and the results of the sentiment analysis. The server analyzes the information in the database and generates a visual correlation diagram that takes into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges indicate their relationships and emotional strengths.

[0943] Users can view and search correlation diagrams generated based on the information they input and the results of emotion analysis on their devices. When searching based on specific criteria, such as occupation, relationship tags, or even emotional state, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[0944] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[0945] Specific examples

[0946] Example 1: Entering information about a new acquaintance

[0947] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[0948] Name: Ichiro Yamada

[0949] Age: 40

[0950] Occupation: Project Manager

[0951] Contact: yamada.ichiro@example.com

[0952] Relationship: Meeting Participant

[0953] Furthermore, the user can input a comment expressing their feelings, such as "Ichiro Yamada was very cooperative." The emotion engine analyzes this comment and recognizes it as a positive emotion. This information and emotional data are sent to the server and stored in a database.

[0954] Example 2: Automatic generation and viewing of correlation diagrams taking emotions into account

[0955] The server receives Ichiro Yamada's information and emotion data, compares it with existing information in the database, and generates a correlation diagram. The correlation diagram visually represents Ichiro Yamada's relationships with other project members. Furthermore, if positive emotions affect the strength of relationships, it highlights that Ichiro Yamada is very cooperative.

[0956] Users can view this correlation diagram on their devices and even search for it based on criteria such as "project manager" or "people with whom they have positive feelings." In this case, the server extracts people with similar attributes and emotional ratings from the database and provides an updated correlation diagram.

[0957] Example 3: Allowing other users to view correlation diagrams

[0958] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to find new talent and business partners by taking into account emotional evaluations, enabling efficient networking.

[0959] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it is possible to achieve more intuitive and reliable evaluation and utilization of human relationships.

[0960] The processing flow will be explained below.

[0961] Step 1:

[0962] The user opens the application on their device. The user selects the form to add a new contact.

[0963] Step 2:

[0964] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[0965] Step 3:

[0966] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[0967] Step 4:

[0968] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[0969] Step 5:

[0970] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[0971] Step 6:

[0972] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[0973] Step 7:

[0974] When a user enters an emotional comment (e.g., "It was cooperative") into the input form, the emotion engine analyzes the comment, and the device generates emotion data as the analysis result.

[0975] Step 8:

[0976] The device then sends the information, including the emotion data, back to the server, which then stores the received emotion data and personal information in a database.

[0977] Step 9:

[0978] The server starts the process of generating a correlation diagram based on the newly stored information and emotion data. The server runs an SQL query to retrieve all relevant contact information and emotion data from the database.

[0979] Step 10:

[0980] The server analyzes the acquired data and converts the relationships and emotional strengths between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[0981] Step 11:

[0982] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[0983] Step 12:

[0984] The server receives the request and executes an SQL query to extract the corresponding information and emotion data from the database. The server generates a correlation diagram based on the extracted information and returns it to the device.

[0985] Step 13:

[0986] The device visually displays the correlation diagram it receives, which shows the strength of the relationships between nodes based on emotion data.

[0987] Step 14:

[0988] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[0989] Step 15:

[0990] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[0991] Step 16:

[0992] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[0993] In this way, it is possible to manage and effectively utilize interpersonal information that takes emotion data into account.

[0994] Example 2

[0995] 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."

[0996] Conventional human relationship information management systems have difficulty taking into account qualitative comments and emotions from users. Furthermore, because the generation and search of correlation diagrams relied on simple data, it was not possible to reflect individual emotions or the strength of relationships. This made it difficult for users to obtain intuitive and reliable information when evaluating and managing relationships.

[0997] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing emotions from the user's input content and voice, means for adjusting the correlation diagram based on the emotion analysis result, and means for saving the emotion analysis result in a database. This makes it possible to generate and search for a correlation diagram with higher accuracy that reflects the user's emotions.

[0998] "Personal information" is information that can be used to identify a specific individual, including name, age, occupation, contact information, relationship tags, etc.

[0999] A "correlation diagram" is a graph that visually shows individual people and their relationships, and uses nodes and edges to express the strength of relationships and emotional states.

[1000] An "emotion engine" is a software or hardware component that identifies emotions from text or voice input by a user and analyzes that emotional state.

[1001] "Emotion analysis results" are data on the user's emotional state analyzed by the emotion engine, and are information classified into emotion categories such as positive, negative, and neutral.

[1002] "Search conditions" are conditions such as specific attributes, tags, and emotional states that a user specifies when searching a correlation diagram.

[1003] A "database" is a digital storage system for systematically storing and managing data such as personal information entered by users and the results of sentiment analysis.

[1004] A "node" is a graph element that represents an individual person in the correlation diagram.

[1005] An "edge" is a line in a graph that represents the relationship between nodes in a correlation diagram, and visually indicates the strength or emotional state of that relationship.

[1006] A "visual display" is a graphical representation that presents information to a user in an easy-to-view manner on a device's display.

[1007] The system of this invention allows users to input information about new acquaintances, perform emotion analysis, and then generate, display, and search correlation diagrams. Specifically, users use a dedicated application to input personal information such as name and occupation from their terminal, and the information is managed and analyzed on the server, which then visually displays the correlation diagram. The main components of this system include a user terminal, a server, an emotion engine, and a database.

[1008] First, the user uses a device with the dedicated application installed. The user enters the name, age, occupation, contact information, relationship tags, etc. into the input form. Specifically, the user enters information such as "Name: Yamada Ichiro," "Age: 40," "Occupation: Project Manager," "Contact: yamada.ichiro@example.com," and "Relationship: Meeting Participant." At the same time, the user can also enter comments expressing their feelings, such as "Yamada Ichiro was very cooperative."

[1009] The entered information is sent from the device to the server. The server stores the received data in a database. The database uses a relational database such as MySQL or PostgreSQL. The server then uses an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze emotions from the user's comments. For example, a comment such as "Yamada Ichiro was very cooperative" is recognized as a positive emotion.

[1010] The server generates a correlation diagram based on the information in the database and the results of the sentiment analysis. This step can be performed using a library that applies graph theory (e.g., Python's NetworkX library). The correlation diagram is composed of nodes and edges, with the nodes representing individual people and the edges representing the relationships and emotional strengths between them. The generated correlation diagram can be viewed visually by the user on their device.

[1011] Users can search the generated correlation diagram based on specific criteria, such as "project managers" or "people with whom they have positive feelings." The server then extracts the relevant information from the database and provides the updated correlation diagram to the user's device.

[1012] Furthermore, users are also provided with a function to request permission to view other users' correlation diagrams. When User A requests permission to view User B's correlation diagram and User B approves, the server grants viewing authority to User A. This allows User A to search and view User B's correlation diagram.

[1013] Prompt Sentence Examples

[1014] "I entered information about Ichiro Yamada, whom I met at a new conference, and found him to be very cooperative. Based on this information, please use the emotion engine to analyze positive emotions and automatically generate a correlation diagram that reflects those emotions."

[1015] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it achieves more intuitive and reliable evaluation and utilization of human relationships.

[1016] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1017] Step 1: Enter your information

[1018] The user uses their device to enter information about a new acquaintance into the application's input form. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags. They can also enter comments that express their feelings. This generates input data (e.g., "Name: Yamada Ichiro," "Age: 40 years old," "Occupation: Project manager," "Relationship: Meeting participant," "Comment: Yamada Ichiro was very cooperative").

[1019] Input: Personal information and emotional comments entered by the user into the terminal

[1020] Output: The input data is generated

[1021] Step 2: Send and store data

[1022] The terminal sends data entered by the user to the server. The server saves the received data in a database. This process uses a POST request using the HTTP protocol, and a relational database (e.g., MySQL or PostgreSQL) is used as the database. For example, the input data is sent to the server in JSON format, where it is analyzed and saved.

[1023] Input: Data entered by the user into the terminal

[1024] Output: Data is saved to the database

[1025] Step 3: Sentiment Analysis

[1026] The server extracts comments from the stored data and performs sentiment analysis using an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding). For example, it analyzes the comment "Yamada Ichiro was very cooperative" and recognizes it as a positive sentiment. The analysis results are then stored back in the database.

[1027] Input: Comment data

[1028] Output: Sentiment analysis results (positive, negative, etc.)

[1029] Step 4: Generate correlation diagram

[1030] The server generates a correlation diagram based on the information in the database and the results of sentiment analysis. The correlation diagram is created using a library that applies graph theory (e.g., Python's NetworkX library), with nodes representing people and edges representing relationships and emotional strength. For example, Ichiro Yamada is visualized as a node, with his relationships with other project members as edges.

[1031] Input: Personal information in the database and sentiment analysis results

[1032] Output: correlation diagram data

[1033] Step 5: Visualize the correlation diagram

[1034] The server sends the generated correlation diagram to the device, which receives it and displays it visually within a dedicated application. For example, the generated correlation diagram is intuitively displayed on the device screen with nodes and edges.

[1035] Input: correlation diagram data

[1036] Output: Correlation diagram displayed on the terminal

[1037] Step 6: Search for correlation diagrams

[1038] The user inputs specific criteria (e.g., "project manager" or "positive emotion") to search for correlation diagrams. At this time, the device sends the search criteria to the server. The server searches the database, generates the corresponding correlation diagram, and sends it to the device. The device receives it and displays it visually.

[1039] Input: Search criteria (tags, emotional state, etc.)

[1040] Output: The corresponding correlation diagram is displayed.

[1041] Step 7: Allow other users to view the correlation diagram

[1042] A user requests permission to view another user's correlation diagram. For example, if user A requests permission to view user B's correlation diagram and user B approves it, the request and approval are processed on the server, and user A is granted permission to view it.

[1043] Input: Viewing permission request and approval

[1044] Output: Viewing permission granted

[1045] By implementing the above steps, users can easily generate, display, and search correlation diagrams that take emotions into account, enabling more reliable evaluation and management of human relationships.

[1046] (Application example 2)

[1047] 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."

[1048] Previously, relationship management systems only collected personal information and generated visual correlation diagrams, but did not have the functionality to adjust the environment in real time based on the user's emotional state. As a result, it was not possible to effectively express personal connections based on the user's emotions, and there was a need for flexible environment adjustments in response to emotional changes.

[1049] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for periodically inputting information about newly acquainted individuals or individuals heard from other people; means for automatically generating a visual correlation diagram based on the input information; means for making the generated correlation diagram searchable; means for obtaining permission to search other users' correlation diagrams and making them searchable; means for using an emotion analysis engine to identify emotions from the user's input text or voice and reflecting the emotional state in the intensity of the correlation diagram; means for updating the generated correlation diagram in real time and displaying it on the smart device; and means for automatically adjusting the device's environmental settings (music, temperature, lighting, etc.) according to the user's emotional state. This allows for visual management of human relationships based on the user's emotions and optimal environmental adjustment in real time.

[1050] "Information about individuals that you have newly met or heard from others" is information about people that you have obtained based on your own experiences or hearsay from others.

[1051] A "visual correlation diagram" is a diagram constructed with nodes and edges based on graph theory that visually represents the relationships and emotional strengths between individual people.

[1052] "Means for making things searchable" refers to a function that allows users to search for information in the database based on specific conditions, and extract and display the relevant correlation diagram.

[1053] "Means of obtaining permission and making it searchable" refers to the ability to request permission to access other users' relationship diagrams and, if permission is granted, make them searchable and viewable.

[1054] An "emotion analysis engine" is an analytical device that identifies emotions from text and voice input by the user and reflects that information in the strength of relationships and environmental settings.

[1055] "Real-time update means" refers to a function that instantly keeps correlation diagrams and environment settings up to date based on user input and sentiment analysis results.

[1056] A "smart device" is a digital terminal for inputting and displaying information, and includes smartphones, smart glasses, head-mounted displays, etc.

[1057] "Means for automatically adjusting environmental settings" refers to a function that appropriately changes the music, temperature, lighting, etc. in the car based on the user's emotional state.

[1058] The system embodying the present invention has functions of information input by a user, data processing and correlation diagram generation by a server, and display and environment adjustment by a smart device.

[1059] First, the user uses a smart device (smartphone, smart glasses, head-mounted display, etc.) to input information about a new acquaintance or someone they have heard from another person. The input form includes items such as name, age, occupation, contact information, and relationship tags, and the user enters this information through the device. In addition, the system has the ability to identify emotions from the text and voice input by the user.

[1060] An emotion analysis engine is used to analyze emotions from input text and voice. This emotion analysis engine uses libraries such as Python's "TextBlob" to identify emotional states such as positive and negative. The server reflects the acquired emotional data in a correlation diagram.

[1061] The server then analyzes the information in the database and generates a visual correlation diagram, taking into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Each node represents an individual person, and the edges indicate their relationships and emotional strengths. Libraries such as "NetworkX" and "Matplotlib" are used to generate the correlation diagram.

[1062] The server updates the generated correlation diagram in real time and displays it on the smart device. Users can search based on specific criteria (e.g., occupation, relationship tags, emotional state), and the server extracts the relevant information from the database and provides the corresponding correlation diagram.

[1063] Furthermore, it has the ability to automatically adjust the environmental settings of smart devices (music, temperature, lighting, etc.) according to the user's emotional state, allowing users to browse and search for information in a comfortable environment.

[1064] As a concrete example, consider the case where User A enters information about "Person A" whom he met at a new meeting. User A uses his smartphone to enter the following information:

[1065] Name: Individual A

[1066] Age: 40

[1067] Occupation: Project Manager

[1068] Contact: example@example.com

[1069] Relationship: Meeting Participant

[1070] Furthermore, a comment expressing an emotion, such as "Individual A was very cooperative," is entered. The emotion analysis engine analyzes this comment and identifies it as a positive emotion. The server generates a correlation diagram based on this information and emotion data and displays it on User A's smart device.

[1071] Examples of prompts generated based on specific examples include:

[1072] "We have a new passenger, Individual B, on board. He's very cooperative and friendly."

[1073] "Passengers seem a little tired at the moment."

[1074] In this way, the present invention can visually manage human relationships based on the user's emotions and provide optimal environmental adjustments in real time.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] The user uses a smart device to input information about a new acquaintance or information they have heard from other people. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags into an input form. This input information is sent from the device to the server and stored in a database. Input: Relationship information entered by the user. Output: Data sent to the server and stored in the database.

[1078] Step 2:

[1079] The text or voice input by the user is sent to the sentiment analysis engine. The sentiment analysis engine is used to analyze emotions from the text or voice. Specifically, it uses the Python "TextBlob" library to classify emotions into positive, negative, or neutral. Input: Text or voice data input by the user. Output: Analyzed sentiment data (emotional state).

[1080] Step 3:

[1081] The server generates a correlation diagram based on the collected interpersonal relationship information and emotional data. NetworkX and Matplotlib are used to generate the correlation diagram. Specifically, each node represents an individual person, and edges indicate their relationships and emotional strength. Input: Interpersonal relationship information and emotional data in the database. Output: Generated correlation diagram (graph).

[1082] Step 4:

[1083] The generated correlation diagram is updated in real time and displayed on the smart device. When a user searches based on specific conditions (e.g., occupation, relationship tags, emotional state), the server extracts the relevant information from the database and provides the corresponding correlation diagram. Input: User's search conditions. Output: Display of the updated correlation diagram.

[1084] Step 5:

[1085] The server automatically adjusts the smart device's environmental settings based on the user's emotional state. These environmental settings include music, temperature, lighting, etc. For example, if the user inputs "I'm tired," the server will play soothing music and dim the lights. Input: User's emotional data. Output: Adjusted device environmental settings.

[1086] Step 6:

[1087] A request is sent to the server to grant access to another user's correlation diagram, and if granted, the correlation diagram becomes searchable and viewable. When User A requests access to User B's correlation diagram and User B approves, the server grants viewing permission to User A. Input: Access permission request. Output: Granting viewing permission and access to the correlation diagram.

[1088] 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.

[1089] 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.

[1090] 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.

[1091] [Fourth embodiment]

[1092] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1093] 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.

[1094] 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).

[1095] 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.

[1096] 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.

[1097] 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).

[1098] 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.

[1099] 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.

[1100] 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.

[1101] 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.

[1102] 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.

[1103] 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.

[1104] 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."

[1105] The system of the present invention includes a user's input of human relationship information, automatic generation of a correlation diagram by a server, and a search function, etc. The present invention can be implemented as follows.

[1106] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[1107] The server then automatically generates a correlation diagram based on the received information. The server analyzes the information in the database and generates a correlation diagram that visually shows the relationships between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges show their relationships.

[1108] Users can view and search correlation diagrams generated based on the information they input on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[1109] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[1110] Specific examples

[1111] Example 1: Entering information about a new acquaintance

[1112] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[1113] Name: Ichiro Yamada

[1114] Age: 40

[1115] Occupation: Project Manager

[1116] Contact: yamada.ichiro@example.com

[1117] Relationship: Meeting Participant

[1118] This information is sent to a server and stored in a database.

[1119] Example 2: Automatic generation and viewing of correlation diagrams

[1120] The server receives Yamada Ichiro's information and compares it with existing information in the database to generate a correlation diagram, which visually shows, for example, Yamada Ichiro's relationships with all the project members he is involved in.

[1121] The user can view this relationship diagram on their device and further search for the occupation "project manager." In this case, the server will extract people with similar occupations from the database and provide an updated relationship diagram.

[1122] Example 3: Allowing other users to view correlation diagrams

[1123] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to easily find new talent and business partners, enabling efficient networking.

[1124] In this way, the present invention can greatly improve the efficiency of managing and utilizing relationships in everyday life and in business environments.

[1125] The processing flow will be explained below.

[1126] Step 1:

[1127] The user opens the application on their device. The user selects the form to add a new contact.

[1128] Step 2:

[1129] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[1130] Step 3:

[1131] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[1132] Step 4:

[1133] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[1134] Step 5:

[1135] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[1136] Step 6:

[1137] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[1138] Step 7:

[1139] The server starts the process of generating a correlation diagram based on the newly saved information: the server runs an SQL query to retrieve all relevant contact information from the database.

[1140] Step 8:

[1141] The server analyzes the acquired data and converts the relationships between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[1142] Step 9:

[1143] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[1144] Step 10:

[1145] The server receives the request, executes an SQL query to extract the corresponding information from the database, and generates a correlation diagram based on the extracted information and returns it to the device.

[1146] Step 11:

[1147] The correlation diagram received by the terminal is visually displayed.

[1148] Step 12:

[1149] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[1150] Step 13:

[1151] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[1152] Step 14:

[1153] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[1154] Example 1

[1155] 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."

[1156] Currently, systems for visually managing interpersonal relationships require a large number of manual operations and are not particularly efficient. It is particularly difficult to centrally manage information about new acquaintances and information obtained from other users, and to automatically generate and search visual correlation diagrams. Furthermore, viewing other users' correlation diagrams requires a time-consuming permission process, which often hinders rapid information sharing and networking. Therefore, there is a need for a system that can manage and visualize relationships, as well as enable efficient information sharing with other users.

[1157] 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.

[1158] In this invention, the server includes means for inputting information on newly acquainted individuals or individuals heard from other users at a terminal, means for transmitting the input information to the server, means for the server to store the received information in a database, means for the server to automatically generate a visual correlation diagram based on the stored information, means for making the generated correlation diagram searchable, means for extracting information from the database based on specific conditions and providing the relevant correlation diagram, and means for obtaining permission to search other users' correlation diagrams and making the correlation diagrams searchable. This makes it possible to efficiently manage and visualize relationships between individuals and quickly share information with other users.

[1159] A "terminal" is an electronic device that allows a user to input and transmit information.

[1160] "Input means" refers to software functionality, including interfaces and forms, through which a user can input information.

[1161] "Means for transmitting information to the server" refers to protocols and communication means for securely transmitting data from the terminal to the server.

[1162] A "server" is a computer system that receives, stores, processes data, and responds to requests from clients.

[1163] A "database" is a system or software for storing data in a structured format.

[1164] The "means for storing information" is a software process for storing the received data in a database.

[1165] A "means for automatically generating correlation diagrams" is an algorithm or software that uses stored data to form nodes and edges based on graph theory, and visually represents relationships.

[1166] A "searchability means" is a software function that allows a user to extract data based on specific criteria and display it visually.

[1167] The "specific conditions" are keywords and filtering conditions used by the user when conducting a search.

[1168] The "means of information extraction" is the query or process used to retrieve data from a database that matches the search criteria.

[1169] The "visual display means" is a software function for displaying the generated correlation diagram on a terminal in a format that is easy for the user to understand.

[1170] "Means for obtaining permission" refers to the process for requesting and managing the permission required to view other users' relationship diagrams.

[1171] The system of this invention allows users to input interpersonal relationship information using a terminal, and the server automatically generates, searches, and displays a correlation diagram based on that information. The operation of the entire system is described in detail below.

[1172] System Configuration

[1173] Hardware and software use cases:

[1174] Terminal: A device used by the user to input information, such as a smartphone, tablet, or laptop. A dedicated application is installed on the device.

[1175] Server: A computer system that receives, stores, and processes data, typically hosted on a cloud service (e.g., AWS).

[1176] Database: A system for storing saved information, typically a NoSQL database such as MongoDB.

[1177] Software Libraries: Python NetworkX and D3.js are used to generate and visualize the correlation diagram.

[1178] Enter and submit information

[1179] Steps for users to enter information:

[1180] The user launches the dedicated application and enters information such as name, age, occupation, contact information, relationship tags, etc. into the input form. The user interface of this application is designed to be easy and intuitive to use.

[1181] Examples:

[1182] The user enters information about the new person he or she has met as follows:

[1183] Name: Example: Yamada Taro

[1184] Age: 40

[1185] Occupation: Project Manager

[1186] Contact: taro.yamada@example.com

[1187] Relationship: Meeting Participant

[1188] The entered information is sent from the terminal to the server, and the data is encrypted and protected using the HTTPS protocol.

[1189] Retention of Information

[1190] The server receives the information and stores it in the database:

[1191] The server analyzes the received information and stores it in a database (e.g., MongoDB), which manages data in document format and allows for easy searching and updating.

[1192] Automatic generation of correlation diagrams

[1193] Steps for the server to generate the correlation diagram:

[1194] The server analyzes the information in the database and automatically generates a correlation diagram based on graph theory. This process uses the Python NetworkX library, where each person is represented as a node and relationships are represented as edges. The generated correlation diagram is then rendered using a visualization library (e.g., D3.js) and later sent to the user's device.

[1195] Examples:

[1196] Based on the information that "Yamada Taro" is participating in a new project, a correlation diagram is generated showing his relationships with all existing project members.

[1197] View and search correlation diagrams

[1198] Steps for users to view and search the generated correlation diagram:

[1199] The user can view the correlation diagram generated on their device and then perform a search based on specific criteria, using tags such as occupations and relationships. The server then extracts the relevant information from the database and provides the user with an updated correlation diagram.

[1200] Examples:

[1201] When a user searches based on the occupation tag "project manager," the server extracts information about people with similar occupations and displays an updated correlation diagram.

[1202] Permission to view other users' correlation diagrams

[1203] Steps for users to request and approve permission to view the relationship diagram:

[1204] User A requests permission to view User B's correlation diagram, and User B approves. The server grants viewing authority to User A, after which User A can search and view User B's correlation diagram. This enables efficient information sharing.

[1205] Examples:

[1206] User A requests to view User B's correlation diagram in order to find a new business partner, and if User B approves, User A can view the correlation diagram.

[1207] Through the above process, the system of the present invention efficiently manages and visualizes interpersonal relationships, enabling rapid information sharing with other users.

[1208] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1209] Step 1:

[1210] User input of information

[1211] The user launches the dedicated application and enters information about the new acquaintance or the person they have received from other users into the input form. Specifically, the input items include name, age, occupation, contact information, and relationship tags.

[1212] Input: Personal information (e.g., name, age, occupation, contact information, relationship tags)

[1213] Output: Personal information data entered

[1214] Specific operation: A user enters "Yamada Taro, age 40, project manager, taro.yamada@example.com, meeting participant" into an input form on a smartphone app.

[1215] Step 2:

[1216] Sending data from the device to the server

[1217] The device sends the entered personal information to the server using the HTTPS protocol, and the data is encrypted.

[1218] Input: Personal information data entered

[1219] Output: Personal information data sent to the server

[1220] Specific operation: Information about "Yamada Taro" is sent from the device to the server in encrypted form.

[1221] Step 3:

[1222] Server storage of information

[1223] The server analyzes the received personal information and stores it in a database using MongoDB, which manages data in document format.

[1224] Input: Personal information data sent to the server

[1225] Output: Personal information data stored in a database

[1226] Specific operation: The server receives the information about "Yamada Taro" and saves it as a document in the "contacts" collection in MongoDB.

[1227] Step 4:

[1228] Automatic generation of correlation diagrams by the server

[1229] The server analyzes the information in the database and automatically generates a correlation diagram that visually represents the relationships between individuals, using the Python NetworkX library.

[1230] Input: All personal information stored in the database

[1231] Output: Automatically generated correlation diagram data

[1232] Specific operation: The server analyzes the relationship between "Yamada Taro" and existing data, generates nodes and edges, and constructs a correlation diagram.

[1233] Step 5:

[1234] Sending correlation diagram from server to terminal

[1235] The server sends the generated correlation diagram to the terminal. The D3.js library is used for visualization, providing a dynamic and interactive correlation diagram.

[1236] Input: Auto-generated correlation diagram data

[1237] Output: Visualized correlation diagram data sent to the device

[1238] Specific operation: The server draws the correlation diagram using D3.js and sends it to the terminal via the Internet.

[1239] Step 6:

[1240] User-friendly correlation diagram browsing and search

[1241] Users view the correlation diagram on their devices and search for information based on specific criteria (e.g., occupation, age, relationship). The server extracts the relevant information from the database and provides an updated correlation diagram.

[1242] Input: User search criteria (e.g., occupation tag "Project Manager")

[1243] Output: Updated correlation diagram data

[1244] Specific operation: The user performs a search narrowed down to "Project Manager," and the server extracts the relevant person information and displays an updated correlation diagram.

[1245] Step 7:

[1246] Request and approve permission to view other users' relationship diagrams

[1247] When User A requests permission to view User B's correlation diagram and User B approves this, the server grants User A permission to view the diagram.

[1248] Input: Permission request from User A, Permission approval from User B

[1249] Output: Viewing permissions granted to User A

[1250] Specific operation: When User A sends a request on the app and User B approves it, the server grants User A permission to view User B's relationship diagram.

[1251] (Application example 1)

[1252] 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."

[1253] Conventional relationship information management systems make it difficult to manually organize and manage information about newly acquainted individuals or individuals heard from others. Furthermore, the visualization and search functions for relationships are limited, making it difficult to update information in real time or share information efficiently. Furthermore, while there is a demand for intuitive operation using smart devices and visual display devices, there has been a lack of systems that can achieve this.

[1254] 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.

[1255] In this invention, the server includes means for periodically inputting information about newly acquainted individuals or individuals heard from other people, means for automatically generating a visual correlation diagram based on the input information, means for making the generated correlation diagram searchable, means for obtaining permission to search other users' correlation diagrams and making them searchable, means for saving the input personal information in a database and updating the correlation diagram in real time, and means for visually displaying the generated correlation diagram. This enables efficient management and updating of interpersonal relationship information and the generation and viewing of visual correlation diagrams in real time. Furthermore, intuitive operation using a smart device can significantly improve user convenience.

[1256] "Information about a person that the user has newly met or has heard from other people" refers to information about a person that the user has newly met or that the user has received through other people.

[1257] "Means for periodic input" refers to a method or device that allows a user to continuously input information at regular intervals.

[1258] A "visual correlation diagram" refers to a diagram that visually shows the relationships between people based on information recorded by the user.

[1259] "Means for automatically generating a correlation diagram" refers to a method or device in which a system automatically visualizes the relationships between people based on input information and creates a correlation diagram.

[1260] The "means for making the diagram searchable" refers to a method or device that allows the generated correlation diagram to be searched for specific conditions or keywords and information to be obtained.

[1261] The "means for obtaining permission and making the correlation diagram searchable" refers to a method or device for obtaining permission to view other users' correlation diagrams and making the correlation diagrams searchable.

[1262] The "means for saving information in a database and updating the correlation diagram in real time" refers to a method or device for saving input information in a database and instantly updating the correlation diagram when the information is added or updated.

[1263] The term "means for visually displaying" refers to a method or device for visually displaying the generated correlation diagram on a terminal used by a user.

[1264] "Tags for occupation, contact information, age, and relationship" refer to attribute information used by the user when entering personal information.

[1265] "Means for representing relationships using nodes and edges based on graph theory" refers to a method or device that uses graph theory to represent people as nodes (points) and depict their relationships as edges (lines).

[1266] "Visual display device" refers to a device that can display graphics or text data, such as a smartphone, tablet, or monitor.

[1267] "Means for real-time updating" refers to a method or device that instantly updates a database or display content each time new information is entered.

[1268] "Means for inputting and viewing correlation diagrams using a device" refers to a method or device that allows a user to input correlation diagram information or view the generated correlation diagram using a smart device, etc.

[1269] The system of this invention is designed to efficiently manage personal information acquired by users and to generate and update a correlation diagram of human relationships in real time. Specific embodiments of this invention will be described below.

[1270] First, a user uses a visual display device such as a smartphone or tablet to enter new personal information into a dedicated application, including name, age, occupation, contact details, and relationship tags. The information acquired through the input form is sent to the server in real time.

[1271] The server uses an SQLite3 database to store the received personal information. The stored information is automatically generated as a correlation diagram of relationships between nodes (points) and edges (lines) based on graph theory. This process uses Python libraries such as NetworkX and Matplotlib. The server immediately updates the correlation diagram each time new information is input and generates data for visual display.

[1272] Users can then view the generated relationship diagram in real time and use the search function on their devices. When searching based on specific criteria, such as occupation or relationship tags, the server extracts the relevant information from the database and provides the corresponding relationship diagram in real time, allowing users to easily obtain the information they need and efficiently manage their relationships.

[1273] It also includes a function to obtain permission to view other users' correlation diagrams. User A requests permission to view a correlation diagram from User B, and if User B approves, the server grants viewing authority to User A. This allows multiple users to share network information and realize efficient networking.

[1274] A specific example is when a sales assistant in a store inputs the personal information of a new customer and then displays the relationships in a correlation diagram in real time. For example, the assistant inputs the information of customer "Taro Tanaka" and records his relationship with an existing customer as a "colleague." This information is immediately saved on the server, and a correlation diagram is generated and updated in real time.

[1275] When creating a program using a generative AI model, an example prompt is as follows:

[1276] "Based on the given customer information, please create a Python program to generate a relationship diagram in real time and save it in a SQLite3 database. Visualize the relationships based on the relationship tags for each customer."

[1277] In this way, the system of the present invention allows users to efficiently manage interpersonal relationship information and visually generate and update correlation diagrams, thereby significantly improving the efficiency of managing and effectively utilizing interpersonal relationships in everyday life and business environments.

[1278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1279] Step 1:

[1280] A user launches a dedicated application on a smartphone or tablet and inputs information about a new acquaintance or a person they have heard from others. At this time, they fill in an input form with information such as name, age, occupation, contact details, and relationship tags. This input data is sent from the device to the server. The input is done through a text field, and as a result, JSON-formatted data is generated and sent to the server.

[1281] Step 2:

[1282] The server parses the received JSON format data and saves it in an SQLite3 database. During the saving process, the entered personal information is added as a record to the corresponding table in the database. Each item in the input data (name, age, occupation, contact information, relationship tags) is mapped to a column in the database to ensure it is saved.

[1283] Step 3:

[1284] The server generates a visual correlation diagram in real time based on the stored information. Specifically, it uses Python's NetworkX library to represent individuals as nodes (points) and relationships as edges (lines). It retrieves the necessary information from the database and creates and updates nodes and edges. This operation generates graph data.

[1285] Step 4:

[1286] Using the generated graph data, the server uses Python's Matplotlib library to draw a visual correlation diagram. The resulting graph image is a graph of human relationships composed of nodes and edges. This image is then converted into a format that can be viewed by users.

[1287] Step 5:

[1288] The server sends the generated correlation diagram to the user's device in real time and displays it visually via an application. The user can check the correlation diagram on the device screen. In the display process, the drawn graph image is converted into a format that can be displayed on the device screen and sent to the user's device.

[1289] Step 6:

[1290] Users use the search function to search and filter the correlation diagram based on specific criteria (e.g., occupation or relationship tags). The user enters the search criteria, and the server again queries the database to extract the relevant data. Based on the search results, a new correlation diagram is generated and displayed.

[1291] Step 7:

[1292] User A requests permission to view another user B's correlation diagram. If user B approves the request through the application, the server grants user A permission to access user B's correlation diagram data. After granting permission, user A can search and view user B's correlation diagram.

[1293] Step 8:

[1294] If the entered personal information is new, the server updates the correlation diagram in real time. During the update process, new nodes and edges are added to the existing graph, and the diagram is redrawn. As a result, a correlation diagram reflecting the latest human relationships is generated and sent to the user's device again for display.

[1295] 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.

[1296] The system of this invention combines a user's input of interpersonal relationship information, automatic generation and search functions of correlation diagrams by a server, and an emotion engine. The emotion engine identifies emotions from user input and voice and uses them to generate and adjust correlation diagrams. The present invention can be implemented as follows.

[1297] First, the user inputs information about a new acquaintance or someone they have heard from another person through their device. The user enters information such as name, age, occupation, contact details, and relationship tags into an input form in a dedicated application. This information is sent from the device to a server and stored in a database.

[1298] Additionally, the system incorporates an emotion engine that analyzes emotions from text and voice inputs by the user. The emotion engine determines the user's emotional state and how that emotion affects the information input. For example, if the user is emotional, the relevance of the information may be emphasized.

[1299] The server then automatically generates a correlation diagram based on the received information and the results of the sentiment analysis. The server analyzes the information in the database and generates a visual correlation diagram that takes into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Nodes represent individual people, and edges indicate their relationships and emotional strengths.

[1300] Users can view and search correlation diagrams generated based on the information they input and the results of emotion analysis on their devices. When searching based on specific criteria, such as occupation, relationship tags, or even emotional state, the server extracts the relevant information from the database and provides the corresponding correlation diagram, which is then visually displayed on the user's device.

[1301] It also includes a function to obtain permission to search other users' correlation diagrams. When User A requests permission to view a correlation diagram from User B, and User B approves this, the server grants viewing authority to User A. User A can then search and view User B's correlation diagram.

[1302] Specific examples

[1303] Example 1: Entering information about a new acquaintance

[1304] The user can input information about "Yamada Ichiro," whom he met at a meeting about a new job, into the terminal. Enter the following information into the input form.

[1305] Name: Ichiro Yamada

[1306] Age: 40

[1307] Occupation: Project Manager

[1308] Contact: yamada.ichiro@example.com

[1309] Relationship: Meeting Participant

[1310] Furthermore, the user can input a comment expressing their feelings, such as "Ichiro Yamada was very cooperative." The emotion engine analyzes this comment and recognizes it as a positive emotion. This information and emotional data are sent to the server and stored in a database.

[1311] Example 2: Automatic generation and viewing of correlation diagrams taking emotions into account

[1312] The server receives Ichiro Yamada's information and emotion data, compares it with existing information in the database, and generates a correlation diagram. The correlation diagram visually represents Ichiro Yamada's relationships with other project members. Furthermore, if positive emotions affect the strength of relationships, it highlights that Ichiro Yamada is very cooperative.

[1313] Users can view this correlation diagram on their devices and even search for it based on criteria such as "project manager" or "people with whom they have positive feelings." In this case, the server extracts people with similar attributes and emotional ratings from the database and provides an updated correlation diagram.

[1314] Example 3: Allowing other users to view correlation diagrams

[1315] User A requests permission to view User B's relationship diagram. If User B approves, the server allows User A to view User B's relationship diagram. This function allows User A to find new talent and business partners by taking into account emotional evaluations, enabling efficient networking.

[1316] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it is possible to achieve more intuitive and reliable evaluation and utilization of human relationships.

[1317] The processing flow will be explained below.

[1318] Step 1:

[1319] The user opens the application on their device. The user selects the form to add a new contact.

[1320] Step 2:

[1321] The device displays an input form, allowing the user to enter name, age, occupation, contact information, relationship tags, etc. The user enters all the required information and presses the send button.

[1322] Step 3:

[1323] The device formats the input information and creates a request to send to the server. The device sends the request to the server.

[1324] Step 4:

[1325] The server receives the request sent from the terminal, analyzes the information in the request, and converts it into a database format.

[1326] Step 5:

[1327] The server connects to the database and creates a SQL query to save the new contact information. The server sends the query to the database and saves the information.

[1328] Step 6:

[1329] The server checks whether the save was successful and returns the result to the device, which then displays the save result to the user.

[1330] Step 7:

[1331] When a user enters an emotional comment (e.g., "It was cooperative") into the input form, the emotion engine analyzes the comment, and the device generates emotion data as the analysis result.

[1332] Step 8:

[1333] The device then sends the information, including the emotion data, back to the server, which then stores the received emotion data and personal information in a database.

[1334] Step 9:

[1335] The server starts the process of generating a correlation diagram based on the newly stored information and emotion data. The server runs an SQL query to retrieve all relevant contact information and emotion data from the database.

[1336] Step 10:

[1337] The server analyzes the acquired data and converts the relationships and emotional strengths between each contact into a graph (nodes and edges) structure. The server generates a correlation diagram and stores the data.

[1338] Step 11:

[1339] The user selects a function on the terminal for viewing or searching a correlation diagram, and the terminal sends a request for the correlation diagram to the server.

[1340] Step 12:

[1341] The server receives the request and executes an SQL query to extract the corresponding information and emotion data from the database. The server generates a correlation diagram based on the extracted information and returns it to the device.

[1342] Step 13:

[1343] The device visually displays the correlation diagram it receives, which shows the strength of the relationships between nodes based on emotion data.

[1344] Step 14:

[1345] When a user requests permission to search another user's correlation diagram, the terminal sends a permission request to the target user, who then checks the permission request on the terminal and approves or rejects it.

[1346] Step 15:

[1347] The terminal transmits the target user's decision to the server. The server receives the target user's decision and, if permission is granted, grants the requesting user permission to search the correlation diagram.

[1348] Step 16:

[1349] Once permission is granted, the user sends a request from their device to the server to search and view the newly authorized correlation diagram. The server provides the information, and the device visually displays the search results to the user.

[1350] In this way, it is possible to manage and effectively utilize interpersonal information that takes emotion data into account.

[1351] Example 2

[1352] 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."

[1353] Conventional human relationship information management systems have difficulty taking into account qualitative comments and emotions from users. Furthermore, because the generation and search of correlation diagrams relied on simple data, it was not possible to reflect individual emotions or the strength of relationships. This made it difficult for users to obtain intuitive and reliable information when evaluating and managing relationships.

[1354] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing emotions from the user's input content and voice, means for adjusting the correlation diagram based on the emotion analysis result, and means for saving the emotion analysis result in a database. This makes it possible to generate and search for a correlation diagram with higher accuracy that reflects the user's emotions.

[1355] "Personal information" is information that can be used to identify a specific individual, including name, age, occupation, contact information, relationship tags, etc.

[1356] A "correlation diagram" is a graph that visually shows individual people and their relationships, and uses nodes and edges to express the strength of relationships and emotional states.

[1357] An "emotion engine" is a software or hardware component that identifies emotions from text or voice input by a user and analyzes that emotional state.

[1358] "Emotion analysis results" are data on the user's emotional state analyzed by the emotion engine, and are information classified into emotion categories such as positive, negative, and neutral.

[1359] "Search conditions" are conditions such as specific attributes, tags, and emotional states that a user specifies when searching a correlation diagram.

[1360] A "database" is a digital storage system for systematically storing and managing data such as personal information entered by users and the results of sentiment analysis.

[1361] A "node" is a graph element that represents an individual person in the correlation diagram.

[1362] An "edge" is a line in a graph that represents the relationship between nodes in a correlation diagram, and visually indicates the strength or emotional state of that relationship.

[1363] A "visual display" is a graphical representation that presents information to a user in an easy-to-view manner on a device's display.

[1364] The system of this invention allows users to input information about new acquaintances, perform emotion analysis, and then generate, display, and search correlation diagrams. Specifically, users use a dedicated application to input personal information such as name and occupation from their terminal, and the information is managed and analyzed on the server, which then visually displays the correlation diagram. The main components of this system include a user terminal, a server, an emotion engine, and a database.

[1365] First, the user uses a device with the dedicated application installed. The user enters the name, age, occupation, contact information, relationship tags, etc. into the input form. Specifically, the user enters information such as "Name: Yamada Ichiro," "Age: 40," "Occupation: Project Manager," "Contact: yamada.ichiro@example.com," and "Relationship: Meeting Participant." At the same time, the user can also enter comments expressing their feelings, such as "Yamada Ichiro was very cooperative."

[1366] The entered information is sent from the device to the server. The server stores the received data in a database. The database uses a relational database such as MySQL or PostgreSQL. The server then uses an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze emotions from the user's comments. For example, a comment such as "Yamada Ichiro was very cooperative" is recognized as a positive emotion.

[1367] The server generates a correlation diagram based on the information in the database and the results of the sentiment analysis. This step can be performed using a library that applies graph theory (e.g., Python's NetworkX library). The correlation diagram is composed of nodes and edges, with the nodes representing individual people and the edges representing the relationships and emotional strengths between them. The generated correlation diagram can be viewed visually by the user on their device.

[1368] Users can search the generated correlation diagram based on specific criteria, such as "project managers" or "people with whom they have positive feelings." The server then extracts the relevant information from the database and provides the updated correlation diagram to the user's device.

[1369] Furthermore, users are also provided with a function to request permission to view other users' correlation diagrams. When User A requests permission to view User B's correlation diagram and User B approves, the server grants viewing authority to User A. This allows User A to search and view User B's correlation diagram.

[1370] Prompt Sentence Examples

[1371] "I entered information about Ichiro Yamada, whom I met at a new conference, and found him to be very cooperative. Based on this information, please use the emotion engine to analyze positive emotions and automatically generate a correlation diagram that reflects those emotions."

[1372] In this way, the present invention significantly improves the efficiency of managing and effectively utilizing human relationships in everyday life and business environments, and by taking the user's emotions into consideration, it achieves more intuitive and reliable evaluation and utilization of human relationships.

[1373] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1374] Step 1: Enter your information

[1375] The user uses their device to enter information about a new acquaintance into the application's input form. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags. They can also enter comments that express their feelings. This generates input data (e.g., "Name: Yamada Ichiro," "Age: 40 years old," "Occupation: Project manager," "Relationship: Meeting participant," "Comment: Yamada Ichiro was very cooperative").

[1376] Input: Personal information and emotional comments entered by the user into the terminal

[1377] Output: The input data is generated

[1378] Step 2: Send and store data

[1379] The terminal sends data entered by the user to the server. The server saves the received data in a database. This process uses a POST request using the HTTP protocol, and a relational database (e.g., MySQL or PostgreSQL) is used as the database. For example, the input data is sent to the server in JSON format, where it is analyzed and saved.

[1380] Input: Data entered by the user into the terminal

[1381] Output: Data is saved to the database

[1382] Step 3: Sentiment Analysis

[1383] The server extracts comments from the stored data and performs sentiment analysis using an emotion engine (e.g., Google Cloud Natural Language API or IBM Watson Natural Language Understanding). For example, it analyzes the comment "Yamada Ichiro was very cooperative" and recognizes it as a positive sentiment. The analysis results are then stored back in the database.

[1384] Input: Comment data

[1385] Output: Sentiment analysis results (positive, negative, etc.)

[1386] Step 4: Generate correlation diagram

[1387] The server generates a correlation diagram based on the information in the database and the results of sentiment analysis. The correlation diagram is created using a library that applies graph theory (e.g., Python's NetworkX library), with nodes representing people and edges representing relationships and emotional strength. For example, Ichiro Yamada is visualized as a node, with his relationships with other project members as edges.

[1388] Input: Personal information in the database and sentiment analysis results

[1389] Output: correlation diagram data

[1390] Step 5: Visualize the correlation diagram

[1391] The server sends the generated correlation diagram to the device, which receives it and displays it visually within a dedicated application. For example, the generated correlation diagram is intuitively displayed on the device screen with nodes and edges.

[1392] Input: correlation diagram data

[1393] Output: Correlation diagram displayed on the terminal

[1394] Step 6: Search for correlation diagrams

[1395] The user inputs specific criteria (e.g., "project manager" or "positive emotion") to search for correlation diagrams. At this time, the device sends the search criteria to the server. The server searches the database, generates the corresponding correlation diagram, and sends it to the device. The device receives it and displays it visually.

[1396] Input: Search criteria (tags, emotional state, etc.)

[1397] Output: The corresponding correlation diagram is displayed.

[1398] Step 7: Allow other users to view the correlation diagram

[1399] A user requests permission to view another user's correlation diagram. For example, if user A requests permission to view user B's correlation diagram and user B approves it, the request and approval are processed on the server, and user A is granted permission to view it.

[1400] Input: Viewing permission request and approval

[1401] Output: Viewing permission granted

[1402] By implementing the above steps, users can easily generate, display, and search correlation diagrams that take emotions into account, enabling more reliable evaluation and management of human relationships.

[1403] (Application example 2)

[1404] 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."

[1405] Previously, relationship management systems only collected personal information and generated visual correlation diagrams, but did not have the functionality to adjust the environment in real time based on the user's emotional state. As a result, it was not possible to effectively express personal connections based on the user's emotions, and there was a need for flexible environment adjustments in response to emotional changes.

[1406] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for periodically inputting information about newly acquainted individuals or individuals heard from other people; means for automatically generating a visual correlation diagram based on the input information; means for making the generated correlation diagram searchable; means for obtaining permission to search other users' correlation diagrams and making them searchable; means for using an emotion analysis engine to identify emotions from the user's input text or voice and reflecting the emotional state in the intensity of the correlation diagram; means for updating the generated correlation diagram in real time and displaying it on the smart device; and means for automatically adjusting the device's environmental settings (music, temperature, lighting, etc.) according to the user's emotional state. This allows for visual management of human relationships based on the user's emotions and optimal environmental adjustment in real time.

[1407] "Information about individuals that you have newly met or heard from others" is information about people that you have obtained based on your own experiences or hearsay from others.

[1408] A "visual correlation diagram" is a diagram constructed with nodes and edges based on graph theory that visually represents the relationships and emotional strengths between individual people.

[1409] "Means for making things searchable" refers to a function that allows users to search for information in the database based on specific conditions, and extract and display the relevant correlation diagram.

[1410] "Means of obtaining permission and making it searchable" refers to the ability to request permission to access other users' relationship diagrams and, if permission is granted, make them searchable and viewable.

[1411] An "emotion analysis engine" is an analytical device that identifies emotions from text and voice input by the user and reflects that information in the strength of relationships and environmental settings.

[1412] "Real-time update means" refers to a function that instantly keeps correlation diagrams and environment settings up to date based on user input and sentiment analysis results.

[1413] A "smart device" is a digital terminal for inputting and displaying information, and includes smartphones, smart glasses, head-mounted displays, etc.

[1414] "Means for automatically adjusting environmental settings" refers to a function that appropriately changes the music, temperature, lighting, etc. in the car based on the user's emotional state.

[1415] The system embodying the present invention has functions of information input by a user, data processing and correlation diagram generation by a server, and display and environment adjustment by a smart device.

[1416] First, the user uses a smart device (smartphone, smart glasses, head-mounted display, etc.) to input information about a new acquaintance or someone they have heard from another person. The input form includes items such as name, age, occupation, contact information, and relationship tags, and the user enters this information through the device. In addition, the system has the ability to identify emotions from the text and voice input by the user.

[1417] An emotion analysis engine is used to analyze emotions from input text and voice. This emotion analysis engine uses libraries such as Python's "TextBlob" to identify emotional states such as positive and negative. The server reflects the acquired emotional data in a correlation diagram.

[1418] The server then analyzes the information in the database and generates a visual correlation diagram, taking into account the relationships and emotional strengths between individuals. This correlation diagram is constructed using nodes and edges based on graph theory. Each node represents an individual person, and the edges indicate their relationships and emotional strengths. Libraries such as "NetworkX" and "Matplotlib" are used to generate the correlation diagram.

[1419] The server updates the generated correlation diagram in real time and displays it on the smart device. Users can search based on specific criteria (e.g., occupation, relationship tags, emotional state), and the server extracts the relevant information from the database and provides the corresponding correlation diagram.

[1420] Furthermore, it has the ability to automatically adjust the environmental settings of smart devices (music, temperature, lighting, etc.) according to the user's emotional state, allowing users to browse and search for information in a comfortable environment.

[1421] As a concrete example, consider the case where User A enters information about "Person A" whom he met at a new meeting. User A uses his smartphone to enter the following information:

[1422] Name: Individual A

[1423] Age: 40

[1424] Occupation: Project Manager

[1425] Contact: example@example.com

[1426] Relationship: Meeting Participant

[1427] Furthermore, a comment expressing an emotion, such as "Individual A was very cooperative," is entered. The emotion analysis engine analyzes this comment and identifies it as a positive emotion. The server generates a correlation diagram based on this information and emotion data and displays it on User A's smart device.

[1428] Examples of prompts generated based on specific examples include:

[1429] "We have a new passenger, Individual B, on board. He's very cooperative and friendly."

[1430] "Passengers seem a little tired at the moment."

[1431] In this way, the present invention can visually manage human relationships based on the user's emotions and provide optimal environmental adjustments in real time.

[1432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1433] Step 1:

[1434] The user uses a smart device to input information about a new acquaintance or information they have heard from other people. Specifically, they enter information such as name, age, occupation, contact information, and relationship tags into an input form. This input information is sent from the device to the server and stored in a database. Input: Relationship information entered by the user. Output: Data sent to the server and stored in the database.

[1435] Step 2:

[1436] The text or voice input by the user is sent to the sentiment analysis engine. The sentiment analysis engine is used to analyze emotions from the text or voice. Specifically, it uses the Python "TextBlob" library to classify emotions into positive, negative, or neutral. Input: Text or voice data input by the user. Output: Analyzed sentiment data (emotional state).

[1437] Step 3:

[1438] The server generates a correlation diagram based on the collected interpersonal relationship information and emotional data. NetworkX and Matplotlib are used to generate the correlation diagram. Specifically, each node represents an individual person, and edges indicate their relationships and emotional strength. Input: Interpersonal relationship information and emotional data in the database. Output: Generated correlation diagram (graph).

[1439] Step 4:

[1440] The generated correlation diagram is updated in real time and displayed on the smart device. When a user searches based on specific conditions (e.g., occupation, relationship tags, emotional state), the server extracts the relevant information from the database and provides the corresponding correlation diagram. Input: User's search conditions. Output: Display of the updated correlation diagram.

[1441] Step 5:

[1442] The server automatically adjusts the smart device's environmental settings based on the user's emotional state. These environmental settings include music, temperature, lighting, etc. For example, if the user inputs "I'm tired," the server will play soothing music and dim the lights. Input: User's emotional data. Output: Adjusted device environmental settings.

[1443] Step 6:

[1444] A request is sent to the server to grant access to another user's correlation diagram, and if granted, the correlation diagram becomes searchable and viewable. When User A requests access to User B's correlation diagram and User B approves, the server grants viewing permission to User A. Input: Access permission request. Output: Granting viewing permission and access to the correlation diagram.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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).

[1452] 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.

[1453] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1454] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1455] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1456] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1457] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1458] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1459] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1460] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1461] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1462] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1463] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1464] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1465] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1466] The following is further disclosed regarding the above embodiment.

[1467] (Claim 1)

[1468] A means of regularly entering personal information about new acquaintances and individuals heard from others;

[1469] A means for automatically generating a visual correlation diagram based on input information;

[1470] A means for making the generated correlation diagram searchable;

[1471] A means for obtaining permission to search other users' correlation diagrams and making them searchable;

[1472] A system including:

[1473] (Claim 2)

[1474] A means for storing the entered personal information in a database;

[1475] means for providing a corresponding correlation diagram based on the search criteria;

[1476] 10. The system of claim 1, comprising:

[1477] (Claim 3)

[1478] The information input means is a means for inputting items including tags of occupation, contact information, age, and relationship;

[1479] When generating a correlation diagram, a means of expressing relationships using nodes and edges based on graph theory is provided.

[1480] 10. The system of claim 1, comprising:

[1481] "Example 1"

[1482] (Claim 1)

[1483] a means for inputting information on a newly acquainted individual or individuals received from other users into the terminal;

[1484] means for transmitting the input information to a server;

[1485] means for the server to store the received information in a database;

[1486] A means for the server to automatically generate a visual correlation diagram based on the stored information;

[1487] A means for making the generated correlation diagram searchable;

[1488] means for extracting information from the database based on specific conditions and providing a corresponding correlation diagram;

[1489] A means for obtaining permission to search other users' correlation diagrams and making them searchable;

[1490] A system including:

[1491] (Claim 2)

[1492] A means for the server to store the input personal information in a database;

[1493] a means for the server to provide a corresponding correlation diagram to a user terminal based on the search conditions;

[1494] 10. The system of claim 1, comprising:

[1495] (Claim 3)

[1496] The information input means is a means for inputting tags such as occupation, contact information, age, and relationship;

[1497] A means for the server to represent relationships using nodes and edges based on graph theory when generating a correlation diagram;

[1498] a means for visually displaying the generated correlation diagram on the terminal;

[1499] 10. The system of claim 1, comprising:

[1500] "Application Example 1"

[1501] (Claim 1)

[1502] A means of regularly entering personal information about new acquaintances and individuals heard from others;

[1503] A means for automatically generating a visual correlation diagram based on input information;

[1504] A means for making the generated correlation diagram searchable;

[1505] A means for obtaining permission to search other users' correlation diagrams and making them searchable;

[1506] A means to store the entered personal information in a database and update the correlation diagram in real time,

[1507] a means for visually displaying the generated correlation diagram;

[1508] A system including:

[1509] (Claim 2)

[1510] The information input means is a means for inputting items including tags of occupation, contact information, age, and relationship;

[1511] When generating a correlation diagram, a means of expressing relationships using nodes and edges based on graph theory is provided.

[1512] A means for updating and displaying the generated correlation diagram in real time,

[1513] A means for users to input and view correlation diagrams using a device;

[1514] 10. The system of claim 1, comprising:

[1515] (Claim 3)

[1516] The information input means uses a visual display device;

[1517] the database storage means stores and updates relationship information in real time;

[1518] a means for visually displaying the generated correlation diagram;

[1519] 10. The system of claim 1, comprising:

[1520] "Example 2: Combining Emotion Engines"

[1521] (Claim 1)

[1522] A means of regularly entering personal information about new acquaintances and individuals heard from others;

[1523] A means for automatically generating a visual correlation diagram based on input information;

[1524] A means for making the generated correlation diagram searchable;

[1525] A means for obtaining permission to search other users' correlation diagrams and making them searchable;

[1526] A means for analyzing emotions from user input and voice;

[1527] A means for adjusting the correlation diagram based on the results of emotion analysis;

[1528] A system including:

[1529] (Claim 2)

[1530] A means for storing the entered personal information in a database;

[1531] means for providing a corresponding correlation diagram based on the search criteria;

[1532] a means for storing the sentiment analysis results in a database;

[1533] means for making the correlation diagram searchable based on emotional states;

[1534] 10. The system of claim 1, comprising:

[1535] (Claim 3)

[1536] The information input means is a means for inputting items including tags of occupation, contact information, age, and relationship;

[1537] When generating a correlation diagram, a means of expressing relationships using nodes and edges based on graph theory is provided.

[1538] 10. The system of claim 1, comprising:

[1539] "Application example 2 when combining emotion engines"

[1540] (Claim 1)

[1541] A means of regularly entering personal information about new acquaintances and individuals heard from others;

[1542] A means for automatically generating a visual correlation diagram based on input information;

[1543] A means for making the generated correlation diagram searchable;

[1544] A means for obtaining permission to search other users' correlation diagrams and making them searchable;

[1545] A means for identifying emotions from the user's input text or voice using an emotion analysis engine and reflecting the emotional state in the strength of the correlation diagram;

[1546] A means for updating the generated correlation diagram in real time and displaying it on a smart device;

[1547] A means to automatically adjust the device's environmental settings (music, temperature, lighting, etc.) according to the user's emotional state;

[1548] A system including:

[1549] (Claim 2)

[1550] A means for storing the entered personal information in a database;

[1551] means for providing a corresponding correlation diagram based on the search criteria;

[1552] A means to store the results of sentiment analysis in a database and reflect them in search results,

[1553] 10. The system of claim 1, comprising:

[1554] (Claim 3)

[1555] The information input means is a means for inputting items including tags of occupation, contact information, age, and relationship;

[1556] When generating a correlation diagram, a means of expressing relationships using nodes and edges based on graph theory is provided.

[1557] a means for setting weights for each node and edge based on emotion evaluation using a sentiment analysis engine;

[1558] a means for adjusting the display style of the correlation diagram based on the emotional state;

[1559] 10. The system of claim 1, comprising: [Explanation of symbols]

[1560] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of regularly entering personal information about new acquaintances and individuals heard from others; A means for automatically generating a visual correlation diagram based on input information; A means for making the generated correlation diagram searchable; A means for obtaining permission to search other users' correlation diagrams and making them searchable; A system including:

2. A means for storing the entered personal information in a database; means for providing a corresponding correlation diagram based on the search criteria; The system of claim 1 , comprising:

3. The information input means is a means for inputting items including tags of occupation, contact information, age, and relationship; When generating a correlation diagram, a means of expressing relationships using nodes and edges based on graph theory is provided. The system of claim 1 , comprising:

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