system

Abstract

We provide the system. [Solution] A means of providing an interface that accepts input from the user, A method using generative models that analyze user input data to identify emotions and needs, A means of searching and presenting the most suitable experts and communities from a database based on the analysis results, A means of displaying search results to the user and performing the selected action, A means of collecting feedback from users after they have used the service, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, many people have personal troubles and mental stresses, and are seeking appropriate support and solutions for them. However, there are problems that it is difficult to access experts or support communities, and the opportunity to obtain appropriate information is limited. Also, the lack of a reliable platform for users to consult their troubles with confidence and receive appropriate support has become an issue.

Means for Solving the Problems

[0005] This invention provides a system that offers an interface for receiving user input, analyzes the user's input data using a generative model to identify emotions and needs, and then quickly and appropriately presents expert support and community information. This system aims for continuous improvement through data encryption for secure transmission, optimal matching based on analysis results, and collection of user feedback.

[0006] A "user" refers to someone who uses the system to input their consultation details and wishes to be matched with experts or communities.

[0007] An "interface" refers to the means of user interaction, including screens and input support tools for users to input their concerns or questions into the system.

[0008] A "generative model" refers to an artificial intelligence model used to analyze input text data and identify emotions and needs.

[0009] "Analysis" is the process of extracting useful information from input data using generative models and identifying the characteristics of the data.

[0010] "Matching" refers to the process of searching for and presenting experts and communities that match the user's needs based on the analysis results.

[0011] A "database" refers to an information aggregation system where information about experts and communities is accumulated and stored in a searchable format.

[0012] "Feedback" refers to opinions such as impressions and suggestions for improvement that users provide after using the system, and includes information used to improve the system.

[0013] "Security" refers to the technologies and methods used to protect user input data and its analysis results from unauthorized access and to maintain privacy.

[0014] "Encryption" refers to the means of converting data using a specific algorithm to prevent unauthorized access and information leakage by intermediaries.

[0015] "Algorithm" refers to a set of procedures that define a series of calculations or processes for solving problems and represents the set of rules applied in matching.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that includes an interface for users to input personal worries and consultation details, analyzes the input data using a generative model, and displays and provides the most suitable experts and support communities based on the user's emotions and needs. This system is characterized by its continuous improvement, taking into account user feedback after the results are displayed.

[0038] To run this system's program, a terminal is required for the user. This terminal is connected to the internet and is responsible for sending data entered by the user to the server. The server analyzes the received data using an advanced generative model to derive specific emotions and needs. For example, if a user enters the concern, "I've been having trouble sleeping lately," the generative model will analyze this as a "sleep-related concern" and identify sleep specialists and relevant online communities.

[0039] The server uses the analysis results to search the database for a list of suitable experts and community information, and sends it to the terminal. The terminal presents this information to the user in a visually clear and easy-to-understand format. Based on the presented information, the user selects specific actions to contact experts or join communities that interest them.

[0040] Furthermore, this system prioritizes user privacy; input data is encrypted before being sent to the server and properly managed throughout the analysis process. Additionally, user feedback is sent to the server and stored in a database, allowing for algorithm updates and more accurate matching. In this way, the entire system is continuously improved, providing users with higher-value support.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters the problem or concern they want to discuss into the text input field on their device screen. After finishing the input, the user presses the "Send" button to send the information to their device.

[0044] Step 2:

[0045] The terminal receives user input data and converts it into a predefined format. After format conversion, it encrypts the data for security and prepares it for transmission to the server.

[0046] Step 3:

[0047] The server receives encrypted data sent from the terminal. The received data is decrypted, and the resulting text data is passed to a generative model to analyze the input content. The generative model uses natural language processing techniques to identify emotions and needs.

[0048] Step 4:

[0049] Based on the analysis results of the generative model, the server searches the database for the most suitable experts and community information for the user. The search results compile a list of relevant experts and community resources.

[0050] Step 5:

[0051] The server formats the search results and sends them to the device in a viewable format. The results include detailed information about experts and communities, contact information, and how to participate.

[0052] Step 6:

[0053] The terminal displays information sent from the server to the user. The user can review the displayed information and choose actions to contact experts or communities that interest them.

[0054] Step 7:

[0055] To perform the action selected by the user, the device sends additional information and launches external links. This allows the user to seek advice or participate in communities.

[0056] Step 8:

[0057] The device presents the user with a feedback form, prompting them to enter their opinions and evaluations about their experience using the system.

[0058] Step 9:

[0059] The user enters feedback and sends it to their device.

[0060] Step 10:

[0061] The server receives feedback sent from users and stores it in a database. This data is used to improve the system, such as adjusting algorithms and enhancing matching accuracy.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In modern society, individuals need to be able to quickly and accurately find appropriate experts and support organizations to address their problems and concerns. However, due to the overwhelming amount of information available, it is difficult for users to find the best support. Furthermore, careful handling of personal information is necessary from a privacy protection standpoint. In addition, a system for continuously improving the quality of support provided is also required.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] This invention includes a server that uses a generative model to analyze user input data and identify emotions and requests, a server that searches for and presents the most suitable expert or collaborating organization from storage based on the analysis results, and a server that enhances information protection by encrypting and communicating the user's input data. This makes it possible for users to easily find the most suitable expert or collaborating organization, achieving a higher level of privacy protection and improved service quality.

[0067] A "communication device" is a device that receives input from a user and transmits that information to a server.

[0068] A "generative model" is a model that utilizes artificial intelligence technology to analyze user input data and derive specific emotions or requests.

[0069] A "memory device" is a device that stores information about experts and collaborating organizations, and has the function of searching and presenting this information as needed.

[0070] "Information protection" refers to measures taken to protect user privacy and ensure secure communication by encrypting user input data.

[0071] A "specialist" is someone who possesses specialized knowledge and skills in a particular field and is capable of addressing users' concerns and providing advice.

[0072] A "cooperative organization" is a group or community whose purpose is to provide support in a specific field or theme.

[0073] An "algorithm" is a set of procedures and formulas that define how to appropriately select experts and collaborating organizations based on input data.

[0074] "Ratings" refer to opinions and feedback provided by users after using a service, and these are used to improve the services and algorithms provided.

[0075] This invention is a system that analyzes personal concerns and consultation details entered by users and suggests appropriate experts and collaborating organizations. This system is primarily implemented using a terminal, a server, and a generative AI model.

[0076] The user uses their device to input their concerns into the interface, such as "I've been having trouble sleeping lately." The device encrypts this input data and securely transmits it to the server. This ensures the security of the information.

[0077] The server analyzes the input data received through the generative AI model it uses to extract specific emotions and requests. An example of a generative AI model used in this process is GPT-4®. The prompt message uses a format such as, "Please enter your current situation and concerns. We will then identify experts and collaborating organizations based on that information."

[0078] Based on the analysis results, the server searches its storage for information on the most suitable experts and collaborating organizations. This information includes profiles of experienced experts in the field and details of online communities that the user can join.

[0079] The server sends search results to the device, which displays them to the user in a visually easy-to-understand format. Based on the information presented, the user can decide to contact experts who interest them or join relevant communities.

[0080] These processes enable a system that protects privacy while providing optimal solutions to the problems faced by individual users.

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

[0082] Step 1:

[0083] Users input personal concerns and questions through the device's interface. For example, they might enter something like, "I've been having trouble sleeping lately." The entered data is output in plain text format and used in the next step.

[0084] Step 2:

[0085] The terminal encrypts the data entered by the user. Specifically, it uses data protection technology to securely convert the entered text data into an encrypted format. The encrypted data is then output and sent to the server.

[0086] Step 3:

[0087] The server receives encrypted data and decrypts it back into its original text format. It decodes the received data and outputs it as plain text data ready for analysis.

[0088] Step 4:

[0089] The server analyzes the input data using a generative AI model. The prompt message, "Please enter the user's current situation and concerns. Based on this, we will identify experts and collaborating organizations," is used to input data into the model. The analysis results output identified emotions and needs.

[0090] Step 5:

[0091] The server searches its storage for the most suitable expert or collaborating organization based on the analysis results. Specifically, it retrieves a list of experts matching the outputted needs and information on communities the user can join from the database, and outputs this as a result.

[0092] Step 6:

[0093] The server sends the search results to the terminal. The terminal then presents the user with a list of experts and community information in a visually easy-to-understand format. The output here is the information displayed on the user's screen.

[0094] Step 7:

[0095] The user selects an action based on the information provided. For example, they might choose to contact a specific expert from the displayed list or decide to join a community. The input in this step represents the user's choice of action, and the result of that choice prepares them for the next step.

[0096] Step 8:

[0097] The terminal collects user feedback through its interface. It gathers the input feedback data and prepares it to be sent to the server. This data is then output as input for the next step.

[0098] Step 9:

[0099] The server analyzes the feedback received from users and stores it in a database. The feedback content is then analyzed and output as data to improve the matching algorithm. This creates a feedback loop that allows the system to provide more precise support in the future.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] For users to effectively resolve their personal problems and concerns, it is essential to recommend appropriate experts and support groups. However, accurately understanding users' emotions and needs and providing optimal recommendations is difficult. Furthermore, continuously improving the accuracy of these recommendations is also a challenge. Additionally, it is necessary to handle user input information securely and protect their privacy.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for providing a terminal to receive input from the user, means for using a generative model to analyze the user's input string to identify emotions and needs, and means for searching for and presenting the most suitable experts or support groups from a knowledge base based on the analysis results. This enables the provision of accurate support based on the user's emotions and needs, as well as information management that respects privacy.

[0105] A "terminal" is a device that allows users to input personal concerns and questions, and is capable of connecting to the internet.

[0106] "Input string" refers to text data that the user provides to the system via their terminal, and is the information that will be analyzed.

[0107] A "generative model" is an algorithm used to analyze user input strings and identify their emotions and needs.

[0108] A "knowledge repository" is a collection of data that stores information on experts and support groups, and is a database from which information can be searched based on analysis results.

[0109] "Feedback" refers to feedback provided by users after using the service, and is data used to improve the system.

[0110] "Security" refers to the level of protection provided for information about user input strings, which is maintained using encryption technology.

[0111] A "recommendation algorithm" is a series of calculation steps used to suggest appropriate experts or support groups to the user based on the analysis results.

[0112] This invention is realized by a system that identifies the user's emotions and needs and suggests the most suitable expert or support group. The system works as follows:

[0113] First, the device plays the role of receiving the input string from the user. This device includes smartphones and tablets connected to the internet. This input string represents the user's current personal concerns or questions.

[0114] Next, the input data is sent to the server via the network. The server then analyzes the received input string. This analysis uses software that implements a generative AI model. Specifically, the generative AI model built on the server uses Google Cloud's natural language processing API to identify the user's emotions and needs.

[0115] Based on the analysis results, the server searches its knowledge base for information on the most suitable experts and support groups and returns the information to the terminal in the form of suggestions. This knowledge base is built using database technologies such as MongoDB. The user is presented with the provided information on the terminal screen in an intuitive manner and can select suggested actions as needed. This user interface is developed using Flutter®.

[0116] Furthermore, to ensure security, user input data is encrypted and protected throughout the entire transmission process. Technologies such as the TLS protocol are used for encryption.

[0117] User feedback received by the system is returned to the server and used to improve the generated AI model and presentation algorithms.

[0118] For example, if a user inputs "I've been feeling depressed lately due to pressure at work," the generating AI model will analyze this and suggest a psychological counselor and a worker support group. Another example of an input prompt is, "I've been working long hours lately, and my mental and physical stress is increasing. What kind of professional or support group should I consult about this problem?" This prompt is sent to the server, where the generating AI model analyzes it and suggests corresponding support.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user inputs a text string through their device. This input represents the user's concerns or questions, and the device receives it and sends it to the server. The input data is encrypted using the TLS protocol before being sent.

[0122] Step 2:

[0123] The server decrypts the received input string and passes it to the generating AI model. The received text is analyzed using natural language processing tools to identify the user's emotions and needs. This process uses Google Cloud's natural language processing API, and the analysis results output an emotion score and needs classification.

[0124] Step 3:

[0125] The server searches the knowledge base based on the analysis results. The knowledge base is built using MongoDB and contains information on the most suitable experts and support groups. The analysis results are used as queries to retrieve relevant information from the database and prepare it as data to be returned to the terminal.

[0126] Step 4:

[0127] The device displays search results received from the server to the user. A Flutter-based user interface presents this information in an intuitive format. Users can choose from the suggested experts and support groups that match their interests and needs and take action.

[0128] Step 5:

[0129] A system is in place to allow users to voluntarily provide feedback after they have finished using the service. This feedback is then sent back to the server, encrypted, and stored in a database. This data will be used to improve future generative AI models and algorithms.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that deeply understands the user's emotions and needs and provides optimal support measures through an interface that accepts user input and an analysis system incorporating an emotion engine. The terminal plays the role of allowing users to input their worries and problems in a natural way and sending that data to the server. The server can analyze the data using an advanced generative model and emotion engine to identify the user's emotional state.

[0132] Specifically, if a user inputs "I've been feeling stressed lately because I've been disagreeing with my friends," the emotion engine analyzes the user's stress level and emotional tone, and uses the results as indicators to make the best match with experts and communities. For example, if the emotion engine detects a high stress level, counselors and support groups specializing in stress management will be suggested preferentially.

[0133] Based on the analysis results from the emotion engine, the server searches the database for the most appropriate resources for the user's situation and sends them to the terminal. The terminal presents this information to the user in an easy-to-understand manner, helping the user choose their next course of action. The user can obtain information on how to contact experts as needed and how to participate in communities.

[0134] Furthermore, the system collects user feedback, and this information is stored on the server to improve the system's performance and usability. In addition, the emotional data shared by the emotion engine is used as a dataset to continuously improve the system's overall matching algorithm, contributing to improved accuracy in services provided to new users.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user inputs their worries and concerns through the terminal's interface. During this process, the terminal formats the input data and prepares it for transmission to the server.

[0138] Step 2:

[0139] The device encrypts user input data and sends it to the server via a secure channel. Encryption is performed to protect data privacy.

[0140] Step 3:

[0141] The server decrypts the received encrypted data and passes it to the generative model and the emotion engine. The emotion engine analyzes the user's text to identify emotions such as stress, joy, and anger.

[0142] Step 4:

[0143] The server integrates the emotion analysis results from the emotion engine and the needs analysis results from the generative model, and searches the database for the most suitable experts and communities for the user. Matching is performed taking into account the type and intensity of the emotion.

[0144] Step 5:

[0145] The server formats information on the most suitable experts and communities and sends it to the device. The data includes details such as contact information, how to participate, and ratings from other users.

[0146] Step 6:

[0147] The device displays information sent from the server to the user. The user can then review the suggested experts and communities to find the option that best suits their needs.

[0148] Step 7:

[0149] When a user selects an action, such as scheduling a consultation with an expert or applying to join a community, the device performs the necessary operations to support the user's action.

[0150] Step 8:

[0151] The terminal requests feedback from the user after they have used the system, displaying a form for them to enter their thoughts and suggestions for improvement.

[0152] Step 9:

[0153] The user enters feedback and sends it to their device.

[0154] Step 10:

[0155] The server receives feedback and stores it in a database. This feedback information is used to improve the algorithm and increase the accuracy of matching. The server also analyzes the feedback and uses it as training data for the emotion engine.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] Modern users face a variety of problems and stresses, and need to quickly obtain appropriate information and support to cope with them. However, providing support tailored to individual emotional states and needs requires sophisticated emotional analysis and accurate individualized responses. Therefore, a system is needed that allows users to accurately understand their own state and effectively receive the support they need.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for using a generative AI model and emotion engine to analyze user input data and identify emotional states and needs; means for searching and presenting optimal support measures and information from a data bank based on the analysis results; and means for continuously learning and improving the matching algorithm for support measures and information sources based on the user's analysis results. This enables the provision of appropriate support measures tailored to each user's emotional state and improves the quality of the service.

[0161] A "terminal" is a device that receives input from a user and sends data to a server.

[0162] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data to identify emotional states and needs.

[0163] The "emotion engine" is an analysis module that works in conjunction with a generative AI model to quantify and evaluate the user's emotional level and tone.

[0164] A "data bank" is a source of information that stores the most suitable support measures and information, which can be searched based on the analysis results.

[0165] "Support measures" is a general term for guidance, information, and support provided according to the user's emotional state and needs.

[0166] A "matching algorithm" is a computational method for optimizing support measures and information sources to match a user's specific needs and emotional state.

[0167] "Feedback" refers to opinions and comments collected from users after they have used a service, regarding their evaluation of the experience and areas for improvement.

[0168] This invention provides an advanced support system that responds to the user's emotions and needs. The system consists of a terminal where the user inputs their worries and problems in natural language, a server that performs data analysis, and a process that provides support based on the analysis results.

[0169] The terminal's role is to receive input from the user. For example, the user might enter a prompt message such as, "I've been feeling stressed lately because I've been disagreeing with my friends." The terminal ensures security by sending this data to the server using an encryption protocol (such as SSL / TLS).

[0170] The server uses a generative AI model and an emotion engine to analyze received user data in detail. The generative AI model interprets text using natural language processing techniques, and the emotion engine quantifies the user's stress level and emotional tone. This allows the server to quantitatively identify the user's emotional state.

[0171] Based on the analysis results, the server searches the database to identify the most suitable support measures and information for the user. For example, if the emotion engine detects a high stress level, information on counselors and support groups specializing in stress management will be prioritized.

[0172] Users can choose their next action based on the information displayed on their device. For example, the steps to contact a suggested counselor or support group are clearly indicated, allowing users to obtain that information with a single click.

[0173] Furthermore, the system collects feedback from users and analyzes that data again on the server, continuously improving the matching algorithm and the quality of the services provided. This makes it possible to improve the accuracy of services for new users.

[0174] In this way, a concrete implementation is realized in which the entire system works together to provide accurate support to users.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The device accepts input from the user. The user inputs their worries or problems in natural language. For example, they might input, "I've been feeling stressed lately because I've been disagreeing with my friends." This input is stored as text data within the device. The specific action taken is for the user to enter text into the device's input field and press the send button.

[0178] Step 2:

[0179] The terminal sends user input data to the server. During this process, the data is transmitted using encryption technologies such as SSL / TLS, ensuring data security. Specifically, the terminal executes a process of sending encrypted data to the server over the network.

[0180] Step 3:

[0181] The server analyzes the user data it receives. This analysis uses a generative AI model and an emotion engine. The text data received as input is tokenized, and prompt sentences are generated and input into the AI ​​model. The generated data is analyzed by the emotion engine, and stress levels and emotional tones are quantified. Specifically, the server receives the data, and the engine that performs the analysis within the program is started.

[0182] Step 4:

[0183] The server searches the database based on the analysis results to identify the most appropriate support measures and information for the user's condition. This search process queries the database based on the stress level values ​​provided by the emotion engine. Specifically, the server uses SQL or similar tools to search the database and saves the retrieved results.

[0184] Step 5:

[0185] The server sends the search results to the terminal. The search results, including helpful suggestions and information, are sent to the terminal as text data, ready for the user to receive. Specifically, the server formats the results and sends them to the terminal via the network.

[0186] Step 6:

[0187] The device presents the received information to the user. The user can then select the next action based on the information displayed on the device. Specifically, the device's display shows the acquired information, and an interface is provided that allows the user to select options on the screen.

[0188] Step 7:

[0189] Users enter feedback into their device after using the service. The device sends this feedback to the server, which is then used to improve the system. Specifically, the user enters their opinion into the feedback form on the device and presses the submit button.

[0190] Step 8:

[0191] The server analyzes the collected feedback and uses it to improve the matching algorithm. This improves the accuracy of the services provided to new users. Specifically, the server receives feedback data as input, retrains the existing algorithm, and improves the system.

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] The present invention aims to provide a system that personalizes the user experience and improves satisfaction by appropriately analyzing the user's emotional state and providing content that matches that emotion in real time. Conventional content delivery services have difficulty adequately considering the user's emotions, and continuous feedback and learning are required to improve accuracy.

[0195] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0196] In this invention, the server includes means for receiving user input and converting it into speech or text, means for using an emotion analysis engine to analyze the user's input data and identify emotions and needs, and means for searching and presenting the most suitable information resources from a database based on the analysis results to provide content that matches the user's emotional state. This enables rapid and accurate analysis of the user's emotions and the provision of personalized content.

[0197] "Information provision means" refers to means of providing an interface for receiving input from users and processing it as audio or text data.

[0198] An "emotion analysis engine" is an analytical system that analyzes user input data to identify their emotions and needs, and is a device used to understand their emotional state.

[0199] "Information resources" refer to content provided based on analysis results and knowledge stored in databases, which are selected appropriately according to the user's emotional state.

[0200] "Feedback information" refers to opinions and evaluations collected from users after a service has been provided, and is data used to optimize the system and improve the service.

[0201] This invention relates to a system comprising a user terminal, a processing server, and provided information resources. In this system, the terminal accepts voice or text input from the user. The user naturally inputs their situation and emotional state, and the input data is transmitted to the server via the terminal.

[0202] The server first uses speech recognition software to convert audio data into text data. Here, it utilizes the Google Cloud Speech-to-Text API. Next, it uses IBM Watson® natural language understanding services to perform sentiment analysis on the converted text data. This sentiment analysis engine plays a crucial role in understanding the emotions and stress levels expressed by the user.

[0203] Based on the analysis results, the server uses Amazon DynamoDB to search for appropriate information resources within the database. This includes content such as videos, music, or articles that match the user's emotional state. The retrieved information resources are sent to the device and presented to the user in an easy-to-understand format.

[0204] This system collects user feedback and uses it to improve the database and sentiment analysis algorithms. This feedback is crucial data for improving the quality of the content provided and optimizing the system.

[0205] For example, if a user enters "I've been feeling tired lately," this text is analyzed by IBM Watson, which identifies the emotion of "fatigue." As a result, the server can recommend relaxing music or entertaining videos.

[0206] Examples of prompt statements are as follows:

[0207] "User input: 'I've been feeling tired lately because work has been so busy.'"

[0208] "Prompt message: 'User's mood: Fatigue, recommend content that can provide relaxation.'"

[0209] This invention allows users to receive entertainment and information that is more tailored to them, enabling them to enjoy a personalized experience.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user inputs their emotions or situation into the device via voice or text. Direct input from the user is received, and the device prepares to process this input in the next step.

[0213] Step 2:

[0214] The device converts voice input into text data. For voice input, the device uses the Google Cloud Speech-to-Text API to generate the text data. The input is voice data, and the output is the corresponding text data.

[0215] Step 3:

[0216] The server receives text data and passes it to the sentiment analysis engine. IBM Watson's natural language understanding service is used to analyze the user's emotions and needs. The input is text data, and the output is the analysis result regarding the emotional state.

[0217] Step 4:

[0218] Based on the analysis results, the server uses Amazon DynamoDB to search for the most suitable information resources within the database. The input is the analysis results regarding the user's emotional state, and the output is a list of content appropriate to the user's emotions.

[0219] Step 5:

[0220] The server transmits selected information resources to the terminal and presents them to the user. The user receives the content visually or audibly and makes appropriate selections. The input is a list of content, and the output is the specific content displayed to the user.

[0221] Step 6:

[0222] After the user has used the presented content, the device collects feedback information. This collected feedback information is sent to the server for system evaluation and improvement. The input is user feedback, and the output is data used for subsequent algorithm improvements.

[0223] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0224] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0225] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0229] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0230] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0231] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0232] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0233] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0234] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0235] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0236] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0237] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0238] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0239] This invention is a system that includes an interface for users to input personal worries and consultation details, analyzes the input data using a generative model, and displays and provides the most suitable experts and support communities based on the user's emotions and needs. This system is characterized by its continuous improvement, taking into account user feedback after the results are displayed.

[0240] To run this system's program, a terminal is required for the user. This terminal is connected to the internet and is responsible for sending data entered by the user to the server. The server analyzes the received data using an advanced generative model to derive specific emotions and needs. For example, if a user enters the concern, "I've been having trouble sleeping lately," the generative model will analyze this as a "sleep-related concern" and identify sleep specialists and relevant online communities.

[0241] The server uses the analysis results to search the database for a list of suitable experts and community information, and sends it to the terminal. The terminal presents this information to the user in a visually clear and easy-to-understand format. Based on the presented information, the user selects specific actions to contact experts or join communities that interest them.

[0242] Furthermore, this system prioritizes user privacy; input data is encrypted before being sent to the server and properly managed throughout the analysis process. Additionally, user feedback is sent to the server and stored in a database, allowing for algorithm updates and more accurate matching. In this way, the entire system is continuously improved, providing users with higher-value support.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user enters the problem or concern they want to discuss into the text input field on their device screen. After finishing the input, the user presses the "Send" button to send the information to their device.

[0246] Step 2:

[0247] The terminal receives user input data and converts it into a predefined format. After format conversion, it encrypts the data for security and prepares it for transmission to the server.

[0248] Step 3:

[0249] The server receives encrypted data sent from the terminal. The received data is decrypted, and the resulting text data is passed to a generative model to analyze the input content. The generative model uses natural language processing techniques to identify emotions and needs.

[0250] Step 4:

[0251] The server uses the results of the generative model analysis to search the database for the most suitable experts and community information for the user. The search results compile a list of relevant experts and community resources.

[0252] Step 5:

[0253] The server formats the search results and sends them to the device in a viewable format. The results include detailed information about experts and communities, contact information, and how to participate.

[0254] Step 6:

[0255] The terminal displays information sent from the server to the user. The user can review the displayed information and choose actions to contact experts or communities that interest them.

[0256] Step 7:

[0257] To perform the action selected by the user, the device sends additional information and launches external links. This allows the user to seek advice or participate in communities.

[0258] Step 8:

[0259] The device presents the user with a feedback form, prompting them to enter their opinions and evaluations about their experience using the system.

[0260] Step 9:

[0261] The user enters feedback and sends it to their device.

[0262] Step 10:

[0263] The server receives feedback sent from users and stores it in a database. This data is used to improve the system, such as adjusting algorithms and enhancing matching accuracy.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In modern society, individuals need to quickly and accurately find appropriate experts and support organizations to address their problems and concerns. However, due to the overwhelming amount of information available, it is difficult for users to find the best support. Furthermore, careful handling of personal information is necessary from a privacy protection standpoint. In addition, a system for continuously improving the quality of support provided is also required.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] This invention includes a server that uses a generative model to analyze user input data and identify emotions and requests, a server that searches for and presents the most suitable expert or collaborating organization from storage based on the analysis results, and a server that enhances information protection by encrypting and communicating the user's input data. This makes it possible for users to easily find the most suitable expert or collaborating organization, achieving a higher level of privacy protection and improved service quality.

[0269] A "communication device" is a device that receives input from a user and transmits that information to a server.

[0270] A "generative model" is a model that utilizes artificial intelligence technology to analyze user input data and derive specific emotions or requests.

[0271] A "memory device" is a device that stores information about experts and collaborating organizations, and has the function of searching and presenting this information as needed.

[0272] "Information protection" refers to measures taken to protect user privacy and ensure secure communication by encrypting user input data.

[0273] A "specialist" is someone who possesses specialized knowledge and skills in a particular field and is capable of addressing users' concerns and providing advice.

[0274] A "cooperative organization" is a group or community whose purpose is to provide support in a specific field or theme.

[0275] An "algorithm" is a set of procedures and formulas that define how to appropriately select experts and collaborating organizations based on input data.

[0276] "Ratings" refer to opinions and feedback provided by users after using a service, and these are used to improve the services and algorithms provided.

[0277] This invention is a system that analyzes the personal troubles and consultation content input by users and proposes appropriate experts and cooperation organizations. This system is mainly implemented using a terminal, a server, and a generative AI model.

[0278] The user uses the terminal to input troubles such as "Recently, I've been having trouble sleeping well" into the interface. The terminal encrypts this input data and securely sends it to the server. This ensures the security of the information.

[0279] The server analyzes the input data received through the generative AI model it uses and extracts specific emotions and requirements. Examples of generative AI models used at this time include GPT-4, etc. The prompt text uses a format such as "Please input the user's current situation and troubles. Based on this, identify experts and cooperation organizations."

[0280] Based on the analysis results, the server searches for information on the most suitable experts and cooperation organizations from the storage device. This information includes the profiles of experienced experts in the field and details of online communities that the user can participate in.

[0281] The server sends the search results to the terminal, and the terminal displays them to the user in a visually easy-to-understand format. Based on the presented information, the user can contact the experts they are interested in or decide to participate in the relevant community.

[0282] Through these processes, a system is realized that provides optimal solutions for the problems faced by individual users while protecting privacy.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user inputs personal troubles and consultation content through the interface of the terminal. For example, input content such as "Recently, I've been having trouble sleeping well." The input data is output in the original text format and used in the next step.

[0286] Step 2:

[0287] The terminal encrypts the data input by the user. Specifically, using data protection technology, the input text data is converted into a securely encrypted format. The encrypted data is output and sent to the server.

[0288] Step 3:

[0289] The server receives the encrypted data and decrypts it back to the original text format. It decodes the received data and outputs it as plain text data ready for analysis.

[0290] Step 4:

[0291] The server analyzes the input data using a generative AI model. Using the prompt text "Please input the user's current situation and troubles. We will identify experts and cooperation organizations based on this." the data is input into the model. As the analysis result, the identified emotions and needs are output.

[0292] Step 5:

[0293] The server searches for the most suitable experts or cooperation organizations from the storage device based on the analysis result. Specifically, it retrieves a list of experts that match the output needs and community information that the user can participate in from the database and outputs this as the result.

[0294] Step 6:

[0295] The server sends the search results to the terminal. The terminal then presents the user with a list of experts and community information in a visually easy-to-understand format. The output here is the information displayed on the user's screen.

[0296] Step 7:

[0297] The user selects an action based on the information provided. For example, they might choose to contact a specific expert from the displayed list or decide to join a community. The input in this step represents the user's choice of action, and the result of that choice prepares them for the next step.

[0298] Step 8:

[0299] The terminal collects user feedback through its interface. It gathers the input feedback data and prepares it to be sent to the server. This data is then output as input for the next step.

[0300] Step 9:

[0301] The server analyzes the feedback received from users and stores it in a database. The feedback content is then analyzed and output as data to improve the matching algorithm. This creates a feedback loop that allows the system to provide more precise support in the future.

[0302] (Application Example 1)

[0303] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0304] In order for users to effectively solve their personal troubles and consultation content, it is essential to propose appropriate experts and support groups. However, it is difficult to accurately grasp users' emotions and needs and make optimal proposals. In addition, it is also an issue to continuously improve the accuracy of proposals. Furthermore, it is necessary to handle users' input information securely and protect their privacy.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for providing a terminal that receives input from a user, means for using a generation model that analyzes the input character string of the user to identify emotions and needs, and means for searching and presenting optimal experts and support groups from a knowledge base based on the analysis result. As a result, it becomes possible to provide accurate support based on users' emotions and needs and manage information while considering privacy.

[0307] The "terminal" is a device for a user to input personal troubles and consultation content, and is a device that can be connected to the Internet.

[0308] The "input character string" is text data provided by the user to the system via the terminal, and is information to be analyzed.

[0309] The "generation model" is an algorithm used to analyze the input character string of the user and identify emotions and needs.

[0310] The "knowledge base" is a collection of data storing information on experts and support groups, and is a database from which information is searched based on the analysis result.

[0311] The "opinion" is feedback provided by the user after using the service, and is data used for improving the system.

[0312] "Security" refers to the level of protection provided for information about user input strings, which is maintained using encryption technology.

[0313] A "recommendation algorithm" is a series of calculation steps used to suggest appropriate experts or support groups to the user based on the analysis results.

[0314] This invention is realized by a system that identifies the user's emotions and needs and suggests the most suitable expert or support group. The system works as follows:

[0315] First, the device plays the role of receiving the input string from the user. This device includes smartphones and tablets connected to the internet. This input string represents the user's current personal concerns or questions.

[0316] Next, the input data is sent to the server via the network. The server then analyzes the received input string. This analysis uses software that implements a generative AI model. Specifically, the generative AI model built on the server utilizes Google Cloud's natural language processing API to identify the user's emotions and needs.

[0317] Based on the analysis results, the server searches its knowledge base for information on the most suitable experts and support groups and returns the information to the terminal in the form of suggestions. This knowledge base is built using database technology such as MongoDB. The user is presented with the provided information on the terminal screen in an intuitive manner and can select suggested actions as needed. This user interface is developed using Flutter.

[0318] Furthermore, to ensure security, user input data is encrypted and protected throughout the entire transmission process. Technologies such as the TLS protocol are used for encryption.

[0319] User feedback received by the system is returned to the server and used to improve the generated AI model and presentation algorithms.

[0320] For example, if a user inputs "I've been feeling depressed lately due to pressure at work," the generating AI model will analyze this and suggest a psychological counselor and a worker support group. Another example of an input prompt is, "I've been working long hours lately, and my mental and physical stress is increasing. What kind of professional or support group should I consult about this problem?" This prompt is sent to the server, where the generating AI model analyzes it and suggests corresponding support.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The user inputs a text string through their device. This input represents the user's concerns or questions, and the device receives it and sends it to the server. The input data is encrypted using the TLS protocol before being sent.

[0324] Step 2:

[0325] The server decrypts the received input string and passes it to the generating AI model. The received text is analyzed using natural language processing tools to identify the user's emotions and needs. This process uses Google Cloud's natural language processing API, and the analysis results output an emotion score and needs classification.

[0326] Step 3:

[0327] The server searches the knowledge base based on the analysis results. The knowledge base is built using MongoDB and contains information on the most suitable experts and support groups. The analysis results are used as queries to retrieve relevant information from the database and prepare it as data to be returned to the terminal.

[0328] Step 4:

[0329] The device displays search results received from the server to the user. A Flutter-based user interface presents this information in an intuitive format. Users can choose from the suggested experts and support groups that match their interests and needs and take action.

[0330] Step 5:

[0331] A system is in place to allow users to voluntarily provide feedback after they have finished using the service. This feedback is then sent back to the server, encrypted, and stored in a database. This data will be used to improve future generative AI models and algorithms.

[0332] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0333] This invention is a system that deeply understands the user's emotions and needs and provides optimal support measures through an interface that accepts user input and an analysis system incorporating an emotion engine. The terminal plays the role of allowing users to input their worries and problems in a natural way and sending that data to the server. The server can analyze the data using an advanced generative model and emotion engine to identify the user's emotional state.

[0334] Specifically, if a user inputs "I've been feeling stressed lately because I've been disagreeing with my friends," the emotion engine analyzes the user's stress level and emotional tone, and uses the results as indicators to make the best match with experts and communities. For example, if the emotion engine detects a high stress level, counselors and support groups specializing in stress management will be suggested preferentially.

[0335] Based on the analysis results from the emotion engine, the server searches the database for the most appropriate resources for the user's situation and sends them to the terminal. The terminal presents this information to the user in an easy-to-understand manner, helping the user choose their next course of action. The user can obtain information on how to contact experts as needed and how to participate in communities.

[0336] Furthermore, the system collects user feedback, and this information is stored on the server to improve the system's performance and usability. In addition, the emotional data shared by the emotion engine is used as a dataset to continuously improve the system's overall matching algorithm, contributing to improved accuracy in services provided to new users.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The user inputs their worries and concerns through the terminal's interface. During this process, the terminal formats the input data and prepares it for transmission to the server.

[0340] Step 2:

[0341] The device encrypts user input data and sends it to the server via a secure channel. Encryption is performed to protect data privacy.

[0342] Step 3:

[0343] The server decrypts the received encrypted data and passes it to the generative model and the emotion engine. The emotion engine analyzes the user's text to identify emotions such as stress, joy, and anger.

[0344] Step 4:

[0345] The server integrates the emotion analysis results from the emotion engine and the needs analysis results from the generative model, and searches the database for the most suitable experts and communities for the user. Matching is performed taking into account the type and intensity of the emotion.

[0346] Step 5:

[0347] The server formats information on the most suitable experts and communities and sends it to the device. The data includes details such as contact information, how to participate, and ratings from other users.

[0348] Step 6:

[0349] The device displays information sent from the server to the user. The user can then review the suggested experts and communities to find the option that best suits their needs.

[0350] Step 7:

[0351] When a user selects an action, such as scheduling a consultation with an expert or applying to join a community, the device performs the necessary operations to support the user's action.

[0352] Step 8:

[0353] The terminal requests feedback from the user after they have used the system, displaying a form for them to enter their thoughts and suggestions for improvement.

[0354] Step 9:

[0355] The user enters feedback and sends it to their device.

[0356] Step 10:

[0357] The server receives feedback and stores it in a database. This feedback information is used to improve the algorithm and increase the accuracy of matching. The server also analyzes the feedback and uses it as training data for the emotion engine.

[0358] (Example 2)

[0359] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0360] Modern users face a variety of problems and stresses, and need to quickly obtain appropriate information and support to cope with them. However, providing support tailored to individual emotional states and needs requires sophisticated emotional analysis and accurate individualized responses. Therefore, a system is needed that allows users to accurately understand their own state and effectively receive the support they need.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0362] In this invention, the server includes means for using a generative AI model and emotion engine to analyze user input data and identify emotional states and needs; means for searching and presenting optimal support measures and information from a data bank based on the analysis results; and means for continuously learning and improving the matching algorithm for support measures and information sources based on the user's analysis results. This enables the provision of appropriate support measures tailored to each user's emotional state and improves the quality of the service.

[0363] A "terminal" is a device that receives input from a user and sends data to a server.

[0364] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data to identify emotional states and needs.

[0365] The "emotion engine" is an analysis module that works in conjunction with a generative AI model to quantify and evaluate the user's emotional level and tone.

[0366] A "data bank" is a source of information that stores the most suitable support measures and information, which can be searched based on the analysis results.

[0367] "Support measures" is a general term for guidance, information, and support provided according to the user's emotional state and needs.

[0368] A "matching algorithm" is a computational method for optimizing support measures and information sources to match a user's specific needs and emotional state.

[0369] "Feedback" refers to opinions and comments collected from users after they have used a service, regarding their evaluation of the experience and areas for improvement.

[0370] This invention provides an advanced support system that responds to the user's emotions and needs. The system consists of a terminal where the user inputs their worries and problems in natural language, a server that performs data analysis, and a process that provides support based on the analysis results.

[0371] The terminal's role is to receive input from the user. The user might enter a prompt message, for example, "I've been feeling stressed lately because I've been disagreeing with my friends." The terminal ensures security by sending this data to the server using an encryption protocol (such as SSL / TLS).

[0372] The server uses a generative AI model and an emotion engine to analyze received user data in detail. The generative AI model interprets text using natural language processing techniques, and the emotion engine quantifies the user's stress level and emotional tone. This allows the server to quantitatively identify the user's emotional state.

[0373] Based on the analysis results, the server searches the database to identify the most suitable support measures and information for the user. For example, if the emotion engine detects a high stress level, information on counselors and support groups specializing in stress management will be prioritized.

[0374] Users can choose their next action based on the information displayed on their device. For example, the steps to contact a suggested counselor or support group are clearly indicated, allowing users to obtain that information with a single click.

[0375] Furthermore, the system collects feedback from users and analyzes that data again on the server, continuously improving the matching algorithm and the quality of the services provided. This makes it possible to improve the accuracy of services for new users.

[0376] In this way, a concrete implementation is realized in which the entire system works together to provide accurate support to users.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The device accepts input from the user. The user inputs their worries or problems in natural language. For example, they might input, "I've been feeling stressed lately because I've been disagreeing with my friends." This input is stored as text data within the device. The specific action taken is for the user to enter text into the device's input field and press the send button.

[0380] Step 2:

[0381] The terminal sends user input data to the server. During this process, the data is transmitted using encryption technologies such as SSL / TLS, ensuring data security. Specifically, the terminal executes a process of sending encrypted data to the server over the network.

[0382] Step 3:

[0383] The server analyzes the user data it receives. This analysis uses a generative AI model and an emotion engine. The text data received as input is tokenized, and prompt sentences are generated and input into the AI ​​model. The generated data is analyzed by the emotion engine, and stress levels and emotional tones are quantified. Specifically, the server receives the data, and the engine that performs the analysis within the program is started.

[0384] Step 4:

[0385] The server searches the database based on the analysis results to identify the most appropriate support measures and information for the user's condition. This search process queries the database based on the stress level values ​​provided by the emotion engine. Specifically, the server uses SQL or similar tools to search the database and saves the retrieved results.

[0386] Step 5:

[0387] The server sends the search results to the terminal. The search results, including helpful suggestions and information, are sent to the terminal as text data, ready for the user to receive. Specifically, the server formats the results and sends them to the terminal via the network.

[0388] Step 6:

[0389] The device presents the received information to the user. The user can then select the next action based on the information displayed on the device. Specifically, the device's display shows the acquired information, and an interface is provided that allows the user to select options on the screen.

[0390] Step 7:

[0391] Users enter feedback into their device after using the service. The device sends this feedback to the server, which is then used to improve the system. Specifically, the user enters their opinion into the feedback form on the device and presses the submit button.

[0392] Step 8:

[0393] The server analyzes the collected feedback and uses it to improve the matching algorithm. This improves the accuracy of the services provided to new users. Specifically, the server receives feedback data as input, retrains the existing algorithm, and improves the system.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] The present invention aims to provide a system that personalizes the user experience and improves satisfaction by appropriately analyzing the user's emotional state and providing content that matches that emotion in real time. Conventional content delivery services have difficulty adequately considering the user's emotions, and continuous feedback and learning are required to improve accuracy.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes means for receiving user input and converting it into speech or text, means for using an emotion analysis engine to analyze the user's input data and identify emotions and needs, and means for searching and presenting the most suitable information resources from a database based on the analysis results to provide content that matches the user's emotional state. This enables rapid and accurate analysis of the user's emotions and the provision of personalized content.

[0399] "Information provision means" refers to means of providing an interface for receiving input from users and processing it as audio or text data.

[0400] An "emotion analysis engine" is an analytical system that analyzes user input data to identify their emotions and needs, and is a device used to understand their emotional state.

[0401] "Information resources" refer to content provided based on analysis results and knowledge stored in databases, which are selected appropriately according to the user's emotional state.

[0402] "Feedback information" refers to opinions and evaluations collected from users after a service has been provided, and is data used to optimize the system and improve the service.

[0403] This invention relates to a system comprising a user terminal, a processing server, and provided information resources. In this system, the terminal accepts voice or text input from the user. The user naturally inputs their situation and emotional state, and the input data is transmitted to the server via the terminal.

[0404] The server first uses speech recognition software to convert audio data into text data. Here, it utilizes the Google Cloud Speech-to-Text API. Next, it uses IBM Watson's natural language understanding service to perform sentiment analysis on the converted text data. This sentiment analysis engine plays a crucial role in understanding the emotions and stress levels expressed by the user.

[0405] Based on the analysis results, the server uses Amazon DynamoDB to search for appropriate information resources within the database. This includes content such as videos, music, or articles that match the user's emotional state. The retrieved information resources are sent to the device and presented to the user in an easy-to-understand format.

[0406] This system collects user feedback and uses it to improve the database and sentiment analysis algorithms. This feedback is crucial data for improving the quality of the content provided and optimizing the system.

[0407] For example, if a user enters "I've been feeling tired lately," this text is analyzed by IBM Watson, which identifies the emotion of "fatigue." As a result, the server can recommend relaxing music or entertaining videos.

[0408] Examples of prompt statements are as follows:

[0409] "User input: 'I've been feeling tired lately because work has been so busy.'"

[0410] "Prompt message: 'User's mood: Fatigue, recommend content that can provide relaxation.'"

[0411] This invention allows users to receive entertainment and information that is more tailored to them, enabling them to enjoy a personalized experience.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The user inputs their emotions or situation into the device via voice or text. Direct input from the user is received, and the device prepares to process this input in the next step.

[0415] Step 2:

[0416] The device converts voice input into text data. For voice input, the device uses the Google Cloud Speech-to-Text API to generate the text data. The input is voice data, and the output is the corresponding text data.

[0417] Step 3:

[0418] The server receives text data and passes it to the sentiment analysis engine. IBM Watson's natural language understanding service is used to analyze the user's emotions and needs. The input is text data, and the output is the analysis result regarding the emotional state.

[0419] Step 4:

[0420] Based on the analysis results, the server uses Amazon DynamoDB to search for the most suitable information resources within the database. The input is the analysis results regarding the user's emotional state, and the output is a list of content appropriate to the user's emotions.

[0421] Step 5:

[0422] The server transmits selected information resources to the terminal and presents them to the user. The user receives the content visually or audibly and makes appropriate selections. The input is a list of content, and the output is the specific content displayed to the user.

[0423] Step 6:

[0424] After the user has used the presented content, the device collects feedback information. This collected feedback information is sent to the server for system evaluation and improvement. The input is user feedback, and the output is data used for subsequent algorithm improvements.

[0425] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0426] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0431] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0433] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0435] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0436] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0437] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0439] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0440] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0441] This invention is a system that includes an interface for users to input personal worries and consultation details, analyzes the input data using a generative model, and displays and provides the most suitable experts and support communities based on the user's emotions and needs. This system is characterized by its continuous improvement, taking into account user feedback after the results are displayed.

[0442] To run this system's program, a terminal is required for the user. This terminal is connected to the internet and is responsible for sending data entered by the user to the server. The server analyzes the received data using an advanced generative model to derive specific emotions and needs. For example, if a user enters the concern, "I've been having trouble sleeping lately," the generative model will analyze this as a "sleep-related concern" and identify sleep specialists and relevant online communities.

[0443] The server uses the analysis results to search the database for a list of suitable experts and community information, and sends it to the terminal. The terminal presents this information to the user in a visually clear and easy-to-understand format. Based on the presented information, the user selects specific actions to contact experts or join communities that interest them.

[0444] Furthermore, this system prioritizes user privacy; input data is encrypted before being sent to the server and properly managed throughout the analysis process. Additionally, user feedback is sent to the server and stored in a database, allowing for algorithm updates and more accurate matching. In this way, the entire system is continuously improved, providing users with higher-value support.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The user enters the problem or concern they want to discuss into the text input field on their device screen. After finishing the input, the user presses the "Send" button to send the information to their device.

[0448] Step 2:

[0449] The terminal receives user input data and converts it into a predefined format. After format conversion, it encrypts the data for security and prepares it for transmission to the server.

[0450] Step 3:

[0451] The server receives encrypted data sent from the terminal. The received data is decrypted, and the resulting text data is passed to a generative model to analyze the input content. The generative model uses natural language processing techniques to identify emotions and needs.

[0452] Step 4:

[0453] The server uses the results of the generative model analysis to search the database for the most suitable experts and community information for the user. The search results compile a list of relevant experts and community resources.

[0454] Step 5:

[0455] The server formats the search results and sends them to the device in a viewable format. The results include detailed information about experts and communities, contact information, and how to participate.

[0456] Step 6:

[0457] The terminal displays information sent from the server to the user. The user can review the displayed information and choose actions to contact experts or communities that interest them.

[0458] Step 7:

[0459] To perform the action selected by the user, the device sends additional information and launches external links. This allows the user to seek advice or participate in communities.

[0460] Step 8:

[0461] The device presents the user with a feedback form, prompting them to enter their opinions and evaluations about their experience using the system.

[0462] Step 9:

[0463] The user enters feedback and sends it to their device.

[0464] Step 10:

[0465] The server receives feedback sent from users and stores it in a database. This data is used to improve the system, such as adjusting algorithms and enhancing matching accuracy.

[0466] (Example 1)

[0467] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In modern society, individuals need to quickly and accurately find appropriate experts and support organizations to address their problems and concerns. However, due to the overwhelming amount of information available, it is difficult for users to find the best support. Furthermore, careful handling of personal information is necessary from a privacy protection standpoint. In addition, a system for continuously improving the quality of support provided is also required.

[0469] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0470] This invention includes a server that uses a generative model to analyze user input data and identify emotions and requests, a server that searches for and presents the most suitable expert or collaborating organization from storage based on the analysis results, and a server that enhances information protection by encrypting and communicating the user's input data. This makes it possible for users to easily find the most suitable expert or collaborating organization, achieving a higher level of privacy protection and improved service quality.

[0471] A "communication device" is a device that receives input from a user and transmits that information to a server.

[0472] A "generative model" is a model that utilizes artificial intelligence technology to analyze user input data and derive specific emotions or requests.

[0473] A "memory device" is a device that stores information about experts and collaborating organizations, and has the function of searching and presenting this information as needed.

[0474] "Information protection" refers to measures taken to protect user privacy and ensure secure communication by encrypting user input data.

[0475] A "specialist" is someone who possesses specialized knowledge and skills in a particular field and is capable of addressing users' concerns and providing advice.

[0476] A "cooperative organization" is a group or community whose purpose is to provide support in a specific field or theme.

[0477] An "algorithm" is a set of procedures and formulas that define how to appropriately select experts and collaborating organizations based on input data.

[0478] "Ratings" refer to opinions and feedback provided by users after using a service, and these are used to improve the services and algorithms provided.

[0479] This invention is a system that analyzes personal concerns and consultation details entered by users and suggests appropriate experts and collaborating organizations. This system is primarily implemented using a terminal, a server, and a generative AI model.

[0480] The user uses their device to input their concerns into the interface, such as "I've been having trouble sleeping lately." The device encrypts this input data and securely transmits it to the server. This ensures the security of the information.

[0481] The server analyzes the input data received through the generative AI model it uses to extract specific emotions and requests. An example of a generative AI model used in this process is GPT-4. The prompt message uses a format such as, "Please enter your current situation and concerns. We will then identify experts and collaborating organizations based on that information."

[0482] Based on the analysis results, the server searches its storage for information on the most suitable experts and collaborating organizations. This information includes profiles of experienced experts in the field and details of online communities that the user can join.

[0483] The server sends search results to the device, which displays them to the user in a visually easy-to-understand format. Based on the information presented, the user can decide to contact experts who interest them or join relevant communities.

[0484] These processes enable a system that protects privacy while providing optimal solutions to the problems faced by individual users.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] Users input personal concerns and questions through the terminal's interface. For example, they might input something like, "I've been having trouble sleeping lately." The entered data is output in plain text format and used in the next step.

[0488] Step 2:

[0489] The terminal encrypts the data entered by the user. Specifically, it uses data protection technology to securely convert the entered text data into an encrypted format. The encrypted data is then output and sent to the server.

[0490] Step 3:

[0491] The server receives encrypted data and decrypts it back into its original text format. It decodes the received data and outputs it as plain text data ready for analysis.

[0492] Step 4:

[0493] The server analyzes the input data using a generative AI model. The prompt message, "Please enter the user's current situation and concerns. Based on this, we will identify experts and collaborating organizations," is used to input data into the model. The analysis results output identified emotions and needs.

[0494] Step 5:

[0495] The server searches its storage for the most suitable expert or collaborating organization based on the analysis results. Specifically, it retrieves a list of experts matching the outputted needs and information on communities the user can join from the database, and outputs this as a result.

[0496] Step 6:

[0497] The server sends the search results to the terminal. The terminal then presents the user with a list of experts and community information in a visually easy-to-understand format. The output here is the information displayed on the user's screen.

[0498] Step 7:

[0499] The user selects an action based on the information provided. For example, they might choose to contact a specific expert from the displayed list or decide to join a community. The input in this step represents the user's choice of action, and the result of that choice prepares them for the next step.

[0500] Step 8:

[0501] The terminal collects user feedback through its interface. It gathers the input feedback data and prepares it to be sent to the server. This data is then output as input for the next step.

[0502] Step 9:

[0503] The server analyzes the feedback received from users and stores it in a database. The feedback content is then analyzed and output as data to improve the matching algorithm. This creates a feedback loop that allows the system to provide more precise support in the future.

[0504] (Application Example 1)

[0505] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] For users to effectively resolve their personal problems and concerns, it is essential to recommend appropriate experts and support groups. However, accurately understanding users' emotions and needs and providing optimal recommendations is difficult. Furthermore, continuously improving the accuracy of these recommendations is also a challenge. Additionally, it is necessary to handle user input information securely and protect their privacy.

[0507] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0508] In this invention, the server includes means for providing a terminal to receive input from the user, means for using a generative model to analyze the user's input string to identify emotions and needs, and means for searching for and presenting the most suitable experts or support groups from a knowledge base based on the analysis results. This enables the provision of accurate support based on the user's emotions and needs, as well as information management that respects privacy.

[0509] A "terminal" is a device that allows users to input personal concerns and questions, and is capable of connecting to the internet.

[0510] "Input string" refers to text data that the user provides to the system via their terminal, and is the information that will be analyzed.

[0511] A "generative model" is an algorithm used to analyze user input strings and identify their emotions and needs.

[0512] A "knowledge repository" is a collection of data that stores information on experts and support groups, and is a database from which information can be searched based on analysis results.

[0513] "Feedback" refers to feedback provided by users after using the service, and is data used to improve the system.

[0514] "Security" refers to the level of protection provided for information about user input strings, which is maintained using encryption technology.

[0515] A "recommendation algorithm" is a series of calculation steps used to suggest appropriate experts or support groups to the user based on the analysis results.

[0516] This invention is realized by a system that identifies the user's emotions and needs and suggests the most suitable expert or support group. The system works as follows:

[0517] First, the device plays the role of receiving the input string from the user. This device includes smartphones and tablets connected to the internet. This input string represents the user's current personal concerns or questions.

[0518] Next, the input data is sent to the server via the network. The server then analyzes the received input string. This analysis uses software that implements a generative AI model. Specifically, the generative AI model built on the server utilizes Google Cloud's natural language processing API to identify the user's emotions and needs.

[0519] Based on the analysis results, the server searches its knowledge base for information on the most suitable experts and support groups and returns the information to the terminal in the form of suggestions. This knowledge base is built using database technology such as MongoDB. The user is presented with the provided information on the terminal screen in an intuitive manner and can select suggested actions as needed. This user interface is developed using Flutter.

[0520] Furthermore, to ensure security, user input data is encrypted and protected throughout the entire transmission process. Technologies such as the TLS protocol are used for encryption.

[0521] User feedback received by the system is returned to the server and used to improve the generated AI model and presentation algorithms.

[0522] For example, if a user inputs "I've been feeling depressed lately due to pressure at work," the generating AI model will analyze this and suggest a psychological counselor and a worker support group. Another example of an input prompt is, "I've been working long hours lately, and my mental and physical stress is increasing. What kind of professional or support group should I consult about this problem?" This prompt is sent to the server, where the generating AI model analyzes it and suggests corresponding support.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The user inputs a text string through their device. This input represents the user's concerns or questions, and the device receives it and sends it to the server. The input data is encrypted using the TLS protocol before being sent.

[0526] Step 2:

[0527] The server decrypts the received input string and passes it to the generating AI model. The received text is analyzed using natural language processing tools to identify the user's emotions and needs. This process uses Google Cloud's natural language processing API, and the analysis results output an emotion score and needs classification.

[0528] Step 3:

[0529] The server searches the knowledge base based on the analysis results. The knowledge base is built using MongoDB and contains information on the most suitable experts and support groups. The analysis results are used as queries to retrieve relevant information from the database and prepare it as data to be returned to the terminal.

[0530] Step 4:

[0531] The device displays search results received from the server to the user. A Flutter-based user interface presents this information in an intuitive format. Users can choose from the suggested experts and support groups that match their interests and needs and take action.

[0532] Step 5:

[0533] A system is in place to allow users to voluntarily provide feedback after they have finished using the service. This feedback is then sent back to the server, encrypted, and stored in a database. This data will be used to improve future generative AI models and algorithms.

[0534] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0535] This invention is a system that deeply understands the user's emotions and needs and provides optimal support measures through an interface that accepts user input and an analysis system incorporating an emotion engine. The terminal plays the role of allowing users to input their worries and problems in a natural way and sending that data to the server. The server can analyze the data using an advanced generative model and emotion engine to identify the user's emotional state.

[0536] Specifically, if a user inputs "I've been feeling stressed lately because I've been disagreeing with my friends," the emotion engine analyzes the user's stress level and emotional tone, and uses the results as indicators to make the best match with experts and communities. For example, if the emotion engine detects a high stress level, counselors and support groups specializing in stress management will be suggested preferentially.

[0537] Based on the analysis results from the emotion engine, the server searches the database for the most appropriate resources for the user's situation and sends them to the terminal. The terminal presents this information to the user in an easy-to-understand manner, helping the user choose their next course of action. The user can obtain information on how to contact experts as needed and how to participate in communities.

[0538] Furthermore, the system collects user feedback, and this information is stored on the server to improve the system's performance and usability. In addition, the emotional data shared by the emotion engine is used as a dataset to continuously improve the system's overall matching algorithm, contributing to improved accuracy in services provided to new users.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The user inputs their worries and concerns through the terminal's interface. During this process, the terminal formats the input data and prepares it for transmission to the server.

[0542] Step 2:

[0543] The device encrypts user input data and sends it to the server via a secure channel. Encryption is performed to protect data privacy.

[0544] Step 3:

[0545] The server decrypts the received encrypted data and passes it to the generative model and the emotion engine. The emotion engine analyzes the user's text to identify emotions such as stress, joy, and anger.

[0546] Step 4:

[0547] The server integrates the emotion analysis results from the emotion engine and the needs analysis results from the generative model, and searches the database for the most suitable experts and communities for the user. Matching is performed taking into account the type and intensity of the emotion.

[0548] Step 5:

[0549] The server formats information on the most suitable experts and communities and sends it to the device. The data includes details such as contact information, how to participate, and ratings from other users.

[0550] Step 6:

[0551] The device displays information sent from the server to the user. The user can then review the suggested experts and communities to find the option that best suits their needs.

[0552] Step 7:

[0553] When a user selects an action, such as scheduling a consultation with an expert or applying to join a community, the device performs the necessary operations to support the user's action.

[0554] Step 8:

[0555] The terminal requests feedback from the user after they have used the system, displaying a form for them to enter their thoughts and suggestions for improvement.

[0556] Step 9:

[0557] The user enters feedback and sends it to their device.

[0558] Step 10:

[0559] The server receives feedback and stores it in a database. This feedback information is used to improve the algorithm and increase the accuracy of matching. The server also analyzes the feedback and uses it as training data for the emotion engine.

[0560] (Example 2)

[0561] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0562] Modern users face a variety of problems and stresses, and need to quickly obtain appropriate information and support to cope with them. However, providing support tailored to individual emotional states and needs requires sophisticated emotional analysis and accurate individualized responses. Therefore, a system is needed that allows users to accurately understand their own state and effectively receive the support they need.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0564] In this invention, the server includes means for using a generative AI model and emotion engine to analyze user input data and identify emotional states and needs; means for searching and presenting optimal support measures and information from a data bank based on the analysis results; and means for continuously learning and improving the matching algorithm for support measures and information sources based on the user's analysis results. This enables the provision of appropriate support measures tailored to each user's emotional state and improves the quality of the service.

[0565] A "terminal" is a device that receives input from a user and sends data to a server.

[0566] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data to identify emotional states and needs.

[0567] The "emotion engine" is an analysis module that works in conjunction with a generative AI model to quantify and evaluate the user's emotional level and tone.

[0568] A "data bank" is a source of information that stores the most suitable support measures and information, which can be searched based on the analysis results.

[0569] "Support measures" is a general term for guidance, information, and support provided according to the user's emotional state and needs.

[0570] A "matching algorithm" is a computational method for optimizing support measures and information sources to match a user's specific needs and emotional state.

[0571] "Feedback" refers to opinions and comments collected from users after they have used a service, regarding their evaluation of the experience and areas for improvement.

[0572] This invention provides an advanced support system that responds to the user's emotions and needs. The system consists of a terminal where the user inputs their worries and problems in natural language, a server that performs data analysis, and a process that provides support based on the analysis results.

[0573] The terminal's role is to receive input from the user. The user might enter a prompt message, for example, "I've been feeling stressed lately because I've been disagreeing with my friends." The terminal ensures security by sending this data to the server using an encryption protocol (such as SSL / TLS).

[0574] The server uses a generative AI model and an emotion engine to analyze received user data in detail. The generative AI model interprets text using natural language processing techniques, and the emotion engine quantifies the user's stress level and emotional tone. This allows the server to quantitatively identify the user's emotional state.

[0575] Based on the analysis results, the server searches the database to identify the most suitable support measures and information for the user. For example, if the emotion engine detects a high stress level, information on counselors and support groups specializing in stress management will be prioritized.

[0576] Users can choose their next action based on the information displayed on their device. For example, the steps to contact a suggested counselor or support group are clearly indicated, allowing users to obtain that information with a single click.

[0577] Furthermore, the system collects feedback from users and analyzes that data again on the server, continuously improving the matching algorithm and the quality of the services provided. This makes it possible to improve the accuracy of services for new users.

[0578] In this way, a concrete implementation is realized in which the entire system works together to provide accurate support to users.

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The device accepts input from the user. The user inputs their worries or problems in natural language. For example, they might input, "I've been feeling stressed lately because I've been disagreeing with my friends." This input is stored as text data within the device. The specific action taken is for the user to enter text into the device's input field and press the send button.

[0582] Step 2:

[0583] The terminal sends user input data to the server. During this process, the data is transmitted using encryption technologies such as SSL / TLS, ensuring data security. Specifically, the terminal executes a process of sending encrypted data to the server over the network.

[0584] Step 3:

[0585] The server analyzes the user data it receives. This analysis uses a generative AI model and an emotion engine. The text data received as input is tokenized, and prompt sentences are generated and input into the AI ​​model. The generated data is analyzed by the emotion engine, and stress levels and emotional tones are quantified. Specifically, the server receives the data, and the engine that performs the analysis within the program is started.

[0586] Step 4:

[0587] The server searches the database based on the analysis results to identify the most appropriate support measures and information for the user's condition. This search process queries the database based on the stress level values ​​provided by the emotion engine. Specifically, the server uses SQL or similar tools to search the database and saves the retrieved results.

[0588] Step 5:

[0589] The server sends the search results to the terminal. The search results, including helpful suggestions and information, are sent to the terminal as text data, ready for the user to receive. Specifically, the server formats the results and sends them to the terminal via the network.

[0590] Step 6:

[0591] The device presents the received information to the user. The user can then select the next action based on the information displayed on the device. Specifically, the device's display shows the acquired information, and an interface is provided that allows the user to select options on the screen.

[0592] Step 7:

[0593] Users enter feedback into their device after using the service. The device sends this feedback to the server, which is then used to improve the system. Specifically, the user enters their opinion into the feedback form on the device and presses the submit button.

[0594] Step 8:

[0595] The server analyzes the collected feedback and uses it to improve the matching algorithm. This improves the accuracy of the services provided to new users. Specifically, the server receives feedback data as input, retrains the existing algorithm, and improves the system.

[0596] (Application Example 2)

[0597] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0598] The present invention aims to provide a system that personalizes the user experience and improves satisfaction by appropriately analyzing the user's emotional state and providing content that matches that emotion in real time. Conventional content delivery services have difficulty adequately considering the user's emotions, and continuous feedback and learning are required to improve accuracy.

[0599] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0600] In this invention, the server includes means for receiving user input and converting it into speech or text, means for using an emotion analysis engine to analyze the user's input data and identify emotions and needs, and means for searching and presenting the most suitable information resources from a database based on the analysis results to provide content that matches the user's emotional state. This enables rapid and accurate analysis of the user's emotions and the provision of personalized content.

[0601] "Information provision means" refers to means of providing an interface for receiving input from users and processing it as audio or text data.

[0602] An "emotion analysis engine" is an analytical system that analyzes user input data to identify their emotions and needs, and is a device used to understand their emotional state.

[0603] "Information resources" refer to content provided based on analysis results and knowledge stored in databases, which are selected appropriately according to the user's emotional state.

[0604] "Feedback information" refers to opinions and evaluations collected from users after a service has been provided, and is data used to optimize the system and improve the service.

[0605] This invention relates to a system comprising a user terminal, a processing server, and provided information resources. In this system, the terminal accepts voice or text input from the user. The user naturally inputs their situation and emotional state, and the input data is transmitted to the server via the terminal.

[0606] The server first uses speech recognition software to convert audio data into text data. Here, it utilizes the Google Cloud Speech-to-Text API. Next, it uses IBM Watson's natural language understanding service to perform sentiment analysis on the converted text data. This sentiment analysis engine plays a crucial role in understanding the emotions and stress levels expressed by the user.

[0607] Based on the analysis results, the server uses Amazon DynamoDB to search for appropriate information resources within the database. This includes content such as videos, music, or articles that match the user's emotional state. The retrieved information resources are sent to the device and presented to the user in an easy-to-understand format.

[0608] This system collects user feedback and uses it to improve the database and sentiment analysis algorithms. This feedback is crucial data for improving the quality of the content provided and optimizing the system.

[0609] For example, if a user enters "I've been feeling tired lately," this text is analyzed by IBM Watson, which identifies the emotion of "fatigue." As a result, the server can recommend relaxing music or entertaining videos.

[0610] Examples of prompt statements are as follows:

[0611] "User input: 'I've been feeling tired lately because work has been so busy.'"

[0612] "Prompt message: 'User's mood: Fatigue, recommend content that can provide relaxation.'"

[0613] This invention allows users to receive entertainment and information that is more tailored to them, enabling them to enjoy a personalized experience.

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The user inputs their emotions or situation into the device via voice or text. Direct input from the user is received, and the device prepares to process this input in the next step.

[0617] Step 2:

[0618] The device converts voice input into text data. For voice input, the device uses the Google Cloud Speech-to-Text API to generate the text data. The input is voice data, and the output is the corresponding text data.

[0619] Step 3:

[0620] The server receives text data and passes it to the sentiment analysis engine. IBM Watson's natural language understanding service is used to analyze the user's emotions and needs. The input is text data, and the output is the analysis result regarding the emotional state.

[0621] Step 4:

[0622] Based on the analysis results, the server uses Amazon DynamoDB to search for the most suitable information resources within the database. The input is the analysis results regarding the user's emotional state, and the output is a list of content appropriate to the user's emotions.

[0623] Step 5:

[0624] The server transmits selected information resources to the terminal and presents them to the user. The user receives the content visually or audibly and makes appropriate selections. The input is a list of content, and the output is the specific content displayed to the user.

[0625] Step 6:

[0626] After the user has used the presented content, the device collects feedback information. This collected feedback information is sent to the server for system evaluation and improvement. The input is user feedback, and the output is data used for subsequent algorithm improvements.

[0627] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0628] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0629] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0633] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0634] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0635] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0636] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0637] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0638] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0639] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0640] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0641] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0642] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0643] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] This invention is a system that includes an interface for users to input personal worries and consultation details, analyzes the input data using a generative model, and displays and provides the most suitable experts and support communities based on the user's emotions and needs. This system is characterized by its continuous improvement, taking into account user feedback after the results are displayed.

[0645] To run this system's program, a terminal is required for the user. This terminal is connected to the internet and is responsible for sending data entered by the user to the server. The server analyzes the received data using an advanced generative model to derive specific emotions and needs. For example, if a user enters the concern, "I've been having trouble sleeping lately," the generative model will analyze this as a "sleep-related concern" and identify sleep specialists and relevant online communities.

[0646] The server uses the analysis results to search the database for a list of suitable experts and community information, and sends it to the terminal. The terminal presents this information to the user in a visually clear and easy-to-understand format. Based on the presented information, the user selects specific actions to contact experts or join communities that interest them.

[0647] Furthermore, this system prioritizes user privacy; input data is encrypted before being sent to the server and properly managed throughout the analysis process. Additionally, user feedback is sent to the server and stored in a database, allowing for algorithm updates and more accurate matching. In this way, the entire system is continuously improved, providing users with higher-value support.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The user enters the problem or concern they want to discuss into the text input field on their device screen. After finishing the input, the user presses the "Send" button to send the information to their device.

[0651] Step 2:

[0652] The terminal receives user input data and converts it into a predefined format. After format conversion, it encrypts the data for security and prepares it for transmission to the server.

[0653] Step 3:

[0654] The server receives encrypted data sent from the terminal. The received data is decrypted, and the resulting text data is passed to a generative model to analyze the input content. The generative model uses natural language processing techniques to identify emotions and needs.

[0655] Step 4:

[0656] The server uses the results of the generative model analysis to search the database for the most suitable experts and community information for the user. The search results compile a list of relevant experts and community resources.

[0657] Step 5:

[0658] The server formats the search results and sends them to the device in a viewable format. The results include detailed information about experts and communities, contact information, and how to participate.

[0659] Step 6:

[0660] The terminal displays information sent from the server to the user. The user can review the displayed information and choose actions to contact experts or communities that interest them.

[0661] Step 7:

[0662] To perform the action selected by the user, the device sends additional information and launches external links. This allows the user to seek advice or participate in communities.

[0663] Step 8:

[0664] The device presents the user with a feedback form, prompting them to enter their opinions and evaluations about their experience using the system.

[0665] Step 9:

[0666] The user enters feedback and sends it to their device.

[0667] Step 10:

[0668] The server receives feedback sent from users and stores it in a database. This data is used to improve the system, such as adjusting algorithms and enhancing matching accuracy.

[0669] (Example 1)

[0670] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] In modern society, individuals need to quickly and accurately find appropriate experts and support organizations to address their problems and concerns. However, due to the overwhelming amount of information available, it is difficult for users to find the best support. Furthermore, careful handling of personal information is necessary from a privacy protection standpoint. In addition, a system for continuously improving the quality of support provided is also required.

[0672] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0673] This invention includes a server that uses a generative model to analyze user input data and identify emotions and requests, a server that searches for and presents the most suitable expert or collaborating organization from storage based on the analysis results, and a server that enhances information protection by encrypting and communicating the user's input data. This makes it possible for users to easily find the most suitable expert or collaborating organization, achieving a higher level of privacy protection and improved service quality.

[0674] A "communication device" is a device that receives input from a user and transmits that information to a server.

[0675] A "generative model" is a model that utilizes artificial intelligence technology to analyze user input data and derive specific emotions or requests.

[0676] A "memory device" is a device that stores information about experts and collaborating organizations, and has the function of searching and presenting this information as needed.

[0677] "Information protection" refers to measures taken to protect user privacy and ensure secure communication by encrypting user input data.

[0678] A "specialist" is someone who possesses specialized knowledge and skills in a particular field and is capable of addressing users' concerns and providing advice.

[0679] A "cooperative organization" is a group or community whose purpose is to provide support in a specific field or theme.

[0680] An "algorithm" is a set of procedures and formulas that define how to appropriately select experts and collaborating organizations based on input data.

[0681] "Ratings" refer to opinions and feedback provided by users after using a service, and these are used to improve the services and algorithms provided.

[0682] This invention is a system that analyzes personal concerns and consultation details entered by users and suggests appropriate experts and collaborating organizations. This system is primarily implemented using a terminal, a server, and a generative AI model.

[0683] The user uses their device to input their concerns into the interface, such as "I've been having trouble sleeping lately." The device encrypts this input data and securely transmits it to the server. This ensures the security of the information.

[0684] The server analyzes the input data received through the generative AI model it uses to extract specific emotions and requests. An example of a generative AI model used in this process is GPT-4. The prompt message uses a format such as, "Please enter your current situation and concerns. We will then identify experts and collaborating organizations based on that information."

[0685] Based on the analysis results, the server searches its storage for information on the most suitable experts and collaborating organizations. This information includes profiles of experienced experts in the field and details of online communities that the user can join.

[0686] The server sends search results to the device, which displays them to the user in a visually easy-to-understand format. Based on the information presented, the user can decide to contact experts who interest them or join relevant communities.

[0687] These processes enable a system that protects privacy while providing optimal solutions to the problems faced by individual users.

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] Users input personal concerns and questions through the terminal's interface. For example, they might input something like, "I've been having trouble sleeping lately." The entered data is output in plain text format and used in the next step.

[0691] Step 2:

[0692] The terminal encrypts the data entered by the user. Specifically, it uses data protection technology to securely convert the entered text data into an encrypted format. The encrypted data is then output and sent to the server.

[0693] Step 3:

[0694] The server receives encrypted data and decrypts it back into its original text format. It decodes the received data and outputs it as plain text data ready for analysis.

[0695] Step 4:

[0696] The server analyzes the input data using a generative AI model. The prompt message, "Please enter the user's current situation and concerns. Based on this, we will identify experts and collaborating organizations," is used to input data into the model. The analysis results output identified emotions and needs.

[0697] Step 5:

[0698] The server searches its storage for the most suitable expert or collaborating organization based on the analysis results. Specifically, it retrieves a list of experts matching the outputted needs and information on communities the user can join from the database, and outputs this as a result.

[0699] Step 6:

[0700] The server sends the search results to the terminal. The terminal then presents the user with a list of experts and community information in a visually easy-to-understand format. The output here is the information displayed on the user's screen.

[0701] Step 7:

[0702] The user selects an action based on the information provided. For example, they might choose to contact a specific expert from the displayed list or decide to join a community. The input in this step represents the user's choice of action, and the result of that choice prepares them for the next step.

[0703] Step 8:

[0704] The terminal collects user feedback through its interface. It gathers the input feedback data and prepares it to be sent to the server. This data is then output as input for the next step.

[0705] Step 9:

[0706] The server analyzes the feedback received from users and stores it in a database. The feedback content is then analyzed and output as data to improve the matching algorithm. This creates a feedback loop that allows the system to provide more precise support in the future.

[0707] (Application Example 1)

[0708] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] For users to effectively resolve their personal problems and concerns, it is essential to recommend appropriate experts and support groups. However, accurately understanding users' emotions and needs and providing optimal recommendations is difficult. Furthermore, continuously improving the accuracy of these recommendations is also a challenge. Additionally, it is necessary to handle user input information securely and protect their privacy.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0711] In this invention, the server includes means for providing a terminal to receive input from the user, means for using a generative model to analyze the user's input string to identify emotions and needs, and means for searching for and presenting the most suitable experts or support groups from a knowledge base based on the analysis results. This enables the provision of accurate support based on the user's emotions and needs, as well as information management that respects privacy.

[0712] A "terminal" is a device that allows users to input personal concerns and questions, and is capable of connecting to the internet.

[0713] "Input string" refers to text data that the user provides to the system via their terminal, and is the information that will be analyzed.

[0714] A "generative model" is an algorithm used to analyze user input strings and identify their emotions and needs.

[0715] A "knowledge repository" is a collection of data that stores information on experts and support groups, and is a database from which information can be searched based on analysis results.

[0716] "Feedback" refers to feedback provided by users after using the service, and is data used to improve the system.

[0717] "Security" refers to the level of protection provided for information about user input strings, which is maintained using encryption technology.

[0718] A "recommendation algorithm" is a series of calculation steps used to suggest appropriate experts or support groups to the user based on the analysis results.

[0719] This invention is realized by a system that identifies the user's emotions and needs and suggests the most suitable expert or support group. The system works as follows:

[0720] First, the device plays the role of receiving the input string from the user. This device includes smartphones and tablets connected to the internet. This input string represents the user's current personal concerns or questions.

[0721] Next, the input data is sent to the server via the network. The server then analyzes the received input string. This analysis uses software that implements a generative AI model. Specifically, the generative AI model built on the server utilizes Google Cloud's natural language processing API to identify the user's emotions and needs.

[0722] Based on the analysis results, the server searches its knowledge base for information on the most suitable experts and support groups and returns the information to the terminal in the form of suggestions. This knowledge base is built using database technology such as MongoDB. The user is presented with the provided information on the terminal screen in an intuitive manner and can select suggested actions as needed. This user interface is developed using Flutter.

[0723] Furthermore, to ensure security, user input data is encrypted and protected throughout the entire transmission process. Technologies such as the TLS protocol are used for encryption.

[0724] User feedback received by the system is returned to the server and used to improve the generated AI model and presentation algorithms.

[0725] For example, if a user inputs "I've been feeling depressed lately due to pressure at work," the generating AI model will analyze this and suggest a psychological counselor and a worker support group. Another example of an input prompt is, "I've been working long hours lately, and my mental and physical stress is increasing. What kind of professional or support group should I consult about this problem?" This prompt is sent to the server, where the generating AI model analyzes it and suggests corresponding support.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The user inputs a text string through their device. This input represents the user's concerns or questions, and the device receives it and sends it to the server. The input data is encrypted using the TLS protocol before being sent.

[0729] Step 2:

[0730] The server decrypts the received input string and passes it to the generating AI model. The received text is analyzed using natural language processing tools to identify the user's emotions and needs. This process uses Google Cloud's natural language processing API, and the analysis results output an emotion score and needs classification.

[0731] Step 3:

[0732] The server searches the knowledge base based on the analysis results. The knowledge base is built using MongoDB and contains information on the most suitable experts and support groups. The analysis results are used as queries to retrieve relevant information from the database and prepare it as data to be returned to the terminal.

[0733] Step 4:

[0734] The device displays search results received from the server to the user. A Flutter-based user interface presents this information in an intuitive format. Users can choose from the suggested experts and support groups that match their interests and needs and take action.

[0735] Step 5:

[0736] A system is in place to allow users to voluntarily provide feedback after they have finished using the service. This feedback is then sent back to the server, encrypted, and stored in a database. This data will be used to improve future generative AI models and algorithms.

[0737] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0738] This invention is a system that deeply understands the user's emotions and needs and provides optimal support measures through an interface that accepts user input and an analysis system incorporating an emotion engine. The terminal plays the role of allowing users to input their worries and problems in a natural way and sending that data to the server. The server can analyze the data using an advanced generative model and emotion engine to identify the user's emotional state.

[0739] Specifically, if a user inputs "I've been feeling stressed lately because I've been disagreeing with my friends," the emotion engine analyzes the user's stress level and emotional tone, and uses the results as indicators to make the best match with experts and communities. For example, if the emotion engine detects a high stress level, counselors and support groups specializing in stress management will be suggested preferentially.

[0740] Based on the analysis results from the emotion engine, the server searches the database for the most appropriate resources for the user's situation and sends them to the terminal. The terminal presents this information to the user in an easy-to-understand manner, helping the user choose their next course of action. The user can obtain information on how to contact experts as needed and how to participate in communities.

[0741] Furthermore, the system collects user feedback, and this information is stored on the server to improve the system's performance and usability. In addition, the emotional data shared by the emotion engine is used as a dataset to continuously improve the system's overall matching algorithm, contributing to improved accuracy in services provided to new users.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The user inputs their worries and concerns through the terminal's interface. During this process, the terminal formats the input data and prepares it for transmission to the server.

[0745] Step 2:

[0746] The device encrypts user input data and sends it to the server via a secure channel. Encryption is performed to protect data privacy.

[0747] Step 3:

[0748] The server decrypts the received encrypted data and passes it to the generative model and the emotion engine. The emotion engine analyzes the user's text to identify emotions such as stress, joy, and anger.

[0749] Step 4:

[0750] The server integrates the emotion analysis results from the emotion engine and the needs analysis results from the generative model, and searches the database for the most suitable experts and communities for the user. Matching is performed taking into account the type and intensity of the emotion.

[0751] Step 5:

[0752] The server formats information on the most suitable experts and communities and sends it to the device. The data includes details such as contact information, how to participate, and ratings from other users.

[0753] Step 6:

[0754] The device displays information sent from the server to the user. The user can then review the suggested experts and communities to find the option that best suits their needs.

[0755] Step 7:

[0756] When a user selects an action, such as scheduling a consultation with an expert or applying to join a community, the device performs the necessary operations to support the user's action.

[0757] Step 8:

[0758] The terminal requests feedback from the user after they have used the system, displaying a form for them to enter their thoughts and suggestions for improvement.

[0759] Step 9:

[0760] The user enters feedback and sends it to their device.

[0761] Step 10:

[0762] The server receives feedback and stores it in a database. This feedback information is used to improve the algorithm and increase the accuracy of matching. The server also analyzes the feedback and uses it as training data for the emotion engine.

[0763] (Example 2)

[0764] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] Modern users face a variety of problems and stresses, and need to quickly obtain appropriate information and support to cope with them. However, providing support tailored to individual emotional states and needs requires sophisticated emotional analysis and accurate individualized responses. Therefore, a system is needed that allows users to accurately understand their own state and effectively receive the support they need.

[0766] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0767] In this invention, the server includes means for using a generative AI model and emotion engine to analyze user input data and identify emotional states and needs; means for searching and presenting optimal support measures and information from a data bank based on the analysis results; and means for continuously learning and improving the matching algorithm for support measures and information sources based on the user's analysis results. This enables the provision of appropriate support measures tailored to each user's emotional state and improves the quality of the service.

[0768] A "terminal" is a device that receives input from a user and sends data to a server.

[0769] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data to identify emotional states and needs.

[0770] The "emotion engine" is an analysis module that works in conjunction with a generative AI model to quantify and evaluate the user's emotional level and tone.

[0771] A "data bank" is a source of information that stores the most suitable support measures and information, which can be searched based on the analysis results.

[0772] "Support measures" is a general term for guidance, information, and support provided according to the user's emotional state and needs.

[0773] A "matching algorithm" is a computational method for optimizing support measures and information sources to match a user's specific needs and emotional state.

[0774] "Feedback" refers to opinions and comments collected from users after they have used a service, regarding their evaluation of the experience and areas for improvement.

[0775] This invention provides an advanced support system that responds to the user's emotions and needs. The system consists of a terminal where the user inputs their worries and problems in natural language, a server that performs data analysis, and a process that provides support based on the analysis results.

[0776] The terminal's role is to receive input from the user. The user might enter a prompt message, for example, "I've been feeling stressed lately because I've been disagreeing with my friends." The terminal ensures security by sending this data to the server using an encryption protocol (such as SSL / TLS).

[0777] The server uses a generative AI model and an emotion engine to analyze received user data in detail. The generative AI model interprets text using natural language processing techniques, and the emotion engine quantifies the user's stress level and emotional tone. This allows the server to quantitatively identify the user's emotional state.

[0778] Based on the analysis results, the server searches the database to identify the most suitable support measures and information for the user. For example, if the emotion engine detects a high stress level, information on counselors and support groups specializing in stress management will be prioritized.

[0779] Users can choose their next action based on the information displayed on their device. For example, the steps to contact a suggested counselor or support group are clearly indicated, allowing users to obtain that information with a single click.

[0780] Furthermore, the system collects feedback from users and analyzes that data again on the server, continuously improving the matching algorithm and the quality of the services provided. This makes it possible to improve the accuracy of services for new users.

[0781] In this way, a concrete implementation is realized in which the entire system works together to provide accurate support to users.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The device accepts input from the user. The user inputs their worries or problems in natural language. For example, they might input, "I've been feeling stressed lately because I've been disagreeing with my friends." This input is stored as text data within the device. The specific action taken is for the user to enter text into the device's input field and press the send button.

[0785] Step 2:

[0786] The terminal sends user input data to the server. During this process, the data is transmitted using encryption technologies such as SSL / TLS, ensuring data security. Specifically, the terminal executes a process of sending encrypted data to the server over the network.

[0787] Step 3:

[0788] The server analyzes the user data it receives. This analysis uses a generative AI model and an emotion engine. The text data received as input is tokenized, and prompt sentences are generated and input into the AI ​​model. The generated data is analyzed by the emotion engine, and stress levels and emotional tones are quantified. Specifically, the server receives the data, and the engine that performs the analysis within the program is started.

[0789] Step 4:

[0790] The server searches the database based on the analysis results to identify the most appropriate support measures and information for the user's condition. This search process queries the database based on the stress level values ​​provided by the emotion engine. Specifically, the server uses SQL or similar tools to search the database and saves the retrieved results.

[0791] Step 5:

[0792] The server sends the search results to the terminal. The search results, including helpful suggestions and information, are sent to the terminal as text data, ready for the user to receive. Specifically, the server formats the results and sends them to the terminal via the network.

[0793] Step 6:

[0794] The device presents the received information to the user. The user can then select the next action based on the information displayed on the device. Specifically, the device's display shows the acquired information, and an interface is provided that allows the user to select options on the screen.

[0795] Step 7:

[0796] Users enter feedback into their device after using the service. The device sends this feedback to the server, which is then used to improve the system. Specifically, the user enters their opinion into the feedback form on the device and presses the submit button.

[0797] Step 8:

[0798] The server analyzes the collected feedback and uses it to improve the matching algorithm. This improves the accuracy of the services provided to new users. Specifically, the server receives feedback data as input, retrains the existing algorithm, and improves the system.

[0799] (Application Example 2)

[0800] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] The present invention aims to provide a system that personalizes the user experience and improves satisfaction by appropriately analyzing the user's emotional state and providing content that matches that emotion in real time. Conventional content delivery services have difficulty adequately considering the user's emotions, and continuous feedback and learning are required to improve accuracy.

[0802] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0803] In this invention, the server includes means for receiving user input and converting it into speech or text, means for using an emotion analysis engine to analyze the user's input data and identify emotions and needs, and means for searching and presenting the most suitable information resources from a database based on the analysis results to provide content that matches the user's emotional state. This enables rapid and accurate analysis of the user's emotions and the provision of personalized content.

[0804] "Information provision means" refers to means of providing an interface for receiving input from users and processing it as audio or text data.

[0805] An "emotion analysis engine" is an analytical system that analyzes user input data to identify their emotions and needs, and is a device used to understand their emotional state.

[0806] "Information resources" refer to content provided based on analysis results and knowledge stored in databases, which are selected appropriately according to the user's emotional state.

[0807] "Feedback information" refers to opinions and evaluations collected from users after a service has been provided, and is data used to optimize the system and improve the service.

[0808] This invention relates to a system comprising a user terminal, a processing server, and provided information resources. In this system, the terminal accepts voice or text input from the user. The user naturally inputs their situation and emotional state, and the input data is transmitted to the server via the terminal.

[0809] The server first uses speech recognition software to convert audio data into text data. Here, it utilizes the Google Cloud Speech-to-Text API. Next, it uses IBM Watson's natural language understanding service to perform sentiment analysis on the converted text data. This sentiment analysis engine plays a crucial role in understanding the emotions and stress levels expressed by the user.

[0810] Based on the analysis results, the server uses Amazon DynamoDB to search for appropriate information resources within the database. This includes content such as videos, music, or articles that match the user's emotional state. The retrieved information resources are sent to the device and presented to the user in an easy-to-understand format.

[0811] This system collects user feedback and uses it to improve the database and sentiment analysis algorithms. This feedback is crucial data for improving the quality of the content provided and optimizing the system.

[0812] For example, if a user enters "I've been feeling tired lately," this text is analyzed by IBM Watson, which identifies the emotion of "fatigue." As a result, the server can recommend relaxing music or entertaining videos.

[0813] Examples of prompt statements are as follows:

[0814] "User input: 'I've been feeling tired lately because work has been so busy.'"

[0815] "Prompt message: 'User's mood: Fatigue, recommend content that can provide relaxation.'"

[0816] This invention allows users to receive entertainment and information that is more tailored to them, enabling them to enjoy a personalized experience.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The user inputs their emotions or situation into the device via voice or text. Direct input from the user is received, and the device prepares to process this input in the next step.

[0820] Step 2:

[0821] The device converts voice input into text data. For voice input, the device uses the Google Cloud Speech-to-Text API to generate the text data. The input is voice data, and the output is the corresponding text data.

[0822] Step 3:

[0823] The server receives text data and passes it to the sentiment analysis engine. IBM Watson's natural language understanding service is used to analyze the user's emotions and needs. The input is text data, and the output is the analysis result regarding the emotional state.

[0824] Step 4:

[0825] Based on the analysis results, the server uses Amazon DynamoDB to search for the most suitable information resources within the database. The input is the analysis results regarding the user's emotional state, and the output is a list of content appropriate to the user's emotions.

[0826] Step 5:

[0827] The server transmits selected information resources to the terminal and presents them to the user. The user receives the content visually or audibly and makes appropriate selections. The input is a list of content, and the output is the specific content displayed to the user.

[0828] Step 6:

[0829] After the user has used the presented content, the device collects feedback information. This collected feedback information is sent to the server for system evaluation and improvement. The input is user feedback, and the output is data used for subsequent algorithm improvements.

[0830] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0831] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0833] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0834] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0835] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0836] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0837] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0838] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0839] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0840] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0841] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0842] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0843] 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.

[0844] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0845] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0846] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0847] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0848] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0849] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0850] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A means of providing an interface that accepts input from the user,

[0854] A method using generative models that analyze user input data to identify emotions and needs,

[0855] A means of searching and presenting the most suitable experts and communities from a database based on the analysis results,

[0856] A means of displaying search results to the user and performing the selected action,

[0857] A means of collecting feedback from users after they have used the service,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which encrypts and transmits data in order to improve the security of user input data.

[0861] (Claim 3)

[0862] The system according to claim 1, which continuously learns and improves its expert and community matching algorithm based on user analysis results.

[0863] "Example 1"

[0864] (Claim 1)

[0865] A means for providing a communication device that accepts input from a user,

[0866] A method using generative models that analyze user input data to identify emotions and requests,

[0867] A means for searching for and presenting the most suitable expert or collaborating organization from storage based on the analysis results,

[0868] A means of displaying search results to the user and performing the selected action,

[0869] A means of collecting user feedback after using the service,

[0870] A means of enhancing information protection by encrypting user input data during transmission,

[0871] A means of continuously learning and adapting the matching algorithm for experts or collaborating organizations based on user evaluations,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which encrypts information during communication in order to improve the security of user input data.

[0875] (Claim 3)

[0876] The system according to claim 1, which continuously learns and improves a matching algorithm for experts or collaborating organizations based on the user's analysis results.

[0877] "Application Example 1"

[0878] (Claim 1)

[0879] A means of providing a terminal that accepts input from users,

[0880] A method using a generative model that analyzes user input strings to identify emotions and needs,

[0881] A means of searching for and presenting the most suitable experts and support groups from a knowledge base based on the analysis results,

[0882] A means of presenting search results to the user and performing the selected action,

[0883] A means of collecting feedback from users after they have used the service,

[0884] A means to train and improve a generative model and presentation algorithm based on the collected opinions,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, which encrypts and transmits information in order to improve the security of user input strings.

[0888] (Claim 3)

[0889] The system according to claim 1, which continuously learns and improves the generative model and presentation algorithm based on the user's analysis results.

[0890] "Example 2 of combining an emotion engine"

[0891] (Claim 1)

[0892] A means of providing a terminal that accepts natural language input from users,

[0893] A method using a generative AI model and emotion engine that analyzes user input data to identify emotional states and needs,

[0894] A means of searching and presenting the most suitable support measures and information from a database based on the analysis results,

[0895] A means of visually presenting search results to the user and executing the next action when an option is selected,

[0896] A means of systematically collecting user feedback regarding service usage,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, which encrypts and transmits user input data to make it easier to keep the data confidential.

[0900] (Claim 3)

[0901] The system according to claim 1, which continuously learns and improves the matching algorithm for support measures and information sources based on the user's analysis results.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] A means of providing information that accepts input from a user, and a means of converting the user's input into speech or text,

[0905] A means of using an emotion analysis engine that analyzes user input data to identify emotions and needs,

[0906] A means of providing content that matches the user's emotional state by searching for and presenting the most suitable information resources from a database based on the analysis results,

[0907] A means of displaying search results to the user and performing the selected action,

[0908] Collecting feedback information after service provision is a means to contribute to the optimization of the entire system,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which encrypts and transfers data in order to guarantee the security of user information.

[0912] (Claim 3)

[0913] The system according to claim 1, which continuously learns and improves an information resource recommendation algorithm based on user sentiment analysis results and feedback. [Explanation of Symbols]

[0914] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface that accepts input from the user, A method using generative models that analyze user input data to identify emotions and needs, A means of searching and presenting the most suitable experts and communities from a database based on the analysis results, A means of displaying search results to the user and performing the selected action, A means of collecting feedback from users after they have used the service, A system that includes this.

2. The system according to claim 1, which encrypts and transmits data in order to improve the security of user input data.

3. The system according to claim 1, which continuously learns and improves its expert and community matching algorithm based on user analysis results.

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