system

Abstract

We provide the system. [Solution] A means of managing user authentication information anonymously, A means of classifying and analyzing the sentiment of consultation content using a generative model that analyzes received natural language input data, Means for providing appropriate advice or links to external resources based on the analysis results, We provide options for contacting experts based on the nature of the consultation, and a means of notifying experts with the user's consent. A means of collecting feedback from users after the consultation and using it to improve the generative model, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern workplace environment, employees have various stresses and worries, but there is a problem that there are insufficient places and opportunities for them to consult. Furthermore, due to the lack of guarantee of anonymity when consulting and the difficulty of connecting with appropriate experts, employees cannot receive sufficient support. For this reason, there is a problem that the productivity of the company and the happiness of employees cannot be improved.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a counseling platform that employees can access anonymously. Specifically, it builds a system that analyzes the received consultation content using natural language processing while maintaining user anonymity, and provides appropriate advice and resources. Furthermore, it provides users with options to connect with experts as needed, and improves the generative model based on continuous feedback to achieve more accurate consultation. As a result, an environment is created where employees can safely discuss stress and worries, making it possible to improve the productivity and well-being of the entire company.

[0006] "User authentication information" refers to identification information used to identify a user when accessing the system and to properly provide system functions.

[0007] "Methods for anonymous management" refer to mechanisms that allow users to use the system without disclosing their personal information.

[0008] "Natural language input data" refers to textual information expressed in everyday language that people normally use.

[0009] A "generative model" is a pre-trained mathematical model used to generate a specific output from data.

[0010] "Means for classifying and emotionally analyzing consultation content" refers to technology that analyzes text input by users, categorizes its content, and determines the emotions contained within it.

[0011] "Means of providing advice or links to external resources" refers to a mechanism that provides users with advice based on analysis results or guides them to external information they should refer to.

[0012] "Providing options for contacting experts and means of notifying experts" refers to options and notification functions that allow users to contact experts if they wish to receive further professional support regarding the content of their consultation.

[0013] "Means for collecting feedback and using it to improve generative models" refers to a system that incorporates evaluations and opinions provided by users and uses them to improve the performance of the model. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides an AI counseling platform where employees can anonymously discuss their stress and worries. This platform is available 24 / 7 and provides users with fast and flexible support.

[0036] First, the user accesses the platform using their device and logs in anonymously. During this process, the server manages the session by generating a temporary ID while protecting the user's anonymity.

[0037] Next, the user inputs their worries and stress into the device in natural language. This data is sent from the device to the server, where it is analyzed using a generative model. The generative model uses natural language processing techniques to analyze the input data and identify the type of emotion and the category of the problem. Based on this analysis, the server provides the user with appropriate advice and links to relevant resources via the device.

[0038] Furthermore, depending on the nature of the consultation, if the user so desires, the server will provide means of contacting specialists such as industrial physicians, in-house counselors, and health staff. If the user agrees, the server will notify the specialist so that they can provide the necessary support.

[0039] After the consultation ends, the user provides feedback through their device. The server receives this feedback, records it in a database, and uses it to improve the performance of the generative model.

[0040] For example, if a user enters a concern such as "I feel a lot of pressure at work and am constantly anxious," the server analyzes this and provides advice on mental health and stress management techniques to the user's device. At the same time, if the server determines it is necessary, it will display an option to contact an industrial physician and open a link to further action if the issue requires more attention.

[0041] This makes it possible for this system to provide mental support in the workplace efficiently and effectively.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user accesses the platform using their device and selects the option to log in anonymously. The server receives the access request, generates a temporary ID to ensure the user's anonymity, and initiates a user session.

[0045] Step 2:

[0046] The user inputs and sends their stress and worries in natural language through their device. The device sends the input text data to the server. The server receives the data and prepares it for the next analysis process.

[0047] Step 3:

[0048] The server uses a generative model to analyze the received text data. The AI ​​model employs natural language processing techniques to identify the emotions, stressors, and categories of the input content. The analysis results are then generated.

[0049] Step 4:

[0050] Based on the analysis results, the server selects appropriate advice and resources for the user. This advice and resources may include stress management techniques and relevant external links.

[0051] Step 5:

[0052] The server sends the selected advice and resources back to the user's terminal. The terminal displays this information to the user. The user reviews the information presented and decides on further actions if necessary.

[0053] Step 6:

[0054] Depending on the nature of the consultation, if the user desires further professional support, the server will provide the option to contact a specialist such as an industrial physician, in-house counselor, or health staff. If the user selects this option and agrees, the server will notify the designated specialist of the necessary information.

[0055] Step 7:

[0056] Users can input feedback on the advice and resources provided via their device. The device sends this feedback to the server. The server records the received feedback in a database and uses it to improve the generative model in the future.

[0057] (Example 1)

[0058] 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."

[0059] In modern times, the number of workers experiencing psychological stress and anxieties in the workplace is increasing, and there is a need for an efficient counseling system that can address these problems quickly and anonymously. However, current systems have challenges in maintaining anonymity, providing appropriate advice for individual consultations, and continuously improving the quality of services.

[0060] 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.

[0061] In this invention, the server includes means for anonymizing and managing user identification information, means for classifying and sentimentally analyzing the content of the problem using a generative information processing model that analyzes received natural-form data, and means for providing appropriate advice or links to external information sources based on the analysis results. This makes it possible to provide appropriate advice tailored to individual consultations while maintaining anonymity, and to continuously improve the service.

[0062] "User identification information" is a general term for data used to identify an individual, and in this invention, it is managed while maintaining anonymity.

[0063] "Anonymization" is the process of preventing the identification of an individual by deleting or transforming information that could identify an individual.

[0064] "Natural form data" refers to information written in human natural language, before it is converted into a format that is easily interpreted by machines.

[0065] A "generative information processing model" is a machine learning model that includes algorithms for generating and analyzing information based on input data.

[0066] "Classification" refers to the process of grouping data based on specific characteristics or criteria.

[0067] "Sentiment analysis" is the process of extracting and analyzing emotional characteristics from text data that are relevant to its context.

[0068] "Advice" refers to professional or practical recommendations or instructions given regarding a specific problem or situation.

[0069] "External information sources" refer to resources and data outside the server that users can access.

[0070] "Contact information" refers to information necessary to establish communication with a specific individual or organization, and includes things like phone numbers and email addresses.

[0071] A "professional" refers to a person who possesses specialized knowledge and skills in a particular field and is qualified to provide appropriate support.

[0072] "Opinion data" refers to feedback and evaluation data collected from users, which can be used to improve the service.

[0073] This invention provides an AI counseling platform that offers anonymous and efficient psychological support within a company. Users first access the platform using a terminal. At this time, users can log in anonymously. When the server recognizes the user's login, it generates temporary identification information to ensure the user's anonymity and manages the session.

[0074] Users input data about their worries and stress in natural language form through the terminal's input interface. This data is sent to a server via the terminal. The server processes the received data using a generative AI model. This model employs a generative information processing model using natural language processing technology, and utilizes open-source libraries such as TENSORFLOW® and PyTorch.

[0075] The generating AI model analyzes the input data, classifies the consultation content, and analyzes the emotions. Based on this information, the server provides the user with advice and links to relevant external information sources via the terminal. Furthermore, if necessary, the server can provide the user with contact information for professionals such as workplace doctors, internal counselors, and health management staff.

[0076] After a user concludes a consultation, they can provide feedback via their device. This feedback data is collected by the server and used as feedback to the generated AI model to further improve the service, thereby enhancing the accuracy of the analysis.

[0077] As a concrete example, consider a case where a user enters a statement such as, "I feel a lot of pressure at work and am constantly anxious." The server analyzes this data and provides helpful advice regarding mental health, as well as offering an option to contact a workplace doctor.

[0078] An example of a prompt message is, "Generate advice to provide to a user who is experiencing stress at work."

[0079] This system will provide users with an environment where they can quickly and flexibly address their problems and receive support with peace of mind.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user accesses the AI ​​counseling platform through their device and logs in anonymously. The device sends this login request to the server. The server receives the login request, generates a unique temporary identifier for the user, and manages the session. This temporary identifier is valid only during the session and serves to maintain the user's anonymity.

[0083] Step 2:

[0084] Users input messages about their worries and stress into their devices as natural-form data. The user's input is recorded on the device as text data and sent directly to the server. The server prepares the received text data for processing, specifically formatting and pre-processing the information while maintaining privacy.

[0085] Step 3:

[0086] The server inputs pre-processed text data into a generating AI model. This model uses natural language processing techniques to analyze patterns in the input data and perform sentiment analysis and problem classification. Specifically, it performs word vectorization and sentiment score calculation, structuring the data. This clearly identifies the category of the consultation content and the emotional state.

[0087] Step 4:

[0088] The server generates appropriate advice and links to external resources for the user based on the analysis results output from the generated AI model. Personalized advice is then generated and sent to the user's device, which displays it to the user. This allows the user to quickly receive information and support tailored to their needs.

[0089] Step 5:

[0090] If a user desires further assistance, they can make a selection on their device. The device then transmits this selection to the server. The server provides a means of contacting a specialist based on the user's request and sends real-time notifications as needed. In this process, the contact information of the selected specialist is automatically selected.

[0091] Step 6:

[0092] After the consultation ends, the user provides feedback through their device. The device sends this feedback information to the server. The server stores the collected feedback in a database and uses it to improve the generated AI model. This provides a mechanism for continuously improving the quality of the service.

[0093] (Application Example 1)

[0094] 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."

[0095] Maintaining personal mental health is crucial in modern society, but opportunities to interact with and receive support from professionals are limited. Therefore, there is a need for a system that provides easily accessible mental health support in daily life. Furthermore, such a system must give due consideration to protecting personal information and ensuring anonymity.

[0096] 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.

[0097] This invention includes a server that includes means for anonymously managing user authentication information, means for classifying and sentimentally analyzing the content of the consultation using a generative model that analyzes the received natural language input data, means for providing appropriate advice or links to external resources based on the analysis results, and means for being incorporated into a home machine as an applied service to provide psychological support through natural dialogue. This makes it possible for users to easily consult about their daily stresses and worries and to connect with professional support as needed.

[0098] "Methods for anonymously managing user authentication information" refers to technologies that allow users to perform necessary authentication while concealing their personal information when using a system.

[0099] A "generative model for analyzing received natural language input data" is an artificial intelligence technology used to analyze natural language input from users, understand its content, and classify it.

[0100] "Means for classifying and emotionally analyzing consultation content" refers to the process of classifying the input consultation into specific categories and evaluating the emotional state.

[0101] "Means of providing appropriate advice or links to external resources based on analysis results" refers to technologies that provide users with access to the most useful information and resources based on the analyzed information.

[0102] "A means of providing psychological support through natural dialogue, integrated into home appliances as an applied service" refers to a process of integrating a system into appliances used in the home and providing mental health support through natural conversations with the user.

[0103] The system realizing this invention provides psychological support to users using smart devices in the home. The system anonymously manages user authentication information and receives input data in natural language format. A generative AI model analyzes the user's input, classifies its content, and analyzes emotions. Based on this analysis, the server generates appropriate advice and provides links to external resources as needed. Furthermore, through applied services, the home device can provide psychological support through natural dialogue with the user.

[0104] The server uses input feedback data to improve the performance of its generative model. This allows it to continuously learn from user feedback and improve analysis accuracy. When a user seeks advice, the server provides the most appropriate advice through sentiment analysis and can connect the user with experts if necessary.

[0105] For example, if a user says to a smart device in their home, "I feel like I've been tired from work lately," the generative AI model can analyze this information and provide advice on how to change their mood or other relevant information. Another example of a prompt would be, "Write code that, when a user says 'I feel like I've been tired from work lately,' analyzes the emotion and category and provides appropriate advice." In this way, the invention is designed to support the mental well-being of users.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user inputs their stress and worries in natural language into the device. The device receives this input and sends it to the server. This input data is in text format and includes the user's intentions and emotions.

[0109] Step 2:

[0110] The server passes the received input data to the generating AI model and begins analysis. At this stage, the server performs text analysis on the input data to identify the type of emotion and the content of the consultation. As part of the data processing, natural language processing techniques are used to classify categories from the input text, extract emotions, and generate output.

[0111] Step 3:

[0112] Based on the analysis results, the server generates the most appropriate advice in text format. This advice can be retrieved from a pre-prepared database or dynamically generated by AI. The output data consists of advice tailored to the user's specific concerns.

[0113] Step 4:

[0114] The server sends the user the generated advice, along with links to external resources and options to contact experts, if necessary. The user can review this information on screen and choose whether to take further action. The result of their choice is then sent back to the server.

[0115] Step 5:

[0116] After the consultation ends, the device receives feedback from the user. This feedback data is sent to the server and recorded to improve the performance of the generated AI model. The feedback is used as input data to train the model, contributing to improved analysis accuracy.

[0117] Throughout each step, the server and terminal can work together to effectively provide individualized psychological support to the user.

[0118] 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.

[0119] This invention combines an emotion engine with a 24 / 7 AI counseling platform where employees can anonymously discuss their mental health issues, thereby more accurately analyzing users' emotions and providing appropriate advice and support. The emotion engine identifies emotions contained in the user's natural language input and analyzes their intensity and type.

[0120] Users can access the platform using their devices and log in anonymously. After logging in, users input their stress and worries in natural language. The device sends this input data to the server. Upon receiving the data, the server uses an emotion engine to analyze the emotions contained in the input text. This analysis identifies the intensity and type of the user's emotions, classifying them into categories such as "anxiety," "anger," and "sadness."

[0121] Based on the results of the emotion analysis, the server selects the most appropriate advice and information resources for the user. Customized advice tailored to the user's emotions is provided through the terminal. Furthermore, connections to specialists are optimized based on the emotional pattern as needed. For example, if strong negative emotions are detected, options to contact an industrial physician or a dedicated counselor are promptly presented.

[0122] If a user requests additional support, the server will notify the selected expert of the necessary information, ensuring that the user receives professional assistance smoothly. After the consultation concludes, the user can provide feedback through their device, which is collected by the server and used to improve the emotion engine and generative model.

[0123] For example, if a user enters "I'm overwhelmed with work and feeling mentally exhausted," the emotion engine recognizes the "anxiety" and "stress" contained in this statement, and the server provides appropriate stress management advice. Furthermore, by promptly contacting specialists as needed, the system can provide the user with optimal support. In this way, the system can comprehensively support the mental health of employees and contribute to improving overall company productivity.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses their device to access the counseling platform and selects anonymous login. The device sends the login information to the server. The server generates a temporary ID and manages the session to protect the user's anonymity.

[0127] Step 2:

[0128] Users input their stress and worries in natural language via their device. This input data is sent to the server by the device when the user clicks the "Send" button.

[0129] Step 3:

[0130] The server passes the received input data to the emotion engine, which analyzes the emotions contained in the user's text data. The emotion engine uses natural language processing to identify the intensity and type of emotion in the text (e.g., "anxiety," "sadness," "anger," etc.).

[0131] Step 4:

[0132] The server inputs the results of the emotion analysis into a generative model to determine the most appropriate advice and resources for the user. In this process, specially customized advice is selected based on the type and intensity of the emotion.

[0133] Step 5:

[0134] The server sends the selected advice and information to the user's terminal. The terminal displays this information to the user, providing visual or auditory feedback.

[0135] Step 6:

[0136] Depending on the nature of the inquiry, the server will determine if the user requires additional support and, if necessary, offer options to contact an industrial physician or in-house counselor. If the user selects this option, the server will notify the specialist of the relevant information.

[0137] Step 7:

[0138] After a user finishes a consultation, they input feedback on the quality of their consultation and the advice they received into their device. The device then sends this feedback to the server. The server stores this feedback information and uses it to improve the emotion engine and generative model.

[0139] This process allows the system to respond quickly and accurately to users' mental health issues.

[0140] (Example 2)

[0141] 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".

[0142] In today's work environment, employees often experience a great deal of stress and mental health issues, yet there is a lack of resources to address these concerns. To address this problem, a system is needed that allows employees to seek advice anonymously and safely, and receive prompt and accurate support. However, traditional methods have made it difficult to properly categorize the content of consultations and facilitate smooth communication with specialists.

[0143] 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.

[0144] In this invention, the server includes means for protecting the user's identification information, means for performing emotion identification and intensity analysis using an emotion engine and generative model that analyzes received natural language input, and means for presenting the user with customized advice or information based on the analyzed emotion data. This enables the user to receive appropriate emotional support while protecting their privacy.

[0145] "User identification information" refers to basic information used to identify an individual when a user accesses the system, but this system employs special processing to ensure anonymity.

[0146] "Natural language input" refers to text data in the language format that humans normally use in conversation and documents, and is data that embodies the user's thoughts and emotions.

[0147] An "emotion engine" is a software module that identifies emotions from natural language input and analyzes their intensity and type.

[0148] A "generative model" refers to an algorithm used to generate new data or create responses based on input data, based on machine learning.

[0149] "Customized advice" refers to advice and suggestions that are individually generated to suit the user's specific emotional state, providing personalized support.

[0150] The "Contact Experts Option" is a feature that allows users to easily contact experts for direct consultation as needed.

[0151] "Feedback information" refers to opinions such as impressions and suggestions for improvement that users provide after using the system, and is data used to improve the system.

[0152] This invention provides an AI counseling system that allows employees to anonymously seek advice on mental health issues. The system includes a terminal, a server, and sentiment analysis software. Users first access the platform through the terminal and log in anonymously. This ensures that the user's identification information is securely protected and their privacy is ensured.

[0153] Users input their emotions or problems into the device using natural language input. The device sends this input to the server. The input data received by the server is analyzed by the emotion engine. The emotion engine uses natural language processing techniques and generative AI models to identify emotions in the input text and analyze their intensity and type. This process is performed based on machine learning algorithms.

[0154] The server generates appropriate customized advice based on the analysis results. This process uses a generative AI model to create prompts that provide the most useful information for the user. For example, if a user inputs "I'm overwhelmed with work and feeling mentally exhausted," the system can sense "anxiety" and "stress" and suggest stress management techniques.

[0155] Furthermore, if strong negative emotions are detected, the user will be presented with an option to quickly contact a professional. After obtaining the user's consent, the server will automate the process of contacting the appropriate professional.

[0156] After the user finishes their consultation, they can provide feedback via their device. This feedback is collected on the server and used to continuously improve the emotion engine and generative model. An example of a prompt might be: "Identify the user's emotional pattern and decide whether to contact an occupational physician if necessary: ​​'I can't take it anymore'."

[0157] In this way, the system can provide comprehensive mental health support to users.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The user accesses the AI ​​counseling system using a terminal and performs an anonymous login. The terminal then performs a procedure to anonymize the user's identification information. The input is the user's login information. The output is anonymized user session data.

[0161] Step 2:

[0162] The user inputs their concerns and feelings using natural language on their device. For example, they might input text such as "I'm having trouble with work stress." The device then sends this input data to the server. In this context, input is the text input by the user, and output is the data sent to the server.

[0163] Step 3:

[0164] The server analyzes the received natural language input using an emotion engine. In this process, the text data is classified by the emotion engine based on emotion categories (e.g., "anxiety," "anger") and their intensity. The input is natural language data, and the output is the result of the emotion category and intensity analysis.

[0165] Step 4:

[0166] The server uses a generative AI model based on the sentiment analysis results to generate appropriate advice for the user. A prompt is used to instruct the generative AI model to "generate advice based on the sentiment analysis results." The input is the sentiment analysis results, and the output is the generated advice.

[0167] Step 5:

[0168] The terminal displays customized advice from the server to the user. The user then reads the presented advice. The input is the advice data from the server, and the output is the displayed advice to the user.

[0169] Step 6:

[0170] The server provides the user with options to contact specialists as needed. If strong negative emotions are detected in the emotion analysis, the user will be presented with options to contact an industrial physician or counselor. The input is a specific condition from the emotion analysis results, and the output is the presentation of contact options.

[0171] Step 7:

[0172] Once a user finishes a consultation, they can enter feedback through their device. This feedback is sent to the server and used for future system improvements. The input is the user's feedback, and the output is the storage of the feedback data on the server.

[0173] (Application Example 2)

[0174] 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".

[0175] In modern society, individual mental health is a crucial issue, and emotional support within the family is particularly needed. Furthermore, there is a lack of systems that provide immediate mental support amidst busy daily life. Therefore, it is necessary to provide a system where individuals can more safely discuss their mental health within their families and receive support from professionals when needed.

[0176] 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.

[0177] In this invention, the server includes means for classifying and sentimentally analyzing the content of a consultation using a sentiment analysis engine that analyzes the received natural language input data, means for presenting appropriate advice or support information based on the results of the sentiment analysis, and means for providing real-time feedback to the user using a robotic device. This makes it possible for individuals to consult anonymously within their homes and receive appropriate advice immediately.

[0178] "User authentication information" refers to information used to identify an individual, and in this system, it is managed anonymously.

[0179] "Natural language input data" refers to data composed of human language used by users during dialogue or consultation.

[0180] An "emotion analysis engine" is used to analyze emotions from input natural language data and classify their intensity and type.

[0181] "Advice or support information" refers to resources of advice and information provided to the user based on the results of sentiment analysis.

[0182] A "robot device" is an electronic device used in the home to provide feedback and emotional support to the user.

[0183] "Feedback" refers to data collected from users, including their opinions and impressions, which is used to improve systems and generative models.

[0184] "External experts" are mental health professionals who are partnered with users to provide additional support.

[0185] One possible embodiment of this invention is a home-based emotional support system. The user engages in natural language-based consultation through a robotic device. The device is equipped with a microphone, speaker, and touchscreen display. This device senses the user's input and converts it into text using a speech recognition engine.

[0186] The text data acquired by the device is sent to the server. The server is equipped with an emotion analysis engine, which analyzes the received data to identify the user's emotions. This analysis utilizes a generative AI model built using TensorFlow. The emotion analysis engine classifies the type and intensity of emotions from the input data and selects and generates appropriate advice and support information as needed.

[0187] Based on the analysis results, the server provides generated advice to the user through a robotic device. For example, if the user inputs "I've been very tired lately and lack motivation," the server analyzes this data and suggests ways to refresh the user. Additionally, if necessary, the server may offer further options such as "Would you consider consulting a specialist?", ensuring the user receives the necessary professional support.

[0188] This system also includes a feedback function, allowing it to collect feedback from users after a consultation. This feedback is used to improve the generative model, which is continuously learned on the server.

[0189] An example of a prompt message might be: "Sentiment analysis: The user said, 'I'm overwhelmed with work and feeling mentally exhausted.' Please provide the best advice." In this way, it is possible to provide an environment where users can feel safe discussing their mental health, even within the home.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The user speaks their request in natural language to the robot device. The device acquires this voice input via its microphone and converts it into text data using a speech recognition engine (e.g., Google® Speech-to-Text API). At this stage, the input is the user's voice, and the output is the converted text data.

[0193] Step 2:

[0194] The device sends the generated text data to the server. The server inputs the received text into the sentiment analysis engine. The sentiment analysis engine uses a generative AI model (using TensorFlow) to analyze the data and identify the user's emotions. Specifically, based on the analysis results, the type of emotion (e.g., "anxiety" or "fatigue") and its intensity are evaluated. The input is text data, and the output is the analyzed type and intensity of emotion.

[0195] Step 3:

[0196] The server selects appropriate advice or support information based on the sentiment analysis results. Using a generative AI model, it generates optimal advice using a prompt (e.g., "Please provide the best advice for this situation."). At this stage, sentiment data is the input, and the output is specific advice or suggestions.

[0197] Step 4:

[0198] The server sends the generated advice back to the robot device, and the terminal communicates that feedback to the user via voice or display. Here, the advice, which is the output from the server, is the input, and the feedback to the user is the output. This process allows the user to receive support in real time.

[0199] Step 5:

[0200] Based on the feedback provided by the user, additional options are selected. For example, if the option to contact an expert is presented, and the user selects it, the server automatically contacts the appropriate expert. The input is the user's selection, and the output is the expert contact information. This step enables the provision of expert support.

[0201] Step 6:

[0202] After the consultation ends, the user provides feedback through their device. The server collects this feedback and uses it to improve the system's sentiment analysis engine and generative model. The input to this final step is the user's feedback, and the output is an updated dataset for system improvement.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention provides an AI counseling platform where employees can anonymously discuss their stress and worries. This platform is available 24 / 7 and provides users with fast and flexible support.

[0220] First, the user accesses the platform using their device and logs in anonymously. During this process, the server manages the session by generating a temporary ID while protecting the user's anonymity.

[0221] Next, the user inputs their worries and stress into the device in natural language. This data is sent from the device to the server, where it is analyzed using a generative model. The generative model uses natural language processing techniques to analyze the input data and identify the type of emotion and the category of the problem. Based on this analysis, the server provides the user with appropriate advice and links to relevant resources via the device.

[0222] Furthermore, depending on the nature of the consultation, if the user so desires, the server will provide means of contacting specialists such as industrial physicians, in-house counselors, and health staff. If the user agrees, the server will notify the specialist so that they can provide the necessary support.

[0223] After the consultation ends, the user provides feedback through their device. The server receives this feedback, records it in a database, and uses it to improve the performance of the generative model.

[0224] For example, if a user enters a concern such as "I feel a lot of pressure at work and am constantly anxious," the server analyzes this and provides advice on mental health and stress management techniques to the user's device. At the same time, if the server determines it is necessary, it will display an option to contact an industrial physician and open a link to further action if the issue requires more attention.

[0225] This makes it possible for this system to provide mental support in the workplace efficiently and effectively.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user accesses the platform using their device and selects the option to log in anonymously. The server receives the access request, generates a temporary ID to ensure the user's anonymity, and initiates a user session.

[0229] Step 2:

[0230] The user inputs and sends their stress and worries in natural language through their device. The device sends the input text data to the server. The server receives the data and prepares it for the next analysis process.

[0231] Step 3:

[0232] The server uses a generative model to analyze the received text data. The AI ​​model employs natural language processing techniques to identify the emotions, stressors, and categories of the input content. The analysis results are then generated.

[0233] Step 4:

[0234] Based on the analysis results, the server selects appropriate advice and resources for the user. This advice and resources may include stress management techniques and relevant external links.

[0235] Step 5:

[0236] The server sends the selected advice and resources back to the user's terminal. The terminal displays this information to the user. The user reviews the information presented and decides on further actions if necessary.

[0237] Step 6:

[0238] Depending on the nature of the consultation, if the user desires further professional support, the server will provide the option to contact a specialist such as an industrial physician, in-house counselor, or health staff. If the user selects this option and agrees, the server will notify the designated specialist of the necessary information.

[0239] Step 7:

[0240] Users can input feedback on the advice and resources provided via their device. The device sends this feedback to the server. The server records the received feedback in a database and uses it to improve the generative model in the future.

[0241] (Example 1)

[0242] 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."

[0243] In modern times, the number of workers experiencing psychological stress and anxieties in the workplace is increasing, and there is a need for an efficient counseling system that can address these problems quickly and anonymously. However, current systems have challenges in maintaining anonymity, providing appropriate advice for individual consultations, and continuously improving the quality of services.

[0244] 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.

[0245] In this invention, the server includes means for anonymizing and managing user identification information, means for classifying and sentimentally analyzing the content of the problem using a generative information processing model that analyzes received natural-form data, and means for providing appropriate advice or links to external information sources based on the analysis results. This makes it possible to provide appropriate advice tailored to individual consultations while maintaining anonymity, and to continuously improve the service.

[0246] "User identification information" is a general term for data used to identify an individual, and in this invention, it is managed while maintaining anonymity.

[0247] "Anonymization" is the process of preventing the identification of an individual by deleting or transforming information that could identify an individual.

[0248] "Natural form data" refers to information written in human natural language, before it is converted into a format that is easily interpreted by machines.

[0249] A "generative information processing model" is a machine learning model that includes algorithms for generating and analyzing information based on input data.

[0250] "Classification" refers to the process of grouping data based on specific characteristics or criteria.

[0251] "Sentiment analysis" is the process of extracting and analyzing emotional characteristics from text data that are relevant to its context.

[0252] "Advice" refers to professional or practical recommendations or instructions given regarding a specific problem or situation.

[0253] "External information sources" refer to resources and data outside the server that users can access.

[0254] "Contact information" refers to information necessary to establish communication with a specific individual or organization, and includes things like phone numbers and email addresses.

[0255] A "professional" refers to a person who possesses specialized knowledge and skills in a particular field and is qualified to provide appropriate support.

[0256] "Opinion data" refers to feedback and evaluation data collected from users, which can be used to improve the service.

[0257] This invention provides an AI counseling platform that offers anonymous and efficient psychological support within a company. Users first access the platform using a terminal. At this time, users can log in anonymously. When the server recognizes the user's login, it generates temporary identification information to ensure the user's anonymity and manages the session.

[0258] Users input data about their worries and stresses in natural language form through the terminal's input interface. This data is sent to a server via the terminal. The server processes the received data using a generative AI model. This model employs a generative information processing model using natural language processing technology, and utilizes open-source libraries such as TensorFlow and PyTorch.

[0259] The generating AI model analyzes the input data, classifies the consultation content, and analyzes the emotions. Based on this information, the server provides the user with advice and links to relevant external information sources via the terminal. Furthermore, if necessary, the server can provide the user with contact information for professionals such as workplace doctors, internal counselors, and health management staff.

[0260] After a user concludes a consultation, they can provide feedback via their device. This feedback data is collected by the server and used as feedback to the generated AI model to further improve the service, thereby enhancing the accuracy of the analysis.

[0261] As a concrete example, consider a case where a user enters a statement such as, "I feel a lot of pressure at work and am constantly anxious." The server analyzes this data and provides helpful advice regarding mental health, as well as offering an option to contact a workplace doctor.

[0262] An example of a prompt message is, "Generate advice to provide to a user who is experiencing stress at work."

[0263] This system will enable users to approach their problems quickly and flexibly, and provide an environment where they can receive support with peace of mind.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user accesses the AI ​​counseling platform through their device and logs in anonymously. The device sends this login request to the server. The server receives the login request, generates a unique temporary identifier for the user, and manages the session. This temporary identifier is valid only during the session and serves to maintain the user's anonymity.

[0267] Step 2:

[0268] Users input messages about their worries and stress into their devices as natural-form data. The user's input is recorded on the device as text data and sent directly to the server. The server prepares the received text data for processing, specifically formatting and pre-processing the information while maintaining privacy.

[0269] Step 3:

[0270] The server inputs pre-processed text data into a generating AI model. This model uses natural language processing techniques to analyze patterns in the input data and perform sentiment analysis and problem classification. Specifically, it performs word vectorization and sentiment score calculation, structuring the data. This clearly identifies the category of the consultation content and the emotional state.

[0271] Step 4:

[0272] The server generates appropriate advice and links to external resources for the user based on the analysis results output from the generated AI model. Personalized advice is then generated and sent to the user's device, which displays it to the user. This allows the user to quickly receive information and support tailored to their needs.

[0273] Step 5:

[0274] If a user desires further assistance, they can make a selection on their device. The device then transmits this selection to the server. The server provides a means of contacting a specialist based on the user's request and sends real-time notifications as needed. In this process, the contact information of the selected specialist is automatically selected.

[0275] Step 6:

[0276] After the consultation ends, the user provides feedback through their device. The device sends this feedback information to the server. The server stores the collected feedback in a database and uses it to improve the generated AI model. This provides a mechanism for continuously improving the quality of the service.

[0277] (Application Example 1)

[0278] 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."

[0279] Maintaining personal mental health is crucial in modern society, but opportunities to interact with and receive support from professionals are limited. Therefore, there is a need for a system that provides easily accessible mental health support in daily life. Furthermore, such a system must give due consideration to protecting personal information and ensuring anonymity.

[0280] 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.

[0281] In this invention, the server includes means for anonymously managing the user's authentication information, means for classifying and sentiment - analyzing the consultation content using a generative model that analyzes the received input data in natural - language form, means for providing appropriate advice or links to external resources based on the analysis results, and means for being incorporated into household machines as an application service and providing psychological support through natural conversation. Thereby, it becomes possible for users to easily consult about their daily stress and worries and lead to professional support if necessary.

[0282] The means for anonymously managing the user's authentication information is a technology for performing necessary authentication while hiding personal information when the user uses the system.

[0283] The generative model for analyzing the received input data in natural - language form is an artificial - intelligence technology used to analyze the natural - language input from the user, understand its content, and classify it.

[0284] The means for classifying and sentiment - analyzing the consultation content is a process of classifying the input consultation into specific categories and evaluating the emotional state.

[0285] The means for providing appropriate advice or links to external resources based on the analysis results is a technology for providing access to the most useful information and resources for the user based on the analyzed information.

[0286] The means for being incorporated into household machines as an application service and providing psychological support through natural conversation is a process of incorporating the system into machines used within the home and providing mental - health support through natural conversation with the user.

[0287] The system realizing this invention provides psychological support to users using smart devices in the home. The system anonymously manages user authentication information and receives input data in natural language format. A generative AI model analyzes the user's input, classifies its content, and analyzes emotions. Based on this analysis, the server generates appropriate advice and provides links to external resources as needed. Furthermore, through applied services, the home device can provide psychological support through natural dialogue with the user.

[0288] The server uses input feedback data to improve the performance of its generative model. This allows it to continuously learn from user feedback and improve analysis accuracy. When a user seeks advice, the server provides the most appropriate advice through sentiment analysis and can connect the user with experts if necessary.

[0289] For example, if a user says to a smart device in their home, "I feel like I've been tired from work lately," the generative AI model can analyze this information and provide advice on how to change their mood or other relevant information. Another example of a prompt would be, "Write code that, when a user says 'I feel like I've been tired from work lately,' analyzes the emotion and category and provides appropriate advice." In this way, the invention is designed to support the mental well-being of users.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The user inputs their stress and worries in natural language into the device. The device receives this input and sends it to the server. This input data is in text format and includes the user's intentions and emotions.

[0293] Step 2:

[0294] The server passes the received input data to the generating AI model and begins analysis. At this stage, the server performs text analysis on the input data to identify the type of emotion and the content of the consultation. As part of the data processing, natural language processing techniques are used to classify categories from the input text, extract emotions, and generate output.

[0295] Step 3:

[0296] The server generates the most suitable advice in text format based on the analysis results. This advice can be retrieved from a pre-prepared database or dynamically generated by AI. The output data is advice tailored to the user's specific concerns.

[0297] Step 4:

[0298] The server sends the user the generated advice, along with links to external resources and options to contact experts, if necessary. The user can review this information on screen and choose whether to take further action. The result of the choice is then sent back to the server.

[0299] Step 5:

[0300] After the consultation ends, the device receives feedback from the user. This feedback data is sent to the server and recorded to improve the performance of the generated AI model. The feedback is used as input data to train the model, contributing to improved analysis accuracy.

[0301] Throughout each step, the server and terminal can work together to effectively provide individualized psychological support to the user.

[0302] 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.

[0303] This invention combines an emotion engine with a 24 / 7 AI counseling platform where employees can anonymously discuss their mental health issues, thereby more accurately analyzing users' emotions and providing appropriate advice and support. The emotion engine identifies emotions contained in the user's natural language input and analyzes their intensity and type.

[0304] Users can access the platform using their devices and log in anonymously. After logging in, users input their stress and worries in natural language. The device sends this input data to the server. Upon receiving the data, the server uses an emotion engine to analyze the emotions contained in the input text. This analysis identifies the intensity and type of the user's emotions, classifying them into categories such as "anxiety," "anger," and "sadness."

[0305] Based on the results of the emotion analysis, the server selects the most appropriate advice and information resources for the user. Customized advice tailored to the user's emotions is provided through the terminal. Furthermore, connections to specialists are optimized based on the emotional pattern as needed. For example, if strong negative emotions are detected, options to contact an industrial physician or a dedicated counselor are promptly presented.

[0306] If a user requests additional support, the server will notify the selected expert of the necessary information, ensuring that the user receives professional assistance smoothly. After the consultation concludes, the user can provide feedback through their device, which is collected by the server and used to improve the emotion engine and generative model.

[0307] For example, when a user inputs "I'm feeling mentally overwhelmed due to too much work", the emotion engine recognizes the "unease" and "stress" contained in this statement, and the server provides appropriate stress management advice in response. Also, by presenting a prompt to quickly contact an expert if necessary, optimal support can be provided to the user. As a result, the system can comprehensively support the mental health of employees and contribute to improving the productivity of the entire company.

[0308] The process flow will be described below.

[0309] Step 1:

[0310] The user uses the terminal to access the counseling platform and selects anonymous login. The terminal sends the login information to the server. To protect the user's anonymity, the server generates a temporary ID and manages the session.

[0311] Step 2:

[0312] The user inputs their stress and worries in natural language via the terminal. This input data is sent by the terminal to the server by clicking the "Send" button.

[0313] Step 3:

[0314] The server passes the received input data to the emotion engine and analyzes the emotions contained in the user's text data. The emotion engine uses natural language processing to identify the intensity and type of emotions in the text (e.g., "unease", "sadness", "anger", etc.).

[0315] Step 4:

[0316] The server inputs the result of the emotion analysis into the generation model to determine the advice and resources most suitable for the user. At this time, specially customized advice is selected based on the type and intensity of the emotion.

[0317] Step 5:

[0318] The server sends the selected advice and information to the user's terminal. The terminal displays this information to the user, providing visual or auditory feedback.

[0319] Step 6:

[0320] Depending on the nature of the inquiry, the server will determine if the user requires additional support and, if necessary, offer options to contact an industrial physician or in-house counselor. If the user selects this option, the server will notify the specialist of the relevant information.

[0321] Step 7:

[0322] After a user finishes a consultation, they input feedback on the quality of their consultation and the advice they received into their device. The device then sends this feedback to the server. The server stores this feedback information and uses it to improve the emotion engine and generative model.

[0323] This process allows the system to respond quickly and accurately to users' mental health issues.

[0324] (Example 2)

[0325] 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".

[0326] In today's work environment, employees often experience a great deal of stress and mental health issues, yet there is a lack of resources to address these concerns. To address this problem, a system is needed that allows employees to seek advice anonymously and safely, and receive prompt and accurate support. However, traditional methods have made it difficult to properly categorize the content of consultations and facilitate smooth communication with specialists.

[0327] 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.

[0328] In this invention, the server includes means for protecting the user's identification information, means for performing emotion identification and intensity analysis using an emotion engine and generative model that analyzes received natural language input, and means for presenting the user with customized advice or information based on the analyzed emotion data. This enables the user to receive appropriate emotional support while protecting their privacy.

[0329] "User identification information" refers to basic information used to identify an individual when a user accesses the system, but this system employs special processing to ensure anonymity.

[0330] "Natural language input" refers to text data in the language format that humans normally use in conversation and documents, and is data that embodies the user's thoughts and emotions.

[0331] An "emotion engine" is a software module that identifies emotions from natural language input and analyzes their intensity and type.

[0332] A "generative model" refers to an algorithm used to generate new data or create responses based on input data, based on machine learning.

[0333] "Customized advice" refers to advice and suggestions that are individually generated to suit the user's specific emotional state, providing personalized support.

[0334] The "Option to Contact Experts" is a set of options that allows users to easily contact experts for direct consultation as needed.

[0335] "Feedback information" refers to opinions such as impressions and suggestions for improvement that users provide after using the system, and is data used to improve the system.

[0336] This invention provides an AI counseling system that allows employees to anonymously seek advice on mental health issues. The system includes a terminal, a server, and sentiment analysis software. Users first access the platform through the terminal and log in anonymously. This ensures that the user's identification information is securely protected and their privacy is ensured.

[0337] Users input their emotions or problems into the device using natural language input. The device sends this input to the server. The input data received by the server is analyzed by the emotion engine. The emotion engine uses natural language processing techniques and generative AI models to identify emotions in the input text and analyze their intensity and type. This process is performed based on machine learning algorithms.

[0338] The server generates appropriate customized advice based on the analysis results. This process uses a generative AI model to create prompts that provide the most useful information for the user. For example, if a user inputs "I'm overwhelmed with work and feeling mentally exhausted," the system can sense "anxiety" and "stress" and suggest stress management techniques.

[0339] Furthermore, if strong negative emotions are detected, the user will be presented with an option to quickly contact a professional. After obtaining the user's consent, the server will automate the process of contacting the appropriate professional.

[0340] After the user finishes their consultation, they can provide feedback via their device. This feedback is collected on the server and used to continuously improve the emotion engine and generative model. An example of a prompt might be: "Identify the user's emotional pattern and decide whether to contact an occupational physician if necessary: ​​'I can't take it anymore'."

[0341] In this way, the system can provide comprehensive mental health support to users.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The user accesses the AI ​​counseling system using a terminal and performs an anonymous login. The terminal then performs a procedure to anonymize the user's identification information. The input is the user's login information. The output is anonymized user session data.

[0345] Step 2:

[0346] The user inputs their concerns and feelings using natural language on their device. For example, they might input text such as "I'm having trouble with work stress." The device then sends this input data to the server. In this context, input is the text input by the user, and output is the data sent to the server.

[0347] Step 3:

[0348] The server analyzes the received natural language input using an emotion engine. In this process, the text data is classified by the emotion engine based on emotion categories (e.g., "anxiety," "anger") and their intensity. The input is natural language data, and the output is the result of the emotion category and intensity analysis.

[0349] Step 4:

[0350] The server uses a generative AI model based on the sentiment analysis results to generate appropriate advice for the user. A prompt is used to instruct the generative AI model to "generate advice based on the sentiment analysis results." The input is the sentiment analysis results, and the output is the generated advice.

[0351] Step 5:

[0352] The terminal displays customized advice from the server to the user. The user then reads the presented advice. The input is the advice data from the server, and the output is the displayed advice to the user.

[0353] Step 6:

[0354] The server provides the user with options to contact specialists as needed. If strong negative emotions are detected in the emotion analysis, the user will be presented with options to contact an industrial physician or counselor. The input is a specific condition from the emotion analysis results, and the output is the presentation of contact options.

[0355] Step 7:

[0356] Once a user finishes a consultation, they can enter feedback through their device. This feedback is sent to the server and used for future system improvements. The input is the user's feedback, and the output is the storage of the feedback data on the server.

[0357] (Application Example 2)

[0358] 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 as the "terminal".

[0359] In modern society, individual mental health is a crucial issue, and emotional support within the family is particularly needed. Furthermore, there is a lack of systems that provide immediate mental support amidst busy daily life. Therefore, it is necessary to provide a system where individuals can more safely discuss their mental health within their families and receive support from professionals when needed.

[0360] 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.

[0361] In this invention, the server includes means for classifying and sentimentally analyzing the content of a consultation using a sentiment analysis engine that analyzes the received natural language input data, means for presenting appropriate advice or support information based on the results of the sentiment analysis, and means for providing real-time feedback to the user using a robotic device. This makes it possible for individuals to consult anonymously within their homes and receive appropriate advice immediately.

[0362] "User authentication information" refers to information used to identify an individual, and in this system, it is managed anonymously.

[0363] "Natural language input data" refers to data composed of human language used by users during dialogue or consultation.

[0364] An "emotion analysis engine" is a tool that analyzes emotions from input natural language data and classifies their intensity and type.

[0365] "Advice or support information" refers to resources of advice and information provided to the user based on the results of sentiment analysis.

[0366] A "robot device" is an electronic device used in the home to provide feedback and emotional support to the user.

[0367] "Feedback" refers to data collected from users, including their opinions and impressions, which is used to improve systems and generative models.

[0368] "External experts" are mental health professionals who are partnered with users to provide additional support.

[0369] One possible embodiment of this invention is a home-based emotional support system. The user engages in natural language-based consultation through a robotic device. The device is equipped with a microphone, speaker, and touchscreen display. This device senses the user's input and converts it into text using a speech recognition engine.

[0370] The text data acquired by the device is sent to the server. The server is equipped with an emotion analysis engine, which analyzes the received data to identify the user's emotions. This analysis utilizes a generative AI model built using TensorFlow. The emotion analysis engine classifies the type and intensity of emotions from the input data and selects and generates appropriate advice and support information as needed.

[0371] Based on the analysis results, the server provides generated advice to the user through a robotic device. For example, if the user inputs "I've been very tired lately and lack motivation," the server analyzes this data and suggests ways to refresh the user. Additionally, if necessary, the server may offer further options such as "Would you consider consulting a specialist?", ensuring the user receives the necessary professional support.

[0372] This system also includes a feedback function, allowing it to collect feedback from users after a consultation. This feedback is used to improve the generative model, which is continuously learned on the server.

[0373] An example of a prompt message might be: "Sentiment analysis: The user said, 'I'm overwhelmed with work and feeling mentally exhausted.' Please provide the best advice." In this way, it is possible to provide an environment where users can feel safe discussing their mental health, even within the home.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The user speaks their request in natural language to the robotic device. The device acquires this voice input via its microphone and converts it into text data using a speech recognition engine (e.g., Google Speech-to-Text API). At this stage, the input is the user's voice, and the output is the converted text data.

[0377] Step 2:

[0378] The device sends the generated text data to the server. The server inputs the received text into the sentiment analysis engine. The sentiment analysis engine uses a generative AI model (using TensorFlow) to analyze the data and identify the user's emotions. Specifically, based on the analysis results, the type of emotion (e.g., "anxiety" or "fatigue") and its intensity are evaluated. The input is text data, and the output is the analyzed type and intensity of emotion.

[0379] Step 3:

[0380] The server selects appropriate advice or support information based on the sentiment analysis results. Using a generative AI model, it generates optimal advice using a prompt (e.g., "Please provide the best advice for this situation."). At this stage, sentiment data is the input, and the output is specific advice or suggestions.

[0381] Step 4:

[0382] The server sends the generated advice back to the robot device, and the terminal communicates that feedback to the user via voice or display. Here, the advice, which is the output from the server, is the input, and the feedback to the user is the output. This process allows the user to receive support in real time.

[0383] Step 5:

[0384] Based on the feedback provided by the user, additional options are selected. For example, if the option to contact an expert is presented, and the user selects it, the server automatically contacts the appropriate expert. The input is the user's selection, and the output is the expert contact information. This step enables the provision of expert support.

[0385] Step 6:

[0386] After the consultation ends, the user provides feedback through their device. The server collects this feedback and uses it to improve the system's sentiment analysis engine and generative model. The input to this final step is the user's feedback, and the output is an updated dataset for system improvement.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention provides an AI counseling platform where employees can anonymously discuss their stress and worries. This platform is available 24 / 7 and provides users with fast and flexible support.

[0404] First, the user accesses the platform using their device and logs in anonymously. During this process, the server manages the session by generating a temporary ID while protecting the user's anonymity.

[0405] Next, the user inputs their worries and stress into the device in natural language. This data is sent from the device to the server, where it is analyzed using a generative model. The generative model uses natural language processing techniques to analyze the input data and identify the type of emotion and the category of the problem. Based on this analysis, the server provides the user with appropriate advice and links to relevant resources via the device.

[0406] Furthermore, depending on the nature of the consultation, if the user so desires, the server will provide means of contacting specialists such as industrial physicians, in-house counselors, and health staff. If the user agrees, the server will notify the specialist so that they can provide the necessary support.

[0407] After the consultation ends, the user provides feedback through their device. The server receives this feedback, records it in a database, and uses it to improve the performance of the generative model.

[0408] For example, if a user enters a concern such as "I feel a lot of pressure at work and am constantly anxious," the server analyzes this and provides advice on mental health and stress management techniques to the user's device. At the same time, if the server determines it is necessary, it will display an option to contact an industrial physician and open a link to further action if the issue requires more attention.

[0409] This makes it possible for this system to provide mental support in the workplace efficiently and effectively.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The user accesses the platform using their device and selects the option to log in anonymously. The server receives the access request, generates a temporary ID to ensure the user's anonymity, and initiates a user session.

[0413] Step 2:

[0414] The user inputs and sends their stress and worries in natural language through their device. The device sends the input text data to the server. The server receives the data and prepares it for the next analysis process.

[0415] Step 3:

[0416] The server uses a generative model to analyze the received text data. The AI ​​model employs natural language processing techniques to identify the emotions, stressors, and categories of the input content. The analysis results are then generated.

[0417] Step 4:

[0418] Based on the analysis results, the server selects appropriate advice and resources for the user. This advice and resources may include stress management techniques and relevant external links.

[0419] Step 5:

[0420] The server sends the selected advice and resources back to the user's terminal. The terminal displays this information to the user. The user reviews the information presented and decides on further actions if necessary.

[0421] Step 6:

[0422] Depending on the nature of the consultation, if the user desires further professional support, the server will provide the option to contact a specialist such as an industrial physician, in-house counselor, or health staff. If the user selects this option and agrees, the server will notify the designated specialist of the necessary information.

[0423] Step 7:

[0424] Users can input feedback on the advice and resources provided via their device. The device sends this feedback to the server. The server records the received feedback in a database and uses it to improve the generative model in the future.

[0425] (Example 1)

[0426] 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."

[0427] In modern times, the number of workers experiencing psychological stress and anxieties in the workplace is increasing, and there is a need for an efficient counseling system that can address these problems quickly and anonymously. However, current systems have challenges in maintaining anonymity, providing appropriate advice for individual consultations, and continuously improving the quality of services.

[0428] 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.

[0429] In this invention, the server includes means for anonymizing and managing user identification information, means for classifying and sentimentally analyzing the content of the problem using a generative information processing model that analyzes received natural-form data, and means for providing appropriate advice or links to external information sources based on the analysis results. This makes it possible to provide appropriate advice tailored to individual consultations while maintaining anonymity, and to continuously improve the service.

[0430] "User identification information" is a general term for data used to identify an individual, and in this invention, it is managed while maintaining anonymity.

[0431] "Anonymization" is the process of preventing the identification of an individual by deleting or transforming information that could identify an individual.

[0432] "Natural form data" refers to information written in human natural language, before it is converted into a format that is easily interpreted by machines.

[0433] A "generative information processing model" is a machine learning model that includes algorithms for generating and analyzing information based on input data.

[0434] "Classification" refers to the process of grouping data based on specific characteristics or criteria.

[0435] "Sentiment analysis" is the process of extracting and analyzing emotional characteristics from text data that are relevant to its context.

[0436] "Advice" refers to professional or practical recommendations or instructions given regarding a specific problem or situation.

[0437] "External information sources" refer to resources and data outside the server that users can access.

[0438] "Contact information" refers to information necessary to establish communication with a specific individual or organization, and includes things like phone numbers and email addresses.

[0439] A "professional" refers to a person who possesses specialized knowledge and skills in a particular field and is qualified to provide appropriate support.

[0440] "Opinion data" refers to feedback and evaluation data collected from users, which can be used to improve the service.

[0441] This invention provides an AI counseling platform that offers anonymous and efficient psychological support within a company. Users first access the platform using a terminal. At this time, users can log in anonymously. When the server recognizes the user's login, it generates temporary identification information to ensure the user's anonymity and manages the session.

[0442] Users input data about their worries and stresses in natural language form through the terminal's input interface. This data is sent to a server via the terminal. The server processes the received data using a generative AI model. This model employs a generative information processing model using natural language processing technology, and utilizes open-source libraries such as TensorFlow and PyTorch.

[0443] The generating AI model analyzes the input data, classifies the consultation content, and analyzes the emotions. Based on this information, the server provides the user with advice and links to relevant external information sources via the terminal. Furthermore, if necessary, the server can provide the user with contact information for professionals such as workplace doctors, internal counselors, and health management staff.

[0444] After a user concludes a consultation, they can provide feedback via their device. This feedback data is collected by the server and used as feedback to the generated AI model to further improve the service, thereby enhancing the accuracy of the analysis.

[0445] As a concrete example, consider a case where a user enters a statement such as, "I feel a lot of pressure at work and am constantly anxious." The server analyzes this data and provides helpful advice regarding mental health, as well as offering an option to contact a workplace doctor.

[0446] An example of a prompt message is, "Generate advice to provide to a user who is experiencing stress at work."

[0447] This system will enable users to approach their problems quickly and flexibly, and provide an environment where they can receive support with peace of mind.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The user accesses the AI ​​counseling platform through their device and logs in anonymously. The device sends this login request to the server. The server receives the login request, generates a unique temporary identifier for the user, and manages the session. This temporary identifier is valid only during the session and serves to maintain the user's anonymity.

[0451] Step 2:

[0452] Users input messages about their worries and stress into their devices as natural-form data. The user's input is recorded on the device as text data and sent directly to the server. The server prepares the received text data for processing, specifically formatting and pre-processing the information while maintaining privacy.

[0453] Step 3:

[0454] The server inputs pre-processed text data into a generating AI model. This model uses natural language processing techniques to analyze patterns in the input data and perform sentiment analysis and problem classification. Specifically, it performs word vectorization and sentiment score calculation, structuring the data. This clearly identifies the category of the consultation content and the emotional state.

[0455] Step 4:

[0456] The server generates appropriate advice and links to external resources for the user based on the analysis results output from the generated AI model. Personalized advice is then generated and sent to the user's device, which displays it to the user. This allows the user to quickly receive information and support tailored to their needs.

[0457] Step 5:

[0458] If a user desires further assistance, they can make a selection on their device. The device then transmits this selection to the server. The server provides a means of contacting a specialist based on the user's request and sends real-time notifications as needed. In this process, the contact information of the selected specialist is automatically selected.

[0459] Step 6:

[0460] After the consultation ends, the user provides feedback through their device. The device sends this feedback information to the server. The server stores the collected feedback in a database and uses it to improve the generated AI model. This provides a mechanism for continuously improving the quality of the service.

[0461] (Application Example 1)

[0462] 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."

[0463] Maintaining personal mental health is crucial in modern society, but opportunities to interact with and receive support from professionals are limited. Therefore, there is a need for a system that provides easily accessible mental health support in daily life. Furthermore, such a system must give due consideration to protecting personal information and ensuring anonymity.

[0464] 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.

[0465] This invention includes a server that includes means for anonymously managing user authentication information, means for classifying and sentimentally analyzing the content of the consultation using a generative model that analyzes the received natural language input data, means for providing appropriate advice or links to external resources based on the analysis results, and means for being incorporated into a home machine as an applied service to provide psychological support through natural dialogue. This makes it possible for users to easily consult about their daily stresses and worries and to connect with professional support as needed.

[0466] "Methods for anonymously managing user authentication information" refers to technologies that allow users to perform necessary authentication while concealing their personal information when using a system.

[0467] A "generative model for analyzing received natural language input data" is an artificial intelligence technology used to analyze natural language input from users, understand its content, and classify it.

[0468] "Means for classifying and emotionally analyzing consultation content" refers to the process of classifying the input consultation into specific categories and evaluating the emotional state.

[0469] "Means of providing appropriate advice or links to external resources based on analysis results" refers to technologies that provide users with access to the most useful information and resources based on the analyzed information.

[0470] "A means of providing psychological support through natural dialogue, integrated into home appliances as an applied service" refers to a process of integrating a system into appliances used in the home and providing mental health support through natural conversations with the user.

[0471] The system realizing this invention provides psychological support to users using smart devices in the home. The system anonymously manages user authentication information and receives input data in natural language format. A generative AI model analyzes the user's input, classifies its content, and analyzes emotions. Based on this analysis, the server generates appropriate advice and provides links to external resources as needed. Furthermore, through applied services, the home device can provide psychological support through natural dialogue with the user.

[0472] The server uses input feedback data to improve the performance of its generative model. This allows it to continuously learn from user feedback and improve analysis accuracy. When a user seeks advice, the server provides the most appropriate advice through sentiment analysis and can connect the user with experts if necessary.

[0473] For example, if a user says to a smart device in their home, "I feel like I've been tired from work lately," the generative AI model can analyze this information and provide advice on how to change their mood or other relevant information. Another example of a prompt would be, "Write code that, when a user says 'I feel like I've been tired from work lately,' analyzes the emotion and category and provides appropriate advice." In this way, the invention is designed to support the mental well-being of users.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The user inputs their stress and worries in natural language into the device. The device receives this input and sends it to the server. This input data is in text format and includes the user's intentions and emotions.

[0477] Step 2:

[0478] The server passes the received input data to the generating AI model and begins analysis. At this stage, the server performs text analysis on the input data to identify the type of emotion and the content of the consultation. As part of the data processing, natural language processing techniques are used to classify categories from the input text, extract emotions, and generate output.

[0479] Step 3:

[0480] The server generates the most suitable advice in text format based on the analysis results. This advice can be retrieved from a pre-prepared database or dynamically generated by AI. The output data is advice tailored to the user's specific concerns.

[0481] Step 4:

[0482] The server sends the user the generated advice, along with links to external resources and options to contact experts, if necessary. The user can review this information on screen and choose whether to take further action. The result of the choice is then sent back to the server.

[0483] Step 5:

[0484] After the consultation ends, the device receives feedback from the user. This feedback data is sent to the server and recorded to improve the performance of the generated AI model. The feedback is used as input data to train the model, contributing to improved analysis accuracy.

[0485] Throughout each step, the server and terminal can work together to effectively provide individualized psychological support to the user.

[0486] 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.

[0487] This invention combines an emotion engine with a 24 / 7 AI counseling platform where employees can anonymously discuss their mental health issues, thereby more accurately analyzing users' emotions and providing appropriate advice and support. The emotion engine identifies emotions contained in the user's natural language input and analyzes their intensity and type.

[0488] Users can access the platform using their devices and log in anonymously. After logging in, users input their stress and worries in natural language. The device sends this input data to the server. Upon receiving the data, the server uses an emotion engine to analyze the emotions contained in the input text. This analysis identifies the intensity and type of the user's emotions, classifying them into categories such as "anxiety," "anger," and "sadness."

[0489] Based on the results of the emotion analysis, the server selects the most appropriate advice and information resources for the user. Customized advice tailored to the user's emotions is provided through the terminal. Furthermore, connections to specialists are optimized based on the emotional pattern as needed. For example, if strong negative emotions are detected, options to contact an industrial physician or a dedicated counselor are promptly presented.

[0490] If a user requests additional support, the server will notify the selected expert of the necessary information, ensuring that the user receives professional assistance smoothly. After the consultation concludes, the user can provide feedback through their device, which is collected by the server and used to improve the emotion engine and generative model.

[0491] For example, if a user enters "I'm overwhelmed with work and feeling mentally exhausted," the emotion engine recognizes the "anxiety" and "stress" contained in this statement, and the server provides appropriate stress management advice. Furthermore, by promptly contacting specialists as needed, the system can provide the user with optimal support. In this way, the system can comprehensively support the mental health of employees and contribute to improving overall company productivity.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The user uses their device to access the counseling platform and selects anonymous login. The device sends the login information to the server. The server generates a temporary ID and manages the session to protect the user's anonymity.

[0495] Step 2:

[0496] Users input their stress and worries in natural language via their device. This input data is sent to the server by the device when the user clicks the "Send" button.

[0497] Step 3:

[0498] The server passes the received input data to the emotion engine, which analyzes the emotions contained in the user's text data. The emotion engine uses natural language processing to identify the intensity and type of emotion in the text (e.g., "anxiety," "sadness," "anger," etc.).

[0499] Step 4:

[0500] The server inputs the results of the emotion analysis into a generative model to determine the most appropriate advice and resources for the user. In this process, specially customized advice is selected based on the type and intensity of the emotion.

[0501] Step 5:

[0502] The server sends the selected advice and information to the user's terminal. The terminal displays this information to the user, providing visual or auditory feedback.

[0503] Step 6:

[0504] Depending on the nature of the inquiry, the server will determine if the user requires additional support and, if necessary, offer options to contact an industrial physician or in-house counselor. If the user selects this option, the server will notify the specialist of the relevant information.

[0505] Step 7:

[0506] After a user finishes a consultation, they input feedback on the quality of their consultation and the advice they received into their device. The device then sends this feedback to the server. The server stores this feedback information and uses it to improve the emotion engine and generative model.

[0507] This process allows the system to respond quickly and accurately to users' mental health issues.

[0508] (Example 2)

[0509] 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."

[0510] In today's work environment, employees often experience a great deal of stress and mental health issues, yet there is a lack of resources to address these concerns. To address this problem, a system is needed that allows employees to seek advice anonymously and safely, and receive prompt and accurate support. However, traditional methods have made it difficult to properly categorize the content of consultations and facilitate smooth communication with specialists.

[0511] 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.

[0512] In this invention, the server includes means for protecting the user's identification information, means for performing emotion identification and intensity analysis using an emotion engine and generative model that analyzes received natural language input, and means for presenting the user with customized advice or information based on the analyzed emotion data. This enables the user to receive appropriate emotional support while protecting their privacy.

[0513] "User identification information" refers to basic information used to identify an individual when a user accesses the system, but this system employs special processing to ensure anonymity.

[0514] "Natural language input" refers to text data in the language format that humans normally use in conversation and documents, and is data that embodies the user's thoughts and emotions.

[0515] An "emotion engine" is a software module that identifies emotions from natural language input and analyzes their intensity and type.

[0516] A "generative model" refers to an algorithm used to generate new data or create responses based on input data, based on machine learning.

[0517] "Customized advice" refers to advice and suggestions that are individually generated to suit the user's specific emotional state, providing personalized support.

[0518] The "Option to Contact Experts" is a set of options that allows users to easily contact experts for direct consultation as needed.

[0519] "Feedback information" refers to opinions such as impressions and suggestions for improvement that users provide after using the system, and is data used to improve the system.

[0520] This invention provides an AI counseling system that allows employees to anonymously seek advice on mental health issues. The system includes a terminal, a server, and sentiment analysis software. Users first access the platform through the terminal and log in anonymously. This ensures that the user's identification information is securely protected and their privacy is ensured.

[0521] Users input their emotions or problems into the device using natural language input. The device sends this input to the server. The input data received by the server is analyzed by the emotion engine. The emotion engine uses natural language processing techniques and generative AI models to identify emotions in the input text and analyze their intensity and type. This process is performed based on machine learning algorithms.

[0522] The server generates appropriate customized advice based on the analysis results. This process uses a generative AI model to create prompts that provide the most useful information for the user. For example, if a user inputs "I'm overwhelmed with work and feeling mentally exhausted," the system can sense "anxiety" and "stress" and suggest stress management techniques.

[0523] Furthermore, if strong negative emotions are detected, the user will be presented with an option to quickly contact a professional. After obtaining the user's consent, the server will automate the process of contacting the appropriate professional.

[0524] After the user finishes their consultation, they can provide feedback via their device. This feedback is collected on the server and used to continuously improve the emotion engine and generative model. An example of a prompt might be: "Identify the user's emotional pattern and decide whether to contact an occupational physician if necessary: ​​'I can't take it anymore'."

[0525] In this way, the system can provide comprehensive mental health support to users.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The user accesses the AI ​​counseling system using a terminal and performs an anonymous login. The terminal then performs a procedure to anonymize the user's identification information. The input is the user's login information. The output is anonymized user session data.

[0529] Step 2:

[0530] The user inputs their concerns and feelings using natural language on their device. For example, they might input text such as "I'm having trouble with work stress." The device then sends this input data to the server. In this context, input is the text input by the user, and output is the data sent to the server.

[0531] Step 3:

[0532] The server analyzes the received natural language input using an emotion engine. In this process, the text data is classified by the emotion engine based on emotion categories (e.g., "anxiety," "anger") and their intensity. The input is natural language data, and the output is the result of the emotion category and intensity analysis.

[0533] Step 4:

[0534] The server uses a generative AI model based on the sentiment analysis results to generate appropriate advice for the user. A prompt is used to instruct the generative AI model to "generate advice based on the sentiment analysis results." The input is the sentiment analysis results, and the output is the generated advice.

[0535] Step 5:

[0536] The terminal displays customized advice from the server to the user. The user then reads the presented advice. The input is the advice data from the server, and the output is the displayed advice to the user.

[0537] Step 6:

[0538] The server provides the user with options to contact specialists as needed. If strong negative emotions are detected in the emotion analysis, the user will be presented with options to contact an industrial physician or counselor. The input is a specific condition from the emotion analysis results, and the output is the presentation of contact options.

[0539] Step 7:

[0540] Once a user finishes a consultation, they can enter feedback through their device. This feedback is sent to the server and used for future system improvements. The input is the user's feedback, and the output is the storage of the feedback data on the server.

[0541] (Application Example 2)

[0542] 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."

[0543] In modern society, individual mental health is a crucial issue, and emotional support within the family is particularly needed. Furthermore, there is a lack of systems that provide immediate mental support amidst busy daily life. Therefore, it is necessary to provide a system where individuals can more safely discuss their mental health within their families and receive support from professionals when needed.

[0544] 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.

[0545] In this invention, the server includes means for classifying and sentimentally analyzing the content of a consultation using a sentiment analysis engine that analyzes the received natural language input data, means for presenting appropriate advice or support information based on the results of the sentiment analysis, and means for providing real-time feedback to the user using a robotic device. This makes it possible for individuals to consult anonymously within their homes and receive appropriate advice immediately.

[0546] "User authentication information" refers to information used to identify an individual, and in this system, it is managed anonymously.

[0547] "Natural language input data" refers to data composed of human language used by users during dialogue or consultation.

[0548] An "emotion analysis engine" is a tool that analyzes emotions from input natural language data and classifies their intensity and type.

[0549] "Advice or support information" refers to resources of advice and information provided to the user based on the results of sentiment analysis.

[0550] A "robot device" is an electronic device used in the home to provide feedback and emotional support to the user.

[0551] "Feedback" refers to data collected from users, including their opinions and impressions, which is used to improve systems and generative models.

[0552] "External experts" are mental health professionals who are partnered with users to provide additional support.

[0553] One possible embodiment of this invention is a home-based emotional support system. The user engages in natural language-based consultation through a robotic device. The device is equipped with a microphone, speaker, and touchscreen display. This device senses the user's input and converts it into text using a speech recognition engine.

[0554] The text data acquired by the device is sent to the server. The server is equipped with an emotion analysis engine, which analyzes the received data to identify the user's emotions. This analysis utilizes a generative AI model built using TensorFlow. The emotion analysis engine classifies the type and intensity of emotions from the input data and selects and generates appropriate advice and support information as needed.

[0555] Based on the analysis results, the server provides generated advice to the user through a robotic device. For example, if the user inputs "I've been very tired lately and lack motivation," the server analyzes this data and suggests ways to refresh the user. Additionally, if necessary, the server may offer further options such as "Would you consider consulting a specialist?", ensuring the user receives the necessary professional support.

[0556] This system also includes a feedback function, allowing it to collect feedback from users after a consultation. This feedback is used to improve the generative model, which is continuously learned on the server.

[0557] An example of a prompt message might be: "Sentiment analysis: The user said, 'I'm overwhelmed with work and feeling mentally exhausted.' Please provide the best advice." In this way, it is possible to provide an environment where users can feel safe discussing their mental health, even within the home.

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The user speaks their request in natural language to the robotic device. The device acquires this voice input via its microphone and converts it into text data using a speech recognition engine (e.g., Google Speech-to-Text API). At this stage, the input is the user's voice, and the output is the converted text data.

[0561] Step 2:

[0562] The device sends the generated text data to the server. The server inputs the received text into the sentiment analysis engine. The sentiment analysis engine uses a generative AI model (using TensorFlow) to analyze the data and identify the user's emotions. Specifically, based on the analysis results, the type of emotion (e.g., "anxiety" or "fatigue") and its intensity are evaluated. The input is text data, and the output is the analyzed type and intensity of emotion.

[0563] Step 3:

[0564] The server selects appropriate advice or support information based on the sentiment analysis results. Using a generative AI model, it generates optimal advice using a prompt (e.g., "Please provide the best advice for this situation."). At this stage, sentiment data is the input, and the output is specific advice or suggestions.

[0565] Step 4:

[0566] The server sends the generated advice back to the robot device, and the terminal communicates that feedback to the user via voice or display. Here, the advice, which is the output from the server, is the input, and the feedback to the user is the output. This process allows the user to receive support in real time.

[0567] Step 5:

[0568] Based on the feedback provided by the user, additional options are selected. For example, if the option to contact an expert is presented, and the user selects it, the server automatically contacts the appropriate expert. The input is the user's selection, and the output is the expert contact information. This step enables the provision of expert support.

[0569] Step 6:

[0570] After the consultation ends, the user provides feedback through their device. The server collects this feedback and uses it to improve the system's sentiment analysis engine and generative model. The input to this final step is the user's feedback, and the output is an updated dataset for system improvement.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention provides an AI counseling platform where employees can anonymously discuss their stress and worries. This platform is available 24 / 7 and provides users with fast and flexible support.

[0589] First, the user accesses the platform using their device and logs in anonymously. During this process, the server manages the session by generating a temporary ID while protecting the user's anonymity.

[0590] Next, the user inputs their worries and stress into the device in natural language. This data is sent from the device to the server, where it is analyzed using a generative model. The generative model uses natural language processing techniques to analyze the input data and identify the type of emotion and the category of the problem. Based on this analysis, the server provides the user with appropriate advice and links to relevant resources via the device.

[0591] Furthermore, depending on the nature of the consultation, if the user so desires, the server will provide means of contacting specialists such as industrial physicians, in-house counselors, and health staff. If the user agrees, the server will notify the specialist so that they can provide the necessary support.

[0592] After the consultation ends, the user provides feedback through their device. The server receives this feedback, records it in a database, and uses it to improve the performance of the generative model.

[0593] For example, if a user enters a concern such as "I feel a lot of pressure at work and am constantly anxious," the server analyzes this and provides advice on mental health and stress management techniques to the user's device. At the same time, if the server determines it is necessary, it will display an option to contact an industrial physician and open a link to further action if the issue requires more attention.

[0594] This makes it possible for this system to provide mental support in the workplace efficiently and effectively.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The user accesses the platform using their device and selects the option to log in anonymously. The server receives the access request, generates a temporary ID to ensure the user's anonymity, and initiates a user session.

[0598] Step 2:

[0599] The user inputs and sends their stress and worries in natural language through their device. The device sends the input text data to the server. The server receives the data and prepares it for the next analysis process.

[0600] Step 3:

[0601] The server uses a generative model to analyze the received text data. The AI ​​model employs natural language processing techniques to identify the emotions, stressors, and categories of the input content. The analysis results are then generated.

[0602] Step 4:

[0603] Based on the analysis results, the server selects appropriate advice and resources for the user. This advice and resources may include stress management techniques and relevant external links.

[0604] Step 5:

[0605] The server sends the selected advice and resources back to the user's terminal. The terminal displays this information to the user. The user reviews the information presented and decides on further actions if necessary.

[0606] Step 6:

[0607] Depending on the nature of the consultation, if the user desires further professional support, the server will provide the option to contact a specialist such as an industrial physician, in-house counselor, or health staff. If the user selects this option and agrees, the server will notify the designated specialist of the necessary information.

[0608] Step 7:

[0609] Users can input feedback on the advice and resources provided via their device. The device sends this feedback to the server. The server records the received feedback in a database and uses it to improve the generative model in the future.

[0610] (Example 1)

[0611] 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".

[0612] In modern times, the number of workers experiencing psychological stress and anxieties in the workplace is increasing, and there is a need for an efficient counseling system that can address these problems quickly and anonymously. However, current systems have challenges in maintaining anonymity, providing appropriate advice for individual consultations, and continuously improving the quality of services.

[0613] 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.

[0614] In this invention, the server includes means for anonymizing and managing user identification information, means for classifying and sentimentally analyzing the content of the problem using a generative information processing model that analyzes received natural-form data, and means for providing appropriate advice or links to external information sources based on the analysis results. This makes it possible to provide appropriate advice tailored to individual consultations while maintaining anonymity, and to continuously improve the service.

[0615] "User identification information" is a general term for data used to identify an individual, and in this invention, it is managed while maintaining anonymity.

[0616] "Anonymization" is the process of preventing the identification of an individual by deleting or transforming information that could identify an individual.

[0617] "Natural form data" refers to information written in human natural language, before it is converted into a format that is easily interpreted by machines.

[0618] A "generative information processing model" is a machine learning model that includes algorithms for generating and analyzing information based on input data.

[0619] "Classification" refers to the process of grouping data based on specific characteristics or criteria.

[0620] "Sentiment analysis" is the process of extracting and analyzing emotional characteristics from text data that are relevant to its context.

[0621] "Advice" refers to professional or practical recommendations or instructions given regarding a specific problem or situation.

[0622] "External information sources" refer to resources and data outside the server that users can access.

[0623] "Contact information" refers to information necessary to establish communication with a specific individual or organization, and includes things like phone numbers and email addresses.

[0624] A "professional" refers to a person who possesses specialized knowledge and skills in a particular field and is qualified to provide appropriate support.

[0625] "Opinion data" refers to feedback and evaluation data collected from users, which can be used to improve the service.

[0626] This invention provides an AI counseling platform that offers anonymous and efficient psychological support within a company. Users first access the platform using a terminal. At this time, users can log in anonymously. When the server recognizes the user's login, it generates temporary identification information to ensure the user's anonymity and manages the session.

[0627] Users input data about their worries and stresses in natural language form through the terminal's input interface. This data is sent to a server via the terminal. The server processes the received data using a generative AI model. This model employs a generative information processing model using natural language processing technology, and utilizes open-source libraries such as TensorFlow and PyTorch.

[0628] The generating AI model analyzes the input data, classifies the consultation content, and analyzes the emotions. Based on this information, the server provides the user with advice and links to relevant external information sources via the terminal. Furthermore, if necessary, the server can provide the user with contact information for professionals such as workplace doctors, internal counselors, and health management staff.

[0629] After a user concludes a consultation, they can provide feedback via their device. This feedback data is collected by the server and used as feedback to the generated AI model to further improve the service, thereby enhancing the accuracy of the analysis.

[0630] As a concrete example, consider a case where a user enters a statement such as, "I feel a lot of pressure at work and am constantly anxious." The server analyzes this data and provides helpful advice regarding mental health, as well as offering an option to contact a workplace doctor.

[0631] An example of a prompt message is, "Generate advice to provide to a user who is experiencing stress at work."

[0632] This system will enable users to approach their problems quickly and flexibly, and provide an environment where they can receive support with peace of mind.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The user accesses the AI ​​counseling platform through their device and logs in anonymously. The device sends this login request to the server. The server receives the login request, generates a unique temporary identifier for the user, and manages the session. This temporary identifier is valid only during the session and serves to maintain the user's anonymity.

[0636] Step 2:

[0637] Users input messages about their worries and stress into their devices as natural-form data. The user's input is recorded on the device as text data and sent directly to the server. The server prepares the received text data for processing, specifically formatting and pre-processing the information while maintaining privacy.

[0638] Step 3:

[0639] The server inputs pre-processed text data into a generating AI model. This model uses natural language processing techniques to analyze patterns in the input data and perform sentiment analysis and problem classification. Specifically, it performs word vectorization and sentiment score calculation, structuring the data. This clearly identifies the category of the consultation content and the emotional state.

[0640] Step 4:

[0641] The server generates appropriate advice and links to external resources for the user based on the analysis results output from the generated AI model. Personalized advice is then generated and sent to the user's device, which displays it to the user. This allows the user to quickly receive information and support tailored to their needs.

[0642] Step 5:

[0643] If a user desires further assistance, they can make a selection on their device. The device then transmits this selection to the server. The server provides a means of contacting a specialist based on the user's request and sends real-time notifications as needed. In this process, the contact information of the selected specialist is automatically selected.

[0644] Step 6:

[0645] After the consultation ends, the user provides feedback through their device. The device sends this feedback information to the server. The server stores the collected feedback in a database and uses it to improve the generated AI model. This provides a mechanism for continuously improving the quality of the service.

[0646] (Application Example 1)

[0647] 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".

[0648] Maintaining personal mental health is crucial in modern society, but opportunities to interact with and receive support from professionals are limited. Therefore, there is a need for a system that provides easily accessible mental health support in daily life. Furthermore, such a system must give due consideration to protecting personal information and ensuring anonymity.

[0649] 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.

[0650] This invention includes a server that includes means for anonymously managing user authentication information, means for classifying and sentimentally analyzing the content of the consultation using a generative model that analyzes the received natural language input data, means for providing appropriate advice or links to external resources based on the analysis results, and means for being incorporated into a home machine as an applied service to provide psychological support through natural dialogue. This makes it possible for users to easily consult about their daily stresses and worries and to connect with professional support as needed.

[0651] "Methods for anonymously managing user authentication information" refers to technologies that allow users to perform necessary authentication while concealing their personal information when using a system.

[0652] A "generative model for analyzing received natural language input data" is an artificial intelligence technology used to analyze natural language input from users, understand its content, and classify it.

[0653] "Means for classifying and emotionally analyzing consultation content" refers to the process of classifying the input consultation into specific categories and evaluating the emotional state.

[0654] "Means of providing appropriate advice or links to external resources based on analysis results" refers to technologies that provide users with access to the most useful information and resources based on the analyzed information.

[0655] "A means of providing psychological support through natural dialogue, integrated into home appliances as an applied service" refers to a process of integrating a system into appliances used in the home and providing mental health support through natural conversations with the user.

[0656] The system realizing this invention provides psychological support to users using smart devices in the home. The system anonymously manages user authentication information and receives input data in natural language format. A generative AI model analyzes the user's input, classifies its content, and analyzes emotions. Based on this analysis, the server generates appropriate advice and provides links to external resources as needed. Furthermore, through applied services, the home device can provide psychological support through natural dialogue with the user.

[0657] The server uses input feedback data to improve the performance of its generative model. This allows it to continuously learn from user feedback and improve analysis accuracy. When a user seeks advice, the server provides the most appropriate advice through sentiment analysis and can connect the user with experts if necessary.

[0658] For example, if a user says to a smart device in their home, "I feel like I've been tired from work lately," the generative AI model can analyze this information and provide advice on how to change their mood or other relevant information. Another example of a prompt would be, "Write code that, when a user says 'I feel like I've been tired from work lately,' analyzes the emotion and category and provides appropriate advice." In this way, the invention is designed to support the mental well-being of users.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The user inputs their stress and worries in natural language into the device. The device receives this input and sends it to the server. This input data is in text format and includes the user's intentions and emotions.

[0662] Step 2:

[0663] The server passes the received input data to the generating AI model and begins analysis. At this stage, the server performs text analysis on the input data to identify the type of emotion and the content of the consultation. As part of the data processing, natural language processing techniques are used to classify categories from the input text, extract emotions, and generate output.

[0664] Step 3:

[0665] The server generates the most suitable advice in text format based on the analysis results. This advice can be retrieved from a pre-prepared database or dynamically generated by AI. The output data is advice tailored to the user's specific concerns.

[0666] Step 4:

[0667] The server sends the user the generated advice, along with links to external resources and options to contact experts, if necessary. The user can review this information on screen and choose whether to take further action. The result of the choice is then sent back to the server.

[0668] Step 5:

[0669] After the consultation ends, the device receives feedback from the user. This feedback data is sent to the server and recorded to improve the performance of the generated AI model. The feedback is used as input data to train the model, contributing to improved analysis accuracy.

[0670] Throughout each step, the server and terminal can work together to effectively provide individualized psychological support to the user.

[0671] 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.

[0672] This invention combines an emotion engine with a 24 / 7 AI counseling platform where employees can anonymously discuss their mental health issues, thereby more accurately analyzing users' emotions and providing appropriate advice and support. The emotion engine identifies emotions contained in the user's natural language input and analyzes their intensity and type.

[0673] Users can access the platform using their devices and log in anonymously. After logging in, users input their stress and worries in natural language. The device sends this input data to the server. Upon receiving the data, the server uses an emotion engine to analyze the emotions contained in the input text. This analysis identifies the intensity and type of the user's emotions, classifying them into categories such as "anxiety," "anger," and "sadness."

[0674] Based on the results of the emotion analysis, the server selects the most appropriate advice and information resources for the user. Customized advice tailored to the user's emotions is provided through the terminal. Furthermore, connections to specialists are optimized based on the emotional pattern as needed. For example, if strong negative emotions are detected, options to contact an industrial physician or a dedicated counselor are promptly presented.

[0675] If a user requests additional support, the server will notify the selected expert of the necessary information, ensuring that the user receives professional assistance smoothly. After the consultation concludes, the user can provide feedback through their device, which is collected by the server and used to improve the emotion engine and generative model.

[0676] For example, if a user enters "I'm overwhelmed with work and feeling mentally exhausted," the emotion engine recognizes the "anxiety" and "stress" contained in this statement, and the server provides appropriate stress management advice. Furthermore, by promptly contacting specialists as needed, the system can provide the user with optimal support. In this way, the system can comprehensively support the mental health of employees and contribute to improving overall company productivity.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The user uses their device to access the counseling platform and selects anonymous login. The device sends the login information to the server. The server generates a temporary ID and manages the session to protect the user's anonymity.

[0680] Step 2:

[0681] Users input their stress and worries in natural language via their device. This input data is sent to the server by the device when the user clicks the "Send" button.

[0682] Step 3:

[0683] The server passes the received input data to the emotion engine, which analyzes the emotions contained in the user's text data. The emotion engine uses natural language processing to identify the intensity and type of emotion in the text (e.g., "anxiety," "sadness," "anger," etc.).

[0684] Step 4:

[0685] The server inputs the results of the emotion analysis into a generative model to determine the most appropriate advice and resources for the user. In this process, specially customized advice is selected based on the type and intensity of the emotion.

[0686] Step 5:

[0687] The server sends the selected advice and information to the user's terminal. The terminal displays this information to the user, providing visual or auditory feedback.

[0688] Step 6:

[0689] Depending on the nature of the inquiry, the server will determine if the user requires additional support and, if necessary, offer options to contact an industrial physician or in-house counselor. If the user selects this option, the server will notify the specialist of the relevant information.

[0690] Step 7:

[0691] After a user finishes a consultation, they input feedback on the quality of their consultation and the advice they received into their device. The device then sends this feedback to the server. The server stores this feedback information and uses it to improve the emotion engine and generative model.

[0692] This process allows the system to respond quickly and accurately to users' mental health issues.

[0693] (Example 2)

[0694] 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".

[0695] In today's work environment, employees often experience a great deal of stress and mental health issues, yet there is a lack of resources to address these concerns. To address this problem, a system is needed that allows employees to seek advice anonymously and safely, and receive prompt and accurate support. However, traditional methods have made it difficult to properly categorize the content of consultations and facilitate smooth communication with specialists.

[0696] 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.

[0697] In this invention, the server includes means for protecting the user's identification information, means for performing emotion identification and intensity analysis using an emotion engine and generative model that analyzes received natural language input, and means for presenting the user with customized advice or information based on the analyzed emotion data. This enables the user to receive appropriate emotional support while protecting their privacy.

[0698] "User identification information" refers to basic information used to identify an individual when a user accesses the system, but this system employs special processing to ensure anonymity.

[0699] "Natural language input" refers to text data in the language format that humans normally use in conversation and documents, and is data that embodies the user's thoughts and emotions.

[0700] An "emotion engine" is a software module that identifies emotions from natural language input and analyzes their intensity and type.

[0701] A "generative model" refers to an algorithm used to generate new data or create responses based on input data, based on machine learning.

[0702] "Customized advice" refers to advice and suggestions that are individually generated to suit the user's specific emotional state, providing personalized support.

[0703] The "Option to Contact Experts" is a set of options that allows users to easily contact experts for direct consultation as needed.

[0704] "Feedback information" refers to opinions such as impressions and suggestions for improvement that users provide after using the system, and is data used to improve the system.

[0705] This invention provides an AI counseling system that allows employees to anonymously seek advice on mental health issues. The system includes a terminal, a server, and sentiment analysis software. Users first access the platform through the terminal and log in anonymously. This ensures that the user's identification information is securely protected and their privacy is ensured.

[0706] Users input their emotions or problems into the device using natural language input. The device sends this input to the server. The input data received by the server is analyzed by the emotion engine. The emotion engine uses natural language processing techniques and generative AI models to identify emotions in the input text and analyze their intensity and type. This process is performed based on machine learning algorithms.

[0707] The server generates appropriate customized advice based on the analysis results. This process uses a generative AI model to create prompts that provide the most useful information for the user. For example, if a user inputs "I'm overwhelmed with work and feeling mentally exhausted," the system can sense "anxiety" and "stress" and suggest stress management techniques.

[0708] Furthermore, if strong negative emotions are detected, the user will be presented with an option to quickly contact a professional. After obtaining the user's consent, the server will automate the process of contacting the appropriate professional.

[0709] After the user finishes their consultation, they can provide feedback via their device. This feedback is collected on the server and used to continuously improve the emotion engine and generative model. An example of a prompt might be: "Identify the user's emotional pattern and decide whether to contact an occupational physician if necessary: ​​'I can't take it anymore'."

[0710] In this way, the system can provide comprehensive mental health support to users.

[0711] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0712] Step 1:

[0713] The user accesses the AI ​​counseling system using a terminal and performs an anonymous login. The terminal then performs a procedure to anonymize the user's identification information. The input is the user's login information. The output is anonymized user session data.

[0714] Step 2:

[0715] The user inputs their concerns and feelings using natural language on their device. For example, they might input text such as "I'm having trouble with work stress." The device then sends this input data to the server. In this context, input is the text input by the user, and output is the data sent to the server.

[0716] Step 3:

[0717] The server analyzes the received natural language input using an emotion engine. In this process, the text data is classified by the emotion engine based on emotion categories (e.g., "anxiety," "anger") and their intensity. The input is natural language data, and the output is the result of the emotion category and intensity analysis.

[0718] Step 4:

[0719] The server uses a generative AI model based on the sentiment analysis results to generate appropriate advice for the user. A prompt is used to instruct the generative AI model to "generate advice based on the sentiment analysis results." The input is the sentiment analysis results, and the output is the generated advice.

[0720] Step 5:

[0721] The terminal displays customized advice from the server to the user. The user then reads the presented advice. The input is the advice data from the server, and the output is the displayed advice to the user.

[0722] Step 6:

[0723] The server provides the user with options to contact specialists as needed. If strong negative emotions are detected in the emotion analysis, the user will be presented with options to contact an industrial physician or counselor. The input is a specific condition from the emotion analysis results, and the output is the presentation of contact options.

[0724] Step 7:

[0725] Once a user finishes a consultation, they can enter feedback through their device. This feedback is sent to the server and used for future system improvements. The input is the user's feedback, and the output is the storage of the feedback data on the server.

[0726] (Application Example 2)

[0727] 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".

[0728] In modern society, individual mental health is a crucial issue, and emotional support within the family is particularly needed. Furthermore, there is a lack of systems that provide immediate mental support amidst busy daily life. Therefore, it is necessary to provide a system where individuals can more safely discuss their mental health within their families and receive support from professionals when needed.

[0729] 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.

[0730] In this invention, the server includes means for classifying and sentimentally analyzing the content of a consultation using a sentiment analysis engine that analyzes the received natural language input data, means for presenting appropriate advice or support information based on the results of the sentiment analysis, and means for providing real-time feedback to the user using a robotic device. This makes it possible for individuals to consult anonymously within their homes and receive appropriate advice immediately.

[0731] "User authentication information" refers to information used to identify an individual, and in this system, it is managed anonymously.

[0732] "Natural language input data" refers to data composed of human language used by users during dialogue or consultation.

[0733] An "emotion analysis engine" is a tool that analyzes emotions from input natural language data and classifies their intensity and type.

[0734] "Advice or support information" refers to resources of advice and information provided to the user based on the results of sentiment analysis.

[0735] A "robot device" is an electronic device used in the home to provide feedback and emotional support to the user.

[0736] "Feedback" refers to data collected from users, including their opinions and impressions, which is used to improve systems and generative models.

[0737] "External experts" are mental health professionals who are partnered with users to provide additional support.

[0738] One possible embodiment of this invention is a home-based emotional support system. The user engages in natural language-based consultation through a robotic device. The device is equipped with a microphone, speaker, and touchscreen display. This device senses the user's input and converts it into text using a speech recognition engine.

[0739] The text data acquired by the device is sent to the server. The server is equipped with an emotion analysis engine, which analyzes the received data to identify the user's emotions. This analysis utilizes a generative AI model built using TensorFlow. The emotion analysis engine classifies the type and intensity of emotions from the input data and selects and generates appropriate advice and support information as needed.

[0740] Based on the analysis results, the server provides generated advice to the user through a robotic device. For example, if the user inputs "I've been very tired lately and lack motivation," the server analyzes this data and suggests ways to refresh the user. Additionally, if necessary, the server may offer further options such as "Would you consider consulting a specialist?", ensuring the user receives the necessary professional support.

[0741] This system also includes a feedback function, allowing it to collect feedback from users after a consultation. This feedback is used to improve the generative model, which is continuously learned on the server.

[0742] An example of a prompt message might be: "Sentiment analysis: The user said, 'I'm overwhelmed with work and feeling mentally exhausted.' Please provide the best advice." In this way, it is possible to provide an environment where users can feel safe discussing their mental health, even within the home.

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The user speaks their request in natural language to the robotic device. The device acquires this voice input via its microphone and converts it into text data using a speech recognition engine (e.g., Google Speech-to-Text API). At this stage, the input is the user's voice, and the output is the converted text data.

[0746] Step 2:

[0747] The device sends the generated text data to the server. The server inputs the received text into the sentiment analysis engine. The sentiment analysis engine uses a generative AI model (using TensorFlow) to analyze the data and identify the user's emotions. Specifically, based on the analysis results, the type of emotion (e.g., "anxiety" or "fatigue") and its intensity are evaluated. The input is text data, and the output is the analyzed type and intensity of emotion.

[0748] Step 3:

[0749] The server selects appropriate advice or support information based on the sentiment analysis results. Using a generative AI model, it generates optimal advice using a prompt (e.g., "Please provide the best advice for this situation."). At this stage, sentiment data is the input, and the output is specific advice or suggestions.

[0750] Step 4:

[0751] The server sends the generated advice back to the robot device, and the terminal communicates that feedback to the user via voice or display. Here, the advice, which is the output from the server, is the input, and the feedback to the user is the output. This process allows the user to receive support in real time.

[0752] Step 5:

[0753] Based on the feedback provided by the user, additional options are selected. For example, if the option to contact an expert is presented, and the user selects it, the server automatically contacts the appropriate expert. The input is the user's selection, and the output is the expert contact information. This step enables the provision of expert support.

[0754] Step 6:

[0755] After the consultation ends, the user provides feedback through their device. The server collects this feedback and uses it to improve the system's sentiment analysis engine and generative model. The input to this final step is the user's feedback, and the output is an updated dataset for system improvement.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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 this memory.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means of managing user authentication information anonymously,

[0780] A means of classifying and analyzing the sentiment of consultation content using a generative model that analyzes received natural language input data,

[0781] Means for providing appropriate advice or links to external resources based on the analysis results,

[0782] We provide options for contacting experts based on the nature of the consultation, and a means of notifying experts with the user's consent.

[0783] A means of collecting feedback from users after the consultation and using it to improve the generative model,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, comprising a generative model that continuously learns from feedback data in order to improve the parsing accuracy when processing a user's natural language input.

[0787] (Claim 3)

[0788] The system according to claim 1, comprising means for automatically selecting contact information for each specialist in order to connect the user with an appropriate industrial physician, in-house counselor, or health staff member based on the content of the consultation.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A means of managing user identification information in an anonymized form,

[0792] A means of classifying and sentimentally analyzing problem content by utilizing generative information processing models that analyze received natural-form data,

[0793] Means for providing appropriate advice or links to external information sources based on the analysis results,

[0794] Depending on the nature of the problem, the system provides options for contacting a specialist, and a means of notifying the specialist if the user agrees.

[0795] After the consultation ends, we will collect feedback from users and use it to improve the generative information processing model.

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, comprising a generative information processing model that continuously learns opinion data in order to improve the accuracy of analysis when processing natural form input from users.

[0799] (Claim 3)

[0800] The system according to claim 1, comprising means for automatically selecting contact information for each professional in order to connect the user with an appropriate workplace physician, internal counselor, or health management staff member based on the nature of the problem.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means of managing user authentication information anonymously,

[0804] A means of classifying and analyzing the sentiment of consultation content using a generative model that analyzes received natural language input data,

[0805] Means for providing appropriate advice or links to external resources based on the analysis results,

[0806] We provide options for contacting experts based on the nature of the consultation, and a means of notifying experts with the user's consent.

[0807] A means of collecting feedback from users after the consultation and using it to improve the generative model,

[0808] As an applied service, it is incorporated into home appliances and provides a means of psychological support through natural dialogue.

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, comprising a generative model that continuously learns from feedback data in order to improve the parsing accuracy when processing a user's natural language input.

[0812] (Claim 3)

[0813] The system according to claim 1, comprising means for automatically selecting contact information for each specialist in order to connect the user with an appropriate industrial physician, in-house counselor, or health staff member based on the content of the consultation.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] Means to protect user identification information,

[0817] A means for performing emotion identification and intensity analysis using an emotion engine and generative model that analyzes received natural language input,

[0818] A means of presenting users with customized advice or information based on analyzed sentiment data,

[0819] The system presents options for contacting a professional based on the user's emotional state, and implements these options only with the user's permission.

[0820] A method for collecting user feedback after the consultation is complete and using it to improve the analysis engine,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising a generative AI model that continuously learns feedback information in order to improve the accuracy of analyzing user emotion data.

[0824] (Claim 3)

[0825] The system according to claim 1, comprising means for automatically selecting contact information for a medical professional or internal counselor suitable for the user based on sentiment analysis.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] A means of managing user authentication information anonymously,

[0829] A means for classifying and analyzing the sentiment of consultation content using an emotion analysis engine that analyzes received natural language input data,

[0830] A means of providing appropriate advice or support information based on the results of emotion analysis,

[0831] We provide options for contacting experts based on the nature of the consultation, and a means of notifying experts with the user's consent.

[0832] A means of providing real-time feedback to users using robotic devices,

[0833] A means of collecting feedback from users after the consultation and using it to improve the sentiment analysis engine and generative model,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising a generative model that continuously learns from feedback data in order to improve the parsing accuracy when processing a user's natural language input.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for automatically selecting and providing contact information for each expert in order to optimize the connection of the user to an appropriate external expert based on the content of the consultation. [Explanation of Symbols]

[0839] 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 managing user authentication information anonymously, A means of classifying and analyzing the sentiment of consultation content using a generative model that analyzes received natural language input data, Means for providing appropriate advice or links to external resources based on the analysis results, We provide options for contacting experts based on the nature of the consultation, and a means of notifying experts with the user's consent. A means of collecting feedback from users after the consultation and using it to improve the generative model, A system that includes this.

2. The system according to claim 1, comprising a generative model that continuously learns from feedback data in order to improve the parsing accuracy when processing a user's natural language input.

3. The system according to claim 1, comprising means for automatically selecting contact information for each specialist in order to connect the user with an appropriate industrial physician, in-house counselor, or health staff member based on the content of the consultation.

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