System

Abstract

To provide a system for providing appropriate and professional advice while protecting privacy against a user's trouble.SOLUTION: A system comprising: means for receiving user information; means for storing the user information in a database; means for authenticating a user; means for having the user input a problem; means for analyzing the input problem; means for classifying the problem based on an analysis result and setting a priority level; means for selecting an appropriate answer system based on the analysis result; means for providing advice to the user by the selected answer system; means for collecting and analyzing post data from a social media account of the user; means for providing additional advice based on the analysis result; and means for periodically sending a follow-up message.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional employee consultation systems involve industrial physicians or the human resources department, making it difficult for users to anonymously discuss their concerns, and privacy is not protected. Other issues include the inability to obtain specialized advice for some concerns, and the wide range of consultation topics makes it difficult to provide appropriate responses. Furthermore, there is a lack of means to detect users' stress and emotional patterns through analysis of social media and provide advice based on those patterns. To address these issues, a system was needed that could anonymously accept and analyze users' concerns and provide appropriate advice. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means. First, a means for accepting user information and a means for storing the user information in a database is provided to authenticate the user. Then, the user is prompted to input their concerns, and a means for analyzing the input concerns is utilized. Next, a means for classifying the concerns and setting priorities based on the analysis results is provided. Furthermore, an appropriate answer system is selected based on the analysis results, and advice is provided to the user using the selected answer system. In addition, with the user's consent, a means for collecting and analyzing posted data from social media accounts is provided, thereby detecting patterns of the user's stress and emotions and providing additional advice. Furthermore, a means for periodically sending follow-up messages is provided to provide continuous support. In this way, a system is realized that provides appropriate and professional advice regarding the user's concerns while protecting privacy.

[0006] "User information" refers to personal identification information such as a user's name, email address, and password, as well as data required for authentication.

[0007] A "database" is an information storage device that stores and manages various data required by the system, such as user information and consultation details.

[0008] "Authentication" is the process of verifying that a user has the proper authority to access a system.

[0009] "Concerns" are text data about personal difficulties or problems that users input into the system.

[0010] "Analysis" is the process of understanding the content of input text data and determining the appropriate classification and response method.

[0011] A "category" is a group of analyzed worries classified according to a specific theme or field.

[0012] "Priority" is an indicator that shows the importance of the problem and the urgency of its resolution.

[0013] An "answer system" is a system that provides appropriate advice and solutions to the analyzed problems.

[0014] A "social media account" is a personal page or profile on a social media platform used by a user.

[0015] "Posted data" refers to information such as messages, comments, and images posted by users on social media.

[0016] "Stress and emotional patterns" are trends in the user's psychological state and emotions detected through analysis.

[0017] A "follow-up message" is a message sent to a user periodically containing additional confirmation or advice. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[0040] Basic system configuration

[0041] 1. User Registration

[0042] The terminal displays a new registration form to the user.

[0043] The user enters information such as name, email address, and password, and clicks the send button.

[0044] The server stores the received user information in a database and sends an authentication email to the user.

[0045] The user clicks on the link in the email to complete the authentication.

[0046] 2. Entering and analyzing worries

[0047] The terminal displays a login form to the user.

[0048] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[0049] The user enters the problem and clicks the send button.

[0050] The server analyzes the received worries, categorizes them, and sets priorities.

[0051] 3. Providing advice

[0052] The server selects an appropriate answering system (psychology expert, health advice AI, etc.) based on the analysis results.

[0053] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[0054] 4. Social Media Analytics

[0055] If the user agrees, the server obtains the user's SNS account information.

[0056] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[0057] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[0058] 5. Follow-up

[0059] The server periodically sends follow-up messages to the user.

[0060] The user can then make a new consultation about the current situation.

[0061] Specific examples

[0062] Example 1: User's concerns

[0063] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[0064] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[0065] The server generates "specific methods for improving communication skills" as advice and displays it on the terminal.

[0066] Example 2: Additional advice based on social media analysis

[0067] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[0068] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[0069] This additional advice is presented to the user through the terminal.

[0070] In this way, the present invention provides a system that can alleviate a user's worries by individually addressing the user's worries and providing appropriate advice while protecting privacy.

[0071] The processing flow will be explained below.

[0072] User Registration and Authentication

[0073] Step 1:

[0074] The terminal displays a new registration form to the user.

[0075] Step 2:

[0076] The user enters the required information such as name, email address, and password.

[0077] Step 3:

[0078] The terminal transmits the input user information to the server.

[0079] Step 4:

[0080] The server stores the user information in a database.

[0081] Step 5:

[0082] The server sends a registration completion email to the user.

[0083] Step 6:

[0084] The user clicks on the link in the registration completion email to complete the authentication.

[0085] Entering and analyzing worries

[0086] Step 7:

[0087] The terminal displays a login form to the user.

[0088] Step 8:

[0089] The user enters their email address and password and clicks the login button.

[0090] Step 9:

[0091] The server compares the entered information with a database and performs authentication.

[0092] Step 10:

[0093] The terminal displays a consultation form to the user.

[0094] Step 11:

[0095] The user enters their concerns in the text box and clicks the send button.

[0096] Step 12:

[0097] The terminal transmits the input worries to the server.

[0098] Problem analysis and classification

[0099] Step 13:

[0100] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[0101] Step 14:

[0102] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[0103] Step 15:

[0104] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[0105] Providing advice

[0106] Step 16:

[0107] The server selects the appropriate response based on the answering system (psychology expert AI, health advice AI, etc.) set for each problem category.

[0108] Step 17:

[0109] The server transmits the advice generated by the selected answering system to the terminal.

[0110] Step 18:

[0111] The terminal displays the advice to the user.

[0112] Social Media Analytics

[0113] Step 19:

[0114] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[0115] Step 20:

[0116] The server collects SNS posting data from users.

[0117] Step 21:

[0118] The server analyzes social media posting data and detects patterns of stress and emotions.

[0119] Step 22:

[0120] The server generates additional advice as needed and sends it to the terminal.

[0121] Step 23:

[0122] The terminal displays additional advice to the user.

[0123] Follow-up

[0124] Step 24:

[0125] The server periodically sends follow-up messages to the user.

[0126] Step 25:

[0127] The user can consult again about new concerns or changes in the situation.

[0128] Example 1

[0129] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] Until now, there have only been limited systems that allow employees to safely consult about their concerns, and it has been difficult to provide sufficiently reliable analysis results and advice. Furthermore, there have been few systems that detect stress and emotional patterns not only from the concerns entered by the user but also from daily social media posting data and provide additional advice. This has led to the issue of not being able to provide comprehensive and individual support to users.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0132] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input worries, means for analyzing the input worries, means for classifying the worries and setting priorities based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for analyzing worries using a natural language processing engine, and means for generating advice using a generative AI model. This makes it possible to analyze stress and emotional patterns from both the user's input content and the social media post data, and provide comprehensive and individual support.

[0133] "User information" refers to personal identification information such as the user's name, email address, and password that is registered in the system.

[0134] A "database" is a management system for storing data such as user information and user concerns.

[0135] "Authentication" refers to the process of verifying that a user is a legitimate subscriber.

[0136] A "problem" is a problem or question that a user wants to discuss with the system.

[0137] "Analysis" is the process of analyzing input data and extracting meaning and patterns.

[0138] A "category" is a classification group for classifying the analyzed worries.

[0139] "Priority" is a numerical value or evaluation criterion that indicates the importance or urgency of the analyzed problem.

[0140] An "answer system" is a device or program that provides appropriate advice to users regarding their concerns.

[0141] "Advice" refers to advice or suggestions provided to the user based on the analysis results.

[0142] "Social Media Account" refers to the account of the social media platform to which the User is registered.

[0143] "Posted data" refers to information such as messages, comments, and photos posted by users on social media.

[0144] A "natural language processing engine" refers to a software system that analyzes input text and understands its meaning and sentiment.

[0145] "Generative AI model" refers to an artificial intelligence model that generates new text or advice based on input data.

[0146] A "follow-up message" is a message that the system periodically sends to the user to prompt follow-up or confirmation.

[0147] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[0148] Basic system configuration

[0149] The system utilizes the following hardware and software:

[0150] Device: A device, including a computer or smartphone, that a user accesses

[0151] Server: Central processing unit that processes data, analyzes, and generates advice

[0152] Database: Relational database such as MySQL (registered trademark) or PostgreSQL

[0153] Natural language processing engine: Google® Cloud Natural Language API

[0154] Generative AI model: OpenAI (registered trademark) GPT-3 (registered trademark)

[0155] Email sending service: SendGrid or Amazon SES

[0156] Social media platform APIs: Twitter API and Facebook Graph API

[0157] Operating procedure

[0158] User Registration

[0159] First, the terminal displays a new registration form to the user. The user enters their name, email address, and password, and clicks the submit button. The server saves the received user information in a database and sends the user an authentication email. The user clicks the link in the email to complete the authentication, and user registration is complete.

[0160] Entering and analyzing worries

[0161] When a user logs in to the system, the terminal displays a consultation form. The user enters their concerns and clicks the send button. The server then analyzes the received concerns using a natural language processing engine, categorizes them, and sets priorities.

[0162] Providing advice

[0163] Based on the analysis results, the server selects an appropriate answering system. Based on the selected system, advice is generated using a generative AI model. The generated advice is displayed on the device for the user to view.

[0164] Social Media Analytics

[0165] If the user agrees, the device provides social media integration functionality and obtains an access token for the social media account using OAuth 2.0. The server periodically collects user posting data from the social media platform and performs automatic analysis. If stress or emotional patterns are detected, additional advice is generated and provided to the user.

[0166] Follow-up

[0167] The server periodically sends follow-up messages to the user, allowing the user to re-enter their concerns about new situations and receive further analysis and advice.

[0168] Specific examples

[0169] For example, if a user inputs "I'm having trouble with my boss and I don't know how to deal with it," the server will categorize this problem as "Work-Relationships" and assign it a medium priority. Using a generative AI model, the server will generate advice on "specific ways to improve communication skills" and display it on the device.

[0170] Additionally, if the user agrees and links their social media account, the server will detect posts such as "Work has been tough lately." In this case, it will determine that the user is in a high-stress state and generate additional advice such as relaxation techniques or counseling. The generated advice will be presented to the user via their device.

[0171] Example prompts for generative AI models

[0172] Analyze the concerns entered by the user, categorize them appropriately, set priorities, generate corresponding advice, and provide it to the user. Also, analyze the user's social media posts and provide additional advice if necessary. Example input: "I'm having trouble with my relationship with my boss and I don't know how to deal with it."

[0173] This system can alleviate users' worries by individually addressing their concerns and providing appropriate advice while protecting their privacy.

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

[0175] Step 1:

[0176] Enter and submit user registration information

[0177] The terminal displays a new registration form. The user enters information such as name, email address, and password, and clicks the submit button. The entered information is sent to the server.

[0178] Input: User information (name, email address, password)

[0179] Output: Data sent to the server

[0180] Step 2:

[0181] Saving user information and sending authentication emails

[0182] The server saves the received user information in a database, then generates an authentication email and sends it to the user using an email sending service.

[0183] Input: User information sent in step 1

[0184] Output: User information entry in database, authentication email

[0185] Step 3:

[0186] Authentication complete

[0187] The user clicks on the authentication link in the email, which contains a unique token that the server verifies and completes the user's authentication.

[0188] Input: Token included in the authentication link

[0189] Output: Authentication state update

[0190] Step 4:

[0191] User Login

[0192] The terminal presents the user with a login form, where the user enters their email address and password and submits it. The server checks the credentials against a database and, once authenticated, presents the user with a personalized dashboard.

[0193] Input: Email address, password

[0194] Output: Authentication results, dashboard display

[0195] Step 5:

[0196] Enter and submit your concerns

[0197] The terminal displays a consultation form. The user enters their concerns and clicks the send button. The details of the concerns are sent to the server.

[0198] Input: Content of concern

[0199] Output: Data sent to the server

[0200] Step 6:

[0201] Analysis and classification of worries

[0202] The server uses a natural language processing engine to analyze the received concerns, categorize them, and set priorities. Data processing involves tokenizing the text, analyzing emotions, and classifying them.

[0203] Input: Content of concern

[0204] Output: Analysis results (category, priority)

[0205] Step 7:

[0206] Answer system selection and advice generation

[0207] The server selects the optimal answering system based on the analysis results, and generates advice using the selected system with a generative AI model.

[0208] Input: Analysis results

[0209] Output: Generated advice

[0210] Step 8:

[0211] Displaying Advice

[0212] The server sends the generated advice to the user's terminal, which displays it.

[0213] Input: Generated advice

[0214] Output: Advice displayed on terminal

[0215] Step 9:

[0216] Linking social media accounts and collecting data

[0217] If the user agrees, the device provides the SNS integration function. It obtains an access token for the SNS account using OAuth 2.0 and sends it to the server. The server then collects the posted data from the SNS platform.

[0218] Input: User consent, SNS access token

[0219] Output: SNS post data

[0220] Step 10:

[0221] Analyzing social media data and generating additional advice

[0222] The server analyzes the collected social media post data using a natural language processing engine to detect stress and emotional patterns, and generates and provides additional advice to users as needed.

[0223] Input: SNS post data

[0224] Output: Further advice, emotion pattern detection

[0225] Step 11:

[0226] Regular follow-up

[0227] The server periodically sends follow-up messages to the user. The user can enter and submit new concerns or questions. These new concerns are also processed in steps 6 to 10.

[0228] Input: Schedule a follow-up message

[0229] Output: Send follow-up message

[0230] (Application example 1)

[0231] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0232] Employees working in brick-and-mortar stores often have concerns about the work environment, interpersonal relationships, stress, and other issues, but lack the means to appropriately discuss and resolve these issues. Even after employees have discussed their concerns, there is a need for a way to continually follow up and provide appropriate advice. Furthermore, adding a function to analyze social media posting data to check employees' mental health would enable more effective support, contributing to improved productivity and employee satisfaction throughout the workplace.

[0233] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0234] In this invention, the server includes means for accepting user information, means for storing the user information in a database, means for authenticating the user, means for having the user input their concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate response system based on the analysis results, means for providing advice to the user using the selected response system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, and means for providing advice on concerns via a device for employees to use in a physical store. This allows employees to easily consult about their concerns and quickly receive appropriate advice. Furthermore, by continuously monitoring employees' mental health through social media analysis and providing additional advice as needed, it is possible to reduce employee stress and improve the work environment.

[0235] "User information" refers to personal user identification information, such as name, email address, and password, that is registered in the system.

[0236] The "database" is a system for organizing, saving, and managing data such as user information, input concerns, analysis results, and advice content.

[0237] "Authentication" is a procedure for verifying that a user has legitimate access rights, and generally involves the use of an email address and password.

[0238] A "problem" is a problem or issue that a user has at work or in their personal life and that they input to the system for consultation.

[0239] "Analysis" is the process of classifying input concerns using AI technology, setting priorities, and deriving appropriate countermeasures.

[0240] An "answer system" is a system that provides appropriate advice to users based on the analysis results, and includes psychology experts and health advice AI.

[0241] "Social media account" refers to the account information of the SNS (social networking service) used by the user.

[0242] "Posted data" refers to content such as text, images, and videos that users publish on social media, and is the subject of analysis.

[0243] A "follow-up message" is a message that the system periodically sends to the user, with the purpose of informing them of progress in their concerns and encouraging them to seek new advice.

[0244] A "device" is hardware that a user uses to access the system, including smartphones and smart glasses.

[0245] This invention is a system for providing advice to employees in brick-and-mortar stores, which can be accessed by employees via smartphones or smart glasses. The configuration and operation of the system are described below.

[0246] Basic system configuration

[0247] 1. User Registration

[0248] The device (smartphone or smart glasses) displays a new registration form to employees working in the physical store.

[0249] The employee enters user information such as name, email address, and password, and clicks the submit button.

[0250] The server stores the received user information in a database and sends an authentication email to the employee.

[0251] Employees click on the link in the email to complete the authentication.

[0252] 2. Entering and analyzing worries

[0253] The terminal presents the authenticated employee with a login form.

[0254] Employees enter their email address and password, and once authentication is complete, a consultation form will be displayed.

[0255] Employees enter their concerns about work or their personal lives and click the send button.

[0256] The server analyzes the received worries, classifies them into categories (e.g., "work," "relationships," etc.), and sets priorities.

[0257] The server uses an AI analysis tool (e.g., GPT-4 (registered trademark)) to appropriately analyze the input concerns.

[0258] 3. Providing advice

[0259] The server selects an appropriate answering system (e.g., psychology expert, health advisor AI) based on the analysis results.

[0260] The selected system will use IBM Watson (registered trademark) to generate appropriate advice and display it on the terminal.

[0261] 4. Social Media Analytics

[0262] If the employee consents, the server collects posting data from the employee's social media accounts (e.g., Twitter and Facebook).

[0263] The server collects the posted data via the Twitter API and Facebook API and analyzes it using GPT-4.

[0264] The server analyzes stress and emotional patterns to determine high stress levels and negative patterns.

[0265] If necessary, the server generates additional advice (e.g., relaxation techniques, counseling recommendations) and displays them on the terminal.

[0266] 5. Follow-up

[0267] The server periodically sends follow-up messages to employees.

[0268] Employees can seek new counseling regarding their current situation.

[0269] Specific examples

[0270] Example 1: User's concerns

[0271] An employee enters into the system, "I've been having trouble communicating with my coworkers lately."

[0272] The server classifies this problem as "human relationships" and assigns it a medium priority.

[0273] Using IBM Watson, advice on "specific ways to improve communication skills" is generated and displayed on the device.

[0274] Example 2: Additional advice based on social media analysis

[0275] If the employee agrees, the server detects posts from Twitter or Facebook saying "Work has been tough lately."

[0276] The server determines this to be a high-stress state and generates additional advice recommending necessary relaxation techniques or counseling.

[0277] This additional advice is presented to employees via a terminal.

[0278] Prompt Sentence Examples

[0279] Example prompt: "Analyze the following problem and provide appropriate advice. Problem: 'Recently, I've been having trouble communicating with my colleagues.'"

[0280] In this way, the present invention provides a system that allows employees working in brick-and-mortar stores to easily consult about their concerns and quickly receive appropriate advice.Continual support through analysis of social media posts can help maintain and improve the mental health of employees.

[0281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0282] Step 1:

[0283] User Registration

[0284] The terminal displays a new registration form to employees working in physical stores.

[0285] Input: An employee enters user information such as name, email address, and password.

[0286] The terminal transmits the input information to the server.

[0287] The server stores the received user information in a database and sends an authentication email to the employee.

[0288] Output: A verification email is sent to the employee.

[0289] Step 2:

[0290] certification

[0291] Employees complete the verification by clicking the link in the verification email.

[0292] Input: Click on the authentication link.

[0293] The server receives the authentication link, verifies the user information, and completes the authentication.

[0294] Output: The user is authenticated and can log into the system.

[0295] Step 3:

[0296] Log in and enter your concerns

[0297] The terminal presents the authenticated employee with a login form.

[0298] Enter your email address and password to log in.

[0299] Employees enter their concerns about the workplace or their personal life into the consultation form and click the send button.

[0300] The terminal transmits the input worries to the server.

[0301] Output: The problem is sent to the server.

[0302] Step 4:

[0303] Analysis of worries

[0304] The server analyzes the received concerns.

[0305] Input: Received trouble data.

[0306] The server uses an AI analysis tool (GPT-4) to classify worries into categories (e.g., "work," "relationships," etc.) and set priorities.

[0307] Output: Categorised worries and priorities.

[0308] Step 5:

[0309] Generating Advice

[0310] The server selects an appropriate answering system based on the analysis results.

[0311] Input: Categorised worries and priorities.

[0312] The server generates appropriate advice using a selected answering system (IBM Watson).

[0313] Output: Generation of advice.

[0314] Step 6:

[0315] Providing advice

[0316] The server transmits the generated advice to the terminal.

[0317] Input: The generated advice.

[0318] The terminal displays the advice to the employee.

[0319] Output: The advice is displayed to the employee.

[0320] Step 7:

[0321] Social Media Analytics

[0322] The server collects posting data from employees' social media accounts if they consent.

[0323] Input: Social media account information with your consent.

[0324] The server collects post data using the Twitter API and Facebook API and analyzes it using GPT-4.

[0325] The server determines stress and emotional patterns and detects high stress states and negative patterns.

[0326] Output: Stress analysis results.

[0327] Step 8:

[0328] Generating additional advice

[0329] The server generates any additional advice needed based on the analysis results.

[0330] Input: Social media analysis results.

[0331] The server generates recommendations for additional relaxation techniques and counseling.

[0332] Output: Additional advice.

[0333] Step 9:

[0334] Providing additional advice

[0335] The server transmits the generated additional advice to the terminal.

[0336] Input: Generated additional advice.

[0337] The terminal displays additional advice to the employee.

[0338] Output: Additional advice is displayed to the employee.

[0339] Step 10:

[0340] Follow-up

[0341] The server periodically sends follow-up messages to employees.

[0342] Input: Follow-up message generation routine.

[0343] Employees can seek new counseling regarding their current situation.

[0344] Output: The follow-up message.

[0345] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0346] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns they have, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the advice.

[0347] Basic system configuration

[0348] 1. User Registration

[0349] The terminal displays a new registration form to the user.

[0350] The user enters information such as name, email address, and password, and clicks the send button.

[0351] The server stores the received user information in a database and sends an authentication email to the user.

[0352] The user clicks on the link in the email to complete the authentication.

[0353] 2. Entering and analyzing worries

[0354] The terminal displays a login form to the user.

[0355] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[0356] The user enters the problem and clicks the send button.

[0357] The server analyzes the received worries, categorizes them, and sets priorities.

[0358] 3. Use of Emotion Engine

[0359] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[0360] The recognized user sentiment is utilized in the subsequent advice generation process.

[0361] 4. Providing advice

[0362] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[0363] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[0364] 5. Social Media Analytics

[0365] If the user agrees, the server obtains the user's SNS account information.

[0366] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[0367] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[0368] 6. Follow-up

[0369] The server periodically sends follow-up messages to the user.

[0370] The user can then make a new consultation about the current situation.

[0371] Specific examples

[0372] Example 1: User concern input and emotion recognition

[0373] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[0374] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[0375] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[0376] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[0377] Example 2: Additional advice based on social media analysis

[0378] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[0379] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[0380] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[0381] The generated additional advice is presented to the user through the terminal.

[0382] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby reducing users' concerns and taking their feelings into consideration.

[0383] The processing flow will be explained below.

[0384] User Registration and Authentication

[0385] Step 1:

[0386] The terminal displays a new registration form to the user.

[0387] Step 2:

[0388] The user enters the required information such as name, email address, and password.

[0389] Step 3:

[0390] The terminal transmits the input user information to the server.

[0391] Step 4:

[0392] The server stores the user information in a database.

[0393] Step 5:

[0394] The server sends a registration completion email to the user.

[0395] Step 6:

[0396] The user clicks on the link in the registration completion email to complete the authentication.

[0397] Entering and analyzing worries

[0398] Step 7:

[0399] The terminal displays a login form to the user.

[0400] Step 8:

[0401] The user enters their email address and password and clicks the login button.

[0402] Step 9:

[0403] The server compares the entered information with a database and performs authentication.

[0404] Step 10:

[0405] The terminal displays a consultation form to the user.

[0406] Step 11:

[0407] The user enters their concerns in the text box and clicks the send button.

[0408] Step 12:

[0409] The terminal transmits the input worries to the server.

[0410] Problem analysis and classification

[0411] Step 13:

[0412] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[0413] Step 14:

[0414] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[0415] Step 15:

[0416] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[0417] Use of emotion engine

[0418] Step 16:

[0419] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[0420] Step 17:

[0421] The emotion engine stores the recognized emotions in a database and uses them in the subsequent advice generation process.

[0422] Providing advice

[0423] Step 18:

[0424] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[0425] Step 19:

[0426] The server transmits the advice generated by the selected answering system to the terminal.

[0427] Step 20:

[0428] The terminal displays the advice to the user.

[0429] Social Media Analytics

[0430] Step 21:

[0431] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[0432] Step 22:

[0433] The server collects SNS posting data from users.

[0434] Step 23:

[0435] The server analyzes social media posting data and detects patterns of stress and emotions.

[0436] Step 24:

[0437] The server generates additional advice as needed and sends it to the terminal.

[0438] Step 25:

[0439] The terminal displays additional advice to the user.

[0440] Follow-up

[0441] Step 26:

[0442] The server periodically sends follow-up messages to the user.

[0443] Step 27:

[0444] The user can consult again about new concerns or changes in the situation.

[0445] The above is a concrete process flow for implementing the invention based on the claims. This process allows users to feel comfortable discussing their concerns and receive professional advice tailored to their individual needs and feelings.

[0446] Example 2

[0447] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] In modern society, employees have a wide range of worries, but there are very few places where they can safely and anonymously seek advice about their worries. Furthermore, systems that can accurately recognize users' emotions and provide advice based on those emotions are not yet fully developed. Furthermore, there is a need for a system that can provide more effective advice by analyzing users' emotions and stress using data posted on social media. Given this background, there is a need to develop a system that users can use with confidence and that can provide appropriate advice that is sensitive to individual emotions.

[0449] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for saving user information in a database, means for authenticating the user, means for allowing the user to input concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for recognizing the user's emotions using an emotion engine, and means for analyzing the user's stress and emotional patterns using the social media post data. This allows the user to safely and anonymously seek advice about their concerns and provides appropriate and emotionally sensitive advice for the concerns.

[0450] "User information" refers to personal information used to identify a user, such as the user's name, email address, and password.

[0451] A "database" is a system for storing, managing, and retrieving specific data.

[0452] "Authentication" is the process of verifying that a user is a legitimate user, usually via a password or authentication link.

[0453] A "problem" is a problem or difficulty that a user has.

[0454] "Analysis" is the process of analyzing input data to find useful information and patterns.

[0455] A "category" is a category for classifying data based on its type or characteristics.

[0456] "Priority" is an indicator of the urgency and importance of processing or response.

[0457] The "answer system" is a system that provides appropriate advice to users regarding their concerns.

[0458] A "social media account" is an account registered by a user on a social media platform.

[0459] "Posted data" refers to information such as text, images, and videos that users post to social media.

[0460] An "emotion engine" is an algorithm or system that analyzes input data and recognizes the user's emotions.

[0461] "Stress" refers to a state or emotion that is mentally or physically taxing.

[0462] A "pattern" is a regular feature or trend that recurs in data.

[0463] A "follow-up message" is a message sent to a user periodically for confirmation or advice.

[0464] The present invention provides a system that allows employees to anonymously and safely seek advice about various concerns they may have, and furthermore recognizes the user's emotions and provides appropriate advice. The following describes in detail the embodiments of the invention.

[0465] System Configuration

[0466] The system includes a series of processes for registering user information, authenticating, inputting worries, analyzing worries, recognizing emotions, providing advice, analyzing social media, and following up.

[0467] 1. User Registration

[0468] The terminal displays a new registration form to the user, who enters information such as their name, email address, and password, and clicks the submit button.

[0469] The server receives this information, stores it in a database, and then sends the user an email for authentication.

[0470] The user clicks on the link in the email they receive to complete the authentication.

[0471] 2. Enter your worries

[0472] The terminal displays a login form to the user, who enters their authentication information and clicks the login button.

[0473] If authentication is successful, a consultation form will be displayed, and the user can enter their concerns and submit them.

[0474] 3. Analysis of worries

[0475] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[0476] The server categorizes the text and sets priorities.

[0477] 4. Emotional Recognition

[0478] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the analysis results and category information.

[0479] 5. Providing advice

[0480] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[0481] The server uses the selected system to generate appropriate advice and displays it on the terminal.

[0482] 6. Social Media Analytics

[0483] If the user agrees, the server requests permission to collect posting data from the user's social media account.

[0484] The server analyzes the collected data using an API (e.g., Twitter API) to identify stress and emotional patterns.

[0485] Generate additional advice as needed and display it on the terminal.

[0486] 7. Follow-up

[0487] The server periodically sends follow-up messages to the user.

[0488] The user can then make a new consultation about the current situation.

[0489] Specific examples

[0490] Example 1: User concern input and emotion recognition

[0491] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[0492] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[0493] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[0494] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[0495] Example 2: Additional advice based on social media analysis

[0496] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[0497] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[0498] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[0499] The generated additional advice is presented to the user through the terminal.

[0500] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby alleviating users' concerns and taking their feelings into consideration.

[0501] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] User Registration

[0504] A user visits the sign-up form.

[0505] The device will prompt the user for their name, email address, and password.

[0506] Input: User information (name, email address, password)

[0507] Output: User information entered

[0508] The user enters the required information and clicks the send button.

[0509] The terminal transmits the input data to the server.

[0510] The server processes the received data and stores it in a database.

[0511] Input: User input information

[0512] Data processing: Save input data to database

[0513] Output: Generate authentication email

[0514] The server generates a verification email and sends it to the user's email address.

[0515] The user clicks on the link in the email to complete the authentication.

[0516] Input: Click on the verification link in the email

[0517] Data calculation: Update authentication status based on link clicks

[0518] Output: User authentication completion notification

[0519] Step 2:

[0520] Enter your worries

[0521] The user accesses the login form.

[0522] The device will prompt the user for an email address and password.

[0523] The user enters their email address and password and clicks the login button.

[0524] The terminal transmits the input data to the server.

[0525] The server checks the authentication information, and if the authentication is successful, displays the consultation form.

[0526] Input: User credentials

[0527] Data processing: Check authentication information

[0528] Output: Display of consultation form

[0529] The user enters the problem they want to discuss and clicks the send button.

[0530] The terminal transmits the input data to the server.

[0531] Input: User's problem text

[0532] Output: Receiving trouble data

[0533] Step 3:

[0534] Analysis of worries

[0535] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[0536] Input: User's problem text

[0537] Data processing: Analyzing text using a natural language processing engine

[0538] Output: Analysis results (category, priority)

[0539] The server categorizes the problems and assigns priorities.

[0540] Step 4:

[0541] Emotion recognition

[0542] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the parsed text and category.

[0543] Input: Analysis results and problem text

[0544] Data Computing: Emotion Identification Using Emotion Recognition API

[0545] Output: Emotion recognition result

[0546] Step 5:

[0547] Providing advice

[0548] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[0549] Input: Analysis results and emotion recognition results

[0550] Data Computing: Choosing the Right Answer System

[0551] Output: Advice generation

[0552] The server uses the selected system to generate appropriate advice for the user and displays it on the terminal.

[0553] Input: Advice from the answer system

[0554] Output: Display advice

[0555] Step 6:

[0556] Social Media Analytics

[0557] The user consents to social media analytics.

[0558] The server requests permission to obtain the user's social networking account information.

[0559] Input: User consent

[0560] Output: Get SNS account information

[0561] The server collects posting data from the social media platform.

[0562] Input: SNS account information

[0563] Data processing: Data collection using API (e.g. Twitter API)

[0564] Output: Collected submission data

[0565] The server analyzes the collected data to identify stress and emotional patterns.

[0566] Input: Collected submission data

[0567] Data Computing: Stress and Emotion Pattern Analysis

[0568] Output: Additional advice

[0569] If necessary, the server generates additional advice and causes it to be displayed on the terminal.

[0570] Step 7:

[0571] Follow-up

[0572] The server periodically sends follow-up messages to the user.

[0573] Input: User registration information

[0574] Output: Send follow-up message

[0575] If the user has any new concerns, he or she can consult the system again.

[0576] (Application example 2)

[0577] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0578] Conventional advice systems are limited to analyzing the problems users face, and have difficulty responding based on their emotions. Furthermore, their ability to provide additional advice that takes into account users' social media posts is limited. Therefore, there is a need for systems that provide more accurate advice while protecting users' privacy. Furthermore, systems that provide continuous support through regular follow-ups are inadequate. These issues must be addressed.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0580] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input a concern, means for analyzing the input concern, means for classifying and prioritizing the concern based on the analysis result, means for selecting an appropriate answer system based on the analysis result, means for providing advice to the user using the selected answer system, means for using an emotion engine for recognizing the user's emotions, means for adjusting the advice content based on the recognized emotions, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis result, and means for periodically sending follow-up messages. This makes it possible to provide highly accurate advice for each user's concern, taking emotions into consideration and protecting privacy.

[0581] "User information" refers to personal identification information, account information, and authentication information related to system users.

[0582] A "database" is a storage device within the system that stores and manages user information and problem data.

[0583] "Authentication" is a procedure for verifying that a user is a legitimate user.

[0584] A "problem" refers to a problem or concern that a user brings to the system for consultation.

[0585] "Analysis" refers to data processing to understand the content of the input concerns and to categorize and prioritize them.

[0586] An "emotion engine" is an algorithm or software module that recognizes emotions from a user's concerns and input text.

[0587] "Social media" refers to the social networking platforms used by users, and also includes data posted on these platforms.

[0588] "Advice" refers to advice or suggestions that the system provides based on the user's concerns and feelings.

[0589] "Follow-up messages" refer to periodic confirmations of support, advice, and encouragement.

[0590] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, an emotion engine is incorporated to recognize the user's emotions and adjust the content of the advice, providing more accurate support.

[0591] Basic system configuration:

[0592] 1. User Registration:

[0593] The server displays a new registration form to the user through the terminal.

[0594] The user enters information such as name, email address, and password, and clicks the send button.

[0595] The server stores the received user information in a database and sends the user an authentication email, which the user must click to complete the authentication.

[0596] 2. Entering and analyzing your concerns:

[0597] The server displays a login form to the user through the terminal.

[0598] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[0599] The user enters the problem and clicks the send button.

[0600] The server analyzes the received worries, categorizes them, and sets priorities.

[0601] 3. Use of Emotion Engine:

[0602] The server runs an emotion engine based on the input problem text and analysis results to recognize the user's emotions. The recognized user emotions are used in the subsequent advice generation process.

[0603] 4. Providing advice:

[0604] The server selects an appropriate answering system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results. Based on the selected system, the server generates appropriate advice for the user and displays it on the device.

[0605] 5. Social Media Analytics:

[0606] If the user agrees, the server will obtain the user's social media account information, collect post data from the social media platform, and analyze stress and emotional patterns. If it determines that additional advice is necessary, the server will generate it and display it on the device.

[0607] 6. Follow-up:

[0608] The server periodically sends follow-up messages to the user, allowing the user to reconsider their current situation.

[0609] Hardware and software used:

[0610] Hardware:

[0611] Server: For data processing and analysis (e.g., AWS (registered trademark) EC2)

[0612] User devices: Smartphones (iPhone (registered trademark), ANDROID (registered trademark), smart glasses, head-mounted displays, robots (e.g., Pepper)

[0613] software:

[0614] Database: Stores user information and problem data (e.g., MySQL)

[0615] Emotion engine: Emotion recognition using natural language processing (NLP) (e.g., BERT model for Hugging Face)

[0616] Development environment: Android Studio, Apple Xcode, Python (for NLP), Node.js (for backend)

[0617] Examples:

[0618] 1. User concern input and emotion recognition:

[0619] User: "I've been having a lot of meetings lately and it's really stressful."

[0620] Server: (Emotion engine recognizes "stress")

[0621] Advice: "Try some relaxation techniques, like deep breathing or gentle exercise. You might also want to reassess your time management."

[0622] 2. Additional advice from social media analysis:

[0623] User's social media post: "I'm frustrated because I don't agree with my coworkers."

[0624] Server: (High stress detected)

[0625] Advice: "Try attending a session to improve your communication skills. Also, take some time to relax."

[0626] 3. Prompt for the generative AI model:

[0627] "I'm having trouble with my boss. How can I deal with this?"

[0628] In this way, this system addresses the user's concerns individually, protects privacy, and provides appropriate and professional advice that takes emotions into consideration, thereby alleviating the user's concerns.

[0629] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0630] Step 1:

[0631] A user fills in the new registration form on the terminal and clicks the submit button. The input data includes name, email address, and password. The server receives this input data and saves it in the database. After saving, the server sends a verification email to the user's email address.

[0632] Step 2:

[0633] The user clicks on the link in the authentication email to complete the authentication. This action gives the user a valid account and allows them to use the system.

[0634] Step 3:

[0635] The user displays a login form on the device and enters their email address and password. The server authenticates the user based on the input data, and if successful, displays a consultation form on the device.

[0636] Step 4:

[0637] The user enters their concerns into the consultation form on their device and clicks the send button. The server receives the entered concern data and analyzes it using an NLP (natural language processing) model.

[0638] Step 5:

[0639] The server categorizes the analyzed worry data and sets priorities, such as "work stress," "interpersonal relationships," and "health issues." Based on this, it decides how to respond to the worry.

[0640] Step 6:

[0641] Based on the analysis results, the server activates an emotion engine to recognize the user's emotions from the text of their worries, such as "anxiety," "stress," or "sadness."

[0642] Step 7:

[0643] Based on the emotion recognition results, the server selects an appropriate response system. Specifically, this could be a psychology expert AI, a health advice AI, or other systems. This selection determines the content of the advice to be provided to the user.

[0644] Step 8:

[0645] The server uses the selected answering system to generate appropriate advice for the user and displays it on the terminal. For example, specific advice such as "relaxation techniques" or "communication skills" is provided.

[0646] Step 9:

[0647] If the user agrees, the server will obtain the user's social media account information and collect posting data from the social media platform, based on which the user's emotional and stress patterns will be analyzed.

[0648] Step 10:

[0649] The server generates additional advice as needed based on the analyzed social media data and displays it on the device, resulting in more accurate and personalized support.

[0650] Step 11:

[0651] The server periodically sends follow-up messages to the user, allowing the server to continuously track changes in the user's concerns and emotions and continue to provide appropriate support and advice.

[0652] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0653] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0654] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0655] [Second embodiment]

[0656] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0657] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0658] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0659] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0660] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0661] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0662] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0663] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0664] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0665] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0666] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0667] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0668] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[0669] Basic system configuration

[0670] 1. User Registration

[0671] The terminal displays a new registration form to the user.

[0672] The user enters information such as name, email address, and password, and clicks the send button.

[0673] The server stores the received user information in a database and sends an authentication email to the user.

[0674] The user clicks on the link in the email to complete the authentication.

[0675] 2. Entering and analyzing worries

[0676] The terminal displays a login form to the user.

[0677] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[0678] The user enters the problem and clicks the send button.

[0679] The server analyzes the received worries, categorizes them, and sets priorities.

[0680] 3. Providing advice

[0681] The server selects an appropriate answering system (psychology expert, health advice AI, etc.) based on the analysis results.

[0682] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[0683] 4. Social Media Analytics

[0684] If the user agrees, the server obtains the user's SNS account information.

[0685] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[0686] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[0687] 5. Follow-up

[0688] The server periodically sends follow-up messages to the user.

[0689] The user can then make a new consultation about the current situation.

[0690] Specific examples

[0691] Example 1: User's concerns

[0692] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[0693] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[0694] The server generates "specific methods for improving communication skills" as advice and displays it on the terminal.

[0695] Example 2: Additional advice based on social media analysis

[0696] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[0697] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[0698] This additional advice is presented to the user through the terminal.

[0699] In this way, the present invention provides a system that can alleviate a user's worries by individually addressing the user's worries and providing appropriate advice while protecting privacy.

[0700] The processing flow will be explained below.

[0701] User Registration and Authentication

[0702] Step 1:

[0703] The terminal displays a new registration form to the user.

[0704] Step 2:

[0705] The user enters the required information such as name, email address, and password.

[0706] Step 3:

[0707] The terminal transmits the input user information to the server.

[0708] Step 4:

[0709] The server stores the user information in a database.

[0710] Step 5:

[0711] The server sends a registration completion email to the user.

[0712] Step 6:

[0713] The user clicks on the link in the registration completion email to complete the authentication.

[0714] Entering and analyzing worries

[0715] Step 7:

[0716] The terminal displays a login form to the user.

[0717] Step 8:

[0718] The user enters their email address and password and clicks the login button.

[0719] Step 9:

[0720] The server compares the entered information with a database and performs authentication.

[0721] Step 10:

[0722] The terminal displays a consultation form to the user.

[0723] Step 11:

[0724] The user enters their concerns in the text box and clicks the send button.

[0725] Step 12:

[0726] The terminal transmits the input worries to the server.

[0727] Problem analysis and classification

[0728] Step 13:

[0729] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[0730] Step 14:

[0731] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[0732] Step 15:

[0733] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[0734] Providing advice

[0735] Step 16:

[0736] The server selects the appropriate response based on the answering system (psychology expert AI, health advice AI, etc.) set for each problem category.

[0737] Step 17:

[0738] The server transmits the advice generated by the selected answering system to the terminal.

[0739] Step 18:

[0740] The terminal displays the advice to the user.

[0741] Social Media Analytics

[0742] Step 19:

[0743] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[0744] Step 20:

[0745] The server collects SNS posting data from users.

[0746] Step 21:

[0747] The server analyzes social media posting data and detects patterns of stress and emotions.

[0748] Step 22:

[0749] The server generates additional advice as needed and sends it to the terminal.

[0750] Step 23:

[0751] The terminal displays additional advice to the user.

[0752] Follow-up

[0753] Step 24:

[0754] The server periodically sends follow-up messages to the user.

[0755] Step 25:

[0756] The user can consult again about new concerns or changes in the situation.

[0757] Example 1

[0758] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] Until now, there have only been limited systems that allow employees to safely consult about their concerns, and it has been difficult to provide sufficiently reliable analysis results and advice. Furthermore, there have been few systems that detect stress and emotional patterns not only from the concerns entered by the user but also from daily social media posting data and provide additional advice. This has led to the issue of not being able to provide comprehensive and individual support to users.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0761] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input worries, means for analyzing the input worries, means for classifying the worries and setting priorities based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for analyzing worries using a natural language processing engine, and means for generating advice using a generative AI model. This makes it possible to analyze stress and emotional patterns from both the user's input content and the social media post data, and provide comprehensive and individual support.

[0762] "User information" refers to personal identification information such as the user's name, email address, and password that is registered in the system.

[0763] A "database" is a management system for storing data such as user information and user concerns.

[0764] "Authentication" refers to the process of verifying that a user is a legitimate subscriber.

[0765] A "problem" is a problem or question that a user wants to discuss with the system.

[0766] "Analysis" is the process of analyzing input data and extracting meaning and patterns.

[0767] A "category" is a classification group for classifying the analyzed worries.

[0768] "Priority" is a numerical value or evaluation criterion that indicates the importance or urgency of the analyzed problem.

[0769] An "answer system" is a device or program that provides appropriate advice to users regarding their concerns.

[0770] "Advice" refers to advice or suggestions provided to the user based on the analysis results.

[0771] "Social Media Account" refers to the account of the social media platform to which the User is registered.

[0772] "Posted data" refers to information such as messages, comments, and photos posted by users on social media.

[0773] A "natural language processing engine" refers to a software system that analyzes input text and understands its meaning and sentiment.

[0774] "Generative AI model" refers to an artificial intelligence model that generates new text or advice based on input data.

[0775] A "follow-up message" is a message that the system periodically sends to the user to prompt follow-up or confirmation.

[0776] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[0777] Basic system configuration

[0778] The system utilizes the following hardware and software:

[0779] Device: A device, including a computer or smartphone, that a user accesses

[0780] Server: Central processing unit that processes data, analyzes, and generates advice

[0781] Database: A relational database such as MySQL or PostgreSQL

[0782] Natural language processing engine: Google Cloud Natural Language API

[0783] Generative AI model: OpenAI GPT-3

[0784] Email sending service: SendGrid or Amazon SES

[0785] Social media platform APIs: Twitter API and Facebook Graph API

[0786] Operating procedure

[0787] User Registration

[0788] First, the terminal displays a new registration form to the user. The user enters their name, email address, and password, and clicks the submit button. The server saves the received user information in a database and sends the user an authentication email. The user clicks the link in the email to complete the authentication, and user registration is complete.

[0789] Entering and analyzing worries

[0790] When a user logs in to the system, the terminal displays a consultation form. The user enters their concerns and clicks the send button. The server then analyzes the received concerns using a natural language processing engine, categorizes them, and sets priorities.

[0791] Providing advice

[0792] Based on the analysis results, the server selects an appropriate answering system. Based on the selected system, advice is generated using a generative AI model. The generated advice is displayed on the device for the user to view.

[0793] Social Media Analytics

[0794] If the user agrees, the device provides social media integration functionality and obtains an access token for the social media account using OAuth 2.0. The server periodically collects user posting data from the social media platform and performs automatic analysis. If stress or emotional patterns are detected, additional advice is generated and provided to the user.

[0795] Follow-up

[0796] The server periodically sends follow-up messages to the user, allowing the user to re-enter their concerns about new situations and receive further analysis and advice.

[0797] Specific examples

[0798] For example, if a user inputs "I'm having trouble with my boss and I don't know how to deal with it," the server will categorize this problem as "Work-Relationships" and assign it a medium priority. Using a generative AI model, the server will generate advice on "specific ways to improve communication skills" and display it on the device.

[0799] Additionally, if the user agrees and links their social media account, the server will detect posts such as "Work has been tough lately." In this case, it will determine that the user is in a high-stress state and generate additional advice such as relaxation techniques or counseling. The generated advice will be presented to the user via their device.

[0800] Example prompts for generative AI models

[0801] Analyze the concerns entered by the user, categorize them appropriately, set priorities, generate corresponding advice, and provide it to the user. Also, analyze the user's social media posts and provide additional advice if necessary. Example input: "I'm having trouble with my relationship with my boss and I don't know how to deal with it."

[0802] This system can alleviate users' worries by individually addressing their concerns and providing appropriate advice while protecting their privacy.

[0803] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0804] Step 1:

[0805] Enter and submit user registration information

[0806] The terminal displays a new registration form. The user enters information such as name, email address, and password, and clicks the submit button. The entered information is sent to the server.

[0807] Input: User information (name, email address, password)

[0808] Output: Data sent to the server

[0809] Step 2:

[0810] Saving user information and sending authentication emails

[0811] The server saves the received user information in a database, then generates an authentication email and sends it to the user using an email sending service.

[0812] Input: User information sent in step 1

[0813] Output: User information entry in database, authentication email

[0814] Step 3:

[0815] Authentication complete

[0816] The user clicks on the authentication link in the email, which contains a unique token that the server verifies and completes the user's authentication.

[0817] Input: Token included in the authentication link

[0818] Output: Authentication state update

[0819] Step 4:

[0820] User Login

[0821] The terminal presents the user with a login form, where the user enters their email address and password and submits it. The server checks the credentials against a database and, once authenticated, presents the user with a personalized dashboard.

[0822] Input: Email address, password

[0823] Output: Authentication results, dashboard display

[0824] Step 5:

[0825] Enter and submit your concerns

[0826] The terminal displays a consultation form. The user enters their concerns and clicks the send button. The details of the concerns are sent to the server.

[0827] Input: Content of concern

[0828] Output: Data sent to the server

[0829] Step 6:

[0830] Analysis and classification of worries

[0831] The server uses a natural language processing engine to analyze the received concerns, categorize them, and set priorities. Data processing involves tokenizing the text, analyzing emotions, and classifying them.

[0832] Input: Content of concern

[0833] Output: Analysis results (category, priority)

[0834] Step 7:

[0835] Answer system selection and advice generation

[0836] The server selects the optimal answering system based on the analysis results, and generates advice using the selected system with a generative AI model.

[0837] Input: Analysis results

[0838] Output: Generated advice

[0839] Step 8:

[0840] Displaying Advice

[0841] The server sends the generated advice to the user's terminal, which displays it.

[0842] Input: Generated advice

[0843] Output: Advice displayed on terminal

[0844] Step 9:

[0845] Linking social media accounts and collecting data

[0846] If the user agrees, the device provides the SNS integration function. It obtains an access token for the SNS account using OAuth 2.0 and sends it to the server. The server then collects the posted data from the SNS platform.

[0847] Input: User consent, SNS access token

[0848] Output: SNS post data

[0849] Step 10:

[0850] Analyzing social media data and generating additional advice

[0851] The server analyzes the collected social media post data using a natural language processing engine to detect stress and emotional patterns, and generates and provides additional advice to users as needed.

[0852] Input: SNS post data

[0853] Output: Further advice, emotion pattern detection

[0854] Step 11:

[0855] Regular follow-up

[0856] The server periodically sends follow-up messages to the user. The user can enter and submit new concerns or questions. These new concerns are also processed in steps 6 to 10.

[0857] Input: Schedule a follow-up message

[0858] Output: Send follow-up message

[0859] (Application example 1)

[0860] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0861] Employees working in brick-and-mortar stores often have concerns about the work environment, interpersonal relationships, stress, and other issues, but lack the means to appropriately discuss and resolve these issues. Even after employees have discussed their concerns, there is a need for a way to continually follow up and provide appropriate advice. Furthermore, adding a function to analyze social media posting data to check employees' mental health would enable more effective support, contributing to improved productivity and employee satisfaction throughout the workplace.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0863] In this invention, the server includes means for accepting user information, means for storing the user information in a database, means for authenticating the user, means for having the user input their concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate response system based on the analysis results, means for providing advice to the user using the selected response system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, and means for providing advice on concerns via a device for employees to use in a physical store. This allows employees to easily consult about their concerns and quickly receive appropriate advice. Furthermore, by continuously monitoring employees' mental health through social media analysis and providing additional advice as needed, it is possible to reduce employee stress and improve the work environment.

[0864] "User information" refers to personal user identification information, such as name, email address, and password, that is registered in the system.

[0865] The "database" is a system for organizing, saving, and managing data such as user information, input concerns, analysis results, and advice content.

[0866] "Authentication" is a procedure for verifying that a user has legitimate access rights, and generally involves the use of an email address and password.

[0867] A "problem" is a problem or issue that a user has at work or in their personal life and that they input to the system for consultation.

[0868] "Analysis" is the process of classifying input concerns using AI technology, setting priorities, and deriving appropriate countermeasures.

[0869] An "answer system" is a system that provides appropriate advice to users based on the analysis results, and includes psychology experts and health advice AI.

[0870] "Social media account" refers to the account information of the SNS (social networking service) used by the user.

[0871] "Posted data" refers to content such as text, images, and videos that users publish on social media, and is the subject of analysis.

[0872] A "follow-up message" is a message that the system periodically sends to the user, with the purpose of informing them of progress in their concerns and encouraging them to seek new advice.

[0873] A "device" is hardware that a user uses to access the system, including smartphones and smart glasses.

[0874] This invention is a system for providing advice to employees in brick-and-mortar stores, which can be accessed by employees via smartphones or smart glasses. The configuration and operation of the system are described below.

[0875] Basic system configuration

[0876] 1. User Registration

[0877] The device (smartphone or smart glasses) displays a new registration form to employees working in the physical store.

[0878] The employee enters user information such as name, email address, and password, and clicks the submit button.

[0879] The server stores the received user information in a database and sends an authentication email to the employee.

[0880] Employees click on the link in the email to complete the authentication.

[0881] 2. Entering and analyzing worries

[0882] The terminal presents the authenticated employee with a login form.

[0883] Employees enter their email address and password, and once authentication is complete, a consultation form will be displayed.

[0884] Employees enter their concerns about work or their personal lives and click the send button.

[0885] The server analyzes the received worries, classifies them into categories (e.g., "work," "relationships," etc.), and sets priorities.

[0886] The server uses an AI analysis tool (e.g., GPT-4) to appropriately analyze the input concerns.

[0887] 3. Providing advice

[0888] The server selects an appropriate answering system (e.g., psychology expert, health advisor AI) based on the analysis results.

[0889] The selected system will use IBM Watson to generate appropriate advice and display it on the device.

[0890] 4. Social Media Analytics

[0891] If the employee consents, the server collects posting data from the employee's social media accounts (e.g., Twitter and Facebook).

[0892] The server collects the posted data via the Twitter API and Facebook API and analyzes it using GPT-4.

[0893] The server analyzes stress and emotional patterns to determine high stress levels and negative patterns.

[0894] If necessary, the server generates additional advice (e.g., relaxation techniques, counseling recommendations) and displays them on the terminal.

[0895] 5. Follow-up

[0896] The server periodically sends follow-up messages to employees.

[0897] Employees can seek new counseling regarding their current situation.

[0898] Specific examples

[0899] Example 1: User's concerns

[0900] An employee enters into the system, "I've been having trouble communicating with my coworkers lately."

[0901] The server classifies this problem as "human relationships" and assigns it a medium priority.

[0902] Using IBM Watson, advice on "specific ways to improve communication skills" is generated and displayed on the device.

[0903] Example 2: Additional advice based on social media analysis

[0904] If the employee agrees, the server detects posts from Twitter or Facebook saying "Work has been tough lately."

[0905] The server determines this to be a high-stress state and generates additional advice recommending necessary relaxation techniques or counseling.

[0906] This additional advice is presented to employees via a terminal.

[0907] Prompt Sentence Examples

[0908] Example prompt: "Analyze the following problem and provide appropriate advice. Problem: 'Recently, I've been having trouble communicating with my colleagues.'"

[0909] In this way, the present invention provides a system that allows employees working in brick-and-mortar stores to easily consult about their concerns and quickly receive appropriate advice.Continual support through analysis of social media posts can help maintain and improve the mental health of employees.

[0910] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0911] Step 1:

[0912] User Registration

[0913] The terminal displays a new registration form to employees working in physical stores.

[0914] Input: An employee enters user information such as name, email address, and password.

[0915] The terminal transmits the input information to the server.

[0916] The server stores the received user information in a database and sends an authentication email to the employee.

[0917] Output: A verification email is sent to the employee.

[0918] Step 2:

[0919] certification

[0920] Employees complete the verification by clicking the link in the verification email.

[0921] Input: Click on the authentication link.

[0922] The server receives the authentication link, verifies the user information, and completes the authentication.

[0923] Output: The user is authenticated and can log into the system.

[0924] Step 3:

[0925] Log in and enter your concerns

[0926] The terminal presents the authenticated employee with a login form.

[0927] Enter your email address and password to log in.

[0928] Employees enter their concerns about the workplace or their personal life into the consultation form and click the send button.

[0929] The terminal transmits the input worries to the server.

[0930] Output: The problem is sent to the server.

[0931] Step 4:

[0932] Analysis of worries

[0933] The server analyzes the received concerns.

[0934] Input: Received trouble data.

[0935] The server uses an AI analysis tool (GPT-4) to classify worries into categories (e.g., "work," "relationships," etc.) and set priorities.

[0936] Output: Categorised worries and priorities.

[0937] Step 5:

[0938] Generating Advice

[0939] The server selects an appropriate answering system based on the analysis results.

[0940] Input: Categorised worries and priorities.

[0941] The server generates appropriate advice using a selected answering system (IBM Watson).

[0942] Output: Generation of advice.

[0943] Step 6:

[0944] Providing advice

[0945] The server transmits the generated advice to the terminal.

[0946] Input: The generated advice.

[0947] The terminal displays the advice to the employee.

[0948] Output: The advice is displayed to the employee.

[0949] Step 7:

[0950] Social Media Analytics

[0951] The server collects posting data from employees' social media accounts if they consent.

[0952] Input: Social media account information with your consent.

[0953] The server collects post data using the Twitter API and Facebook API and analyzes it using GPT-4.

[0954] The server determines stress and emotional patterns and detects high stress states and negative patterns.

[0955] Output: Stress analysis results.

[0956] Step 8:

[0957] Generating additional advice

[0958] The server generates any additional advice needed based on the analysis results.

[0959] Input: Social media analysis results.

[0960] The server generates recommendations for additional relaxation techniques and counseling.

[0961] Output: Additional advice.

[0962] Step 9:

[0963] Providing additional advice

[0964] The server transmits the generated additional advice to the terminal.

[0965] Input: Generated additional advice.

[0966] The terminal displays additional advice to the employee.

[0967] Output: Additional advice is displayed to the employee.

[0968] Step 10:

[0969] Follow-up

[0970] The server periodically sends follow-up messages to employees.

[0971] Input: Follow-up message generation routine.

[0972] Employees can seek new counseling regarding their current situation.

[0973] Output: The follow-up message.

[0974] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0975] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns they have, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the advice.

[0976] Basic system configuration

[0977] 1. User Registration

[0978] The terminal displays a new registration form to the user.

[0979] The user enters information such as name, email address, and password, and clicks the send button.

[0980] The server stores the received user information in a database and sends an authentication email to the user.

[0981] The user clicks on the link in the email to complete the authentication.

[0982] 2. Entering and analyzing worries

[0983] The terminal displays a login form to the user.

[0984] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[0985] The user enters the problem and clicks the send button.

[0986] The server analyzes the received worries, categorizes them, and sets priorities.

[0987] 3. Use of Emotion Engine

[0988] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[0989] The recognized user sentiment is utilized in the subsequent advice generation process.

[0990] 4. Providing advice

[0991] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[0992] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[0993] 5. Social Media Analytics

[0994] If the user agrees, the server obtains the user's SNS account information.

[0995] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[0996] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[0997] 6. Follow-up

[0998] The server periodically sends follow-up messages to the user.

[0999] The user can then make a new consultation about the current situation.

[1000] Specific examples

[1001] Example 1: User concern input and emotion recognition

[1002] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1003] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1004] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[1005] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[1006] Example 2: Additional advice based on social media analysis

[1007] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1008] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1009] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[1010] The generated additional advice is presented to the user through the terminal.

[1011] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby reducing users' concerns and taking their feelings into consideration.

[1012] The processing flow will be explained below.

[1013] User Registration and Authentication

[1014] Step 1:

[1015] The terminal displays a new registration form to the user.

[1016] Step 2:

[1017] The user enters the required information such as name, email address, and password.

[1018] Step 3:

[1019] The terminal transmits the input user information to the server.

[1020] Step 4:

[1021] The server stores the user information in a database.

[1022] Step 5:

[1023] The server sends a registration completion email to the user.

[1024] Step 6:

[1025] The user clicks on the link in the registration completion email to complete the authentication.

[1026] Entering and analyzing worries

[1027] Step 7:

[1028] The terminal displays a login form to the user.

[1029] Step 8:

[1030] The user enters their email address and password and clicks the login button.

[1031] Step 9:

[1032] The server compares the entered information with a database and performs authentication.

[1033] Step 10:

[1034] The terminal displays a consultation form to the user.

[1035] Step 11:

[1036] The user enters their concerns in the text box and clicks the send button.

[1037] Step 12:

[1038] The terminal transmits the input worries to the server.

[1039] Problem analysis and classification

[1040] Step 13:

[1041] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[1042] Step 14:

[1043] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[1044] Step 15:

[1045] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[1046] Use of emotion engine

[1047] Step 16:

[1048] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[1049] Step 17:

[1050] The emotion engine stores the recognized emotions in a database and uses them in the subsequent advice generation process.

[1051] Providing advice

[1052] Step 18:

[1053] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1054] Step 19:

[1055] The server transmits the advice generated by the selected answering system to the terminal.

[1056] Step 20:

[1057] The terminal displays the advice to the user.

[1058] Social Media Analytics

[1059] Step 21:

[1060] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[1061] Step 22:

[1062] The server collects SNS posting data from users.

[1063] Step 23:

[1064] The server analyzes social media posting data and detects patterns of stress and emotions.

[1065] Step 24:

[1066] The server generates additional advice as needed and sends it to the terminal.

[1067] Step 25:

[1068] The terminal displays additional advice to the user.

[1069] Follow-up

[1070] Step 26:

[1071] The server periodically sends follow-up messages to the user.

[1072] Step 27:

[1073] The user can consult again about new concerns or changes in the situation.

[1074] The above is a concrete process flow for implementing the invention based on the claims. This process allows users to feel comfortable discussing their concerns and receive professional advice tailored to their individual needs and feelings.

[1075] Example 2

[1076] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1077] In modern society, employees have a wide range of worries, but there are very few places where they can safely and anonymously seek advice about their worries. Furthermore, systems that can accurately recognize users' emotions and provide advice based on those emotions are not yet fully developed. Furthermore, there is a need for a system that can provide more effective advice by analyzing users' emotions and stress using data posted on social media. Given this background, there is a need to develop a system that users can use with confidence and that can provide appropriate advice that is sensitive to individual emotions.

[1078] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for saving user information in a database, means for authenticating the user, means for allowing the user to input concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for recognizing the user's emotions using an emotion engine, and means for analyzing the user's stress and emotional patterns using the social media post data. This allows the user to safely and anonymously seek advice about their concerns and provides appropriate and emotionally sensitive advice for the concerns.

[1079] "User information" refers to personal information used to identify a user, such as the user's name, email address, and password.

[1080] A "database" is a system for storing, managing, and retrieving specific data.

[1081] "Authentication" is the process of verifying that a user is a legitimate user, usually via a password or authentication link.

[1082] A "problem" is a problem or difficulty that a user has.

[1083] "Analysis" is the process of analyzing input data to find useful information and patterns.

[1084] A "category" is a category for classifying data based on its type or characteristics.

[1085] "Priority" is an indicator of the urgency and importance of processing or response.

[1086] The "answer system" is a system that provides appropriate advice to users regarding their concerns.

[1087] A "social media account" is an account registered by a user on a social media platform.

[1088] "Posted data" refers to information such as text, images, and videos that users post to social media.

[1089] An "emotion engine" is an algorithm or system that analyzes input data and recognizes the user's emotions.

[1090] "Stress" refers to a state or emotion that is mentally or physically taxing.

[1091] A "pattern" is a regular feature or trend that recurs in data.

[1092] A "follow-up message" is a message sent to a user periodically for confirmation or advice.

[1093] The present invention provides a system that allows employees to anonymously and safely seek advice about various concerns they may have, and furthermore recognizes the user's emotions and provides appropriate advice. The following describes in detail the embodiments of the invention.

[1094] System Configuration

[1095] The system includes a series of processes for registering user information, authenticating, inputting worries, analyzing worries, recognizing emotions, providing advice, analyzing social media, and following up.

[1096] 1. User Registration

[1097] The terminal displays a new registration form to the user, who enters information such as their name, email address, and password, and clicks the submit button.

[1098] The server receives this information, stores it in a database, and then sends the user an email for authentication.

[1099] The user clicks on the link in the email they receive to complete the authentication.

[1100] 2. Enter your worries

[1101] The terminal displays a login form to the user, who enters their authentication information and clicks the login button.

[1102] If authentication is successful, a consultation form will be displayed, and the user can enter their concerns and submit them.

[1103] 3. Analysis of worries

[1104] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[1105] The server categorizes the text and sets priorities.

[1106] 4. Emotional Recognition

[1107] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the analysis results and category information.

[1108] 5. Providing advice

[1109] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1110] The server uses the selected system to generate appropriate advice and displays it on the terminal.

[1111] 6. Social Media Analytics

[1112] If the user agrees, the server requests permission to collect posting data from the user's social media account.

[1113] The server analyzes the collected data using an API (e.g., Twitter API) to identify stress and emotional patterns.

[1114] Generate additional advice as needed and display it on the terminal.

[1115] 7. Follow-up

[1116] The server periodically sends follow-up messages to the user.

[1117] The user can then make a new consultation about the current situation.

[1118] Specific examples

[1119] Example 1: User concern input and emotion recognition

[1120] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1121] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1122] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[1123] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[1124] Example 2: Additional advice based on social media analysis

[1125] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1126] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1127] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[1128] The generated additional advice is presented to the user through the terminal.

[1129] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby alleviating users' concerns and taking their feelings into consideration.

[1130] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] User Registration

[1133] A user visits the sign-up form.

[1134] The device will prompt the user for their name, email address, and password.

[1135] Input: User information (name, email address, password)

[1136] Output: User information entered

[1137] The user enters the required information and clicks the send button.

[1138] The terminal transmits the input data to the server.

[1139] The server processes the received data and stores it in a database.

[1140] Input: User input information

[1141] Data processing: Save input data to database

[1142] Output: Generate authentication email

[1143] The server generates a verification email and sends it to the user's email address.

[1144] The user clicks on the link in the email to complete the authentication.

[1145] Input: Click on the verification link in the email

[1146] Data calculation: Update authentication status based on link clicks

[1147] Output: User authentication completion notification

[1148] Step 2:

[1149] Enter your worries

[1150] The user accesses the login form.

[1151] The device will prompt the user for an email address and password.

[1152] The user enters their email address and password and clicks the login button.

[1153] The terminal transmits the input data to the server.

[1154] The server checks the authentication information, and if the authentication is successful, displays the consultation form.

[1155] Input: User credentials

[1156] Data processing: Check authentication information

[1157] Output: Display of consultation form

[1158] The user enters the problem they want to discuss and clicks the send button.

[1159] The terminal transmits the input data to the server.

[1160] Input: User's problem text

[1161] Output: Receiving trouble data

[1162] Step 3:

[1163] Analysis of worries

[1164] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[1165] Input: User's problem text

[1166] Data processing: Analyzing text using a natural language processing engine

[1167] Output: Analysis results (category, priority)

[1168] The server categorizes the problems and assigns priorities.

[1169] Step 4:

[1170] Emotion recognition

[1171] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the parsed text and category.

[1172] Input: Analysis results and problem text

[1173] Data Computing: Emotion Identification Using Emotion Recognition API

[1174] Output: Emotion recognition result

[1175] Step 5:

[1176] Providing advice

[1177] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1178] Input: Analysis results and emotion recognition results

[1179] Data Computing: Choosing the Right Answer System

[1180] Output: Advice generation

[1181] The server uses the selected system to generate appropriate advice for the user and displays it on the terminal.

[1182] Input: Advice from the answer system

[1183] Output: Display advice

[1184] Step 6:

[1185] Social Media Analytics

[1186] The user consents to social media analytics.

[1187] The server requests permission to obtain the user's social networking account information.

[1188] Input: User consent

[1189] Output: Get SNS account information

[1190] The server collects posting data from the social media platform.

[1191] Input: SNS account information

[1192] Data processing: Data collection using API (e.g. Twitter API)

[1193] Output: Collected submission data

[1194] The server analyzes the collected data to identify stress and emotional patterns.

[1195] Input: Collected submission data

[1196] Data Computing: Stress and Emotion Pattern Analysis

[1197] Output: Additional advice

[1198] If necessary, the server generates additional advice and causes it to be displayed on the terminal.

[1199] Step 7:

[1200] Follow-up

[1201] The server periodically sends follow-up messages to the user.

[1202] Input: User registration information

[1203] Output: Send follow-up message

[1204] If the user has any new concerns, he or she can consult the system again.

[1205] (Application example 2)

[1206] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1207] Conventional advice systems are limited to analyzing the problems users face, and have difficulty responding based on their emotions. Furthermore, their ability to provide additional advice that takes into account users' social media posts is limited. Therefore, there is a need for systems that provide more accurate advice while protecting users' privacy. Furthermore, systems that provide continuous support through regular follow-ups are inadequate. These issues must be addressed.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1209] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input a concern, means for analyzing the input concern, means for classifying and prioritizing the concern based on the analysis result, means for selecting an appropriate answer system based on the analysis result, means for providing advice to the user using the selected answer system, means for using an emotion engine for recognizing the user's emotions, means for adjusting the advice content based on the recognized emotions, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis result, and means for periodically sending follow-up messages. This makes it possible to provide highly accurate advice for each user's concern, taking emotions into consideration and protecting privacy.

[1210] "User information" refers to personal identification information, account information, and authentication information related to system users.

[1211] A "database" is a storage device within the system that stores and manages user information and problem data.

[1212] "Authentication" is a procedure for verifying that a user is a legitimate user.

[1213] A "problem" refers to a problem or concern that a user brings to the system for consultation.

[1214] "Analysis" refers to data processing to understand the content of the input concerns and to categorize and prioritize them.

[1215] An "emotion engine" is an algorithm or software module that recognizes emotions from a user's concerns and input text.

[1216] "Social media" refers to the social networking platforms used by users, and also includes data posted on these platforms.

[1217] "Advice" refers to advice or suggestions that the system provides based on the user's concerns and feelings.

[1218] "Follow-up messages" refer to periodic confirmations of support, advice, and encouragement.

[1219] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, an emotion engine is incorporated to recognize the user's emotions and adjust the content of the advice, providing more accurate support.

[1220] Basic system configuration:

[1221] 1. User Registration:

[1222] The server displays a new registration form to the user through the terminal.

[1223] The user enters information such as name, email address, and password, and clicks the send button.

[1224] The server stores the received user information in a database and sends the user an authentication email, which the user must click to complete the authentication.

[1225] 2. Entering and analyzing your concerns:

[1226] The server displays a login form to the user through the terminal.

[1227] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[1228] The user enters the problem and clicks the send button.

[1229] The server analyzes the received worries, categorizes them, and sets priorities.

[1230] 3. Use of Emotion Engine:

[1231] The server runs an emotion engine based on the input problem text and analysis results to recognize the user's emotions. The recognized user emotions are used in the subsequent advice generation process.

[1232] 4. Providing advice:

[1233] The server selects an appropriate answering system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results. Based on the selected system, the server generates appropriate advice for the user and displays it on the device.

[1234] 5. Social Media Analytics:

[1235] If the user agrees, the server will obtain the user's social media account information, collect post data from the social media platform, and analyze stress and emotional patterns. If it determines that additional advice is necessary, the server will generate it and display it on the device.

[1236] 6. Follow-up:

[1237] The server periodically sends follow-up messages to the user, allowing the user to reconsider their current situation.

[1238] Hardware and software used:

[1239] Hardware:

[1240] Server: For data processing and analysis (e.g. AWS EC2)

[1241] User devices: Smartphones (iPhone, Android), smart glasses, head-mounted displays, robots (e.g., Pepper)

[1242] software:

[1243] Database: Stores user information and problem data (e.g., MySQL)

[1244] Emotion engine: Emotion recognition using natural language processing (NLP) (e.g., BERT model for Hugging Face)

[1245] Development environment: Android Studio, Apple Xcode, Python (for NLP), Node.js (for backend)

[1246] Examples:

[1247] 1. User concern input and emotion recognition:

[1248] User: "I've been having a lot of meetings lately and it's really stressful."

[1249] Server: (Emotion engine recognizes "stress")

[1250] Advice: "Try some relaxation techniques, like deep breathing or gentle exercise. You might also want to reassess your time management."

[1251] 2. Additional advice from social media analysis:

[1252] User's social media post: "I'm frustrated because I don't agree with my coworkers."

[1253] Server: (High stress detected)

[1254] Advice: "Try attending a session to improve your communication skills. Also, take some time to relax."

[1255] 3. Prompt for the generative AI model:

[1256] "I'm having trouble with my boss. How can I deal with this?"

[1257] In this way, this system addresses the user's concerns individually, protects privacy, and provides appropriate and professional advice that takes emotions into consideration, thereby alleviating the user's concerns.

[1258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1259] Step 1:

[1260] A user fills in the new registration form on the terminal and clicks the submit button. The input data includes name, email address, and password. The server receives this input data and saves it in the database. After saving, the server sends a verification email to the user's email address.

[1261] Step 2:

[1262] The user clicks on the link in the authentication email to complete the authentication. This action gives the user a valid account and allows them to use the system.

[1263] Step 3:

[1264] The user displays a login form on the device and enters their email address and password. The server authenticates the user based on the input data, and if successful, displays a consultation form on the device.

[1265] Step 4:

[1266] The user enters their concerns into the consultation form on their device and clicks the send button. The server receives the entered concern data and analyzes it using an NLP (natural language processing) model.

[1267] Step 5:

[1268] The server categorizes the analyzed worry data and sets priorities, such as "work stress," "interpersonal relationships," and "health issues." Based on this, it decides how to respond to the worry.

[1269] Step 6:

[1270] Based on the analysis results, the server activates an emotion engine to recognize the user's emotions from the text of their worries, such as "anxiety," "stress," or "sadness."

[1271] Step 7:

[1272] Based on the emotion recognition results, the server selects an appropriate response system. Specifically, this could be a psychology expert AI, a health advice AI, or other systems. This selection determines the content of the advice to be provided to the user.

[1273] Step 8:

[1274] The server uses the selected answering system to generate appropriate advice for the user and displays it on the terminal. For example, specific advice such as "relaxation techniques" or "communication skills" is provided.

[1275] Step 9:

[1276] If the user agrees, the server will obtain the user's social media account information and collect posting data from the social media platform, based on which the user's emotional and stress patterns will be analyzed.

[1277] Step 10:

[1278] The server generates additional advice as needed based on the analyzed social media data and displays it on the device, resulting in more accurate and personalized support.

[1279] Step 11:

[1280] The server periodically sends follow-up messages to the user, allowing the server to continuously track changes in the user's concerns and emotions and continue to provide appropriate support and advice.

[1281] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1282] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1283] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1284] [Third embodiment]

[1285] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1286] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1287] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1288] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1289] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1290] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1291] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1292] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1293] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1294] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1295] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1296] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1297] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[1298] Basic system configuration

[1299] 1. User Registration

[1300] The terminal displays a new registration form to the user.

[1301] The user enters information such as name, email address, and password, and clicks the send button.

[1302] The server stores the received user information in a database and sends an authentication email to the user.

[1303] The user clicks on the link in the email to complete the authentication.

[1304] 2. Entering and analyzing worries

[1305] The terminal displays a login form to the user.

[1306] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[1307] The user enters the problem and clicks the send button.

[1308] The server analyzes the received worries, categorizes them, and sets priorities.

[1309] 3. Providing advice

[1310] The server selects an appropriate answering system (psychology expert, health advice AI, etc.) based on the analysis results.

[1311] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[1312] 4. Social Media Analytics

[1313] If the user agrees, the server obtains the user's SNS account information.

[1314] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[1315] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[1316] 5. Follow-up

[1317] The server periodically sends follow-up messages to the user.

[1318] The user can then make a new consultation about the current situation.

[1319] Specific examples

[1320] Example 1: User's concerns

[1321] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1322] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1323] The server generates "specific methods for improving communication skills" as advice and displays it on the terminal.

[1324] Example 2: Additional advice based on social media analysis

[1325] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1326] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1327] This additional advice is presented to the user through the terminal.

[1328] In this way, the present invention provides a system that can alleviate a user's worries by individually addressing the user's worries and providing appropriate advice while protecting privacy.

[1329] The processing flow will be explained below.

[1330] User Registration and Authentication

[1331] Step 1:

[1332] The terminal displays a new registration form to the user.

[1333] Step 2:

[1334] The user enters the required information such as name, email address, and password.

[1335] Step 3:

[1336] The terminal transmits the input user information to the server.

[1337] Step 4:

[1338] The server stores the user information in a database.

[1339] Step 5:

[1340] The server sends a registration completion email to the user.

[1341] Step 6:

[1342] The user clicks on the link in the registration completion email to complete the authentication.

[1343] Entering and analyzing worries

[1344] Step 7:

[1345] The terminal displays a login form to the user.

[1346] Step 8:

[1347] The user enters their email address and password and clicks the login button.

[1348] Step 9:

[1349] The server compares the entered information with a database and performs authentication.

[1350] Step 10:

[1351] The terminal displays a consultation form to the user.

[1352] Step 11:

[1353] The user enters their concerns in the text box and clicks the send button.

[1354] Step 12:

[1355] The terminal transmits the input worries to the server.

[1356] Problem analysis and classification

[1357] Step 13:

[1358] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[1359] Step 14:

[1360] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[1361] Step 15:

[1362] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[1363] Providing advice

[1364] Step 16:

[1365] The server selects the appropriate response based on the answering system (psychology expert AI, health advice AI, etc.) set for each problem category.

[1366] Step 17:

[1367] The server transmits the advice generated by the selected answering system to the terminal.

[1368] Step 18:

[1369] The terminal displays the advice to the user.

[1370] Social Media Analytics

[1371] Step 19:

[1372] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[1373] Step 20:

[1374] The server collects SNS posting data from users.

[1375] Step 21:

[1376] The server analyzes social media posting data and detects patterns of stress and emotions.

[1377] Step 22:

[1378] The server generates additional advice as needed and sends it to the terminal.

[1379] Step 23:

[1380] The terminal displays additional advice to the user.

[1381] Follow-up

[1382] Step 24:

[1383] The server periodically sends follow-up messages to the user.

[1384] Step 25:

[1385] The user can consult again about new concerns or changes in the situation.

[1386] Example 1

[1387] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1388] Until now, there have only been limited systems that allow employees to safely consult about their concerns, and it has been difficult to provide sufficiently reliable analysis results and advice. Furthermore, there have been few systems that detect stress and emotional patterns not only from the concerns entered by the user but also from daily social media posting data and provide additional advice. This has led to the issue of not being able to provide comprehensive and individual support to users.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1390] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input worries, means for analyzing the input worries, means for classifying the worries and setting priorities based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for analyzing worries using a natural language processing engine, and means for generating advice using a generative AI model. This makes it possible to analyze stress and emotional patterns from both the user's input content and the social media post data, and provide comprehensive and individual support.

[1391] "User information" refers to personal identification information such as the user's name, email address, and password that is registered in the system.

[1392] A "database" is a management system for storing data such as user information and user concerns.

[1393] "Authentication" refers to the process of verifying that a user is a legitimate subscriber.

[1394] A "problem" is a problem or question that a user wants to discuss with the system.

[1395] "Analysis" is the process of analyzing input data and extracting meaning and patterns.

[1396] A "category" is a classification group for classifying the analyzed worries.

[1397] "Priority" is a numerical value or evaluation criterion that indicates the importance or urgency of the analyzed problem.

[1398] An "answer system" is a device or program that provides appropriate advice to users regarding their concerns.

[1399] "Advice" refers to advice or suggestions provided to the user based on the analysis results.

[1400] "Social Media Account" refers to the account of the social media platform to which the User is registered.

[1401] "Posted data" refers to information such as messages, comments, and photos posted by users on social media.

[1402] A "natural language processing engine" refers to a software system that analyzes input text and understands its meaning and sentiment.

[1403] "Generative AI model" refers to an artificial intelligence model that generates new text or advice based on input data.

[1404] A "follow-up message" is a message that the system periodically sends to the user to prompt follow-up or confirmation.

[1405] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[1406] Basic system configuration

[1407] The system utilizes the following hardware and software:

[1408] Device: A device, including a computer or smartphone, that a user accesses

[1409] Server: Central processing unit that processes data, analyzes, and generates advice

[1410] Database: A relational database such as MySQL or PostgreSQL

[1411] Natural language processing engine: Google Cloud Natural Language API

[1412] Generative AI model: OpenAI GPT-3

[1413] Email sending service: SendGrid or Amazon SES

[1414] Social media platform APIs: Twitter API and Facebook Graph API

[1415] Operating procedure

[1416] User Registration

[1417] First, the terminal displays a new registration form to the user. The user enters their name, email address, and password, and clicks the submit button. The server saves the received user information in a database and sends the user an authentication email. The user clicks the link in the email to complete the authentication, and user registration is complete.

[1418] Entering and analyzing worries

[1419] When a user logs in to the system, the terminal displays a consultation form. The user enters their concerns and clicks the send button. The server then analyzes the received concerns using a natural language processing engine, categorizes them, and sets priorities.

[1420] Providing advice

[1421] Based on the analysis results, the server selects an appropriate answering system. Based on the selected system, advice is generated using a generative AI model. The generated advice is displayed on the device for the user to view.

[1422] Social Media Analytics

[1423] If the user agrees, the device provides social media integration functionality and obtains an access token for the social media account using OAuth 2.0. The server periodically collects user posting data from the social media platform and performs automatic analysis. If stress or emotional patterns are detected, additional advice is generated and provided to the user.

[1424] Follow-up

[1425] The server periodically sends follow-up messages to the user, allowing the user to re-enter their concerns about new situations and receive further analysis and advice.

[1426] Specific examples

[1427] For example, if a user inputs "I'm having trouble with my boss and I don't know how to deal with it," the server will categorize this problem as "Work-Relationships" and assign it a medium priority. Using a generative AI model, the server will generate advice on "specific ways to improve communication skills" and display it on the device.

[1428] Additionally, if the user agrees and links their social media account, the server will detect posts such as "Work has been tough lately." In this case, it will determine that the user is in a high-stress state and generate additional advice such as relaxation techniques or counseling. The generated advice will be presented to the user via their device.

[1429] Example prompts for generative AI models

[1430] Analyze the concerns entered by the user, categorize them appropriately, set priorities, generate corresponding advice, and provide it to the user. Also, analyze the user's social media posts and provide additional advice if necessary. Example input: "I'm having trouble with my relationship with my boss and I don't know how to deal with it."

[1431] This system can alleviate users' worries by individually addressing their concerns and providing appropriate advice while protecting their privacy.

[1432] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1433] Step 1:

[1434] Enter and submit user registration information

[1435] The terminal displays a new registration form. The user enters information such as name, email address, and password, and clicks the submit button. The entered information is sent to the server.

[1436] Input: User information (name, email address, password)

[1437] Output: Data sent to the server

[1438] Step 2:

[1439] Saving user information and sending authentication emails

[1440] The server saves the received user information in a database, then generates an authentication email and sends it to the user using an email sending service.

[1441] Input: User information sent in step 1

[1442] Output: User information entry in database, authentication email

[1443] Step 3:

[1444] Authentication complete

[1445] The user clicks on the authentication link in the email, which contains a unique token that the server verifies and completes the user's authentication.

[1446] Input: Token included in the authentication link

[1447] Output: Authentication state update

[1448] Step 4:

[1449] User Login

[1450] The terminal presents the user with a login form, where the user enters their email address and password and submits it. The server checks the credentials against a database and, once authenticated, presents the user with a personalized dashboard.

[1451] Input: Email address, password

[1452] Output: Authentication results, dashboard display

[1453] Step 5:

[1454] Enter and submit your concerns

[1455] The terminal displays a consultation form. The user enters their concerns and clicks the send button. The details of the concerns are sent to the server.

[1456] Input: Content of concern

[1457] Output: Data sent to the server

[1458] Step 6:

[1459] Analysis and classification of worries

[1460] The server uses a natural language processing engine to analyze the received concerns, categorize them, and set priorities. Data processing involves tokenizing the text, analyzing emotions, and classifying them.

[1461] Input: Content of concern

[1462] Output: Analysis results (category, priority)

[1463] Step 7:

[1464] Answer system selection and advice generation

[1465] The server selects the optimal answering system based on the analysis results, and generates advice using the selected system with a generative AI model.

[1466] Input: Analysis results

[1467] Output: Generated advice

[1468] Step 8:

[1469] Displaying Advice

[1470] The server sends the generated advice to the user's terminal, which displays it.

[1471] Input: Generated advice

[1472] Output: Advice displayed on terminal

[1473] Step 9:

[1474] Linking social media accounts and collecting data

[1475] If the user agrees, the device provides the SNS integration function. It obtains an access token for the SNS account using OAuth 2.0 and sends it to the server. The server then collects the posted data from the SNS platform.

[1476] Input: User consent, SNS access token

[1477] Output: SNS post data

[1478] Step 10:

[1479] Analyzing social media data and generating additional advice

[1480] The server analyzes the collected social media post data using a natural language processing engine to detect stress and emotional patterns, and generates and provides additional advice to users as needed.

[1481] Input: SNS post data

[1482] Output: Further advice, emotion pattern detection

[1483] Step 11:

[1484] Regular follow-up

[1485] The server periodically sends follow-up messages to the user. The user can enter and submit new concerns or questions. These new concerns are also processed in steps 6 to 10.

[1486] Input: Schedule a follow-up message

[1487] Output: Send follow-up message

[1488] (Application example 1)

[1489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1490] Employees working in brick-and-mortar stores often have concerns about the work environment, interpersonal relationships, stress, and other issues, but lack the means to appropriately discuss and resolve these issues. Even after employees have discussed their concerns, there is a need for a way to continually follow up and provide appropriate advice. Furthermore, adding a function to analyze social media posting data to check employees' mental health would enable more effective support, contributing to improved productivity and employee satisfaction throughout the workplace.

[1491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1492] In this invention, the server includes means for accepting user information, means for storing the user information in a database, means for authenticating the user, means for having the user input their concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate response system based on the analysis results, means for providing advice to the user using the selected response system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, and means for providing advice on concerns via a device for employees to use in a physical store. This allows employees to easily consult about their concerns and quickly receive appropriate advice. Furthermore, by continuously monitoring employees' mental health through social media analysis and providing additional advice as needed, it is possible to reduce employee stress and improve the work environment.

[1493] "User information" refers to personal user identification information, such as name, email address, and password, that is registered in the system.

[1494] The "database" is a system for organizing, saving, and managing data such as user information, input concerns, analysis results, and advice content.

[1495] "Authentication" is a procedure for verifying that a user has legitimate access rights, and generally involves the use of an email address and password.

[1496] A "problem" is a problem or issue that a user has at work or in their personal life and that they input to the system for consultation.

[1497] "Analysis" is the process of classifying input concerns using AI technology, setting priorities, and deriving appropriate countermeasures.

[1498] An "answer system" is a system that provides appropriate advice to users based on the analysis results, and includes psychology experts and health advice AI.

[1499] "Social media account" refers to the account information of the SNS (social networking service) used by the user.

[1500] "Posted data" refers to content such as text, images, and videos that users publish on social media, and is the subject of analysis.

[1501] A "follow-up message" is a message that the system periodically sends to the user, with the purpose of informing them of progress in their concerns and encouraging them to seek new advice.

[1502] A "device" is hardware that a user uses to access the system, including smartphones and smart glasses.

[1503] This invention is a system for providing advice to employees in brick-and-mortar stores, which can be accessed by employees via smartphones or smart glasses. The configuration and operation of the system are described below.

[1504] Basic system configuration

[1505] 1. User Registration

[1506] The device (smartphone or smart glasses) displays a new registration form to employees working in the physical store.

[1507] The employee enters user information such as name, email address, and password, and clicks the submit button.

[1508] The server stores the received user information in a database and sends an authentication email to the employee.

[1509] Employees click on the link in the email to complete the authentication.

[1510] 2. Entering and analyzing worries

[1511] The terminal presents the authenticated employee with a login form.

[1512] Employees enter their email address and password, and once authentication is complete, a consultation form will be displayed.

[1513] Employees enter their concerns about work or their personal lives and click the send button.

[1514] The server analyzes the received worries, classifies them into categories (e.g., "work," "relationships," etc.), and sets priorities.

[1515] The server uses an AI analysis tool (e.g., GPT-4) to appropriately analyze the input concerns.

[1516] 3. Providing advice

[1517] The server selects an appropriate answering system (e.g., psychology expert, health advisor AI) based on the analysis results.

[1518] The selected system will use IBM Watson to generate appropriate advice and display it on the device.

[1519] 4. Social Media Analytics

[1520] If the employee consents, the server collects posting data from the employee's social media accounts (e.g., Twitter and Facebook).

[1521] The server collects the posted data via the Twitter API and Facebook API and analyzes it using GPT-4.

[1522] The server analyzes stress and emotional patterns to determine high stress levels and negative patterns.

[1523] If necessary, the server generates additional advice (e.g., relaxation techniques, counseling recommendations) and displays them on the terminal.

[1524] 5. Follow-up

[1525] The server periodically sends follow-up messages to employees.

[1526] Employees can seek new counseling regarding their current situation.

[1527] Specific examples

[1528] Example 1: User's concerns

[1529] An employee enters into the system, "I've been having trouble communicating with my coworkers lately."

[1530] The server classifies this problem as "human relationships" and assigns it a medium priority.

[1531] Using IBM Watson, advice on "specific ways to improve communication skills" is generated and displayed on the device.

[1532] Example 2: Additional advice based on social media analysis

[1533] If the employee agrees, the server detects posts from Twitter or Facebook saying "Work has been tough lately."

[1534] The server determines this to be a high-stress state and generates additional advice recommending necessary relaxation techniques or counseling.

[1535] This additional advice is presented to employees via a terminal.

[1536] Prompt Sentence Examples

[1537] Example prompt: "Analyze the following problem and provide appropriate advice. Problem: 'Recently, I've been having trouble communicating with my colleagues.'"

[1538] In this way, the present invention provides a system that allows employees working in brick-and-mortar stores to easily consult about their concerns and quickly receive appropriate advice.Continual support through analysis of social media posts can help maintain and improve the mental health of employees.

[1539] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1540] Step 1:

[1541] User Registration

[1542] The terminal displays a new registration form to employees working in physical stores.

[1543] Input: An employee enters user information such as name, email address, and password.

[1544] The terminal transmits the input information to the server.

[1545] The server stores the received user information in a database and sends an authentication email to the employee.

[1546] Output: A verification email is sent to the employee.

[1547] Step 2:

[1548] certification

[1549] Employees complete the verification by clicking the link in the verification email.

[1550] Input: Click on the authentication link.

[1551] The server receives the authentication link, verifies the user information, and completes the authentication.

[1552] Output: The user is authenticated and can log into the system.

[1553] Step 3:

[1554] Log in and enter your concerns

[1555] The terminal presents the authenticated employee with a login form.

[1556] Enter your email address and password to log in.

[1557] Employees enter their concerns about the workplace or their personal life into the consultation form and click the send button.

[1558] The terminal transmits the input worries to the server.

[1559] Output: The problem is sent to the server.

[1560] Step 4:

[1561] Analysis of worries

[1562] The server analyzes the received concerns.

[1563] Input: Received trouble data.

[1564] The server uses an AI analysis tool (GPT-4) to classify worries into categories (e.g., "work," "relationships," etc.) and set priorities.

[1565] Output: Categorised worries and priorities.

[1566] Step 5:

[1567] Generating Advice

[1568] The server selects an appropriate answering system based on the analysis results.

[1569] Input: Categorised worries and priorities.

[1570] The server generates appropriate advice using a selected answering system (IBM Watson).

[1571] Output: Generation of advice.

[1572] Step 6:

[1573] Providing advice

[1574] The server transmits the generated advice to the terminal.

[1575] Input: The generated advice.

[1576] The terminal displays the advice to the employee.

[1577] Output: The advice is displayed to the employee.

[1578] Step 7:

[1579] Social Media Analytics

[1580] The server collects posting data from employees' social media accounts if they consent.

[1581] Input: Social media account information with your consent.

[1582] The server collects post data using the Twitter API and Facebook API and analyzes it using GPT-4.

[1583] The server determines stress and emotional patterns and detects high stress states and negative patterns.

[1584] Output: Stress analysis results.

[1585] Step 8:

[1586] Generating additional advice

[1587] The server generates any additional advice needed based on the analysis results.

[1588] Input: Social media analysis results.

[1589] The server generates recommendations for additional relaxation techniques and counseling.

[1590] Output: Additional advice.

[1591] Step 9:

[1592] Providing additional advice

[1593] The server transmits the generated additional advice to the terminal.

[1594] Input: Generated additional advice.

[1595] The terminal displays additional advice to the employee.

[1596] Output: Additional advice is displayed to the employee.

[1597] Step 10:

[1598] Follow-up

[1599] The server periodically sends follow-up messages to employees.

[1600] Input: Follow-up message generation routine.

[1601] Employees can seek new counseling regarding their current situation.

[1602] Output: The follow-up message.

[1603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1604] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns they have, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the advice.

[1605] Basic system configuration

[1606] 1. User Registration

[1607] The terminal displays a new registration form to the user.

[1608] The user enters information such as name, email address, and password, and clicks the send button.

[1609] The server stores the received user information in a database and sends an authentication email to the user.

[1610] The user clicks on the link in the email to complete the authentication.

[1611] 2. Entering and analyzing worries

[1612] The terminal displays a login form to the user.

[1613] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[1614] The user enters the problem and clicks the send button.

[1615] The server analyzes the received worries, categorizes them, and sets priorities.

[1616] 3. Use of Emotion Engine

[1617] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[1618] The recognized user sentiment is utilized in the subsequent advice generation process.

[1619] 4. Providing advice

[1620] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1621] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[1622] 5. Social Media Analytics

[1623] If the user agrees, the server obtains the user's SNS account information.

[1624] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[1625] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[1626] 6. Follow-up

[1627] The server periodically sends follow-up messages to the user.

[1628] The user can then make a new consultation about the current situation.

[1629] Specific examples

[1630] Example 1: User concern input and emotion recognition

[1631] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1632] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1633] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[1634] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[1635] Example 2: Additional advice based on social media analysis

[1636] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1637] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1638] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[1639] The generated additional advice is presented to the user through the terminal.

[1640] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby reducing users' concerns and taking their feelings into consideration.

[1641] The processing flow will be explained below.

[1642] User Registration and Authentication

[1643] Step 1:

[1644] The terminal displays a new registration form to the user.

[1645] Step 2:

[1646] The user enters the required information such as name, email address, and password.

[1647] Step 3:

[1648] The terminal transmits the input user information to the server.

[1649] Step 4:

[1650] The server stores the user information in a database.

[1651] Step 5:

[1652] The server sends a registration completion email to the user.

[1653] Step 6:

[1654] The user clicks on the link in the registration completion email to complete the authentication.

[1655] Entering and analyzing worries

[1656] Step 7:

[1657] The terminal displays a login form to the user.

[1658] Step 8:

[1659] The user enters their email address and password and clicks the login button.

[1660] Step 9:

[1661] The server compares the entered information with a database and performs authentication.

[1662] Step 10:

[1663] The terminal displays a consultation form to the user.

[1664] Step 11:

[1665] The user enters their concerns in the text box and clicks the send button.

[1666] Step 12:

[1667] The terminal transmits the input worries to the server.

[1668] Problem analysis and classification

[1669] Step 13:

[1670] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[1671] Step 14:

[1672] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[1673] Step 15:

[1674] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[1675] Use of emotion engine

[1676] Step 16:

[1677] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[1678] Step 17:

[1679] The emotion engine stores the recognized emotions in a database and uses them in the subsequent advice generation process.

[1680] Providing advice

[1681] Step 18:

[1682] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1683] Step 19:

[1684] The server transmits the advice generated by the selected answering system to the terminal.

[1685] Step 20:

[1686] The terminal displays the advice to the user.

[1687] Social Media Analytics

[1688] Step 21:

[1689] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[1690] Step 22:

[1691] The server collects SNS posting data from users.

[1692] Step 23:

[1693] The server analyzes social media posting data and detects patterns of stress and emotions.

[1694] Step 24:

[1695] The server generates additional advice as needed and sends it to the terminal.

[1696] Step 25:

[1697] The terminal displays additional advice to the user.

[1698] Follow-up

[1699] Step 26:

[1700] The server periodically sends follow-up messages to the user.

[1701] Step 27:

[1702] The user can consult again about new concerns or changes in the situation.

[1703] The above is a concrete process flow for implementing the invention based on the claims. This process allows users to feel comfortable discussing their concerns and receive professional advice tailored to their individual needs and feelings.

[1704] Example 2

[1705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1706] In modern society, employees have a wide range of worries, but there are very few places where they can safely and anonymously seek advice about their worries. Furthermore, systems that can accurately recognize users' emotions and provide advice based on those emotions are not yet fully developed. Furthermore, there is a need for a system that can provide more effective advice by analyzing users' emotions and stress using data posted on social media. Given this background, there is a need to develop a system that users can use with confidence and that can provide appropriate advice that is sensitive to individual emotions.

[1707] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for saving user information in a database, means for authenticating the user, means for allowing the user to input concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for recognizing the user's emotions using an emotion engine, and means for analyzing the user's stress and emotional patterns using the social media post data. This allows the user to safely and anonymously seek advice about their concerns and provides appropriate and emotionally sensitive advice for the concerns.

[1708] "User information" refers to personal information used to identify a user, such as the user's name, email address, and password.

[1709] A "database" is a system for storing, managing, and retrieving specific data.

[1710] "Authentication" is the process of verifying that a user is a legitimate user, usually via a password or authentication link.

[1711] A "problem" is a problem or difficulty that a user has.

[1712] "Analysis" is the process of analyzing input data to find useful information and patterns.

[1713] A "category" is a category for classifying data based on its type or characteristics.

[1714] "Priority" is an indicator of the urgency and importance of processing or response.

[1715] The "answer system" is a system that provides appropriate advice to users regarding their concerns.

[1716] A "social media account" is an account registered by a user on a social media platform.

[1717] "Posted data" refers to information such as text, images, and videos that users post to social media.

[1718] An "emotion engine" is an algorithm or system that analyzes input data and recognizes the user's emotions.

[1719] "Stress" refers to a state or emotion that is mentally or physically taxing.

[1720] A "pattern" is a regular feature or trend that recurs in data.

[1721] A "follow-up message" is a message sent to a user periodically for confirmation or advice.

[1722] The present invention provides a system that allows employees to anonymously and safely seek advice about various concerns they may have, and furthermore recognizes the user's emotions and provides appropriate advice. The following describes in detail the embodiments of the invention.

[1723] System Configuration

[1724] The system includes a series of processes for registering user information, authenticating, inputting worries, analyzing worries, recognizing emotions, providing advice, analyzing social media, and following up.

[1725] 1. User Registration

[1726] The terminal displays a new registration form to the user, who enters information such as their name, email address, and password, and clicks the submit button.

[1727] The server receives this information, stores it in a database, and then sends the user an email for authentication.

[1728] The user clicks on the link in the email they receive to complete the authentication.

[1729] 2. Enter your worries

[1730] The terminal displays a login form to the user, who enters their authentication information and clicks the login button.

[1731] If authentication is successful, a consultation form will be displayed, and the user can enter their concerns and submit them.

[1732] 3. Analysis of worries

[1733] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[1734] The server categorizes the text and sets priorities.

[1735] 4. Emotional Recognition

[1736] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the analysis results and category information.

[1737] 5. Providing advice

[1738] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1739] The server uses the selected system to generate appropriate advice and displays it on the terminal.

[1740] 6. Social Media Analytics

[1741] If the user agrees, the server requests permission to collect posting data from the user's social media account.

[1742] The server analyzes the collected data using an API (e.g., Twitter API) to identify stress and emotional patterns.

[1743] Generate additional advice as needed and display it on the terminal.

[1744] 7. Follow-up

[1745] The server periodically sends follow-up messages to the user.

[1746] The user can then make a new consultation about the current situation.

[1747] Specific examples

[1748] Example 1: User concern input and emotion recognition

[1749] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1750] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1751] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[1752] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[1753] Example 2: Additional advice based on social media analysis

[1754] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1755] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1756] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[1757] The generated additional advice is presented to the user through the terminal.

[1758] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby alleviating users' concerns and taking their feelings into consideration.

[1759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1760] Step 1:

[1761] User Registration

[1762] A user visits the sign-up form.

[1763] The device will prompt the user for their name, email address, and password.

[1764] Input: User information (name, email address, password)

[1765] Output: User information entered

[1766] The user enters the required information and clicks the send button.

[1767] The terminal transmits the input data to the server.

[1768] The server processes the received data and stores it in a database.

[1769] Input: User input information

[1770] Data processing: Save input data to database

[1771] Output: Generate authentication email

[1772] The server generates a verification email and sends it to the user's email address.

[1773] The user clicks on the link in the email to complete the authentication.

[1774] Input: Click on the verification link in the email

[1775] Data calculation: Update authentication status based on link clicks

[1776] Output: User authentication completion notification

[1777] Step 2:

[1778] Enter your worries

[1779] The user accesses the login form.

[1780] The device will prompt the user for an email address and password.

[1781] The user enters their email address and password and clicks the login button.

[1782] The terminal transmits the input data to the server.

[1783] The server checks the authentication information, and if the authentication is successful, displays the consultation form.

[1784] Input: User credentials

[1785] Data processing: Check authentication information

[1786] Output: Display of consultation form

[1787] The user enters the problem they want to discuss and clicks the send button.

[1788] The terminal transmits the input data to the server.

[1789] Input: User's problem text

[1790] Output: Receiving trouble data

[1791] Step 3:

[1792] Analysis of worries

[1793] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[1794] Input: User's problem text

[1795] Data processing: Analyzing text using a natural language processing engine

[1796] Output: Analysis results (category, priority)

[1797] The server categorizes the problems and assigns priorities.

[1798] Step 4:

[1799] Emotion recognition

[1800] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the parsed text and category.

[1801] Input: Analysis results and problem text

[1802] Data Computing: Emotion Identification Using Emotion Recognition API

[1803] Output: Emotion recognition result

[1804] Step 5:

[1805] Providing advice

[1806] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[1807] Input: Analysis results and emotion recognition results

[1808] Data Computing: Choosing the Right Answer System

[1809] Output: Advice generation

[1810] The server uses the selected system to generate appropriate advice for the user and displays it on the terminal.

[1811] Input: Advice from the answer system

[1812] Output: Display advice

[1813] Step 6:

[1814] Social Media Analytics

[1815] The user consents to social media analytics.

[1816] The server requests permission to obtain the user's social networking account information.

[1817] Input: User consent

[1818] Output: Get SNS account information

[1819] The server collects posting data from the social media platform.

[1820] Input: SNS account information

[1821] Data processing: Data collection using API (e.g. Twitter API)

[1822] Output: Collected submission data

[1823] The server analyzes the collected data to identify stress and emotional patterns.

[1824] Input: Collected submission data

[1825] Data Computing: Stress and Emotion Pattern Analysis

[1826] Output: Additional advice

[1827] If necessary, the server generates additional advice and causes it to be displayed on the terminal.

[1828] Step 7:

[1829] Follow-up

[1830] The server periodically sends follow-up messages to the user.

[1831] Input: User registration information

[1832] Output: Send follow-up message

[1833] If the user has any new concerns, he or she can consult the system again.

[1834] (Application example 2)

[1835] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1836] Conventional advice systems are limited to analyzing the problems users face, and have difficulty responding based on their emotions. Furthermore, their ability to provide additional advice that takes into account users' social media posts is limited. Therefore, there is a need for systems that provide more accurate advice while protecting users' privacy. Furthermore, systems that provide continuous support through regular follow-ups are inadequate. These issues must be addressed.

[1837] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1838] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input a concern, means for analyzing the input concern, means for classifying and prioritizing the concern based on the analysis result, means for selecting an appropriate answer system based on the analysis result, means for providing advice to the user using the selected answer system, means for using an emotion engine for recognizing the user's emotions, means for adjusting the advice content based on the recognized emotions, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis result, and means for periodically sending follow-up messages. This makes it possible to provide highly accurate advice for each user's concern, taking emotions into consideration and protecting privacy.

[1839] "User information" refers to personal identification information, account information, and authentication information related to system users.

[1840] A "database" is a storage device within the system that stores and manages user information and problem data.

[1841] "Authentication" is a procedure for verifying that a user is a legitimate user.

[1842] A "problem" refers to a problem or concern that a user brings to the system for consultation.

[1843] "Analysis" refers to data processing to understand the content of the input concerns and to categorize and prioritize them.

[1844] An "emotion engine" is an algorithm or software module that recognizes emotions from a user's concerns and input text.

[1845] "Social media" refers to the social networking platforms used by users, and also includes data posted on these platforms.

[1846] "Advice" refers to advice or suggestions that the system provides based on the user's concerns and feelings.

[1847] "Follow-up messages" refer to periodic confirmations of support, advice, and encouragement.

[1848] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, an emotion engine is incorporated to recognize the user's emotions and adjust the content of the advice, providing more accurate support.

[1849] Basic system configuration:

[1850] 1. User Registration:

[1851] The server displays a new registration form to the user through the terminal.

[1852] The user enters information such as name, email address, and password, and clicks the send button.

[1853] The server stores the received user information in a database and sends the user an authentication email, which the user must click to complete the authentication.

[1854] 2. Entering and analyzing your concerns:

[1855] The server displays a login form to the user through the terminal.

[1856] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[1857] The user enters the problem and clicks the send button.

[1858] The server analyzes the received worries, categorizes them, and sets priorities.

[1859] 3. Use of Emotion Engine:

[1860] The server runs an emotion engine based on the input problem text and analysis results to recognize the user's emotions. The recognized user emotions are used in the subsequent advice generation process.

[1861] 4. Providing advice:

[1862] The server selects an appropriate answering system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results. Based on the selected system, the server generates appropriate advice for the user and displays it on the device.

[1863] 5. Social Media Analytics:

[1864] If the user agrees, the server will obtain the user's social media account information, collect post data from the social media platform, and analyze stress and emotional patterns. If it determines that additional advice is necessary, the server will generate it and display it on the device.

[1865] 6. Follow-up:

[1866] The server periodically sends follow-up messages to the user, allowing the user to reconsider their current situation.

[1867] Hardware and software used:

[1868] Hardware:

[1869] Server: For data processing and analysis (e.g. AWS EC2)

[1870] User devices: Smartphones (iPhone, Android), smart glasses, head-mounted displays, robots (e.g., Pepper)

[1871] software:

[1872] Database: Stores user information and problem data (e.g., MySQL)

[1873] Emotion engine: Emotion recognition using natural language processing (NLP) (e.g., BERT model for Hugging Face)

[1874] Development environment: Android Studio, Apple Xcode, Python (for NLP), Node.js (for backend)

[1875] Examples:

[1876] 1. User concern input and emotion recognition:

[1877] User: "I've been having a lot of meetings lately and it's really stressful."

[1878] Server: (Emotion engine recognizes "stress")

[1879] Advice: "Try some relaxation techniques, like deep breathing or gentle exercise. You might also want to reassess your time management."

[1880] 2. Additional advice from social media analysis:

[1881] User's social media post: "I'm frustrated because I don't agree with my coworkers."

[1882] Server: (High stress detected)

[1883] Advice: "Try attending a session to improve your communication skills. Also, take some time to relax."

[1884] 3. Prompt for the generative AI model:

[1885] "I'm having trouble with my boss. How can I deal with this?"

[1886] In this way, this system addresses the user's concerns individually, protects privacy, and provides appropriate and professional advice that takes emotions into consideration, thereby alleviating the user's concerns.

[1887] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1888] Step 1:

[1889] A user fills in the new registration form on the terminal and clicks the submit button. The input data includes name, email address, and password. The server receives this input data and saves it in the database. After saving, the server sends a verification email to the user's email address.

[1890] Step 2:

[1891] The user clicks on the link in the authentication email to complete the authentication. This action gives the user a valid account and allows them to use the system.

[1892] Step 3:

[1893] The user displays a login form on the device and enters their email address and password. The server authenticates the user based on the input data, and if successful, displays a consultation form on the device.

[1894] Step 4:

[1895] The user enters their concerns into the consultation form on their device and clicks the send button. The server receives the entered concern data and analyzes it using an NLP (natural language processing) model.

[1896] Step 5:

[1897] The server categorizes the analyzed worry data and sets priorities, such as "work stress," "interpersonal relationships," and "health issues." Based on this, it decides how to respond to the worry.

[1898] Step 6:

[1899] Based on the analysis results, the server activates an emotion engine to recognize the user's emotions from the text of their worries, such as "anxiety," "stress," or "sadness."

[1900] Step 7:

[1901] Based on the emotion recognition results, the server selects an appropriate response system. Specifically, this could be a psychology expert AI, a health advice AI, or other systems. This selection determines the content of the advice to be provided to the user.

[1902] Step 8:

[1903] The server uses the selected answering system to generate appropriate advice for the user and displays it on the terminal. For example, specific advice such as "relaxation techniques" or "communication skills" is provided.

[1904] Step 9:

[1905] If the user agrees, the server will obtain the user's social media account information and collect posting data from the social media platform, based on which the user's emotional and stress patterns will be analyzed.

[1906] Step 10:

[1907] The server generates additional advice as needed based on the analyzed social media data and displays it on the device, resulting in more accurate and personalized support.

[1908] Step 11:

[1909] The server periodically sends follow-up messages to the user, allowing the server to continuously track changes in the user's concerns and emotions and continue to provide appropriate support and advice.

[1910] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1911] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1912] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1913] [Fourth embodiment]

[1914] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1915] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1916] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1917] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1918] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1919] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1920] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1921] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1922] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1923] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1924] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1925] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1926] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1927] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[1928] Basic system configuration

[1929] 1. User Registration

[1930] The terminal displays a new registration form to the user.

[1931] The user enters information such as name, email address, and password, and clicks the send button.

[1932] The server stores the received user information in a database and sends an authentication email to the user.

[1933] The user clicks on the link in the email to complete the authentication.

[1934] 2. Entering and analyzing worries

[1935] The terminal displays a login form to the user.

[1936] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[1937] The user enters the problem and clicks the send button.

[1938] The server analyzes the received worries, categorizes them, and sets priorities.

[1939] 3. Providing advice

[1940] The server selects an appropriate answering system (psychology expert, health advice AI, etc.) based on the analysis results.

[1941] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[1942] 4. Social Media Analytics

[1943] If the user agrees, the server obtains the user's SNS account information.

[1944] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[1945] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[1946] 5. Follow-up

[1947] The server periodically sends follow-up messages to the user.

[1948] The user can then make a new consultation about the current situation.

[1949] Specific examples

[1950] Example 1: User's concerns

[1951] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[1952] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[1953] The server generates "specific methods for improving communication skills" as advice and displays it on the terminal.

[1954] Example 2: Additional advice based on social media analysis

[1955] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[1956] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[1957] This additional advice is presented to the user through the terminal.

[1958] In this way, the present invention provides a system that can alleviate a user's worries by individually addressing the user's worries and providing appropriate advice while protecting privacy.

[1959] The processing flow will be explained below.

[1960] User Registration and Authentication

[1961] Step 1:

[1962] The terminal displays a new registration form to the user.

[1963] Step 2:

[1964] The user enters the required information such as name, email address, and password.

[1965] Step 3:

[1966] The terminal transmits the input user information to the server.

[1967] Step 4:

[1968] The server stores the user information in a database.

[1969] Step 5:

[1970] The server sends a registration completion email to the user.

[1971] Step 6:

[1972] The user clicks on the link in the registration completion email to complete the authentication.

[1973] Entering and analyzing worries

[1974] Step 7:

[1975] The terminal displays a login form to the user.

[1976] Step 8:

[1977] The user enters their email address and password and clicks the login button.

[1978] Step 9:

[1979] The server compares the entered information with a database and performs authentication.

[1980] Step 10:

[1981] The terminal displays a consultation form to the user.

[1982] Step 11:

[1983] The user enters their concerns in the text box and clicks the send button.

[1984] Step 12:

[1985] The terminal transmits the input worries to the server.

[1986] Problem analysis and classification

[1987] Step 13:

[1988] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[1989] Step 14:

[1990] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[1991] Step 15:

[1992] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[1993] Providing advice

[1994] Step 16:

[1995] The server selects the appropriate response based on the answering system (psychology expert AI, health advice AI, etc.) set for each problem category.

[1996] Step 17:

[1997] The server transmits the advice generated by the selected answering system to the terminal.

[1998] Step 18:

[1999] The terminal displays the advice to the user.

[2000] Social Media Analytics

[2001] Step 19:

[2002] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[2003] Step 20:

[2004] The server collects SNS posting data from users.

[2005] Step 21:

[2006] The server analyzes social media posting data and detects patterns of stress and emotions.

[2007] Step 22:

[2008] The server generates additional advice as needed and sends it to the terminal.

[2009] Step 23:

[2010] The terminal displays additional advice to the user.

[2011] Follow-up

[2012] Step 24:

[2013] The server periodically sends follow-up messages to the user.

[2014] Step 25:

[2015] The user can consult again about new concerns or changes in the situation.

[2016] Example 1

[2017] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2018] Until now, there have only been limited systems that allow employees to safely consult about their concerns, and it has been difficult to provide sufficiently reliable analysis results and advice. Furthermore, there have been few systems that detect stress and emotional patterns not only from the concerns entered by the user but also from daily social media posting data and provide additional advice. This has led to the issue of not being able to provide comprehensive and individual support to users.

[2019] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2020] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input worries, means for analyzing the input worries, means for classifying the worries and setting priorities based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for analyzing worries using a natural language processing engine, and means for generating advice using a generative AI model. This makes it possible to analyze stress and emotional patterns from both the user's input content and the social media post data, and provide comprehensive and individual support.

[2021] "User information" refers to personal identification information such as the user's name, email address, and password that is registered in the system.

[2022] A "database" is a management system for storing data such as user information and user concerns.

[2023] "Authentication" refers to the process of verifying that a user is a legitimate subscriber.

[2024] A "problem" is a problem or question that a user wants to discuss with the system.

[2025] "Analysis" is the process of analyzing input data and extracting meaning and patterns.

[2026] A "category" is a classification group for classifying the analyzed worries.

[2027] "Priority" is a numerical value or evaluation criterion that indicates the importance or urgency of the analyzed problem.

[2028] An "answer system" is a device or program that provides appropriate advice to users regarding their concerns.

[2029] "Advice" refers to advice or suggestions provided to the user based on the analysis results.

[2030] "Social Media Account" refers to the account of the social media platform to which the User is registered.

[2031] "Posted data" refers to information such as messages, comments, and photos posted by users on social media.

[2032] A "natural language processing engine" refers to a software system that analyzes input text and understands its meaning and sentiment.

[2033] "Generative AI model" refers to an artificial intelligence model that generates new text or advice based on input data.

[2034] A "follow-up message" is a message that the system periodically sends to the user to prompt follow-up or confirmation.

[2035] This invention provides a system that allows employees to consult with peace of mind about their concerns. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. Furthermore, it can analyze users' social media posting data and provide additional advice based on that data.

[2036] Basic system configuration

[2037] The system utilizes the following hardware and software:

[2038] Device: A device, including a computer or smartphone, that a user accesses

[2039] Server: Central processing unit that processes data, analyzes, and generates advice

[2040] Database: A relational database such as MySQL or PostgreSQL

[2041] Natural language processing engine: Google Cloud Natural Language API

[2042] Generative AI model: OpenAI GPT-3

[2043] Email sending service: SendGrid or Amazon SES

[2044] Social media platform APIs: Twitter API and Facebook Graph API

[2045] Operating procedure

[2046] User Registration

[2047] First, the terminal displays a new registration form to the user. The user enters their name, email address, and password, and clicks the submit button. The server saves the received user information in a database and sends the user an authentication email. The user clicks the link in the email to complete the authentication, and user registration is complete.

[2048] Entering and analyzing worries

[2049] When a user logs in to the system, the terminal displays a consultation form. The user enters their concerns and clicks the send button. The server then analyzes the received concerns using a natural language processing engine, categorizes them, and sets priorities.

[2050] Providing advice

[2051] Based on the analysis results, the server selects an appropriate answering system. Based on the selected system, advice is generated using a generative AI model. The generated advice is displayed on the device for the user to view.

[2052] Social Media Analytics

[2053] If the user agrees, the device provides social media integration functionality and obtains an access token for the social media account using OAuth 2.0. The server periodically collects user posting data from the social media platform and performs automatic analysis. If stress or emotional patterns are detected, additional advice is generated and provided to the user.

[2054] Follow-up

[2055] The server periodically sends follow-up messages to the user, allowing the user to re-enter their concerns about new situations and receive further analysis and advice.

[2056] Specific examples

[2057] For example, if a user inputs "I'm having trouble with my boss and I don't know how to deal with it," the server will categorize this problem as "Work-Relationships" and assign it a medium priority. Using a generative AI model, the server will generate advice on "specific ways to improve communication skills" and display it on the device.

[2058] Additionally, if the user agrees and links their social media account, the server will detect posts such as "Work has been tough lately." In this case, it will determine that the user is in a high-stress state and generate additional advice such as relaxation techniques or counseling. The generated advice will be presented to the user via their device.

[2059] Example prompts for generative AI models

[2060] Analyze the concerns entered by the user, categorize them appropriately, set priorities, generate corresponding advice, and provide it to the user. Also, analyze the user's social media posts and provide additional advice if necessary. Example input: "I'm having trouble with my relationship with my boss and I don't know how to deal with it."

[2061] This system can alleviate users' worries by individually addressing their concerns and providing appropriate advice while protecting their privacy.

[2062] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2063] Step 1:

[2064] Enter and submit user registration information

[2065] The terminal displays a new registration form. The user enters information such as name, email address, and password, and clicks the submit button. The entered information is sent to the server.

[2066] Input: User information (name, email address, password)

[2067] Output: Data sent to the server

[2068] Step 2:

[2069] Saving user information and sending authentication emails

[2070] The server saves the received user information in a database, then generates an authentication email and sends it to the user using an email sending service.

[2071] Input: User information sent in step 1

[2072] Output: User information entry in database, authentication email

[2073] Step 3:

[2074] Authentication complete

[2075] The user clicks on the authentication link in the email, which contains a unique token that the server verifies and completes the user's authentication.

[2076] Input: Token included in the authentication link

[2077] Output: Authentication state update

[2078] Step 4:

[2079] User Login

[2080] The terminal presents the user with a login form, where the user enters their email address and password and submits it. The server checks the credentials against a database and, once authenticated, presents the user with a personalized dashboard.

[2081] Input: Email address, password

[2082] Output: Authentication results, dashboard display

[2083] Step 5:

[2084] Enter and submit your concerns

[2085] The terminal displays a consultation form. The user enters their concerns and clicks the send button. The details of the concerns are sent to the server.

[2086] Input: Content of concern

[2087] Output: Data sent to the server

[2088] Step 6:

[2089] Analysis and classification of worries

[2090] The server uses a natural language processing engine to analyze the received concerns, categorize them, and set priorities. Data processing involves tokenizing the text, analyzing emotions, and classifying them.

[2091] Input: Content of concern

[2092] Output: Analysis results (category, priority)

[2093] Step 7:

[2094] Answer system selection and advice generation

[2095] The server selects the optimal answering system based on the analysis results, and generates advice using the selected system with a generative AI model.

[2096] Input: Analysis results

[2097] Output: Generated advice

[2098] Step 8:

[2099] Displaying Advice

[2100] The server sends the generated advice to the user's terminal, which displays it.

[2101] Input: Generated advice

[2102] Output: Advice displayed on terminal

[2103] Step 9:

[2104] Linking social media accounts and collecting data

[2105] If the user agrees, the device provides the SNS integration function. It obtains an access token for the SNS account using OAuth 2.0 and sends it to the server. The server then collects the posted data from the SNS platform.

[2106] Input: User consent, SNS access token

[2107] Output: SNS post data

[2108] Step 10:

[2109] Analyzing social media data and generating additional advice

[2110] The server analyzes the collected social media post data using a natural language processing engine to detect stress and emotional patterns, and generates and provides additional advice to users as needed.

[2111] Input: SNS post data

[2112] Output: Further advice, emotion pattern detection

[2113] Step 11:

[2114] Regular follow-up

[2115] The server periodically sends follow-up messages to the user. The user can enter and submit new concerns or questions. These new concerns are also processed in steps 6 to 10.

[2116] Input: Schedule a follow-up message

[2117] Output: Send follow-up message

[2118] (Application example 1)

[2119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2120] Employees working in brick-and-mortar stores often have concerns about the work environment, interpersonal relationships, stress, and other issues, but lack the means to appropriately discuss and resolve these issues. Even after employees have discussed their concerns, there is a need for a way to continually follow up and provide appropriate advice. Furthermore, adding a function to analyze social media posting data to check employees' mental health would enable more effective support, contributing to improved productivity and employee satisfaction throughout the workplace.

[2121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2122] In this invention, the server includes means for accepting user information, means for storing the user information in a database, means for authenticating the user, means for having the user input their concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate response system based on the analysis results, means for providing advice to the user using the selected response system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, and means for providing advice on concerns via a device for employees to use in a physical store. This allows employees to easily consult about their concerns and quickly receive appropriate advice. Furthermore, by continuously monitoring employees' mental health through social media analysis and providing additional advice as needed, it is possible to reduce employee stress and improve the work environment.

[2123] "User information" refers to personal user identification information, such as name, email address, and password, that is registered in the system.

[2124] The "database" is a system for organizing, saving, and managing data such as user information, input concerns, analysis results, and advice content.

[2125] "Authentication" is a procedure for verifying that a user has legitimate access rights, and generally involves the use of an email address and password.

[2126] A "problem" is a problem or issue that a user has at work or in their personal life and that they input to the system for consultation.

[2127] "Analysis" is the process of classifying input concerns using AI technology, setting priorities, and deriving appropriate countermeasures.

[2128] An "answer system" is a system that provides appropriate advice to users based on the analysis results, and includes psychology experts and health advice AI.

[2129] "Social media account" refers to the account information of the SNS (social networking service) used by the user.

[2130] "Posted data" refers to content such as text, images, and videos that users publish on social media, and is the subject of analysis.

[2131] A "follow-up message" is a message that the system periodically sends to the user, with the purpose of informing them of progress in their concerns and encouraging them to seek new advice.

[2132] A "device" is hardware that a user uses to access the system, including smartphones and smart glasses.

[2133] This invention is a system for providing advice to employees in brick-and-mortar stores, which can be accessed by employees via smartphones or smart glasses. The configuration and operation of the system are described below.

[2134] Basic system configuration

[2135] 1. User Registration

[2136] The device (smartphone or smart glasses) displays a new registration form to employees working in the physical store.

[2137] The employee enters user information such as name, email address, and password, and clicks the submit button.

[2138] The server stores the received user information in a database and sends an authentication email to the employee.

[2139] Employees click on the link in the email to complete the authentication.

[2140] 2. Entering and analyzing worries

[2141] The terminal presents the authenticated employee with a login form.

[2142] Employees enter their email address and password, and once authentication is complete, a consultation form will be displayed.

[2143] Employees enter their concerns about work or their personal lives and click the send button.

[2144] The server analyzes the received worries, classifies them into categories (e.g., "work," "relationships," etc.), and sets priorities.

[2145] The server uses an AI analysis tool (e.g., GPT-4) to appropriately analyze the input concerns.

[2146] 3. Providing advice

[2147] The server selects an appropriate answering system (e.g., psychology expert, health advisor AI) based on the analysis results.

[2148] The selected system will use IBM Watson to generate appropriate advice and display it on the device.

[2149] 4. Social Media Analytics

[2150] If the employee consents, the server collects posting data from the employee's social media accounts (e.g., Twitter and Facebook).

[2151] The server collects the posted data via the Twitter API and Facebook API and analyzes it using GPT-4.

[2152] The server analyzes stress and emotional patterns to determine high stress levels and negative patterns.

[2153] If necessary, the server generates additional advice (e.g., relaxation techniques, counseling recommendations) and displays them on the terminal.

[2154] 5. Follow-up

[2155] The server periodically sends follow-up messages to employees.

[2156] Employees can seek new counseling regarding their current situation.

[2157] Specific examples

[2158] Example 1: User's concerns

[2159] An employee enters into the system, "I've been having trouble communicating with my coworkers lately."

[2160] The server classifies this problem as "human relationships" and assigns it a medium priority.

[2161] Using IBM Watson, advice on "specific ways to improve communication skills" is generated and displayed on the device.

[2162] Example 2: Additional advice based on social media analysis

[2163] If the employee agrees, the server detects posts from Twitter or Facebook saying "Work has been tough lately."

[2164] The server determines this to be a high-stress state and generates additional advice recommending necessary relaxation techniques or counseling.

[2165] This additional advice is presented to employees via a terminal.

[2166] Prompt Sentence Examples

[2167] Example prompt: "Analyze the following problem and provide appropriate advice. Problem: 'Recently, I've been having trouble communicating with my colleagues.'"

[2168] In this way, the present invention provides a system that allows employees working in brick-and-mortar stores to easily consult about their concerns and quickly receive appropriate advice.Continual support through analysis of social media posts can help maintain and improve the mental health of employees.

[2169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2170] Step 1:

[2171] User Registration

[2172] The terminal displays a new registration form to employees working in physical stores.

[2173] Input: An employee enters user information such as name, email address, and password.

[2174] The terminal transmits the input information to the server.

[2175] The server stores the received user information in a database and sends an authentication email to the employee.

[2176] Output: A verification email is sent to the employee.

[2177] Step 2:

[2178] certification

[2179] Employees complete the verification by clicking the link in the verification email.

[2180] Input: Click on the authentication link.

[2181] The server receives the authentication link, verifies the user information, and completes the authentication.

[2182] Output: The user is authenticated and can log into the system.

[2183] Step 3:

[2184] Log in and enter your concerns

[2185] The terminal presents the authenticated employee with a login form.

[2186] Enter your email address and password to log in.

[2187] Employees enter their concerns about the workplace or their personal life into the consultation form and click the send button.

[2188] The terminal transmits the input worries to the server.

[2189] Output: The problem is sent to the server.

[2190] Step 4:

[2191] Analysis of worries

[2192] The server analyzes the received concerns.

[2193] Input: Received trouble data.

[2194] The server uses an AI analysis tool (GPT-4) to classify worries into categories (e.g., "work," "relationships," etc.) and set priorities.

[2195] Output: Categorised worries and priorities.

[2196] Step 5:

[2197] Generating Advice

[2198] The server selects an appropriate answering system based on the analysis results.

[2199] Input: Categorised worries and priorities.

[2200] The server generates appropriate advice using a selected answering system (IBM Watson).

[2201] Output: Generation of advice.

[2202] Step 6:

[2203] Providing advice

[2204] The server transmits the generated advice to the terminal.

[2205] Input: The generated advice.

[2206] The terminal displays the advice to the employee.

[2207] Output: The advice is displayed to the employee.

[2208] Step 7:

[2209] Social Media Analytics

[2210] The server collects posting data from employees' social media accounts if they consent.

[2211] Input: Social media account information with your consent.

[2212] The server collects post data using the Twitter API and Facebook API and analyzes it using GPT-4.

[2213] The server determines stress and emotional patterns and detects high stress states and negative patterns.

[2214] Output: Stress analysis results.

[2215] Step 8:

[2216] Generating additional advice

[2217] The server generates any additional advice needed based on the analysis results.

[2218] Input: Social media analysis results.

[2219] The server generates recommendations for additional relaxation techniques and counseling.

[2220] Output: Additional advice.

[2221] Step 9:

[2222] Providing additional advice

[2223] The server transmits the generated additional advice to the terminal.

[2224] Input: Generated additional advice.

[2225] The terminal displays additional advice to the employee.

[2226] Output: Additional advice is displayed to the employee.

[2227] Step 10:

[2228] Follow-up

[2229] The server periodically sends follow-up messages to employees.

[2230] Input: Follow-up message generation routine.

[2231] Employees can seek new counseling regarding their current situation.

[2232] Output: The follow-up message.

[2233] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2234] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns they have, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the advice.

[2235] Basic system configuration

[2236] 1. User Registration

[2237] The terminal displays a new registration form to the user.

[2238] The user enters information such as name, email address, and password, and clicks the send button.

[2239] The server stores the received user information in a database and sends an authentication email to the user.

[2240] The user clicks on the link in the email to complete the authentication.

[2241] 2. Entering and analyzing worries

[2242] The terminal displays a login form to the user.

[2243] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[2244] The user enters the problem and clicks the send button.

[2245] The server analyzes the received worries, categorizes them, and sets priorities.

[2246] 3. Use of Emotion Engine

[2247] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[2248] The recognized user sentiment is utilized in the subsequent advice generation process.

[2249] 4. Providing advice

[2250] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[2251] Based on the selected system, the server generates appropriate advice for the user and displays it on the terminal.

[2252] 5. Social Media Analytics

[2253] If the user agrees, the server obtains the user's SNS account information.

[2254] The server collects posting data from social media platforms and analyzes stress and emotional patterns.

[2255] If it is determined that additional advice is needed, the server generates it and displays it on the terminal.

[2256] 6. Follow-up

[2257] The server periodically sends follow-up messages to the user.

[2258] The user can then make a new consultation about the current situation.

[2259] Specific examples

[2260] Example 1: User concern input and emotion recognition

[2261] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[2262] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[2263] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[2264] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[2265] Example 2: Additional advice based on social media analysis

[2266] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[2267] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[2268] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[2269] The generated additional advice is presented to the user through the terminal.

[2270] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby reducing users' concerns and taking their feelings into consideration.

[2271] The processing flow will be explained below.

[2272] User Registration and Authentication

[2273] Step 1:

[2274] The terminal displays a new registration form to the user.

[2275] Step 2:

[2276] The user enters the required information such as name, email address, and password.

[2277] Step 3:

[2278] The terminal transmits the input user information to the server.

[2279] Step 4:

[2280] The server stores the user information in a database.

[2281] Step 5:

[2282] The server sends a registration completion email to the user.

[2283] Step 6:

[2284] The user clicks on the link in the registration completion email to complete the authentication.

[2285] Entering and analyzing worries

[2286] Step 7:

[2287] The terminal displays a login form to the user.

[2288] Step 8:

[2289] The user enters their email address and password and clicks the login button.

[2290] Step 9:

[2291] The server compares the entered information with a database and performs authentication.

[2292] Step 10:

[2293] The terminal displays a consultation form to the user.

[2294] Step 11:

[2295] The user enters their concerns in the text box and clicks the send button.

[2296] Step 12:

[2297] The terminal transmits the input worries to the server.

[2298] Problem analysis and classification

[2299] Step 13:

[2300] The server analyzes the received concerns using natural language processing (NLP) algorithms.

[2301] Step 14:

[2302] Based on the analysis results, the server classifies the worries into preset categories (e.g., work, relationships, health).

[2303] Step 15:

[2304] The server sets the priority of the concern (high, medium, low) based on the analysis results.

[2305] Use of emotion engine

[2306] Step 16:

[2307] The server operates an emotion engine based on the input worry text and analysis results to recognize the user's emotions.

[2308] Step 17:

[2309] The emotion engine stores the recognized emotions in a database and uses them in the subsequent advice generation process.

[2310] Providing advice

[2311] Step 18:

[2312] The server selects an appropriate response system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[2313] Step 19:

[2314] The server transmits the advice generated by the selected answering system to the terminal.

[2315] Step 20:

[2316] The terminal displays the advice to the user.

[2317] Social Media Analytics

[2318] Step 21:

[2319] If the user permits SNS analysis, the server obtains the user's account information from the SNS platform.

[2320] Step 22:

[2321] The server collects SNS posting data from users.

[2322] Step 23:

[2323] The server analyzes social media posting data and detects patterns of stress and emotions.

[2324] Step 24:

[2325] The server generates additional advice as needed and sends it to the terminal.

[2326] Step 25:

[2327] The terminal displays additional advice to the user.

[2328] Follow-up

[2329] Step 26:

[2330] The server periodically sends follow-up messages to the user.

[2331] Step 27:

[2332] The user can consult again about new concerns or changes in the situation.

[2333] The above is a concrete process flow for implementing the invention based on the claims. This process allows users to feel comfortable discussing their concerns and receive professional advice tailored to their individual needs and feelings.

[2334] Example 2

[2335] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2336] In modern society, employees have a wide range of worries, but there are very few places where they can safely and anonymously seek advice about their worries. Furthermore, systems that can accurately recognize users' emotions and provide advice based on those emotions are not yet fully developed. Furthermore, there is a need for a system that can provide more effective advice by analyzing users' emotions and stress using data posted on social media. Given this background, there is a need to develop a system that users can use with confidence and that can provide appropriate advice that is sensitive to individual emotions.

[2337] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for saving user information in a database, means for authenticating the user, means for allowing the user to input concerns, means for analyzing the input concerns, means for classifying and prioritizing the concerns based on the analysis results, means for selecting an appropriate answer system based on the analysis results, means for providing advice to the user using the selected answer system, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis results, means for periodically sending follow-up messages, means for recognizing the user's emotions using an emotion engine, and means for analyzing the user's stress and emotional patterns using the social media post data. This allows the user to safely and anonymously seek advice about their concerns and provides appropriate and emotionally sensitive advice for the concerns.

[2338] "User information" refers to personal information used to identify a user, such as the user's name, email address, and password.

[2339] A "database" is a system for storing, managing, and retrieving specific data.

[2340] "Authentication" is the process of verifying that a user is a legitimate user, usually via a password or authentication link.

[2341] A "problem" is a problem or difficulty that a user has.

[2342] "Analysis" is the process of analyzing input data to find useful information and patterns.

[2343] A "category" is a category for classifying data based on its type or characteristics.

[2344] "Priority" is an indicator of the urgency and importance of processing or response.

[2345] The "answer system" is a system that provides appropriate advice to users regarding their concerns.

[2346] A "social media account" is an account registered by a user on a social media platform.

[2347] "Posted data" refers to information such as text, images, and videos that users post to social media.

[2348] An "emotion engine" is an algorithm or system that analyzes input data and recognizes the user's emotions.

[2349] "Stress" refers to a state or emotion that is mentally or physically taxing.

[2350] A "pattern" is a regular feature or trend that recurs in data.

[2351] A "follow-up message" is a message sent to a user periodically for confirmation or advice.

[2352] The present invention provides a system that allows employees to anonymously and safely seek advice about various concerns they may have, and furthermore recognizes the user's emotions and provides appropriate advice. The following describes in detail the embodiments of the invention.

[2353] System Configuration

[2354] The system includes a series of processes for registering user information, authenticating, inputting worries, analyzing worries, recognizing emotions, providing advice, analyzing social media, and following up.

[2355] 1. User Registration

[2356] The terminal displays a new registration form to the user, who enters information such as their name, email address, and password, and clicks the submit button.

[2357] The server receives this information, stores it in a database, and then sends the user an email for authentication.

[2358] The user clicks on the link in the email they receive to complete the authentication.

[2359] 2. Enter your worries

[2360] The terminal displays a login form to the user, who enters their authentication information and clicks the login button.

[2361] If authentication is successful, a consultation form will be displayed, and the user can enter their concerns and submit them.

[2362] 3. Analysis of worries

[2363] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[2364] The server categorizes the text and sets priorities.

[2365] 4. Emotional Recognition

[2366] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the analysis results and category information.

[2367] 5. Providing advice

[2368] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[2369] The server uses the selected system to generate appropriate advice and displays it on the terminal.

[2370] 6. Social Media Analytics

[2371] If the user agrees, the server requests permission to collect posting data from the user's social media account.

[2372] The server analyzes the collected data using an API (e.g., Twitter API) to identify stress and emotional patterns.

[2373] Generate additional advice as needed and display it on the terminal.

[2374] 7. Follow-up

[2375] The server periodically sends follow-up messages to the user.

[2376] The user can then make a new consultation about the current situation.

[2377] Specific examples

[2378] Example 1: User concern input and emotion recognition

[2379] The user inputs to the system, "I'm having trouble with my boss and I don't know how to handle it."

[2380] The server categorizes this problem as "work-human relationships" and assigns it a medium priority.

[2381] The emotion engine recognizes from the input text that the user's emotion is "anxiety."

[2382] The server takes into account the emotion recognition results and generates advice including "specific methods for improving communication skills" as well as relaxation techniques to ease emotions, which are then displayed on the device.

[2383] Example 2: Additional advice based on social media analysis

[2384] The user agrees, and the server detects a post from the user's social media account saying, "Work has been tough lately."

[2385] The server determines this to be a high-stress state and generates additional advice such as relaxation techniques or counseling.

[2386] The emotion engine recognizes from social media posts that a user's emotion is "stress" and provides advice that takes those emotions into consideration.

[2387] The generated additional advice is presented to the user through the terminal.

[2388] In this way, the present invention provides a system that addresses users' concerns individually, provides appropriate and professional advice while protecting privacy, thereby alleviating users' concerns and taking their feelings into consideration.

[2389] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2390] Step 1:

[2391] User Registration

[2392] A user visits the sign-up form.

[2393] The device will prompt the user for their name, email address, and password.

[2394] Input: User information (name, email address, password)

[2395] Output: User information entered

[2396] The user enters the required information and clicks the send button.

[2397] The terminal transmits the input data to the server.

[2398] The server processes the received data and stores it in a database.

[2399] Input: User input information

[2400] Data processing: Save input data to database

[2401] Output: Generate authentication email

[2402] The server generates a verification email and sends it to the user's email address.

[2403] The user clicks on the link in the email to complete the authentication.

[2404] Input: Click on the verification link in the email

[2405] Data calculation: Update authentication status based on link clicks

[2406] Output: User authentication completion notification

[2407] Step 2:

[2408] Enter your worries

[2409] The user accesses the login form.

[2410] The device will prompt the user for an email address and password.

[2411] The user enters their email address and password and clicks the login button.

[2412] The terminal transmits the input data to the server.

[2413] The server checks the authentication information, and if the authentication is successful, displays the consultation form.

[2414] Input: User credentials

[2415] Data processing: Check authentication information

[2416] Output: Display of consultation form

[2417] The user enters the problem they want to discuss and clicks the send button.

[2418] The terminal transmits the input data to the server.

[2419] Input: User's problem text

[2420] Output: Receiving trouble data

[2421] Step 3:

[2422] Analysis of worries

[2423] The server analyzes the received problem text using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API).

[2424] Input: User's problem text

[2425] Data processing: Analyzing text using a natural language processing engine

[2426] Output: Analysis results (category, priority)

[2427] The server categorizes the problems and assigns priorities.

[2428] Step 4:

[2429] Emotion recognition

[2430] The server identifies the user's emotion using an emotion recognition API (e.g., IBM Watson Tone Analyzer) based on the parsed text and category.

[2431] Input: Analysis results and problem text

[2432] Data Computing: Emotion Identification Using Emotion Recognition API

[2433] Output: Emotion recognition result

[2434] Step 5:

[2435] Providing advice

[2436] The server selects an appropriate response system (e.g., psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results.

[2437] Input: Analysis results and emotion recognition results

[2438] Data Computing: Choosing the Right Answer System

[2439] Output: Advice generation

[2440] The server uses the selected system to generate appropriate advice for the user and displays it on the terminal.

[2441] Input: Advice from the answer system

[2442] Output: Display advice

[2443] Step 6:

[2444] Social Media Analytics

[2445] The user consents to social media analytics.

[2446] The server requests permission to obtain the user's social networking account information.

[2447] Input: User consent

[2448] Output: Get SNS account information

[2449] The server collects posting data from the social media platform.

[2450] Input: SNS account information

[2451] Data processing: Data collection using API (e.g. Twitter API)

[2452] Output: Collected submission data

[2453] The server analyzes the collected data to identify stress and emotional patterns.

[2454] Input: Collected submission data

[2455] Data Computing: Stress and Emotion Pattern Analysis

[2456] Output: Additional advice

[2457] If necessary, the server generates additional advice and causes it to be displayed on the terminal.

[2458] Step 7:

[2459] Follow-up

[2460] The server periodically sends follow-up messages to the user.

[2461] Input: User registration information

[2462] Output: Send follow-up message

[2463] If the user has any new concerns, he or she can consult the system again.

[2464] (Application example 2)

[2465] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2466] Conventional advice systems are limited to analyzing the problems users face, and have difficulty responding based on their emotions. Furthermore, their ability to provide additional advice that takes into account users' social media posts is limited. Therefore, there is a need for systems that provide more accurate advice while protecting users' privacy. Furthermore, systems that provide continuous support through regular follow-ups are inadequate. These issues must be addressed.

[2467] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2468] In this invention, the server includes means for accepting user information, means for saving the user information in a database, means for authenticating the user, means for having the user input a concern, means for analyzing the input concern, means for classifying and prioritizing the concern based on the analysis result, means for selecting an appropriate answer system based on the analysis result, means for providing advice to the user using the selected answer system, means for using an emotion engine for recognizing the user's emotions, means for adjusting the advice content based on the recognized emotions, means for collecting and analyzing post data from the user's social media account, means for providing additional advice based on the analysis result, and means for periodically sending follow-up messages. This makes it possible to provide highly accurate advice for each user's concern, taking emotions into consideration and protecting privacy.

[2469] "User information" refers to personal identification information, account information, and authentication information related to system users.

[2470] A "database" is a storage device within the system that stores and manages user information and problem data.

[2471] "Authentication" is a procedure for verifying that a user is a legitimate user.

[2472] A "problem" refers to a problem or concern that a user brings to the system for consultation.

[2473] "Analysis" refers to data processing to understand the content of the input concerns and to categorize and prioritize them.

[2474] An "emotion engine" is an algorithm or software module that recognizes emotions from a user's concerns and input text.

[2475] "Social media" refers to the social networking platforms used by users, and also includes data posted on these platforms.

[2476] "Advice" refers to advice or suggestions that the system provides based on the user's concerns and feelings.

[2477] "Follow-up messages" refer to periodic confirmations of support, advice, and encouragement.

[2478] This invention provides a system that allows employees to anonymously and securely seek advice about various concerns, and is characterized by recognizing the user's emotions and providing appropriate advice. This system registers, saves, and authenticates user information, allows users to input their concerns, analyzes those concerns, and provides appropriate advice. It can also analyze users' social media posting data and provide additional advice based on that data. Furthermore, an emotion engine is incorporated to recognize the user's emotions and adjust the content of the advice, providing more accurate support.

[2479] Basic system configuration:

[2480] 1. User Registration:

[2481] The server displays a new registration form to the user through the terminal.

[2482] The user enters information such as name, email address, and password, and clicks the send button.

[2483] The server stores the received user information in a database and sends the user an authentication email, which the user must click to complete the authentication.

[2484] 2. Entering and analyzing your concerns:

[2485] The server displays a login form to the user through the terminal.

[2486] The user enters their email address and password, and once authentication is complete, a consultation form is displayed.

[2487] The user enters the problem and clicks the send button.

[2488] The server analyzes the received worries, categorizes them, and sets priorities.

[2489] 3. Use of Emotion Engine:

[2490] The server runs an emotion engine based on the input problem text and analysis results to recognize the user's emotions. The recognized user emotions are used in the subsequent advice generation process.

[2491] 4. Providing advice:

[2492] The server selects an appropriate answering system (psychology expert AI, health advice AI, etc.) based on the analysis results and emotion recognition results. Based on the selected system, the server generates appropriate advice for the user and displays it on the device.

[2493] 5. Social Media Analytics:

[2494] If the user agrees, the server will obtain the user's social media account information, collect post data from the social media platform, and analyze stress and emotional patterns. If it determines that additional advice is necessary, the server will generate it and display it on the device.

[2495] 6. Follow-up:

[2496] The server periodically sends follow-up messages to the user, allowing the user to reconsider their current situation.

[2497] Hardware and software used:

[2498] Hardware:

[2499] Server: For data processing and analysis (e.g. AWS EC2)

[2500] User devices: Smartphones (iPhone, Android), smart glasses, head-mounted displays, robots (e.g., Pepper)

[2501] software:

[2502] Database: Stores user information and problem data (e.g., MySQL)

[2503] Emotion engine: Emotion recognition using natural language processing (NLP) (e.g., BERT model for Hugging Face)

[2504] Development environment: Android Studio, Apple Xcode, Python (for NLP), Node.js (for backend)

[2505] Examples:

[2506] 1. User concern input and emotion recognition:

[2507] User: "I've been having a lot of meetings lately and it's really stressful."

[2508] Server: (Emotion engine recognizes "stress")

[2509] Advice: "Try some relaxation techniques, like deep breathing or gentle exercise. You might also want to reassess your time management."

[2510] 2. Additional advice from social media analysis:

[2511] User's social media post: "I'm frustrated because I don't agree with my coworkers."

[2512] Server: (High stress detected)

[2513] Advice: "Try attending a session to improve your communication skills. Also, take some time to relax."

[2514] 3. Prompt for the generative AI model:

[2515] "I'm having trouble with my boss. How can I deal with this?"

[2516] In this way, this system addresses the user's concerns individually, protects privacy, and provides appropriate and professional advice that takes emotions into consideration, thereby alleviating the user's concerns.

[2517] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2518] Step 1:

[2519] A user fills in the new registration form on the terminal and clicks the submit button. The input data includes name, email address, and password. The server receives this input data and saves it in the database. After saving, the server sends a verification email to the user's email address.

[2520] Step 2:

[2521] The user clicks on the link in the authentication email to complete the authentication. This action gives the user a valid account and allows them to use the system.

[2522] Step 3:

[2523] The user displays a login form on the device and enters their email address and password. The server authenticates the user based on the input data, and if successful, displays a consultation form on the device.

[2524] Step 4:

[2525] The user enters their concerns into the consultation form on their device and clicks the send button. The server receives the entered concern data and analyzes it using an NLP (natural language processing) model.

[2526] Step 5:

[2527] The server categorizes the analyzed worry data and sets priorities, such as "work stress," "interpersonal relationships," and "health issues." Based on this, it decides how to respond to the worry.

[2528] Step 6:

[2529] Based on the analysis results, the server activates an emotion engine to recognize the user's emotions from the text of their worries, such as "anxiety," "stress," or "sadness."

[2530] Step 7:

[2531] Based on the emotion recognition results, the server selects an appropriate response system. Specifically, this could be a psychology expert AI, a health advice AI, or other systems. This selection determines the content of the advice to be provided to the user.

[2532] Step 8:

[2533] The server uses the selected answering system to generate appropriate advice for the user and displays it on the terminal. For example, specific advice such as "relaxation techniques" or "communication skills" is provided.

[2534] Step 9:

[2535] If the user agrees, the server will obtain the user's social media account information and collect posting data from the social media platform, based on which the user's emotional and stress patterns will be analyzed.

[2536] Step 10:

[2537] The server generates additional advice as needed based on the analyzed social media data and displays it on the device, resulting in more accurate and personalized support.

[2538] Step 11:

[2539] The server periodically sends follow-up messages to the user, allowing the server to continuously track changes in the user's concerns and emotions and continue to provide appropriate support and advice.

[2540] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2541] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2542] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2543] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2544] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2545] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2546] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2547] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2548] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2549] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2550] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2551] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2552] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2553] 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.

[2554] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2555] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2556] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2557] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2558] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2559] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2560] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2561] The following is further disclosed regarding the above embodiment.

[2562] (Claim 1)

[2563] means for accepting user information;

[2564] a means for storing user information in a database;

[2565] a means for authenticating a user;

[2566] A means for allowing a user to input a concern;

[2567] A means for analyzing the input worries,

[2568] A means of categorizing and prioritizing concerns based on the analysis results;

[2569] A means for selecting an appropriate response system based on the analysis results;

[2570] means for providing advice to the user by a selected answer system;

[2571] means for collecting and analyzing posting data from users' social media accounts;

[2572] A means of providing additional advice based on the analysis results; and

[2573] a means of sending periodic follow-up messages;

[2574] A system including:

[2575] (Claim 2)

[2576] 2. The system of claim 1, wherein the means for collecting and analyzing posting data from a user's social media account operates based on the user's consent.

[2577] (Claim 3)

[2578] 10. The system of claim 1, further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results.

[2579] "Example 1"

[2580] (Claim 1)

[2581] means for accepting user information;

[2582] a means for storing user information in a database;

[2583] a means for authenticating a user;

[2584] A means for allowing a user to input a concern;

[2585] A means for analyzing the input worries,

[2586] A means of categorizing and prioritizing concerns based on the analysis results;

[2587] A means for selecting an appropriate response system based on the analysis results;

[2588] means for providing advice to the user by a selected answer system;

[2589] means for collecting and analyzing posting data from users' social media accounts;

[2590] A means of providing additional advice based on the analysis results; and

[2591] a means of sending periodic follow-up messages;

[2592] A means of analyzing worries using a natural language processing engine,

[2593] a means for generating advice using a generative AI model;

[2594] A system including:

[2595] (Claim 2)

[2596] 2. The system of claim 1, wherein the means for collecting and analyzing posting data from a user's social media account operates based on the user's consent.

[2597] (Claim 3)

[2598] 10. The system of claim 1, further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results.

[2599] "Application Example 1"

[2600] (Claim 1)

[2601] means for accepting user information;

[2602] a means for storing user information in a database;

[2603] a means for authenticating a user;

[2604] A means for allowing a user to input a concern;

[2605] A means for analyzing the input worries,

[2606] A means of categorizing and prioritizing concerns based on the analysis results;

[2607] A means for selecting an appropriate response system based on the analysis results;

[2608] means for providing advice to the user by a selected answer system;

[2609] means for collecting and analyzing posting data from users' social media accounts;

[2610] A means of providing additional advice based on the analysis results; and

[2611] a means of sending periodic follow-up messages;

[2612] A means for employees to receive advice on their concerns via devices for use in physical stores, and

[2613] A system including:

[2614] (Claim 2)

[2615] 2. The system of claim 1, wherein the means for collecting and analyzing posting data from a user's social media account operates based on the user's consent.

[2616] (Claim 3)

[2617] 10. The system of claim 1, further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results.

[2618] "Example 2: Combining Emotion Engines"

[2619] (Claim 1)

[2620] means for accepting user information;

[2621] a means for storing user information in a database;

[2622] a means for authenticating a user;

[2623] A means for allowing a user to input a concern;

[2624] A means for analyzing the input worries,

[2625] A means of categorizing and prioritizing concerns based on the analysis results;

[2626] A means for selecting an appropriate response system based on the analysis results;

[2627] means for providing advice to the user by a selected answer system;

[2628] means for collecting and analyzing posting data from users' social media accounts;

[2629] A means of providing additional advice based on the analysis results; and

[2630] a means of sending periodic follow-up messages;

[2631] means for recognizing a user's emotion using an emotion engine;

[2632] A method for analyzing users' stress and emotional patterns using social media posting data;

[2633] A system including:

[2634] (Claim 2)

[2635] 2. The system of claim 1, wherein the means for collecting and analyzing posting data from a user's social media account operates based on the user's consent.

[2636] (Claim 3)

[2637] 10. The system of claim 1, further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results.

[2638] "Application example 2 when combining emotion engines"

[2639] (Claim 1)

[2640] means for accepting user information;

[2641] a means for storing user information in a database;

[2642] a means for authenticating a user;

[2643] A means for allowing a user to input a concern;

[2644] A means for analyzing the input worries,

[2645] A means of categorizing and prioritizing concerns based on the analysis results;

[2646] A means for selecting an appropriate response system based on the analysis results;

[2647] means for providing advice to the user by a selected answer system;

[2648] A means using an emotion engine to recognize the emotion of a user;

[2649] a means for tailoring advice content based on the perceived emotions;

[2650] means for collecting and analyzing posting data from users' social media accounts;

[2651] A means of providing additional advice based on the analysis results; and

[2652] a means of sending periodic follow-up messages;

[2653] A system including:

[2654] (Claim 2)

[2655] 2. The system of claim 1, wherein the means for collecting and analyzing posting data from a user's social media account operates based on the user's consent.

[2656] (Claim 3)

[2657] 10. The system of claim 1, further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results. [Explanation of symbols]

[2658] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting user information; a means for storing user information in a database; a means for authenticating a user; A means for allowing a user to input a concern; A means for analyzing the input worries, A means of categorizing and prioritizing concerns based on the analysis results; A means for selecting an appropriate response system based on the analysis results; means for providing advice to the user by a selected answer system; means for collecting and analyzing posting data from users' social media accounts; A means of providing additional advice based on the analysis results; and a means of sending periodic follow-up messages; A system including:

2. 2. The system according to claim 1, wherein the means for collecting and analyzing posted data from a user's social media account operates based on the user's consent.

3. The system according to claim 1 , further comprising means for detecting a pattern of stress or emotion of the user based on the analysis results.

Images